AI-enhanced tools are rapidly being utilized at the cutting edge of modern IT operations. Tasks such as overseeing network complexity at scale and defending against sophisticated cyberattacks are increasingly being entrusted to AIOps platforms and AI-driven security tools, respectively.

As a result, the repeated use of these platforms has granted channel partners faster access to large yields of data.

Nevertheless, as Managed Service Providers (MSPs) strive to scale their AIOps and AI-driven security practices for customers, a hard truth is emerging: more data doesn’t necessarily translate to better outcomes. In fact, AI platforms are only ever as effective and reliable as the data they ingest and analyze. Managing sampled, siloed, and fragmented network telemetry across multi-vendor, multi-tenant environments severely limits the effectiveness of partners' managed services, instead amplifying noise and inflating observability costs. This kind of data makes for a brittle blade, undercutting the AI integration designed to improve operational efficiency and strengthen security.

For channel partners, the priority must shift from data quantity to data quality. The competitive advantage of AIOps is not found in the sheer accumulation of data, but in the ability to finely cut away the noise to achieve high-signal, low-noise telemetry ready to benefit the systems it feeds. Partners who understand this shift are best positioned to help their customers capitalize on AI's true capabilities.

The strain of fragmented telemetry

In the era of static, on-premises infrastructure, tracking data was rather straightforward. Applications changed slowly, and a high volume of server logs generally equated to better control over the digital ecosystem. But modern, cloud-based environments have changed this dynamic. Legacy tools now face a data burden they were never designed to process effectively. Despite this radical shift, many service providers still cling to outdated ingestion habits, attempting to collect everything without a clear filtering strategy.

Rather than creating a unified view of system performance, this approach often results in telemetry being fragmented across multiple disconnected tools, leaving critical insights isolated and difficult to correlate.

This operational stubbornness can prove expensive. Allowing unrefined, chaotic data to flood telemetry pipelines results in bloated tool investments that actively bury critical performance signals. For MSPs, relying on siloed and sampled data feeds is tantamount to operating blindly. When visibility is fractured, minor glitches go unnoticed until they trigger massive infrastructure failures, disrupting the client's cloud environment and undermining their trust in the provider.

Furthermore, disconnected monitoring point solutions create a compounding operational problem: an endless, unmanageable barrage of noise. When every tool fires alerts independently, distinguishing a critical systemic failure from routine background noise becomes extremely challenging.

Without a unified telemetry strategy, IT teams waste valuable time correlating alerts across multiple platforms instead of resolving the underlying issue. This chronic information overload can exhaust frontline technicians, destroying engineering productivity and stalling active threat detection.

The ensuing organizational toll is heavy. Over time, operators become dangerously dependent on a small group of experts for emergency incident response. These seasoned firefighters become the first and last point of call for late-night rescues and system support. This constant state of reactive crisis management accelerates burnout among top technical talent.

More importantly, it creates an operational trap in which a partner’s best minds are permanently sidelined, forced to manually prop up broken telemetry systems instead of driving innovation and service improvement. When teams are trapped in this cycle of reactive firefighting, the efficiency and growth promised by advanced AIOps platforms remain out of reach.

Tempering true ‘AI-ready’ data

Rather than adopting a patchwork of new tools to manage an ever-expanding data burden, the ultimate solution lies in ensuring telemetry is inherently fit for purpose. A channel partner’s true competitive edge rests on their ability and capacity to deliver solutions that can curate high-signal, low-noise data streams that enable immediate, informed action.

Telemetry is only truly "AI-ready” when it meets a specific, measurable standard. It must be comprehensive, accurate, contextually enriched, and available in real time. Meeting this baseline eliminates the critical operational blind spots caused by data sampling, fragmented toolsets, and inconsistent collection methods across client networks.

Maintaining real-time observability is therefore an operational necessity. Without it, customers cannot fully realize the value of investments in network automation and performance optimization. With it, their service assurance improves, threat detection becomes more effective, and operational blind spots are significantly reduced.

Achieving high-fidelity telemetry requires continuous, packet-level visibility. This provides AI-driven systems with the granular and contextual information needed to establish reliable baselines of normal network behavior, identify anomalies, and help IT teams address issues before they affect performance or disrupt operations.

AI-ready data must also be contextually enriched and correlated across domains. Raw telemetry can indicate that an event has occurred, but context explains what happened, where it happened, and why it matters. Application-aware insights transform telemetry into actionable intelligence, enabling IT teams to identify root causes faster and prioritize remediation. By delivering this richer context, channel partners convert raw network data into meaningful operational insight.

When this foundation is in place, its value extends far beyond network performance. AI-ready telemetry becomes the engine behind next-generation security capabilities, creating new opportunities for MSPs to deliver higher-value services and generate more consistent revenue opportunities.

Unsheathing new revenue streams in next-gen threat detection

The need for AI-ready telemetry becomes even more apparent in cybersecurity. As next-generation cyber threats evolve to evade traditional defenses, AI-driven detection platforms demand complete, accurate, and context-rich telemetry. When security models ingest fragmented telemetry, threats slip through unnoticed, leaving organizations unable to identify malicious activity before it disrupts operations.

For channel partners, addressing this challenge creates a significant opportunity. By delivering comprehensive, contextual, and actionable network-derived intelligence, partners can develop higher-value threat detection and response services, helping customers identify and respond to malicious activity earlier.

High-quality telemetry can also reduce false positives, helping MSPs scale their security operations, support more customers, and use their existing resources more efficiently. As clean, high-quality data helps customers demonstrate stronger operational control and support compliance with strict regulations, it also creates another route for partners to provide valuable, recurring services and keep systems audit-ready.

Striking while the iron is hot

The competitive divide across the IT channel won't simply be defined by which partners adopt the most AI into their offerings. The true advantage will lie with those who provide these AI technologies with the precise data quality they need to perform to the best of their abilities.

AI systems and their potential are readily apparent and available to everyone. However, partners that continue delivering fragmented, sampled, and siloed telemetry into their clients’ AI systems will drive up infrastructure costs, increase operational noise, and ultimately fall behind in operational efficiency and security effectiveness. Instead, the competitive advantage will likely belong to MSPs with the cleanest, most contextual, and most actionable telemetry.

By shifting their focus from data quantity to data quality – specifically towards AI-ready data, channel leaders can cut through the operational noise and unlock more of the commercial and operational value promised by AI-driven tools.

There's a moment in every tech cycle when a product shifts from a novelty to a standard. The partners who recognize that moment early are typically the ones who end up seeing the best returns. When it comes to agentic AI, we're already at that stage, with a McKinsey report stating that 62% of organizations say they’re at least experimenting with agents, and 23% are deploying and scaling them.

Those who've been in the game long enough have seen this before. Think back to endpoint management. A decade ago, the partners who moved fast to own device management built durable, recurring revenue. In creating their own frameworks, service level agreements, and accountability models, they set the standard for what ‘managed’ really meant in their markets.

The interest in endpoint management operated along an S-curve, which saw a slow incubation period, followed by a rapid acceleration of interest. But those who waited found themselves playing catch-up in a category that had already been defined. This same dynamic is unfolding right now with agentic AI. The only difference is that the clock is running a lot faster.

Agent accountability

AI agents can now act as fully integrated team members. They handle day-to-day work: routing support tickets, assisting in the sales cycle through pipeline forecasting, CRM upkeep, etc., running IT operations, responding to cybersecurity breaches, and much more. However, it’s unclear who runs, governs, and holds accountability for these new workers.

Let’s say an IT support company deploys an AI agent that auto-routes support tickets from users to the relevant support department. There’s an expectation that the agent will receive the ticket, route the inquiry properly, and report back to the user without any guidance. If a human were handling these tickets, it would be obvious who to report to if something went wrong. The same isn’t immediately obvious with an autonomous agent, which creates an accountability gap.

This is where Managed Service Providers (MSPs) come in. They're well equipped to guide the partners and vendors they work with who may not be able to handle agent issues on their own, especially considering how new this technology is. Customers deploy the agents, but MSPs should govern them.

Adoption anxiety

There’s been a recent notable change in the questions being asked around AI agents. Six months ago, customers were asking whether they should be adopting AI agents at all, and what ROI they’d see from adopting them. Now, they’re asking whether agents are working properly, and who to call when they aren't.

While the early adopters have already been through this shift, the next wave of customers are only now reaching it. This is the point where interest turns into widespread adoption. Customers are not wearing tin-foil hats and don’t buy into the fears and hyperbole around AI. Most are AI-curious and open to reviewing and utilizing agents, but are naturally anxious about employing autonomous bots.

Data is another source of anxiety that MSPs need to address. Our AI at Work report found that 40% of business directors cited data privacy and security concerns as their top barrier to wider AI adoption. Unlike a chatbot that responds to a prompt, an agent can access systems, use data, and take action on a customer's behalf.

Organizations need to know what data an agent can access and where human oversight begins. That's where MSPs add value, giving customers the visibility and ongoing monitoring they need to adopt agents with confidence.

Governance and the ‘three waves’ of AI

The value of the partner relationship is riding out the waves of AI, then guiding customers through end-to-end implementation. Product knowledge and context are both key for MSPs to really understand a customer's needs. This means learning the business logic, risk tolerance, compliance requirements, and being able to apply that context to every tech decision, from onboarding agents to having them work autonomously alongside employees.

AI agent governance is a natural extension of that process. An MSP who already knows a customer's workflows, data sensitivities, and operational boundaries is positioned to ask the right questions before deploying agents. They can then course-correct if an agent's behavior starts drifting.

That last part matters more than most people realize because agents don't exist in a static environment. Processes, regulations, and business priorities are changing in real-time. The same is true for AI adoption, which unfolds in three waves: first, employees using accessible LLMs like ChatGPT at work; second, vibe coding and structured agents that handle specific, simple tasks; and finally, autonomous agents that understand their tasks and self-improve. Navigating these stages is complex, which is where trusted partners come in to ease the burden.

Early management

MSPs should start by separating upfront work from ongoing management. The initial consulting and deployment phase, starting with understanding a customer's use cases, defining their success criteria, mapping risks, configuring the agent, and going live, is a one-time professional service.

Ongoing management is where long-term value lies. This includes monitoring agent performance, reviewing outputs for errors, and maintaining an audit trail that keeps the compliance team happy. Additionally, different-sized businesses will have different requirements, and where major providers may have dedicated IT support teams, smaller businesses may not have access to the same support structures. MSPs must individualize their plans for their customers, depending on their needs.

This ‘agent as managed service’ model maps naturally onto how MSPs already structure their work with customers. In addition to selling devices, MSPs ensure agents are secure, updated, and performing well over time. MSPs ensure that agents are doing their job correctly and remain adaptable. Without continued management, one-time investments into AI agents are unlikely to return real ROI for customers.

Acceleration has started

MSPs that saw the best returns from managing endpoints weren't slow to realize the value, nor did they treat endpoint management as a one-time service. Instead, they created a whole new managed service model. That's the opportunity available to the channel right now with AI agent oversight.

With the majority of businesses now arriving at agent adoption, this may be the last chance for MSPs to set the standard for an agent management service model. While there is no name for this service model, by managing customer anxieties around agent adoption, offering ongoing support for agents, and adapting services in line with customer needs, forward-looking MSPs will write the rules for agent governance across the channel.

To date, the AI opportunity for managed service providers (MSPs) has largely centered on deployment. While early stages focused on which platforms to buy and how to start piloting, the market has reached a tipping point. Businesses are no longer looking for experiments; they are looking for infrastructure.

That picture is shifting. Today, most organizations are no longer asking whether AI has value. They are asking a more complex question: how do we make AI part of the way our business actually operates?

A recent McKinsey report showed 88% of global organizations are now using AI in at least one business function, yet only around one-third have begun scaling it across the enterprise, with the highest-performing focusing on redesigning workflows rather than simply deploying new tools.

For channel partners, this shift represents the next commercial opportunity. As AI becomes more accessible, selling AI tools is becoming less of a differentiator. The real value is in moving upstream: helping customers redesign workflows, build employee confidence, and embed AI into everyday business operations.

Successful AI depends on successful workflows

Many organizations have already demonstrated that AI works. They've run pilots, tested new use cases, and proved it can deliver productivity gains. Yet many of these projects struggle to scale beyond a single department.

Our data highlights the scale of that challenge. More than half (54%) of UK organizations remain in the research, exploration, or pilot phase of AI adoption, while only 16% have fully deployed AI-powered digital workers. The reason is straightforward: AI has often been added as another standalone application instead of being embedded into the everyday workflows where people already spend their time.

The organizations making the greatest progress are taking a different approach. Rather than asking where they can deploy another AI tool, they're asking where AI can remove friction from everyday work.

Communications is a natural place to start because every customer conversation, meeting, and interaction generates valuable business intelligence. When AI is embedded into those experiences, it can automatically capture actions, surface insights, reduce administration, and improve customer experiences without employees changing the way they work.

The MSP role is changing

This is where the channel has an opportunity to evolve. Historically, success for enterprises was measured by delivering projects on time and deploying new technology. Increasingly, customers need help answering broader business questions.

Which processes should change to make AI genuinely useful? How should AI fit into customer service and employee workflows? How do organizations measure whether adoption is actually delivering a return? How do they introduce governance without slowing innovation?

These are strategic challenges rather than technical ones, and they create opportunities for partners to build much deeper customer relationships.

Take customer service as an example. Deploying AI to summarize calls or recommend next actions is relatively straightforward. Embedding those capabilities into day-to-day operations, training teams to use them effectively, redesigning processes around them, and measuring their business impact is where long-term value is created.

That is also where recurring services revenue begins. Helping customers embed AI into everyday operations and refine workflows creates an ongoing partnership, rather than a one-off implementation project.

Learning from the cloud playbook when it comes to AI

When organizations moved to cloud platforms, the biggest opportunity for partners wasn't simply selling licenses. It came from helping customers migrate, redesign processes, improve adoption, and continuously optimize their environments. AI is following much the same trajectory.

As deployment becomes easier, customers will increasingly look for partners who understand their business rather than simply their technology stack. They'll need trusted advisors who can connect AI to business outcomes, whether that's improving customer experience, increasing employee productivity, or streamlining operations.

For Managed Service Providers (MSPs), that represents an opportunity to move beyond implementation projects and become long-term strategic partners.

The next phase of channel growth

The AI market is entering its next chapter. The first stage rewarded partners for helping customers buy AI, and the second will reward those who help customers operationalize it.

Customers don't need or want any more disconnected AI tools. They need AI embedded into the conversations, workflows, and business processes that already power their organizations.

The partners that help customers make that transition won't simply deliver better AI projects. They'll create stronger customer relationships, unlock new recurring service opportunities, and establish a role that extends well beyond deployment.

Ultimately, the biggest opportunity for the channel isn't just selling AI tools. It's helping customers change the way work gets done.

The cybersecurity industry is awash with products.

Every time a new attack technique is discovered, vendors will rush to develop a product to address the challenge.

Whether it be malware prevention, privileged access management, next-generation firewalls, or email security platforms, the industry is flooded with platforms promising to improve defences and make it harder for attackers to infiltrate systems.

But in reality, this has made security very noisy.

Instead of improving security, the volume of tools organisations must manage has actually amplified risks.

There are too many alerts coming in to understand and identify threats quickly. Too many platforms to manage, which overburdens resources and amplifies costs, and too many interfaces to navigate, which makes managing security far from seamless.

Overall, while the objective of the platforms is to strengthen security, the products can actually jeopardise it.

However, these problems are significantly worse when it comes to Managed Service Providers (MSPs).

Tool sprawl in the channel

Consider an MSP delivering security services to 50 customers.

One customer may use Microsoft security technologies, another CrowdStrike, while others could have different SIEM, vulnerability management, ticketing, and endpoint security platforms.

However, the MSP is often forced to operate them all.

All simultaneously, and all expertly, knowing the capabilities of each platform and how to operate them.

The MSP must maintain the knowledge, processes, and integrations required to operate across all of the platforms they manage for their clients. However, not only does this create resourcing challenges it also amplifies costs.

There is the cost of purchasing each platform that clients depend on, plus there is the cost of employees to manage the platforms.

Furthermore, with MSP staff continually navigating between platform interfaces, this wastes valuable time, reduces productivity, and can make managing security for clients more cumbersome.

Individually, these delays can appear insignificant. However, across hundreds or thousands of incidents, tickets, and customer interactions, they quickly accumulate.

Plus, as an MSP grows its customer base, the challenges can become even harder to manage.

When growth introduces more complexity

Every business wants to grow its customer base, but for an MSP this can also mean introducing more tools into their environments.

If every new customer introduces another combination of technologies, integrations and processes, onboarding customers also introduces additional operational complexity.

Providers can find themselves in a position where growing the business requires continually adding more people to manage the additional workload.

This has obvious implications for margins, but it can also put pressure on existing teams.

Skilled cyber security professionals are already difficult and expensive to recruit. Using their time to perform repetitive administrative tasks or manually move between disconnected platforms is neither efficient nor sustainable.

Ultimately, the channel therefore needs to look beyond simply adding more technology and consider how existing technologies are operated.

There will always be customers that want or need different technologies. Channel providers therefore need to find ways to embrace this diversity without allowing it to dictate how efficiently they operate.

But how can this be achieved?

The importance of technology-agnostic platforms

One of the best ways to overcome this challenge is by working with partners that deliver technology-agnostic platforms that simplify the management of security for MSPs.

These platforms can seamlessly integrate with the security platforms MSPs rely on for their customers, but they can be managed via a single dashboard.

This allows an MSP to more efficiently manage security, but without having to navigate across multiple platforms.

These platforms can deliver everything a partner needs to track, monitor, and manage the security of their customers, without overburdening resources or amplifying costs.

Instead, everything can be managed via a single unified platform, reducing complexity and cutting out the chaos of managing multiple solutions.

A technology-agnostic platform doesn't replace customer investments; it provides a common operational layer across them.

Analysts can investigate incidents, automate workflows, manage tickets, and monitor security posture from one interface while still supporting whichever technologies each customer has chosen.

The channel doesn't need fewer security technologies; it needs a better way to operate them.

MSPs that standardize their operations across diverse customer environments will reduce costs, improve analyst productivity, and deliver a more consistent service.

In an increasingly competitive market, operational efficiency isn't just an internal advantage; ultimately, it's a differentiator that leads to better security outcomes for customers and healthier margins for MSPs.

In recent years, there have been tremendous technology developments and industry shifts. The rapid adoption of generative AI has been a significant disruptor, forcing both vendors and the channel to re-evaluate their offerings and take a more agile stance to expand market reach and boost operational efficiency.

New business opportunities are being driven by trends such as AI storage integration, cloud sovereignty, and the demand for proactive cybersecurity measures. However, given this rapid pace of change, with some partners still focused on legacy resale and non-proactive, break-fix models, are resellers really in a prime position for success?

The biggest trap channel partners could fall into is failing to recognise new opportunities and respond with agility. With AI-driven workflows, regulations, and shifting buyer expectations, those who stick to old approaches risk being sidelined and seeing their margins shrink.

Key industry and technology shifts

Major trends reshaping the channel landscape include the integration of AI with storage infrastructures, the rise of sovereign clouds for data compliance, and the development of advanced data protection platforms.

Another area we see skyrocketing is the edge-cloud-hybrid ecosystem. Organizations are seeking to operate across edge, on-premises, and public/hybrid cloud with seamless data mobility and unified management. With multi-cloud and hybrid architectures becoming the norm, especially for backup and disaster recovery (DR), scalability and cost control remain paramount issues for organizations.

Equally apparent is the need for consistent security and compliance measures across these environments, along with ensuring flexibility to prevent reliance on a single vendor. Data sovereignty, which requires that data be governed by the laws and regulations of the country where it is stored or processed, marks a shift away from generic cloud-first strategies towards more localized management, hybrid-cloud arrangements, and thorough sovereignty evaluations.

With organizations struggling to balance increasing data volumes against rising infrastructure costs, there is also growth in smart multi-tiered storage. This includes storage analytics and tools for real-time monitoring and automated management to enable visibility across different tiers. With the capability to assign "hot" or frequently accessed data to high-speed, premium storage and "cold" or archival data to more affordable options, organizations can avoid the costly mistake of over-provisioning with high-performance drives.

Meanwhile, legacy hardware sales and on-prem-only solutions will continue their sharp decline, falling short for partners who rely on them. The shift to cloud and as-a-service models, combined with economic pressures, has made this a low-margin area.

Channel opportunities for business growth

As global data volumes near 200 zettabytes and AI-native technologies become increasingly sophisticated, customers are demanding comprehensive services that extend beyond traditional project-based hardware or standard cybersecurity offerings.

Customers are seeking partners who can provide AI-ready, sovereign, and resilient data protection that works seamlessly across on-prem, edge, and cloud. With this in mind, key growth areas for the channel are:

AI-driven services and automation

Cyber-resilient data storage solutions and integrated, AI-powered security platforms are driving significant growth opportunities for channel partners. These companies can tap into new opportunities by providing services focused on AI integration that extend beyond just implementing AI tools. These offerings may include automating IT operations, AI monitoring, and intelligence in data protection.

The recent geopolitical shift created by the U.S. government’s unprecedented export-control directive - which restricts global access to the most powerful AI models developed by Anthropic (the prominent US-based AI research and safety company) - has created immediate channel opportunities. If Value-added Resellers (VARs) and Managed Service Providers (MSPs) pivot quickly, they can offer services such as sovereign AI consulting (providing data residency, technological autonomy, and jurisdictional control), regional cloud hosting with sovereign cloud space, as well as advisory services on cross-border regulatory compliance, such as offering risk mitigation audits.

Hybrid-managed offerings

Customers are increasingly adopting multi-tiered architectures that integrate edge computing, on-premises systems, and public cloud solutions. This shift calls for seamless data mobility, unified management, and real-time analytics to ensure efficiency. For channel partners, this trend opens new opportunities for designing, managing, and securing these distributed, complex environments.

The ubiquity of cloud has highlighted a challenge: the importance of operational visibility. Managing data across multiple environments without a unified dashboard has introduced unnecessary complexity and increased the risk of security blind spots. The lack of visibility across edge-cloud-hybrid ecosystems presents an opportunity for partners to act as trusted advisors while also delivering unified management and monitoring across fragmented infrastructures.

Cybersecurity and compliance-as-a-service

Cybersecurity continues to be the foremost growth driver for the channel. Providers are already broadening their offerings to counter the rising frequency of cyberattacks, introducing services such as managed detection and response (MDR), ransomware protection strategies, and compliance solutions. With the emergence of AI-driven threats, there is a growing demand for integrated cyber recovery solutions, presenting lucrative opportunities for partners capable of delivering robust managed security services.

Many opportunities will come from customers who are ready to adopt AI-powered infrastructures and hybrid governance models (the framework that enables managing operations across both physical offices and remote endpoints) in response to data sovereignty pressures. As organizations incorporate AI into their operations, they must address increased security vulnerabilities while ensuring their workloads and data adhere to relevant jurisdictional and privacy laws. Therefore, partners who can offer high-value consulting, data auditing, automation, and managed services, as well as architecting hybrid or sovereign cloud environments, will see the most growth.

Potential challenges

Obstacles to capitalizing on opportunities in the AI era include a lack of sufficient expertise (the talent gap) and longer sales cycles. The channel could struggle to find people who can manage hybrid ecosystems, navigate compliance, and handle the complexity of AI-driven environments. This is an area where vendors can really add value, especially in helping partners bridge that gap with hands-on training, workshops, and webinars.

Another hurdle is that most customers, especially outside the enterprise segment, will face tighter budgets and more cautious spending, which could slow adoption and delay new projects.

There could also be setbacks resulting from vendor complexity, too many tools, programs, and licensing models, which could further slow down the momentum if not simplified early in the year.

The path to success

To embrace these new opportunities, the channel must anticipate changes among customers, vendors, and markets. Buying technology is no longer what customers are looking for; they want solutions that deliver resilience, compliance, and sustainability. The channel must move from being generalists to specialists, guiding and supporting customers and accelerating the shift to service-led models in areas like AI and cybersecurity (such as delivering Backup-as-a-Service and cyber recovery).

Meanwhile, vendors must support this shift by making things easier: offering flexible licensing, providing specialised training and integration tools, and driving closer collaboration with partners. Vendors will also be expected to simplify hybrid and sovereign architectures, provide automation tools, and jointly develop go-to-market initiatives that help partners scale their operations.

The current industry transformation presents a huge opportunity for channel businesses to shift from just reselling to delivering high-value, integrated solutions. As the rise of AI-native and autonomous storage creates a demand for consulting and managed services, clients will most definitely need expert guidance to implement and manage these complex systems.

Cyberattacks are getting faster, more evasive, and easier to execute with both scale and precision. The window of time it takes for an attack to escalate is often measured in minutes, not hours or days, as attackers use automation, phishing-as-a-service kits, and AI tools to move faster and evade detection.

Traditional reactive security defences were not designed for this.

With customers looking to them for protection against ever-evolving threats, Managed Service Providers (MSPs) must learn to navigate this era of generative and agentic AI. This requires continuous visibility and response across the full attack lifecycle. Becoming fluent in AI and combining intelligent automation with human judgement is essential to building cyber resilience in customer environments.

This is a great opportunity for MSPs. Partners that can move beyond the traditional model of blocking known threats and static signatures will gain a powerful competitive advantage in this new threat environment.

How MSPs can get ahead

It’s never been easier for threat actors to launch campaigns. Service-based platforms have industrialized credential theft, initial access, malware distribution, and more, lowering the barrier to entry while increasing attack volume and consistency.

Our research found that 90% of high-volume phishing campaigns in 2025 used kits, a significant jump from 30% the year before. We’re also seeing a growing number of attacks incorporating AI tools, such as using generative AI to rapidly craft deceptive messages and quickly shift tactics.

When an attack can progress from initial access to persistence and device compromise in five minutes, organizations need partners that can detect and respond in real-time.

The answer lies in moving from reactive support to proactive resilience.

That means continuous monitoring, earlier detection, and automated containment of suspicious incidents and anomalies rather than waiting for an incident ticket to land.

That shift changes the customer relationship, too. When an MSP identifies and addresses a threat before the customer is aware of it, the conversation moves from damage limitation to strategic guidance. That’s a different kind of value, building a stronger and longer-lasting relationship.

The MSPs best placed to make this transition are those investing now in the tools, workflows, and expertise to deliver security that is proactive by design rather than reactive by default.

Embracing AI and automation

Integrating AI and automation into MSP security offerings isn’t about replacing human expertise, but about making that expertise scale.

Manual monitoring across fragmented customer environments, including email, identity, endpoints, networks, and cloud infrastructure, isn’t viable at the speed at which modern threats move.

AI changes that paradigm, with automated monitoring tools providing continuous oversight, correlating signals across the full environment rather than treating each layer in isolation. Anomalies that could take a human analyst hours to qualify can be flagged in seconds. Routine threats can be contained automatically, without an analyst needing to intervene.

That last point is especially important, as alert fatigue is a pressing problem for security teams managing multiple customer environments simultaneously. When automation handles the high-volume, lower-complexity end of the threat spectrum, analysts can concentrate on the incidents that require business context, judgment, and experience to resolve.

Personalized solutions

The strongest security outcomes combine intelligent automation with human expertise, not substituting one for the other. Automation delivers speed and scale. People deliver understanding and context. Together, they allow MSPs to provide protection that is continuous, adaptive and aligned to what customers actually need – oversight that keeps pace with the threat environment rather than perpetually chasing it.

Predictive AI analytics also has huge potential for helping MSPs anticipate the needs of their customers. With greater insight into resourcing needs, security threats and growth opportunities, MSPs can provide personalized services which align with each customer's business priorities and future needs.

Building for what comes next

The gap between the speed of attacks and the speed of defence is widening.

AI and automation give MSPs a credible path to closing that gap, not by removing the human element, but by ensuring that human expertise is applied where it matters most. Providers that invest in building that capability now will be better positioned to protect their customers, reduce operational strain, and have more meaningful conversations about resilience rather than recovery.

MSPs that move toward a proactive, AI-augmented security model stand to differentiate themselves in a crowded market, not just as service providers, but as the kind of trusted advisors that customers need against increasingly fast and unpredictable threats.

Walk into almost any business today, and you will find employees using AI tools that IT teams haven’t approved or paid for. The use of unsanctioned applications, known as shadow AI, is coming from the top.

I’ve heard bosses and senior leaders boast about maxing out on their token limits for personal AI tools at work, some saying they would risk disciplinary action to continue. My opinion, based on research we’ve done, is that sensitive company information is being fed into unsecured platforms with knowing and active encouragement from leadership teams focused on speed and output.

With Gartner estimating that 79% of cybersecurity leaders have evidence of unsanctioned AI use, we know reckless behavior isn’t limited to the boardroom. IBM's research shows a persistent gap between leaders' AI ambition and governance readiness in organizations of all sizes. Employees are using whichever AI most increases their productivity, consequences be damned.

Business AI tools aren’t meeting employee needs. Whether that’s because of cost, AI readiness, or lack of understanding, Managed Service Providers (MSPs) and IT providers have a unique window to support businesses needing to adopt correctly. The channel is now reaching wider business managers who direct company culture, not just IT teams. With the way things are going, the channel has an opportunity to become strategic if we can meet the ask.

The pressure point

The window for the channel to move from IT vendors to strategic business partners is now. From a product perspective, upcoming licensing changes and cost pressures are likely to force decisions that many organizations have so far delayed. When budgets are tight, downgrading access to AI tools is a logical step. However, this often pushes employees back toward unsanctioned options.

Employees have already demonstrated that they will seek out tools that help them work more efficiently. Our research found that a significant proportion of employees said they would turn to personal AI tools if their employer restricted access on cost grounds. That’s a small profit gain one year and a data leak, boardroom scandal, and reputation loss the next.

For MSPs and Microsoft partners, this represents both a warning and an opportunity. Businesses must respond to employee needs and top-down rule-breaking by optimizing licensing, deploying AI properly, and putting the right software in place.

For the channel, a renewed focus on adopting AI that fits the budget and the needs of the workforce will open new business opportunities.

The government is paying attention

It is also worth noting that this is not just a commercial conversation or pandering to employee desires for the latest tech. At London Tech Week, the UK government focused on improving AI adoption among small and midsize businesses, with a caveat that tech transformation needs to happen safely and effectively.

To me, these policies and investments are a clear sign that AI enablement for SMBs is a national priority, and the channel is one of the primary routes through which it will be delivered. There has been a buzz in the channel for some time around how MSPs need to move from transactional resellers to trusted and strategic technology advisors.

Now is the time to meet the needs of providers seeking guidance on adopting quickly and safely. With renewed government focus on scaling AI for all businesses, the channel ecosystem is welcoming more than just IT buyers, presenting a long-term opportunity for the MSP.

What good readiness actually looks like

When our customers ask us about the latest AI software, financial, structural, and cultural concerns immediately crop up. They are trying to understand whether they are ready to adopt AI in a way that delivers value without introducing unnecessary risk. In most cases, the answer is more complex than a simple technology decision. Most know that the answer is more complicated than a licence purchase.

The practical work includes an honest AI readiness assessment, a clear policy framework that governs what tools can be used and how, user training that goes beyond a PowerPoint, licensing optimization, and a thorough security and compliance review that accounts for the data risks that shadow AI has already introduced.

For businesses already suffering from shadow AI use, especially where it’s driven from the top, it might be an uncomfortable conversation. Without it, businesses will keep losing money on poorly implemented and poorly performing software that their employees aren’t using, whilst the leadership team sets an example of risking sensitive data just to hit KPIs.

This conversation isn’t beyond the capability of a well-positioned MSP. But it requires a trusted relationship and the confidence to have a broader conversation with the customer than MSPs are used to having.

The window is open

Businesses have been allowing their AI tools to underperform for a multitude of reasons, but pricing changes, risk, and legislation are all about to force decisions.

If they aren’t already, businesses will soon be coming to the channel needing guidance. It’s been talked about for some time, but now really is the moment for partners and channel actors to make themselves known as AI readiness advisors. Now, before the next wave of shadow AI incidents, before a competitor gets there first.

The key question is not whether customers need support with AI; that’s obvious. For me, the channel needs to be assessing whether they can take advantage of this window and provide that support and more.

AI is becoming an infrastructure race, not just a software one. Across Europe, governments, hyperscalers and investors are competing to build the compute, connectivity and operational backbone required to support AI at scale. The UK has made clear that it has been and intends to be part of that conversation.

There are good reasons to take that ambition seriously. Through its UK Compute Roadmap, the government has placed AI infrastructure at the centre of its industrial strategy, with plans to expand public compute capacity, establish AI Growth Zones, accelerate data center planning and invest in compute and semiconductor capabilities.

Private investment has followed, including Blackstone’s £10 billion AI data center project in Northumberland, CoreWeave’s multi-billion-pound investment in UK AI compute capacity, AWS’s £8 billion commitment to UK data centers and Google’s continued investment in expanding its UK infrastructure footprint.

But ambition on its own is not enough.

The UK’s competitive advantages

The UK enters this race with several meaningful strengths. It has one of Europe’s largest AI ecosystems, a mature cloud market, an established hyperscale data center footprint and a deep enterprise customer base that is actively exploring AI adoption.

London remains one of the continent’s leading financial centres, helping attract investment while bringing together organizations with both the capital and appetite to invest in AI initiatives.

Combined with world-class universities and research institutions that continue to produce talent and innovation, the UK has many of the ingredients needed to position itself as a long-term hub for European AI innovation.

The UK’s infrastructure challenges

Power availability may be the UK’s greatest obstacle. Industrial electricity prices remain among the highest in the developed world, while grid connections for major infrastructure projects can take years to secure. The UK’s AI ambitions ultimately depend on a resource that cannot be scaled overnight: energy.

That matters because AI workloads, particularly GPU-intensive ones, are unusually power-hungry. Over time, energy economics will influence not only where infrastructure is built, but where workloads actually run. As enterprises look more closely at the cost of scaling AI, location becomes an operating decision, not just a property or planning decision.

The UK also faces growing competition from across Europe. France benefits from strong government backing and abundant nuclear energy. The Nordic countries offer renewable power, cooler climates, and lower operating costs that naturally support large-scale data centers. Germany combines industrial scale with significant enterprise demand for AI, while Ireland continues to attract hyperscale cloud investment even as it grapples with its own energy constraints.

Rather than competing on identical strengths, each market has the chance to build a compelling proposition. The UK’s competitive edge lies less in offering the cheapest power and more in combining enterprise demand, financial investment, cloud maturity and a growing AI innovation ecosystem. Whether that will be enough to secure long-term advantage remains an open question.

What does this mean for the channel?

For channel partners, focusing only on which country “wins” risks missing the bigger opportunity.

As enterprises move from AI experimentation to production deployments, infrastructure decisions are becoming business decisions. Clients are asking where AI workloads should run, how to balance cloud and on-premises environments, control costs, meet sovereignty and compliance requirements, and scale without creating operational drag.

That shift moves the conversation beyond products and into outcomes. The most relevant partners will be the ones that can help clients make better decisions across architecture, operations, governance, and cost, not just deploy another piece of technology.

For Managed Service Providers (MSPs), systems integrators, and other channel partners, this creates an opportunity to move further up the value chain. Rather than simply helping clients select and deploy infrastructure for AI, partners can help assess AI readiness, modernize data center environments, design hybrid AI architectures, optimize networking and storage, strengthen security, implement governance, and build a more disciplined approach to long-term AI cost management.

Energy will remain central to that conversation. GPU-intensive workloads already place significant demands on power and cooling, making infrastructure efficiency a real business concern. As customers seek to understand why AI deployments cost what they do, partners that can connect technology decisions with operational improvements will stand out.

The bottom line is that the opportunity for channel partners is not tied to a particular postcode or data center location. Instead, it is tied to helping clients navigate the growing complexity of AI infrastructure itself.

Looking beyond the build-out

While public attention often focuses on where compute capacity will be built, the more durable opportunity lies in supporting AI inference: the day-to-day execution of AI workloads that power business applications and operational decisions. As AI adoption matures, these workloads must run securely, efficiently and cost-effectively, creating sustained demand for the design, integration and managed services that channel partners are well positioned to provide.

Whether the UK ultimately establishes itself as Europe’s primary AI infrastructure hub remains an open question. The country has clear strengths, but also some constraints that need long-term solutions, such as around power, planning, and long-term capacity. Geography alone will not determine the winners.

For channel partners, taking a wait-and-see approach would be a mistake. The smarter move is to help clients design and refine AI environments around their operational needs now. Those that bring expertise in AI architecture, workload optimisation, cost management, energy efficiency and governance will be best positioned to lead, regardless of which country comes out ahead.

For years, channel partners have made their margins on cloud migration and security, helping clients move data somewhere safe and keep it that way. Both have become the default, but solving that problem has quietly created a new one: data that is safe and well governed is not the same as data people can actually use.

Backend systems have grown more sophisticated while the interfaces sitting on top of them have stayed clunky, fragmented, and largely ignored, leaving valuable information locked away from the people who need it most, and this is where the value has now shifted.

Relying on cloud migration and baseline security as a primary revenue engine will leave channel partners behind because providers continue to sell infrastructure solutions to an enterprise market that has already moved past the infrastructure phase.

Storage and security handle yesterday's issues, while data silos resulting in data fragmentation are now the urgent challenges of today. Data silos trap critical information within isolated, non-communicating systems, scattering corporate data unevenly across public clouds and legacy applications. This inevitably leads to data fragmentation: a chaotic state in which corporate data is unevenly distributed across multiple public clouds, legacy systems, and disconnected applications.

The consequences of this fragmentation are crippling, leaving organizations without a single source of truth. In fact, 77% of respondents in an IBM Institute for Business Value study strongly agreed that these silos actively hinder real-time analytics and data-driven decisions.

For the past decade, managed service providers (MSPs), value-added resellers (VARs), and IT consultants have built highly profitable practices by guiding organizations through cloud infrastructure modernisation. But today, the next margin growth opportunity isn’t where data is stored; it is how that data is experienced: a shift known as the Digital Experience Platform (DXP) space, which helps organizations unify, govern, and present fragmented backend data into usable, front-end interfaces.

The multi-million dollar data disconnect

Organizations continue to pour substantial capital into upgrading their cloud infrastructure. An Omdia survey shows how spending reached US$110.9 billion in late 2025, a 29% year-on-year surge, with another 27% growth forecast through 2026 on global cloud infrastructure. However, a massive operational problem persists: the data sits perfectly organized in the backend but remains functionally inaccessible to the end user.

Simply owning data is no longer a competitive advantage; the real value is in how easily people can use it. This structural gap between backend complexity and front-end utility defines the "Data Experience" gap.

For a business-to-business (B2B) buyer, this gap looks like a frustrating customer portal that cannot display real-time shipping updates because the shipping database does not talk to the web interface. For a supplier, it means relying on manual emails because procurement platforms do not share information.

Transitioning to data experience architecture

To capture higher profit margins in this landscape, channel partners must transition from storage infrastructure providers into data experience builders.

This evolution requires moving away from traditional, slow, and expensive data migration methods, such as moving information into the cloud, and instead focuses on building smart, lightweight integration layers. By seamlessly connecting legacy systems to modern cloud environments, partners can consolidate fragmented data into a single, cohesive view without disrupting the underlying storage architecture.

This ability to deliver integration without interruption represents a highly specialized skill set for which corporate clients are willing to pay a premium.

Beyond merely connecting these disparate systems, opening the flow of data introduces an urgent need for smart permissions and sophisticated governance.

A distributor, a B2B customer, and an internal sales representative may all require access to information stored in the same database, yet what actually appears on their respective screens must be uniquely tailored.

Designing and implementing these complex, context-aware access control systems serves as a high-value advisory service. It establishes a level of security and compliance that basic software resellers simply cannot replicate, deeply embedding the partner into the client's day-to-day operations.

Ultimately, these seamless backend connections enable the delivery of consumer-style B2B experiences that modern buyers now expect as standard. Digital experience insights tracked by platform providers indicate that the primary hurdle for B2B companies isn't designing an attractive user interface; rather, it is untangling the complex web of integrations sitting right beneath the surface.

Channel partners who can bridge this gap by building tailored vendor portals and collaborative digital workspaces resolve their clients' most critical technical friction points, effectively making themselves indispensable business allies.

The high-margin advisory model

When a partner fixes an organization's data experience, they are doing far more than installing software. They are mapping out how the business actually runs, improving daily operations, and building resilient integration systems.

This results in higher client retention, robust profit protection, and a steady stream of recurring revenue as the client’s digital needs continue to scale.

Architecting these solutions requires a technology foundation designed specifically for backend complexity. Platforms that act as a unified hub connecting content, workflows, and backend systems to deliver personalized experiences are built for this transition, offering an evolvable architecture that allows partners to bridge the data experience gap across the entire business ecosystem.

By building security and context-aware compliance directly into the core of the integration layer, partners can safely deliver highly localised, consumer-grade experiences across diverse global markets. Furthermore, utilizing integrated AI and scalable SaaS or PaaS environments ensures that as client traffic and campaign demands fluctuate, the underlying data layer remains resilient.

The foundational infrastructure of the modern enterprise has already been laid. The channel partners who will win over the next decade will be those who don’t focus solely on storage boxes, but instead upskill their teams in integration governance and start unlocking the value of the data inside them.

The market has moved past the plumbing phase to build data experience, and channel partners need to move with it.

Exclaimer has announced the launch of MSP Connect, a new partner program designed to help managed service providers (MSPs) provision, manage, and scale email signature services across customer environments.

Available immediately, the initiative brings consumption-based billing, centralized self-service provisioning, and dedicated channel support together with integrations for ConnectWise, Kaseya, and Microsoft Marketplace.

Exclaimer said the program has been designed to address the operational challenges MSPs face when managing email signatures across Microsoft 365 and Google Workspace environments, where updates can otherwise require manual, user-by-user administration.

With MSP Connect, billing adjusts according to actual customer usage, allowing MSPs’ costs to increase or decrease as customers add or remove users.

MSPs will also have access to a redesigned partner portal that allows them to provision and manage signatures across multiple customer environments from a single location, while native integrations with PSA platforms aim to reduce administrative work.

In an announcement, Exclaimer chief operating officer Jim Turner said MSP Connect was developed in response to feedback from partners around the ease of use and billing model of the program’s predecessor.

"MSPs have been clear about what they need from us: a platform that is easy to provision at scale, a commercial model that flexes with their customers and commercial support that helps them grow," he explained.

“MSP Connect is the reset. It removes unnecessary steps, gives partners more control through self-service, and aligns billing with what their customers use."

Strengthening MSP revenue generation

Exclaimer’s MSP Connect launch marks the next phase of its channel-first growth strategy as the email signature management specialist looks to strengthen its partner network.

Partners already account for 30% of the firm’s annual recurring revenue, while its global channel ecosystem includes around 5,000 partners.

The company said MSP Connect will now enable partners to add email signature management to existing Microsoft 365 and managed services relationships without introducing a complex deployment or support burden.

MSPs can generate license margin, bundle Exclaimer’s technology into wider managed services packages, as well as create additional revenue through services such as signature design, deployment, and ongoing management.

"An MSP is a business within a business. It needs to manage hundreds of customer environments without adding operational drag," commented Louise Taylor, vice president of channel at Exclaimer. "If getting one customer live requires a long sales process or a services project, the economics stop working."

Reducing friction for MSPs

Exclaimer’s new program includes not-for-resale (NFR) licensing, dedicated channel account management, as well as co-brandable partner enablement materials.

The vendor said its platform can be deployed by MSPs in under an hour depending on the customer environment, using directory data to populate approved signature attributes across Microsoft 365 and Google Workspace without requiring any heavy integration project or mandatory professional services.

Additionally, MSPs can build additional services around the platform, including signature template design and template catalogue creation, while their customers’ marketing teams can utilize approved campaign banners, engagement analytics, and rule-based signature content.

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For years, much of the relationship was anchored by the renewal itself. Customers turned to partners for help making sense of license positions and vendor terms, and the transaction carried enough margin to support the account work around it.

With Gartner expecting worldwide IT spending to reach $6.31 trillion this year, driven by software and AI infrastructure, the market is still growing around partners. The difficulty is that growth no longer flows through the channel in the same way.

A license can still open the account. Its value now depends on whether the partner can use that moment to extend their services to support the broader software estate.

Buying is moving around the reseller

A large part of the traditional reseller model grew around Microsoft licensing and the incentives attached to it. Backend fees historically gave partners dependable income around the customer relationship, often through repeatable work across accounts.

As those economics have narrowed, customers have gained more ways to buy technology. A reseller may spend months shaping the right solution, then see the purchase move through a marketplace because the customer wants to draw down an existing cloud commitment. The advice remains valuable, even when the transaction lands somewhere else.

Months of account work can suddenly become detached from the order. Understanding how the customer intends to buy has become just as important as understanding what they intend to buy.

A renewal now sits on top of a software estate that may have moved faster than the agreement in front of the customer. SaaS spreads through different parts of the business before central teams have a full grip on ownership. Cloud costs shift the economics of tools that once looked predictable. On top of this, AI capabilities are arriving inside the platforms customers use every day.

By the time the renewal comes around, the agreement today may not show enough about whether spend still accurately reflects how the business operates.

The license should open a wide view of the estate

To protect the relationship, partners need to turn the renewal into a clearer view of the estate behind it.

The license position shows what the customer is entitled to use. Usage data shows whether the business still depends on it. The gap between those two things often reveals important opportunities for optimization, cost control, and better decision-making.

Partners with Software Asset Management (SAM) and IT Asset Management (ITAM) expertise already have much of that capability. They understand how quickly entitlement can drift away from real use, especially when software is purchased centrally and then adopted unevenly across the organization. Turning that knowledge into a managed service gives customers a clearer view before the renewal deadline forces a decision.

SaaS management builds naturally from there. Applications can spread through teams long before central IT has a clean view of ownership or spend. A partner that can show which tools are active and which contracts no longer reflect use is helping the customer make a better renewal decision.

AI adds the same pressure in a newer form. Capabilities are being added into platforms customers already run, so the cost can build inside familiar contracts before the business has worked out where the value sits. Connecting that spend back to usage gives partners a stronger role than simply helping the customer process the next agreement.

Our 2026 State of the Cloud report findings suggest the services market is already moving this way, with nearly half of Managed Service Providers (MSPs) planning to offer AI consulting and SaaS management services. Enterprise use of MSPs has also risen year on year, which points to larger organizations looking for specialist help as their estates become harder to manage.

Margin must come from the services around the license

Partners that stay closest to the customer will be the ones that make the estate clearer between renewals. The license gives them a route into that work, then the service relationship has to carry it forward.

Account teams need enough visibility into usage and consumption to challenge assumptions before procurement turns the renewal into a price negotiation. Sales teams also need to be measured on the service opportunities created around the license, not only on the order itself.

Resale remains a key part of the channel, and the license still opens the door. More of the margin now comes from helping customers understand the full technology estate behind it. Understanding what they own, what they use, where costs are increasing, and where technology investments are delivering value.

In that environment, the most successful partners will be defined by the insight they provide and the outcomes they help customers achieve.

For years, selling physical security meant knowing one buyer. The director of physical security, or facilities, signed off on cameras, locks, and badge readers, and the conversation rarely left that room. That buyer is being moved aside.

Physical security budgets are rising sharply. EY research found that nearly 80% of organizations increased spending in the last budget cycle, and more than a quarter have now shifted oversight to the CISO, a role built for network defense, not card readers and lockdown logic. The money is growing, and the person controlling it has changed. For Value-Added-Resellers (VARs), Managed Service Providers (MSPs) and integrators, that is the most important shift in this market, and the partners who haven’t adjusted their go-to-market are still pitching buyers who no longer control the budget.

Here is what that looks like on the ground. A reseller who used to walk in with a door schedule now sits across from a CISO who wants to see NIST CSF mappings. An integrator arrives for a campus deployment and finds the cybersecurity team holds policy authority over systems they have never seen up close. Deals that should close stall, because no one in the room feels accountable for a physical incident. The handoff is happening faster than buying committees are used to.

Four things will determine which partners own this shift - and which get left behind.

Who actually owns the budget now?

Map the buying committee before you pitch anything. In most organizations going through this shift, the CISO owns the budget and the risk, but the physical security or facilities lead still owns day-to-day operations. Both are in the room.

If you assume the old buyer is still in charge, you will lose to a competitor who figured out the money moved. Your first job on any new opportunity is to establish who signs, who operates, and who is accountable when something goes wrong. In a convergence deal, those are often three different stakeholders.

How do you earn credibility with a CISO who has never run a physical system?

Learn their language. A CISO does not think in door schedules and panel counts; they think in frameworks, risk, and auditability. When you can map a physical access control deployment to NIST CSF, explain how it changes their attack surface, and show how you would prove it works under audit, you stop being a hardware vendor and become someone they can defend a budget line to.

The mistake we see most often is the reverse: walking a CISO through the technical detail of the physical install. That buyer does not want the schematic. They want to know what risk you remove, what you can attest to, and what happens when something is compromised. Translate physical implementation into security outcomes, and you will be in conversations your competitors never reach.

Where can partners attach services?

The opportunity sits in the gap nobody owns: the space between IT policy and physical implementation.

In the deployments we work on at Acre, that gap is almost always wider than the organization expects. The CISO sets policy, the facilities team runs the hardware, but almost no one owns the interface between them- the integration that decides what the physical system does when the identity provider is compromised, or what happens to a badge when a credential is exposed.

That is billable work, and it recurs: assessments that map physical controls to the security framework, integration between access control and identity systems, and managed services that keep the two estates talking and produce the evidence an auditor will ask for. This is where partners move from one-time installers to retained advisors, which is the more durable revenue anyway.

How do you structure the sale when the buying committee has changed?

Sell to the committee, not the individual. The deals that stall are the ones pitched to one stakeholder while another quietly holds a veto. Get the CISO, the physical security lead, and, often, IT into the same conversation early, and make accountability explicit.

Who owns a physical breach? Who owns a credential compromise that has physical consequences? When you name those owners in the room, you remove the ambiguity that kills deals, and you position yourself as the partner who understands the new org chart rather than the one still selling to the old one.

None of this requires the channel to become a cybersecurity practice overnight. It requires recognizing that the buyer has changed, learning enough of the CISO’s language to be credible, and building services around the integration gap the convergence created.

The partners who make that shift will own the relationship as physical and digital security keep merging. The ones who don’t will keep pitching to a room that’s already moved on.

The budgets are there. The question is: Are your conversations reaching the people who control that money?

Modern applications rely heavily on open-source packages and third-party dependencies, meaning almost every application organizations run is built on layers of code written by people outside the organization.

Channel partners are often responsible for recommending, integrating and managing these environments. As a result, when a dependency is compromised, accountability increasingly lands with the partner managing the stack.

Why vulnerable and malicious packages remain in production

Unfortunately, public disclosure does not equal remediation. Organizations continue running outdated or vulnerable dependencies months after Common Vulnerabilities and Exposures (CVEs) become public, and this is increasingly becoming the norm. CVE volume has grown by 263% since 2020, with 2026 already seeing a further 33% increase. At this pace, security teams are struggling to digest the volume of vulnerabilities, let alone prioritise and implement effective remediation plans.

Many Managed Service Providers (MSPs) and consultancies also inherit environments they didn't originally architect. Combined with security frameworks that were designed around infrastructure and endpoints rather than continuously evolving software dependencies, the challenge becomes even greater. Moreover, the growing use of AI-assisted development is adding another layer of complexity, accelerating software creation while increasing the volume of third-party code and dependencies entering production.

Modern applications can contain hundreds of transitive dependencies, making it difficult to maintain an accurate inventory of what's actually running in production. Limited adoption of Software Bills of Materials (SBOMs), alongside the challenge of auditing applications thoroughly, only compounds the problem. Meanwhile, CVE severity scores don't always reflect real-world exploitability, making triage more difficult and further delaying remediation.

How supply chain attacks are changing the threat model

A single compromised dependency can now create risk across multiple customer environments simultaneously and at speed. Attackers are increasingly targeting shared development tooling and open-source repositories, exploiting assumptions around shared responsibility and the belief that someone else is managing the risk.

Traditional perimeter-based security was never designed for trusted software components becoming the attack vector. Increasingly, nation-state actors and organized cybercriminal groups are targeting open-source maintainers directly, recognising that compromising a widely used dependency offers far greater scale than attacking individual endpoints.

Because these attacks are delivered through trusted, signed software components, they can bypass many traditional detection controls. In many cases, organizations receive few, if any, alerts, leaving security teams unaware until the compromise has already spread.

New expectations

Clients increasingly expect partners to explain software supply chain risk in business terms, marking a shift from reactive remediation to demonstrable governance. Visibility, software inventories, and continuous monitoring are quickly becoming baseline expectations rather than value-added services.

Cyber insurance underwriters are also asking for evidence of SBOM practices and software inventory controls. Partners who cannot demonstrate these capabilities risk creating challenges for clients during policy renewals, which can ultimately affect the services they are trusted to deliver. At the same time, legal and procurement teams are beginning to include software supply chain requirements in vendor contracts, meaning partners need to be prepared for increasingly detailed conversations.

For partners, this represents more than another security challenge. Clients increasingly need help understanding software supply chain risk, interpreting SBOMs, assessing third-party dependencies, and embedding these practices into procurement and governance. Those who can provide this expertise move from being technology providers to trusted advisors.

The responsibility and accountability expansion

Software supply chain security is becoming a defining issue for partners operating across cloud and DevSecOps environments. As responsibility for managing modern development environments expands, so too does accountability when something goes wrong. To retain client trust, partners must move beyond fragmented tooling and demonstrate a clear, structured approach to managing software supply chain risk at scale.

Partners that get ahead of this have an opportunity to differentiate themselves. Rather than viewing software supply chain security as another compliance exercise, they can provide credible answers to the questions clients and their boards are already asking.

The conversation is also changing commercially. It has shifted from the value proposition of fixing vulnerabilities after the event to providing continuous assurance. For partners, that's an opportunity to deepen customer relationships while developing new security services that generate recurring revenue.

Within boardrooms, conversations are increasingly focused on who owns software supply chain risk, what impact it could have on the business, and what the financial implications might be.

Partners that cannot answer those questions risk losing credibility and, ultimately, customer relationships. Partners that can answer those questions with a proven strategy are much more likely to have stronger client adoption, expanded revenue opportunity, and longer-lasting relationships.

Voice remains one of the most effective channels for customer relationships. For customers, having access to a human on the other end of the line, especially in times of confusion or crisis, is increasingly important in an era driven by automation and AI adoption.

This is exactly why so many Managed Service Providers (MSPs) are involved in running or supporting contact center infrastructure on behalf of their customers. However, despite the rewards, it naturally doesn’t come without some risks.

In April 2026, the UK government published the findings of its 2025/26 Cyber Security Breaches Survey, which highlighted some sobering statistics. According to the data, nearly half of all UK businesses (43%) experienced a cyber security breach or attack in the past year.

Phishing remained the dominant attack vector, cited by 38% of businesses, and rated as the most disruptive incident type by 69% of those affected. However, of those phishing incidents, a growing share is no longer delivered by email. Rather, they’re arriving by phone call, and thanks to AI, they are increasingly convincing.

The evolving threat

Voice phishing, or “vishing”, is not a new phenomenon. Bad actors and criminals have been spoofing voices and using fraudulent calls since the popularization of the telephone. What is new are the features that make voice phishing more convincing.

The proliferation of AI-generated voice deepfaking, caller ID spoofing tools, and lack of traceability have contributed to a new wave of fraudulent calls that appear more real than ever before.

Many organizations have spent significant resources securing their email environments, including spam filters, user training, and sandboxing. While this is a sensible investment, fraudsters have noticed and are adapting, which may explain why voice phishing has become an increasingly favored tactic for some bad actors.

Why voice is being targeted

The basic mechanics of voice phishing have always relied on social engineering. Impersonating a trusted person and creating a sense of urgency help to manipulate victims into taking a malicious action, whether that be authorizing a payment, sharing credentials, or granting undue access to a system. While traditionally a mismatched voice was a potential giveaway to fraud, AI deepfakes have made this process much more convincing.

Voice cloning tools that once required real technical knowledge can now be replicated by most people with a commercial PC. It’s now easier than ever to create a convincing replica of a known voice from a short audio sample, for example, from a CEO's recorded video message, a support call that was unknowingly captured, or a social media clip.

If a cloned voice calls a finance team member and asks them to process an urgent payment, citing relevant details such as a live deal, a regulatory deadline, or a supplier relationship, very few red flags are present for the human on the receiving end of the call.

Number spoofing compounds the issue. Fraudsters may be able to secure the capability to present any caller ID they choose, making a call appear to originate from a trusted source. The result of mixing emerging tech with good due diligence is a call that looks and sounds real.

MSPs can carry disproportionate risk

MSPs that support or run contact center infrastructure are in the middle of communications flows for multiple organizations and partners simultaneously. Critically, this also makes them the first port of call for customers when something technical goes wrong, which can offer an easy alibi for fraudsters looking to exploit them.

There are two distinct risks for MSPs: one operational, one reputational. The operational risk is straightforward: IT help desks control credentials, systems, and sensitive customer data, making them a target for malicious actors. The reputational risk, however, has a wider-reaching impact. If a customer is defrauded through infrastructure managed by an MSP, customers may find the MSP liable. Even if the MSP bears no technical responsibility, they are the supplier in the middle.

There is also a less visible third risk: the channel as a route of compromise. Fraudsters who have already gained access, whether through an earlier breach, open-source intelligence, or a compromised supplier, will probe the path that offers the least resistance. If an MSP's call handling has not been safeguarded with fraud in mind, that becomes the path of least resistance.

Practical steps to take right now

The tools to address this problem exist today, but gaps remain in general adoption and awareness. Firstly, MSPs should be implementing proper caller authentication where it is available. For example, in some markets, protocols exist to authenticate calls before they’re answered, such as the “STIR/SHAKEN” protocols used in the USA. These act as a digital "passport" for telephone calls, ensuring the number a call handler sees on their caller ID is legitimate and matches the actual person or business calling. These act as a strong defense against number spoofing.

Next, establish comprehensive verification workflows. Agents should never rely on voice recognition alone. Callbacks to verified numbers, challenge questions, and two-factor authentication must be embedded as standard security procedures. Call handlers must verify who is requesting sensitive information and challenge even those who appear familiar.

Additionally, staff should be trained to recognize the specific warning signs of voice phishing. Most security training currently focuses on email. Staff need scenario-based training covering AI-generated voice hallmarks, such as unnatural pauses and scripted rigidity, alongside classic social engineering tactics: creating a sense of urgency and requesting to bypass usual processes.

Finally, participate in industry data-sharing. No single provider sees the full picture. In the UK, mechanisms like the Home Office's Telecoms Fraud Sector Charter seek to improve cross-sector visibility of active campaigns. MSPs engaged in these networks get earlier warnings to ensure they’re not operating with only a partial view.

Voice is a channel worth championing

Voice is a communication channel with many strengths, and the human touch can be the differentiator between satisfied and dissatisfied customers. For so many reasons, it’s a critical channel that’s worth championing, and worth safeguarding.

Voice fraud is the channel's problem. But the channel is very well placed to address it.

Steps such as appropriate due diligence through the supply chain, monitoring for suspicious traffic patterns, and validating caller IDs used by customers are all effective. It’s also crucial that MSPs and contact centers take the appropriate steps now to ensure that criminals can’t get in and customers remain confident.

Most small and mid-sized businesses (SMBs) are grappling with networks that have evolved one tool at a time, as new vendors were added to solve individual challenges as they emerged.

The result is a patchwork of solutions, from wireless access points and firewalls to monitoring tools, bolted together with little strategic planning. Each one has its own interface, licensing model, and operating expenses.

This is costing SMBs time, money, and resources. Too often, the IT infrastructure and management tools businesses rely on today were designed for enterprise environments, making them unnecessarily complex for smaller organizations. Disjointed systems and multiple dashboards make it harder to identify issues, and if a problem arises, which support service should a stretched IT team contact first? Add in the licensing cost for supporting multiple tools from multiple vendors, and the burden quickly grows.

If SMBs are to escape from the network tool maze, they need to think about how all of their systems fit together and work with managed service partners to move toward a simpler, more effective cloud-based IT infrastructure.

From fragmented tools to unified management

No longer reserved for the deep pockets of enterprises, unified, cloud-managed platforms are transforming network operations for SMBs. By bringing together networking, security, and monitoring into one interface, IT teams get a single pane of glass view across their entire environment.

For businesses, this means clearer visibility across devices such as routers and switches, insights into performance and power consumption, and the ability to identify and resolve issues before they impact operations.

Many Managed Service Providers (MSPs) are recommending cloud-based systems because they recognize the operational and business benefits they deliver. With less hardware to oversee, time-consuming on-site maintenance visits are reduced, and the path from purchase to deployment is fast and straightforward.

A more strategic role for MSPs

For MSPs, the shift to the cloud also has advantages.

Remote management and automated software updates save time and costs. The advantages of this can be purely practical – looking after one unified platform will always be easier than overseeing several fragmented ones.

Moving clients to the cloud also allows MSPs to shift from reactive support when something goes wrong to proactive monitoring, optimization, and vulnerability management. They can deliver technical expertise, helping customers to scale seamlessly to meet market demands, and act as an outsourced IT department that transforms IT from being a burden to a strategic advantage.

Helping SMBs adopt cloud-managed platforms delivers another significant benefit - security. As compliance requirements and legal regulations change and cyber threats grow exponentially every year, the built-in cyber frameworks of cloud platforms become essential.

Compliance certifications, automated updates, and integrated monitoring provide a level of protection that was once available primarily to enterprise customers, and without the associated overhead.

Simplifying the path forward

If SMBs are to simplify their networks, they need to move away from tool sprawl and towards a unified platform with security built-in, not bolted on. The right solution will not only deliver improved visibility and simpler operations, but also the features businesses need without unnecessary complexity. This also makes implementation easier as the company grows.

MSPs can help identify the right solution for the customer, but vendors must demonstrate how their platforms work in real-world conditions.

And while even the best unified platform cannot do everything, integrating specialist tools should be seamless through open APIs and interoperability frameworks.

The future of networking is about using fewer tools to achieve more through simpler architecture, centralized control, and systems designed for how SMBs and their partners actually operate.

As the old saying goes, sometimes “you have to fight fire with fire”. That’s certainly true in 2026, with cyber threats increasingly coming from ever more sophisticated use of AI tools.

There’s a problem with that, though: most businesses are not yet fully set up to deal with AI-based attacks and don’t trust the tools to do the job. A 2026 Proofpoint AI and Human Risk Landscape Report revealed that half of organizations using AI-based security controls still experienced suspicious or confirmed AI-related incidents.

There is a fundamental security gap in many organizations: AI adoption has outpaced the right security measures. In the report, nearly 9 in 10 (87%) organizations had moved AI assistants beyond the pilot stage, and 76% were actively piloting or rolling out autonomous agents. But that activity has outpaced security maturity: 63% had AI security controls in place, but 52% weren’t completely confident those controls would detect a compromised AI.

AI tools are embedded directly into communications to increase productivity and speed, but in the rush to be efficient and compete, AI permissions and access to sensitive data are being left unchecked.

These aren’t complex security engineering problems: they are organizational and process gaps that can be addressed without waiting for the tooling market to mature. Currently, only a few organizations have developed their incident response playbooks, logging coverage, or forensic tools needed to investigate a compromised AI agent.

For many businesses lacking the in-house skills needed to take on these items, which can be significant, contracting a trusted managed service provider (MSP) can help bridge the gap. Many MSPs have been assisting businesses with AI adoption for some time now, and many have the deep understanding and technical skills needed to help businesses of all sizes see tangible benefits to AI while keeping critical data safe.

The origins of AI attacks

Most attacks start with unrestrained access and end with autonomous systems exposing sensitive data from these environments. When threat actors target agentic systems that lack proper controls, they don’t need to trick employees to access internal intelligence; they only need to manipulate the AI.

Prompt injection attacks are a common way to do this. A bad actor might send a target user seemingly helpful AI instructions while posing as a trusted authority or co-worker. A well-intentioned employee may then ask an AI to answer what seems like a simple inquiry.

Depending on the attacker’s instructions, the AI agent may instead be tricked into reading manipulated webpages (i.e., white text on a white background) to unwittingly extract internal data and send it to the attacker’s server.

For prompt injection attacks, it’s important to limit AI agents’ access to only the tools and data they need to complete the designed task. This is a good solution to prevent AI from giving out more information than required. This can limit the scope of an external threat actor’s reach.

That said, the employee’s role isn’t lost in all AI-based attacks. An ongoing cybersecurity skills gap severely impacts defenses, and threat actors know this.

Over the last decade, multi-factor authentication (MFA) has been an important step toward stronger security authentication. But today, attackers can pair AI-generated phishing with ‘MFA bypass kits,’ such as open-source Evilginx (known as a penetration testing utility for these styles of attacks) and the W3LL panel (a private phishing kit) to deceive employees into handing over that ‘extra step’ of security.

Tools like Evilginx and the W3LL phishing kit are used to create realistic sign-in pages that mimic those of Google, Microsoft, and others. Without proper security training, employees may unwittingly be signing into these while attackers capture their session tokens – even those with MFA.

A good defense against MFA bypass kits is adopting phishing-resistant MFA technologies such as FIDO2 hardware keys, Windows Hello for Business, Certificate-based Authentication (CBA), and Passkeys. These methods are tied to legitimate sign-in pages and don’t work on fake pages.

However, stopping the threat from ever materializing starts with having proper cybersecurity awareness training. Nearly half of all organizations lack this training or simply adopt a checkbox approach, which is why implementing these programs is an important step to closing the gap.

These programs teach users to spot a myriad of cyber threats, including AI-based threats. Tools of this type are also a good example of using AI in defensive security, as some can leverage AI to customize the training an end user receives based on their performance in past training.

This is another area where MSPs are highly qualified to assist. MSPs typically run training programs across a vast number of users and industry types. They understand what training works and what doesn’t, and can help position the best security awareness training for a given organization.

How does AI enhance threat detection?

To understand the power and importance of AI-powered cybersecurity, it helps to understand how it works. Once trained, a detection model becomes exceptionally good at spotting the characteristics of malicious activity. It can take into account thousands of different characteristics to spot anomalies, outliers, and similarities that humans are unable to correlate.

It’s important to have a reliable cybersecurity service provider with REAL AI skills, because machine learning systems aren’t perfect and (while rare) can produce false positives. A strong partner can minimize these false positives while offering real-time threat detection and analysis, as well as faster, well-informed response times.

A trusted MSP will have a wide range of capabilities and will have a deep understanding of the protection methods that work well within their target industries. By leveraging that deep knowledge from their partner MSPs, businesses will benefit from great protection, even with today’s AI-powered attacks.

Preparedness in 2026 and beyond

To operate in this new era, businesses must treat every AI agent as a high-risk workload identity.

In practice, this requires working with reputable MSPs to implement strict least-privilege access to avoid data leaks, constant monitoring to protect the integrity of the data, and comprehensive employee awareness training.

The cybersecurity market has long added to the stack every time a new threat or gap emerges. Another tool, another agent, another gateway, another monitoring layer.

Over time, enterprises end up with sprawling stacks that are expensive to license, difficult to manage, and increasingly hard to justify. Channel partners likewise ended up with a bloated offering that can feel redundant and hard to manage.

And worse, for all their complexity, many of them are pointed in the wrong direction. One of the biggest security gaps today is an area few are actively dealing with – securing the browser.

Where the working day actually lives

For most employees, the browser is no longer a gateway to work; it has become the primary workspace. SaaS platforms, collaboration tools, and AI applications have moved the working day almost entirely online.

Yet security hasn't kept pace. The controls most organizations rely on were built around corporate networks, managed devices, and applications behind a firewall. They control whether a user can reach an application. They cannot see what happens once that user is inside it — whether data is being pasted into a public AI tool, copied to a personal account, or handled in ways that create real exposure.

AI adoption is accelerating the market need. Employees use generative AI tools daily, often through personal accounts and outside IT visibility. For SMEs relying on their MSP to bridge the gap between ambition and capability, the question of how to adopt AI safely at scale is already on the table.

The enterprise browser difference

The idea of a secure browser isn’t a very exciting sell, but the enterprise browser has a lot more on offer. It embeds security and data protection controls directly into the session, at the point where the user interacts with the application and the data. That is fundamentally different from tools that inspect traffic or attempt to intercept data movement after the fact.

The shift goes from managing access to managing behavior. Traditional security asks whether a user can reach an application. The more consequential question is what happens once they are inside it, and that’s also where most stacks go dark. Policy applied at the point of action makes those moments governable without disrupting workflow.

Where conventional controls block and restrict, enterprise browsers enable, guide, and make users more productive, applying controls contextually rather than as blanket restrictions. Zero trust is more fully realized, because rather than a one-time identity check at login, session context is assessed continuously, adjusting as circumstances change.

The consolidation opportunity for MSPs

With most organizations already dealing with bloated security stacks, consolidation has become a more valuable proposition than expansion.

An enterprise browser plays into this by replacing multiple parts of the existing stack rather than simply adding another tool. VDI and AVD deployments, VPN infrastructure, CASB and DLP layers, even managed device logistics exist largely to compensate for what consumer browsers cannot do. Solve the problem at the source, and many become redundant.

Further, once deployed, MSPs gain a persistent foothold from which to roll out additional policies and capabilities without returning to the customer environment. With 84% of MSP clients now expecting cybersecurity as a core part of the service offering, up from 65% the previous year, delivering that through a consolidated architecture separates competitive partners from the rest.

Why now? And where to start?

Partners who build capability now will meet demand as it accelerates; those who wait will be competing for ground already taken.

The SME market needs architectural innovation from the industry, with many organizations depending on their MSP precisely because they lack the internal resources to run complex security programs.

Sectors like hospitality also stand out: cost-conscious, high turnover, and acutely exposed to credential and token theft. Legal services present a similar profile, being heavily regulated but without the in-house expertise of financial services peers.

Across all of them, AI governance is the accelerant. Every customer asking how to adopt AI safely is, whether they know it or not, asking a question the browser layer is uniquely placed to answer.

The stack got big by solving the wrong problem. The opportunity for MSPs is to solve the right one starting in the browser.

On July 13, 2026, the Department of War suspended the Cybersecurity Maturity Model Certification (CMMC) Phase 2, the third-party assessment mandate that was set to take effect November 10.

Within days, managed service providers (MSPs) across the defense channel started fielding the same question from clients: does this mean CMMC is dead?

It doesn't. And the MSPs who answer that question wrong are about to hand their most defensible service line to competitors who read the suspension memo more carefully.

Reality check

Here's what actually happened. Department of War CIO Kirsten Davies suspended the requirement for defense contractors to pass a Certified Third-Party Assessor Organization (C3PAO) audit before winning Level 2 contract awards. She did not suspend Phase 1. Self-assessment, SPRS score submission, and annual affirmation obligations under DFARS 252.204-7012 remain fully enforced, and NIST SP 800-171 Revision 2 stays the standard the Department checks against through self-assessments and select government-led reviews.

The reasoning behind the decision was blunt. Davies told reporters “the math just simply doesn’t math” for the roughly 100,000 companies in the defense industrial base needing third-party assessment against the roughly 100 approved C3PAOs able to deliver it, with Small Business Administration data pointing to compliance costs approaching $7 billion a year for small and mid-sized contractors. A CMMC Reform Task Force review is due back to the Department CIO by mid-September, and officials have explicitly declined to rule out ending the third-party model altogether.

That's the pattern MSPs need to see clearly. The Department suspended a verification mechanism. It did not suspend the obligation that mechanism existed to verify.

Access controls

Here's where it gets uncomfortable for MSPs specifically. Self-assessment doesn't mean a client certifies in a vacuum. On most defense-adjacent accounts, the MSP is the party actually configuring the access controls, generating the audit logs, and often shaping the SPRS score the client submits to the government. When that score doesn't match reality, the False Claims Act attaches liability to whoever made the certification. Civil penalties currently run $14,308 to $28,619 per false claim, plus treble damages, regardless of whether a C3PAO ever shows up to check. The suspension removed the outside assessor who might have caught an inflated score before it became a submitted claim. It didn't remove the claim.

That's not theoretical. On June 18, 2026, the Department of Justice announced a $507,144 settlement with LOGZONE Inc., an Alabama defense contractor, over allegations it failed to implement NIST SP 800-171 controls required under two Department of the Navy contracts -- no C3PAO ever flagged the gap.

The case moved through DOJ's Civil Cyber-Fraud Initiative, which has settled fifteen cybersecurity-related False Claims Act cases since 2021, more than half of them in the past fiscal year alone. Many of these cases start with a whistleblower under the FCA's qui tam provisions, not a government audit, which means the C3PAO pause does nothing to shrink the pool of people who can bring one. The suspension changes who checks the box before awarding. It does nothing to the enforcement pipeline that starts after the box is checked wrong.

Reforming rather than removing

MSPs who read “Phase 2 suspended” as “stand down” are making the wrong bet twice. First, they're leaving a client's self-attestation exposure unmanaged during the exact window when nobody else is checking it.

Second, they're assuming the review ends in cancellation, when the Department has said only that it's reforming the model. Not abandoning the requirement to prove contractors protect controlled unclassified information. The 2021 transition from CMMC 1.0 to 2.0 set the precedent: the verification mechanism changed, and the underlying NIST 800-171 obligation never moved.

The MSPs who win this window aren't the ones still pitching “get certified before Phase 2 hits.” They're the ones using the pause to get the evidence layer right while the pressure is off. SPRS scores tied to real configuration data instead of a best guess, continuous monitoring instead of a point-in-time snapshot, and an audit trail that a future assessor can read without a client scrambling to reconstruct six months of change history. Build that once, on a platform that keeps every access log and configuration change in a single, unalterable record, and it holds up whether the Task Force brings back C3PAO assessments, swaps in a lighter validation model, or lands somewhere nobody has floated yet.

None of this requires exotic tooling. It requires the same discipline MSPs already apply to other compliance regimes. Version-controlled system security plans, POA&Ms with real remediation dates and supporting evidence, and SPRS scores a client's own configuration data that can reproduce on demand, not just defend on paper. Treating this as a documentation exercise misses the point. The documentation only holds up if it matches what the systems actually do.

That gap is wider than most MSPs assume. Kiteworks and Coalfire's State of CMMC 2.0 Preparedness in the DIB report found only 46% of surveyed defense industrial base organizations considered themselves ready for Level 2 certification. What’s more, just 44% had continuous monitoring in place across in-scope systems. Gaps that predate the suspension and that a pause in third-party audits does nothing to close.

The suspension bought the defense industrial base time. It didn't buy MSPs an excuse. The ones who spend the next two months building evidence architecture, instead of waiting on the Task Force report, will own the client relationship no matter what that report recommends.

Factors such as rising geopolitical tensions, cyber threats, energy instability, and supply chain disruption continue to reshape global technology. As a result, businesses are rethinking where critical workloads should sit.

Organizations are looking at data sovereignty to achieve greater control and resilience during turbulent times, shifting the concept away from a legal consideration and toward an infrastructure design decision.

In fact, sovereignty is cited as important to almost all (98%) IT service providers when choosing cloud or data center providers for their organization, according to our research. With many businesses opting for local data control, reassessing where their infrastructure, workloads, and critical data physically reside will be key.

The factors driving infrastructure changes

Geopolitical risk was once a secondary consideration in infrastructure planning, but it has quickly become a board-level concern. Now, 96% of IT service providers cite geopolitical threats when devising infrastructure strategies. They are thinking more about geographic exposure and the dependencies that sit underneath digital operations. The shift was already underway before recent geopolitical tensions intensified this shift. Gartner figures from 2025 showed that 61% of CIOs and IT leaders in Western Europe were looking to increase reliance on local cloud providers to protect their data.

The key threats shaping moves toward localization include energy price spikes from global conflicts (95%) and disruptions to shipping routes and cyber campaigns by hostile states (both 93%). Tightening data sovereignty laws and regulatory frameworks are also playing a part. For example, the ICO updated its international transfer guidance in January 2026, with the introduction of a three-step test to decide whether a data transfer should be restricted, such as when the UK’s GDPR applies.

Organizations want to know which jurisdictions apply when opting for a provider in a particular region, and whether clear evidence of data movement can be provided if regulators ask the question. 46% of IT service providers also cite ISO 27001/22301 and similar certifications as a main factor when it comes to choosing a UK data centre provider for sovereignty-sensitive workloads.

Compliance in an ideal scenario should inform infrastructure decisions from the very beginning, and certified, carrier-neutral data centres can play a big role here. They offer predictable governance and clearer evidence that regulatory requirements are being met, alongside low-latency connectivity to ensure innovation can continue to happen across borders.

Cloud strategies are evolving

Sovereignty concerns are greatly impacting cloud, colocation, and hybrid infrastructure decisions. In particular, cloud strategies are changing, with much more consideration around where workloads are being housed.

An often opted-for approach is the most sensitive data being kept in-country, while the less sensitive data is pushed out to the bigger cloud platforms. This distinction is important because the more sensitive data requires much tighter jurisdictional control, perhaps due to regulatory requirements or sometimes because of the market they serve. Global cloud platforms offer the needed reach for other services that rely on less-critical data.

Organizations must keep in mind that while sovereign cloud initiatives can enhance resilience and control, they must do so in a way that doesn’t foster isolated environments. Sovereignty should never be treated as a boundary. Of course, effective governance of data is crucial, but applications must continue to provide value and workloads that can move if circumstances change. When executed correctly, sovereignty should provide tested failover routes, jurisdictional control, and the ability to leverage the services offered by cloud and network partners.

With geopolitical tensions creating uncertainty, businesses want to be sure that if one part of their infrastructure is negatively affected by an incident, they can continue operating without losing control of sensitive workloads. Data centre interconnections support sovereignty by offering operational redundancy, such as multiple backup power sources.

The role of channel partners

Channel partners can play a central role in the sovereignty shift from board-level concern to practical infrastructure consideration.

As organizations look to achieve greater control over sensitive data and make use of secure access to cloud services, managed service providers (MSPs) and system integrators (SIs) have an opportunity to guide them through the right infrastructure choices.

Channel partners can essentially act more as strategic advisors, helping inform decisions around colocation, cloud connectivity, workload resilience, and secure data movement. The value is in helping those businesses to make sovereignty actionable.

How sovereignty’s success will be judged

With global risks escalating and compliance requirements tightening, data sovereignty is becoming a greater infrastructure priority. Local control is important, but it only creates value when supported by clear governance, strong security and the flexibility to adapt.

Careful decisions must be made around which data stays local and where cloud and colocation can provide value, alongside maintaining continuity during disruption.

For channel partners, this creates an opportunity to help customers connect sovereignty with resilience and performance. Overall, sovereignty’s success will be judged on how well it can support control, connectivity, and continuity.

Ten years ago, a server outage might have affected a single office or application. IT was simpler, stakes were lower and customers were willing, and able to accept a reactive relationship. Managed Service Providers (MSPs) built their value on ticket closure rates, uptime stats, and how quickly they could put out fires. It worked. But that era is over.

Today, a single point of failure can ripple across hybrid cloud environments, disrupt customer experiences globally and trigger regulatory risk. Businesses aren’t looking for faster helpdesks anymore; they’re demanding strategic IT partners that can provide true visibility into their environments and clear guidance on how to use technology to drive business outcomes. And they want all of this while budgets tighten and risk intensifies.

IT leaders are also operating under financial pressures. They’re no longer judged solely on system stability, but are expected to help drive revenue, support innovation and manage risk.

At the same time, many IT leaders are being asked to deliver AI projects, strengthen cybersecurity and drive digital transformation projects, without proportional increases in IT budgets. This is resulting in a more detailed examination of infrastructure spending, particularly around refresh cycles and long-term support strategies.

In this landscape, MSPs that still rely on a break-fix model aren’t just behind the curve; they’re becoming irrelevant. Support capabilities are still important, but the real difference now is made by those who go beyond fixing issues to become more of a strategic partner to their clients.

From visibility to insight

Today’s enterprise estates are fragmented by design, spanning on-premises infrastructure, colocation, multiple public clouds and an expanding edge. Layer in legacy systems, cloud-native applications and a patchwork of vendors and operating models, and the result is an environment that isn’t just complex; it’s opaque.

It is that opacity that’s the real challenge.

It’s no longer sufficient to detect when something breaks. By the time an alert triggers, the damage is often already underway. What IT leaders need now isn’t more monitoring noise; it’s clarity. They need partners who can cut through the sprawl to reveal how infrastructure is actually performing, where risk is accumulating and where inefficiency is quietly draining value.

This is where infrastructure intelligence comes in. Infrastructure intelligence goes beyond traditional monitoring tools to bring together data from across hybrid environments and layers in analytics, automation and contextual awareness. Rather than generating more noise, infrastructure intelligence is about filtering, correlating and interpreting signals in real time.

This enables MSPs to understand not just that something is happening, but why it’s happening and what the downstream impact will be. For example, instead of flagging a spike in CPU usage as an isolated event, it can tie that signal to broader workload behaviour, capacity trends or emerging resource constraints across environments.

This shift allows MSPs to move away from reactive incident management and towards predictive and preventative operations. Patterns can be identified before they escalate into outages, capacity can be optimised ahead of demand, and risks can be surfaced long before they impact users. In this model, insight replaces hindsight, giving both the MSP and the customer far greater control over performance, cost and resilience.

Redefining infrastructure lifecycle strategy

Traditional OEM-led refresh cycles are also coming under scrutiny. It no longer makes financial sense to replace hardware on a fixed timeline when it’s still running effectively. As a result, organizations are more likely to extend infrastructure lifecycles, provided they have the expertise to manage risk and performance. For MSPs, this creates an opportunity.

Partners that can help customers balance performance, reliability and cost optimization are playing a far more strategic role in IT decision-making. Third-party maintenance (TPM), once seen as purely a cost-cutting tactic, is now playing a key part in broader infrastructure optimisation. In hybrid environments, it gives organizations the flexibility to keep reliable systems in place longer, while putting their investment into the areas that really need it.

This allows MSPs to go beyond service delivery and play a more strategic advisory role, helping customers understand what to replace, what to keep and how to align support with what the customer actually needs. By combining lifecycle data, performance insights and support histories, MSPs can make far more informed recommendations about when to extend, when to replace and where to optimize.

This evolution doesn’t diminish the role of OEMs or traditional managed services. It reflects a shift towards the idea that customers now expect flexibility. In multi-vendor, hybrid environments, MSPs are increasingly judged not by vendor alignment, but by their ability to deliver outcome-driven, objective guidance.

The future of the MSP

The definition of value in managed services is being fundamentally rewritten. Closed ticket metrics still matter, but they no longer define the leaders in the market. Value is now measured by what doesn’t happen - outages avoided, inefficiencies eliminated, and unnecessary spend reduced. It’s reflected in better utilization, longer asset lifecycles and infrastructure that performs predictably under pressure.

To meet these expectations, MSPs need to evolve both what they offer and how they deliver it. They also need a mindset shift away from simply managing infrastructure and towards making sense of the data it generates and turning it into meaningful business insight.

This shift is changing the growth trajectory of the channel. MSPs that stick to a break-fix model risk being reduced to a commodity, competing primarily on price as margins continue to shrink.

The next phase of growth belongs to MSPs that can move upstream, translating data into insight, insight into action, and action into measurable outcomes. That requires building infrastructure intelligence capabilities that go beyond visibility, integrating optimisation and lifecycle management into a continuous, insight-driven model of service delivery.

As IT estates continue to expand and fragment, success will depend on the ability to provide direction as much as delivery. MSPs that can simplify complexity and help organisations make better decisions will move beyond day-to-day operational support to become true strategic partners, trusted not just to run infrastructure, but to help shape how it develops over time.

Most Managed Service Provider (MSP) service desks already have several layers of automation. From chatbots and self-service portals to AI routing and virtual agents, the tooling is widely used. Some of it resolves tech issues well, ensuring that skilled engineers are not wasting time on password resets and basic access requests.

However, problems usually start when a ticket is passed between multiple teams or arrives without the context needed for troubleshooting.

Even though MSPs are already tracking the usual metrics: ticket volume, average handling time, first-contact resolution, and technician workload, L1 teams can spend huge chunks of the day just trying to gather context. Some support teams jokingly call it the “20 Questions” phase. Who is the user? What device are they using? Has anyone already touched the machine? Did the VPN fail before the update or after it? Is this even the right queue?

A lot of that happens because support teams still lack clear visibility into what users are experiencing on the endpoint. Most MSPs now operate across ticketing, endpoint management, monitoring, remote access, and documentation tools that do not always share information particularly well. So technicians end up jumping between systems trying to piece together context that should already be sitting in front of them.

According to our analysis presented at the Gartner Digital Workplace Summit London recently, saving just one minute of context-switching time across 10,000 monthly tickets equates to 166 hours of recovered support capacity.

That is basically a full-time technician disappearing into tab-switching and context rebuilding, which is not exactly a great use of skilled people.

Escalation is where things really start getting expensive

In reality, many escalations just restart the troubleshooting process from scratch. Support teams have all kinds of names for this: verification tax, re-diagnosis, or ticket ping-pong. In some environments, AI-assisted triage has actually made this harder to spot because tickets arrive looking neatly categorized while still missing critical context.

Nobody fully trusts the notes attached to the ticket, so the next technician repeats the same checks anyway. And honestly, sometimes they have a point. By the third reassignment, half the ticket notes barely make sense anymore.

That gets expensive fast once senior engineers start getting dragged into tickets that never should have reached them. L2 teams can spend 15–30 minutes rechecking information already confirmed earlier in the support chain. L3 engineers may still insist on verifying the root cause themselves before touching anything important. Leaving some tickets basically on a doomed escalation path from the moment the initial diagnosis goes wrong.

If the underlying ticket data and context are weak, automation can just accelerate bad routing decisions. Tickets land in the wrong queues, bounce between teams, or get escalated before anybody has properly understood the issue in the first place. Some queues basically become dumping grounds for badly categorized tickets.

Why AI support struggles with unpredictable problems

Every MSP wants users to handle the simple stuff themselves rather than flooding the queue with password resets and printer tickets.

The trouble is that self-service tends to fall apart pretty quickly once the issue is no longer straightforward. Somebody reports a “slow laptop.” The system suggests a few generic fixes. Nothing changes. The ticket lands in the wrong queue anyway. Then an L1 tech has to start from scratch, figuring out whether the problem is Wi-Fi, memory usage, a bad update, or some background process chewing through the CPU.

Failed self-service gets expensive fast. Failed self-service attempts can push incident-handling costs from roughly $6 to $53 once escalation and repeated troubleshooting are involved.

Part of the problem stems from AI-driven support tools that are trained on historical ticket data and static workflows, rather than real-time device information. After all, real support environments drift all the time. Devices fall out of policy. VPN issues hit one office but not another.

That is where the bad assumptions creep in. Tickets look neatly categorized even when the underlying diagnosis is wrong. By the time the issue escalates, half of the support recommendations are due to AI hallucinations. Skilled technicians recognize that almost immediately. However, the other automations usually do not.

Reducing wasted effort in the service desk

Just throwing more AI at the service desk doesn’t fix the underlying diagnosis problem. In reality, technicians still spend way too much time piecing together context, rerunning the same diagnostics, and trying to figure out how a ticket even ended up in their queue after failing First Correct Assignment.

Smart MSPs are starting to tackle the problem at the source. They’re rebuilding their self-service portals so users can just explain what’s wrong in plain English, rather than forcing them to pick from clunky categories. After all, no one ever reports a “DHCP lease failure”; they say the internet’s down. L1 techs are also gaining real-time visibility into endpoints, rather than relying on patchy ticket notes and whatever the user happens to mention.

When you can instantly see failed updates, VPN drops, crashed services, or a machine grinding to a halt, you waste a lot less time playing detective. But even with those improvements, most MSPs still hit the same wall: all the important information lives in separate systems.

Ticketing, RMM, monitoring tools, and endpoint agents don’t communicate effectively. So every time a ticket escalates, the next person basically has to start from scratch. That’s why some of the better-run MSPs are now creating a single shared record for endpoint health and diagnostic history. When L1, L2, and L3 are all looking at the same up-to-date facts, you cut out the endless re-checking and stop tickets from getting stuck on a doomed escalation path.

At the end of the day, automation and AI are only as good as the foundation they’re built on. If self-service keeps generating dirty tickets, or tickets keep getting escalated too early, or rebuilt every time they move queues, AI is just helping you make the same mistakes faster. The teams getting real results are the ones giving every support tier access to the same live endpoint context; that’s when AI actually starts pulling its weight.

Netgear is making strides in helping businesses make the move into AI-powered network operations and management with the launch of Netgear Insight 10.0.

This next-generation cloud network management platform will be game-changing for small and medium-sized businesses (SMBs) and Managed Service Providers (MSPs), plugging the gap between need and resource, according to the firm.

Insight 10.0 is designed to support organizations through delivery of enterprise-grade AIOps, intelligence, and operational simplicity, helping them to make decisions faster and move their business forward.

One key benefit of the new platform is empowering network administrators to move from being purely reactive to being proactive, according to Luca Marinelli, Netgear’s head of Europe.

In terms of launches, Marinelli said this is a pretty significant one for the company and a key focus during his time with the business so far, following his appointment in October last year.

“The opportunity to have a single pane of glass, which will be managing simultaneously – like with multi-tenancy – several networks will free up time for [MSPs] to maybe start providing other additional services to attract new customers, which is always something very healthy to do in business.”

“[Also] if you perform certain types of activities in a reduced amount of time, your margin can be better. This results in a much more profitable business. So, there are multiple impacts [from] using more sophisticated state-of-the-art tools," Marinelli added.

"Sometimes the feeling is that AI is just a nice slogan that you need to just add in everything you say. [But] I think the important thing is to know what to do with this superpower. If you know what you want to do, it is extremely valuable.”

Outside of product innovation, Netgear has been heavily focused on how it partners to do business. Indeed, in November 2025 it unveiled changes to its partner program in a bid to make it easier to work together and drive joint success.

At launch, Netgear’s president and general manager, Pramod Badjate, said partners were at the center of everything the company does.

“Our big play at Netgear is delivering enterprise-grade products with the simplicity that the SME market needs. That all boils down to a product that is ready for those environments, but also, from a TCO point of view, fits that customer experience really well,” said Jordan Hobday, Netgear’s UK country manager.

“The Netgear Drive Partner Program is three tiers. It's there to really incentivize and reward our partners. There's a commercial benefit to that, but equally it's about how we [can] provide a good experience to our partners, like certifications, self-serve material, all the good things that you would expect.

“A big part for us is simplicity, and how easy it is to work with or do business with Netgear.”

Netgear’s EMEA business is in good health, according to Luca, but he said the company’s turnaround journey was not finished yet. Indeed, one priority for him is not just growing partner numbers for numbers' sake, but really focusing on deeper engagement.

“I think we have, in my opinion, enough partners that we are working with. The game is to keep them interested; I don't necessarily think we need to increase the number of partners broadly. It's going to be a very selective move in both the AV and IT markets to capture the real players that we need to work with, in a given territory, in a given market,” he said.

“We work with a lot of partners, but the number of partners we have this business intimacy with is not at all at the level where it should be. It’s about ways of working with partners; it's about tools that we keep evolving; it's about the value we bring to those partners and the way we spend time. We do spend time [with partners] already, of course, but [it’s about] the way we spend [that] time. It has to evolve.

“It's [about] more quality, more depth, more intimacy, more shifting the way we work with partners, which takes a little while. It doesn't happen overnight.“

Dropzone AI has announced an exclusive EMEA distribution partnership with QBS Software, in a move the company said will expand the availability of its AI-powered security operations platform to MSSPs and VARs across the region.

The agreement will see QBS distribute Dropzone AI’s Agentic SOC platform, which uses a team of AI agents to automate security alert investigations across existing security environments.

According to the vendor, the partnership aims to help channel partners address the growing demand for security operations capabilities as organizations contend with rising alert volumes, increasingly sophisticated cyber attacks, as well as an ongoing shortage of skilled SOC analysts.

For MSSPs, Dropzone said the platform can help scale managed SOC services without requiring a proportional increase in analyst headcount, while VARs will be able to offer the technology to customers operating in-house security operations centers.

"Security teams are under immense pressure to scale operations, meet increasingly stringent SLAs, and ensure regulatory compliance while maintaining resilience against rising alert volumes,” said Brett Candon, Dropzone AI’s vice president of international, in an announcement.

“Through this partnership with QBS, Dropzone AI will extend its reach to both MSSPs and VARs across EMEA. MSSP partners can scale service delivery and improve profitability, while VAR partners can help customers with in-house SOCs move from alert chaos to incident focus."

Dropzone’s Agentic SOC solution is built around its AI SOC Analyst, which investigates security alerts from across an organization’s existing security stack before escalating confirmed threats to human analysts for response.

The technology integrates with existing SIEM, SOAR, EDR, and case management platforms, allowing organizations to augment existing security operations without replacing current infrastructure.

Benefits for MSSPs and VARs

Dropzone said its platform will help MSSPs improve SLA performance, onboard customers faster, increase SOC capacity, and free up analysts to focus on high-value tasks while building higher-margin managed security services.

Meanwhile, VARs will be able to support customers operating their own SOCs by leveraging the offering to accelerate investigations, reduce false positives, ease alert fatigue, and address temporary cyber security skills shortages.

The companies said the agreement expands the channel opportunity across EMEA by enabling partners to support customers with managed, co-managed, and in-house security operations models while helping address the ongoing shortage of skilled SOC analysts.

"Our partnership with Dropzone AI strengthens QBS' security portfolio with advanced autonomous AI SOC capabilities that addresses a real challenge across the channel,” commented Tom Corrigan, chief revenue officer at QBS Software.

“By bringing category-defining AI SOC capabilities to our partner ecosystem, we're supporting growth, improving service delivery, and helping partners deliver stronger customer outcomes.”

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UK Managed Service Providers (MSPs) have embraced flat-rate support because it’s made delivery scalable: standardize the stack, package the service, and give customers a predictable monthly cost.

However, support has expanded faster than fixed fees can keep up with. Users expect instant fixes, boards expect resilience, and compliance teams, insurers, and auditors increasingly expect proof.

At the same time, the modern customer estate - SaaS, cloud, remote endpoints, third parties, and integrations - creates more noise, more incidents, and more out-of-hours work.

The somewhat predictable result is margin erosion: more tickets, more tools, more engineer time, and the same recurring revenue.

That’s why, for MSPs, observability needs to become a clearly defined, billable capability focused on outcomes customers recognize - fewer avoidable incidents, faster resolution, better user experience, and evidence that stands up in audits and insurance questionnaires.

Flat-rate contracts stay profitable when demand is reduced, not just responded to, and managed observability is one of the most practical levers MSPs have to do that.

Support is scalable when demand is predictable

Flat-rate support only works when MSPs can predict and reduce demand. But customer estates keep getting more complex while contract values stay flat, so every extra ticket and escalation eats margin.The usual culprits are ticket volatility, tool sprawl that slows triage and drives up costs, and escalation creep that pushes routine issues onto senior engineers. When resolution slips, SLA penalties and reputational damage follow.

That’s exactly what observability should fix, preventing avoidable incidents and cutting time-to-resolution, but too often it’s treated as “free”, and the service desk absorbs the cost.

Observability becomes billable when it’s auditable

In the channel, monitoring can sound like a checkbox. Customers assume it’s already included, like antivirus used to be, until you translate it into outcomes they care about:

• Less downtime (and fewer productivity losses)
• Faster incident resolution (and fewer escalations)
• Better user experience (especially for SaaS and remote work)
• Evidence for audits, regulators, and cyber insurers (not just “we think it’s fine)
• Capacity and cost control (no surprise renewals, no shadow IT growth)

For many organizations, that evidence now affects audit outcomes and cyber insurance terms, including whether cover is available and how premiums are priced. That shift in language matters because it changes observability from a feature to a service line: something with a scope, deliverables, and a price.

Tier it: response vs assurance

MSPs don’t need to rip up contracts to monetise observability; they can add outcome-based tiers that make proactive work visible and separate reactive support from assurance.

A simple model:

• Baseline covers availability checks, basic alerting, and standard response
• Managed Observability is a paid add-on: alert tuning, SLOs, dependency mapping, service reviews, proactive fixes, plus monthly reporting that supports audits and insurer questionnaires
• Service Assurance is the premium layer for customers who need stronger governance: continuous optimisation, capacity planning, change-impact analysis, and board-ready resilience reporting

The key differentiator isn’t more alerts, it’s less noise and more action. Fewer false positives, faster diagnosis, and a credible narrative of what you prevented, not just what you fixed after the damage was done.

Packaging that doesn’t feel like platform marketing

The fastest way to make observability feel promotional is to centre it on a platform. The fastest way to make it feel valuable is to centre it on operations.

Three practical packaging moves MSPs are using:

1. Define the observable estate: Be explicit about what’s covered (networks, cloud, critical SaaS, key user journeys, core systems) so the scope is clear.
2. Make proactive work visible: A monthly assurance summary showing issues removed, top drivers, automation time saved, and early risks - reusable for audits and insurance.
3. Tie it to business-critical moments: Position it as risk control during migrations, site moA simple model: ves, mergers, major upgrades, and security hardening, not just BAU monitoring.

SLOs, not uptime: measure what users feel

Packaging won’t protect margins if the NOC is already drowning. Observability only becomes a revenue engine when it’s operationally credible, and that starts with alert quality. Duplicate, low-value alerts create noise, slow triage, and trigger unnecessary escalations, inflating delivery costs. Good alert hygiene means de-duplicating, setting thresholds that reflect real user experience, and tuning continuously based on what actually drives incidents.

Consistency matters, too: standard runbooks keep first-line triage predictable and reduce senior-engineer drag. Measure what customers feel with SLOs (latency, transaction time, failed logins, API errors), not uptime alone, and “single pane of glass” should mean one accountable operational picture, not one tool.

The unglamorous truth is that MSPs protect margin not by collecting more telemetry, but by turning telemetry into fewer tickets.

Compliance pressure is rewriting the value conversation

Budgets are tight, suppliers are being rationalised, and procurement is increasingly asking why they are paying if things still break.

Cyber insurance and regulation are sharpening that pressure. Insurers want evidence of monitoring and operational oversight, and frameworks like FCA operational resilience, DORA, and NIS2 are raising expectations for mapping and proof. In many cases, observability is moving from nice to have to a commercial or contractual requirement.

That’s why monetising observability works now: it shifts the conversation from reactive support to measurable assurance, differentiates you from low-cost providers, and protects margin without adding headcount by preventing failures from becoming tickets in the first place.

Analysts and Managed Service Provider (MSP) leaders have warned that the industry must evolve beyond traditional support services if it is to remain competitive in the AI era.

Speaking at Kaseya Connect Europe in Prague, Omdia principal analyst Maxine Holt described a move from “MSP 2.0” to “MSP 3.0” – a model built around platforms, automation, compliance, and business outcomes rather than reactive IT support.

“MSP 3.0 isn’t just about doing what you’ve always done, but with better tools,” explained Holt.

Instead, she argued, MSPs need to rethink how they create value for customers, moving from technology operators to strategic advisors.

The end of the traditional MSP?

Jack Peploe, founder of MSP Veterinary IT Services, agrees with the need to overhaul the services offered to customers.

“The world of an MSP is dying. I genuinely believe that,” he told Channel Pro.

Peploe points to how the traditional MSP model built around monitoring, patching, and first-line support is becoming increasingly automated - and MSPs are being pushed higher up the value chain. His own company has already removed its traditional first-line support team.

“We haven’t lost anyone, we’ve upskilled them,” he told ChannelPro.

For Peploe, success increasingly depends on understanding customer industries, workflows, and business challenges rather than simply managing technology.

“What [customers] want is someone that really understands them, gets them, understands what they can do, understands their processes, their flows, the inefficiencies,” he said.

AI adoption remains slow

While AI dominated discussions at the event, there were warnings that adoption remains slower than many organisations would like.

Omdia research presented at the event found that fewer than one in five AI proof-of-concept projects ultimately make it into production, highlighting the gap between experimentation and deployment. The biggest barriers include integrating AI with existing infrastructure, a lack of in-house expertise, and difficulties defining clear business outcomes.

Kaseya CEO Rania Succar argued that partners have an important role to play in helping customers bridge the gap between experimentation and deployment.

“The adoption of agentic solutions to date has been too slow,” she told ChannelPro.

Similarly, Holt suggested that customers need practical guidance rather than more hype around AI.

“SMBs don’t need more AI hype,” she said. “They need practical guidance on how to actually implement AI in ways that integrate with existing systems, deliver measurable value, and don’t require them to become AI experts overnight.”

Platforms become the foundation

Underlying many of these changes is the growing importance of platform integration. As MSPs adopt more automation and AI capabilities, fragmented toolsets are becoming increasingly difficult to manage.

“It’s not about the best tool for each of the areas operating independently,” said Holt, who said MSP 3.0 will be built on “fewer platforms with much better integration”.

She added, “MSP 3.0 providers don’t just respond to problems, they anticipate them. They don’t just implement solutions; they recommend strategies. And they don’t just manage technology; they enable business outcomes.”

Kaseya has outlined an ambitious plan to unify its sprawling product portfolio around a common data layer, open APIs, and AI-driven automation, as the vendor seeks to move beyond its acquisition-led past and position itself for what it calls the era of autonomous IT.

The announcement, made at Kaseya Connect Europe in Prague this week, marks a strategic shift for a company that’s spent much of the past decade expanding through acquisitions. Rather than adding more products, execs are now focused on integrating the technologies already inside the portfolio.

“We’re doing the hard work to connect the data and open up the APIs,” Kaseya CEO Rania Succar told ChannelPro.

Focus shifts to platform architecture

The strategy is being led by CTO Pratik Wadher, who joined the company last year and has been tasked with building a common platform architecture across Kaseya’s product set. The long-term goal is to create a unified data layer capable of supporting AI agents that can automate ticket triage, remediation, reporting, onboarding, and other routine operational tasks.

Succar argued that many vendors have focused on adding AI features without first addressing the underlying data challenges.

“We’re actually doing the hard work to connect the data and open up the APIs,” she said, adding that AI becomes easier once those foundations are in place.

Elsewhere, Kaseya is expanding API access to allow MSPs to build their own integrations, customer experiences, and business intelligence capabilities on top of the platform.

Larger MSPs in the spotlight

The move reflects a growing focus on larger and more sophisticated MSPs. Historically, Kaseya has been strongest among small and mid-sized service providers, but execs now believe larger MSPs increasingly want access to the underlying data and APIs to build their own solutions.

“We are going to really focus on helping the largest MSPs be successful,” said Succar.

Partners at the event suggested that many of the changes introduced under Succar’s leadership are already being noticed, particularly around openness, product integration, and a greater focus on engineering execution.

“That change of management has really seen that progressive change,” said Simon Gurner, managing director of UK-based MSP Sunrise Technologies. “They were very, very siloed before, and they’re really pulling together now.”

Execution remains key

Alongside the positive reception, MSPs acknowledge that delivering a truly integrated platform will take time.

Jason Fry, managing director at MSP Global Four, said his organization had already invested heavily in building its own automation and reporting layer across multiple Kaseya products. And while welcoming the direction of travel, Fry cautioned that the work is only just beginning.

“It’s going to take them another year or so to fully develop and get all of that in place.”

The first capabilities built on the new platform are expected to become available later this year, with Kaseya planning to expand the functionality over time as more products are brought onto the common architecture.

Supply chain cyber risk is now a top concern for managed service providers (MSPs) and their customers, according to new research from CyberSmart.

The cyber risk management provider’s 2026 MSP Survey found that 43% of MSPs and their customers experienced a cyber incident caused by or originating from a supplier or third-party vendor during the last 12 months.

Conducted by OnePoll, the research gathered responses from 350 MSP leaders across the UK and Ireland spanning a range of industries and customer sizes.

The results suggest that MSPs are becoming increasingly exposed to supply chain risk due to their privileged access to customer environments – making them attractive targets for cyber criminals seeking to use them as a gateway to potentially hundreds of organizations.

Among the respondents that experienced a supply chain incident, 39% said the breach affected only the customer, while 16% said it only affected the MSP. Meanwhile, 39% said both were impacted.

According to CyberSmart, this means over half (55%) of incidents involved the MSP in some capacity.

Despite this figure, the research found that 55% of MSPs still do not monitor for supply chain risk – while over a third (37%) only assess risk quarterly and 11% do so annually.

In terms of challenges, MSPs cited managing and enforcing security requirements in contracts (39%), third-party risk assessment and monitoring (37%), and the cost of securing and monitoring supply chains (36%) as the biggest hurdles.

“Supply chain risk has become a central concern for MSPs and SMEs as cybercriminals increasingly target interconnected business ecosystems,” commented CyberSmart CEO and co-founder Jamie Akhtar.

“MSPs sit at the centre of these environments, which means a single weak link can have far-reaching consequences for customers, suppliers and partners.”

Growing regulatory pressure

Elsewhere, the report also explored MSP preparedness ahead of the UK’s Cyber Security and Resilience Bill (CSRB), which was introduced back in November 2025 and brings providers into the scope of formal regulation for the first time.

The CSRB includes mandatory security requirements, stricter incident reporting, and greater accountability as MSPs become increasingly critical components of national cyber resilience.

According to the findings, 96% of respondents said they felt prepared for the legislation to a certain extent, while 45% described themselves as fully prepared.

Addressing key CSRB concerns

However, MSPs pointed to operational and organizational concerns rather than technology and software limitations as the biggest barrier to readiness.

Instead, they cited skills (41%), clearer customer expectations (41%), stronger support for managing third-party risk (41%), as well as better-defined roles and liability (39%) as key requirements going forward.

Increased liability and legal exposure emerged as the biggest concern linked to the new legislation, noted by 42% of the survey’s participants, with MSP leaders expressing concern over undefined accountability and how risk will be operationalized in practice.

Improving accountability and resilience

Despite these concerns, 77% said they believe CSRB goes far enough in helping to protect supply chain organizations – including MSPs themselves – from cyber risk.

When it comes to what can be done to improve protection for MSPs, participants highlighted clearer guidance and best practice standards (54%), stronger protections around shared liability (52%), and clearer regulatory frameworks specifically for MSPs (51%) as the top three potential improvements.

“What our research shows is that the industry understands the need for greater accountability and resilience, but MSPs also need clearer guidance, shared responsibility and continuous risk visibility to make that possible in practice,” Akhtar added.

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Kaseya has cut the ribbon on MSP Success, a new growth ecosystem designed to help managed service providers (MSPs) scale more efficiently through a combination of marketing enablement, peer collaboration, and community support.

The initiative unifies the firm’s growth and business acceleration programs, including MSP Success Digital Marketing, MSP Success Peer, and the Kaseya Community.

According to the vendor, the new-look ecosystem will help MSPs raise their competitiveness and drive measurable business outcomes at a time when customer acquisition remains a significant industry challenge.

Citing findings from its 2026 State of the MSP Report, Kaseya said 71% of MSPs identified acquiring new customers as their biggest hurdle.

Kaseya eyes pipeline growth with MSP Success

At the heart of the firm’s new initiative is MSP Success Digital Marketing, a new purpose-built platform designed to help MSPs strengthen their market presence and accelerate pipeline growth.

The platform equips partners with a host of tools and services, from search engine and answer engine optimization (SEO/AEO), content marketing, and email and social media campaign assistance, to reputation management, analytics, and lead capture capabilities.

MSPs also gain access to a dedicated marketing success specialist, Kaseya said.

“While MSPs excel at technology, many struggle with marketing in a challenging landscape,” said Kaseya executive vice president of channel, Dan Tomaszewski.

“That’s why we’ve built a team of experienced practitioners, along with the right tools and processes, to help MSPs grow and succeed."

Strengthening peer collaboration

Alongside the digital marketing program, Kaseya has also combined its MSP peer programs – TruMethods Peer and Technology Marketing Toolkit – into a single peer collaboration initiative, dubbed MSP Success Peer.

The combined program aims to provide partners with an improved platform for collaboration and access to a larger network focused on operational and business growth opportunities.

Through the initiative, MSPs can participate in quarterly in-person meetings across North America, EMEA, and APAC, as well as online sessions geared towards building community and accountability.

Expanding growth-focused leadership

To support the continued expansion of the peer program, Kaseya also announced the appointment of seasoned leader Jay Dixon as director of MSP Success Peer Education, with further additions anticipated in due course.

“Building strong leadership starts with people who have lived it,” added Tomaszewski. “Jay spent four years in our Peer program as a captain, exited his business, and is now coming back to help others do the same.

“His experience reinforces our approach to investing in leaders who understand what success looks like.”

Meanwhile, Tomaszewski will lead Kaseya’s wider MSP Success initiative alongside Greg Jones, senior vice president of MSP Success EMEA, and Mike Stodola, vice president of marketing enablement strategy.

According to the company, the program is backed by a global team of 140 employees focused on MSP growth trends, AI-driven business enablement, and partner community development.

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The majority (89%) of MSPs have adopted a vertical-specific market strategy, according to recent research we carried out with Omida.

The reason? As AI spreads, compliance and governance challenges have become more complex, particularly in regulated industries; that’s why more MSPs are tackling these challenges by offering integrated solutions that are tailored to meet the needs of specific verticals.

Governance and compliance are the top barriers to AI adoption

Governance and compliance have hindered AI adoption for years, as many end-customers have struggled to implement the technology securely, effectively, and broadly across sectors and regions.

In 2025, for example, Moody’s found that many organizations “have no rules in place to govern the use of AI,” an unambiguous indictment of how pervasive and serious this problem has become.

The Omdia research shows that these challenges are more pronounced than ever. Indeed, it found that over half of MSPs identify governance and compliance as the main barrier to AI adoption, surpassing technical concerns, including data and security management (14% listed as top barrier), value realization (14%), and technical expertise gaps (13%).

As AI becomes more advanced and deeply embedded across workflows and regions, governance and compliance will only become more challenging and important. The rise of agentic AI amplifies risk. According to one report, for example, less than half of agents are governed, while there are roughly 3 million ungoverned agents in the UK and the US alone. That’s why it’s crucial that end-customers get these problems under control quickly, and MSPs have a huge role to play in that transformation.

As Robin Ody, Practice Leader, MSP Analysis, at Omdia says: “One of the bottlenecks for end-customer AI adoption today is the operational burden of data governance and compliance, which has become more complex and essential as AI continues to spread.”

MSPs turn to integration and specialization to beat AI adoption woes

With legislation like the EU AI Act already in force, and new legislation like the UK’s new Cyber Security and Resilience Bill well on its way to becoming law, AI compliance and governance are only becoming more complicated, and research shows that more MSPs are turning to integrated platforms and specialized market strategies to deliver seamless and effective compliance and governance frameworks.

More end-customers and MSPs are turning to integrated data protection, governance, and compliance platforms to solve and preempt AI adoption challenges.

This is an acceleration of a trend that predates the AI era. End-customers have preferred integrated platforms for crucial functions like data protection for years because these platforms offer greater control and functionality than a patchwork of point solutions that might theoretically offer similar functionality. As AI spreads and adoption issues continue to mount, the push for an integrated, platform-oriented approach to data protection, governance, and compliance has become even more important, creating significant opportunity for MSPs that are able to capitalize on this demand.

Like platform integration, vertical specialization also simplifies compliance and governance, particularly when it’s paired with a platform-oriented approach. By tailoring their market strategies and service delivery to fit the compliance and governance needs of specific regulated verticals, MSPs can win and keep more business than competitors.

Challenges loom, but so does opportunity

Most end-customers still have some work to do before they’re able to use AI securely and effectively. That said, the problems driving stalled AI adoption have become increasingly clear, and MSPs have a leading role to play when it comes to meeting this demand, which is likely to represent a several-billion-dollar opportunity in the coming years.

By adopting an integrated, platform-driven approach to the related priorities of data protection, AI governance, and AI compliance, MSPs can meet end-customers where they are and drive long-term AI success while building reliable, recurring revenue streams.

We know what the market needs. Now, we have to work together to bring the right solutions and services to market and meet this opportunity.

Identity Access Management (IAM) is a key building block to successful risk management at a time when ID theft is a key route for threat actors into company networks.

Since 2020, successful cyber breaches leveraging identity theft have become widespread, with recent examples such as the Visa, Marks & Spencer, Jaguar, and Harrods cases illustrating how even well-resourced companies are not immune to these types of attacks.

These breaches were linked to the Scattered Spider group, which has undergone a merger with another prolific cybercriminal group known as ShinyHunters. In just four months, the new group has successfully targeted and infiltrated multiple targets across the US and Europe, including the March 2026 breach of the European Commission, resulting in a 350GB data leak.

The anatomy of an identity attack

In these Identity attacks, Social Engineering is the primary initial vector, with cyber criminals leveraging Vishing and Phishing attack vectors to bypass SSO and multi-factor authentication (MFA) identity access controls. Attackers will often masquerade as internal IT, calling users on their work or personal phones to re-enroll or reset their IAM credentials, then send a modified account reset link to bypass non-phish-resistant MFA.

With credentials successfully hijacked, the attacker can then replay the MFA token to access SaaS resources and exfiltrate corporate data for the extortion phase.

The Social Engineering vector is popular as it offers an easy way to understand a company’s internal structure by leveraging social media. Employee names are usually linked to job titles, which reveal potential access privileges. Personal posts, interests, and commentary give insight into effective tactics for acquiring personal data through phishing, and, as corporate email addresses follow well-known conventions, they are easy to determine.

With identity being a pillar of cybersecurity but also a key attack vector, there are some key capabilities that IAM should deliver.

Rethinking IAM for a more complex threat landscape

Identity access management is not a one-size-fits-all solution. Customer environments and business objectives determine which identity controls will be the most effective.

Nevertheless, there are key capabilities an IAM solution should provide:

1. Coverage based on thorough integration within the corporate environment ensures a ‘single source of truth’, allowing visibility over the whole network – including legacy systems.
2. Correlation of login data used to identify potential anomalies. The more complex the environment, the more important this capability. Automated analysis can flag potential issues to be investigated manually as a second step, to uncover more details.
3. Reporting that enables pertinent and concise alerts to be raised by the IAM solution and follow a clear escalation path to ensure key stakeholders have actionable intelligence for decision-making.

Out of these capabilities, it’s the correlation element that is most important for the early detection of potential breaches in IAM integrity.

To improve the chance of early detection, it’s more effective to focus on looking for anomalies within the environment. These could be related to the user identity behavior, such as “Impossible Travel”, the user identity “location” represented by changes in IP address, or the service identities in the environment spiking in activity during off-hours.

There are several strategies that organizations can adopt to identify anomalous sign-ins without disrupting user experience; these fall under the concept of Risk-Based Authentication (RBA). Organizations can implement User and Entity Behavior Analytics (UEBA), which creates a profile of user behavior and can trigger a biometric or MFA check if a user activity falls outside of the baseline of the usual profile.

Conditional Access is another option, triggering authentication when a user activity exceeds a defined risk score threshold. Integrating FIDO2 passkeys, either software-based or hardware tokens, with one of the above RBA methods will greatly improve the efficacy of RBA by eliminating 90% of the common “anomalous sign-in” flags generated by password guessing or phishing.

IAM as a managed service opportunity

With threats on the rise and limited in-house cybersecurity expertise, companies of all sizes increasingly rely on managed cybersecurity services to strengthen and maintain their security posture. The IT channel is in a privileged position to deliver tailored and effective solutions incorporating IAM as an essential element of corporate cyber-resilience. But what should a robust IAM managed service include?

A true managed identity service should include an MFA or Passkey (FIDO2) capability, allow for customized policy management, and be able to deliver identity services to both users and non-human systems. It should also be capable of risk analysis powered by machine learning and AI, and deliver workflow orchestration. The service should be continuously reviewed and updated to keep up with the fast-evolving threat landscape.

For partners building IAM-managed services, it is recommended that they first conduct housekeeping in their own environment. Supply chain compromise is one of the top concerns in 2026, and partners must be able to show that their own environments are secure.

Secondly, if you have access to multiple vendors, you should standardize your solution stack. Ideally, you would have two core identity platforms, with one likely to be Microsoft Entra ID.

Thirdly, you should develop a comprehensive onboarding blueprint. The success of the service will depend on a positive customer onboarding experience, minimizing any outages in the process to ensure business continuity.

Ultimately, identity is no longer just an administrative layer. It is central to how organizations defend their environments.

As attackers increasingly target credentials, access pathways, and identity stores, businesses need IAM strategies that combine visibility, detection, and strong authentication. For partners, the opportunity lies not simply in selling another security tool, but in helping customers build a more resilient and adaptive approach to identity-led risk.

Vendor satisfaction among service providers has fallen sharply.

Indeed, the proportion of ITSPs describing themselves as “very satisfied” with vendor relationships dropped from 37% last year to just 19% this year, according to GTIA’s State of the Channel 2026 report.

However, Carolyn April, VP of research and market intelligence at GTIA, said the findings reflect broader disruption rather than straightforward vendor failure.

“This isn’t a case of vendors failing their partners,” she said. “It’s more of the market, the landscape, the technology has changed so quickly and in such a way that there’s a misalignment between what partners need right now and what they currently offer.”

The research is based on a survey of 130 channel pros across the UK and Ireland conducted in December 2025. It suggests many MSPs are now wrestling with bigger questions about where long-term value sits in an AI-driven market.

Peter Strahan, director at MSP Lantech, believes the drop in vendor satisfaction reflects wider anxiety across the MSP market.

“MSPs are not suddenly upset because vendors forgot to send them a Christmas card,” he told ChannelPro. “I think it’s more to do with anxiety. AI is forcing a lot of providers to confront uncomfortable questions about what parts of their business will still hold value in three to five years.”

For years, many MSPs built successful businesses around service desk operations, endpoint management, and recurring software revenue. AI and vendor automation are beginning to challenge parts of that model.

“When businesses are asking those questions internally, tolerance for vendor friction drops quickly. Programme complexity, certification requirements, commercial changes, and unclear profitability outlooks may feel more painful when confidence is under pressure,” said Strahan.

“This is as much a confidence story as it is a vendor satisfaction story.”

A changing vendor landscape

GTIA’s April said that AI has triggered the biggest reassessment of vendor relationships the channel has seen in years.

Historically, many MSPs maintained relatively settled vendor portfolios, relying on familiar products and predictable commercial returns. AI has disrupted that stability by introducing new suppliers and entirely new categories of tools.

“There’s a lot of new vendors out there,” said April. “For the first time in a long time, you have a partner community and MSP community that has a whole host of newer products that are offering products that they want to look at, and so the vetting process has really ramped up.”

At the same time, some established vendors such as Microsoft and VMware have frustrated parts of the partner ecosystem through pricing changes and evolving licensing models. However, April argued the bigger issue is structural.

“What that does to business models, and what that does to the types of benefits the partners are going to be looking for, and what’s going to benefit them the most to help them drive growth for their own business – those things are misaligned with some of the older programmes partners currently have in place,” said April.

AI could reshape MSP pricing

One of the biggest unresolved questions emerging from the AI transition is pricing. Traditional MSP charging structures based on users, devices or labour are increasingly being questioned as automation and AI agents begin taking over operational tasks.

April said both vendors and partners are still trying to work out what sustainable pricing models will look like in an AI-driven services market.

“The pricing model or models that are going to work best are largely unknown, both on the vendor side and on the partner side. That’s going to be probably one of the biggest headaches that needs to be dealt with over the next couple of years,” she said.

Customers are also likely to question why pricing should remain unchanged if AI reduces the amount of human labour involved in delivering services.

“In the old way of doing things, the per-user, per-device model – I think that’s going to get blown up,” said April.

That may accelerate the move towards consumption-based or outcome-led pricing models, something the industry has discussed since the rise of cloud computing but never fully embraced.

Hybrid models remain dominant

Despite years of discussion around the move to pure-play managed services, GTIA’s findings show the UK and Ireland channel remains firmly hybrid.

Most providers still combine managed services, consulting, product sales, and project work. Rather than converging around a single model, MSPs appear to be diversifying revenue streams to manage uncertainty and changing customer demand.

Austen Clark, CEO at Jera IT, explained to ChannelPro that vendor relationships have shifted significantly over the past five years as suppliers matured and became increasingly investment-focused.

“One point of view is that this is very welcome; we have stable and reliable solutions that are deployed repeatedly at a competitive rate,” he said. “The other view is we have seen a decline in product innovation and advancement – more tinkering with updates to front interfaces than step change progress.”

Clark also pointed to growing frustration around cybersecurity tooling, where overlapping products and market saturation have made it harder for MSPs to differentiate.

MSPs rethink where they add value

The findings suggest the vendor satisfaction decline is less about individual suppliers and more about uncertainty over where MSPs fit in an AI-driven market.

Clark believes AI is already reshaping how MSPs engage with customers. “The MSP industry was born out of ‘solving’ problems,” he said. “There is evidence with the development of AI that this has changed; problems once again are being solved by MSP organizations through engaging with their clients.”

That may also explain why vendors are coming under greater scrutiny. MSPs are no longer just looking for reliable technology platforms or predictable partner margins. Increasingly, they are evaluating which suppliers can help them stay relevant as customer expectations evolve around AI adoption.

For Strahan, that uncertainty sits beneath much of the frustration reflected in the GTIA data, concluding: “AI is creating genuine opportunity, but it is also forcing many MSPs to ask some difficult questions about their own future business models,” he said.

Periods of high demand, whether driven by seasonal spikes, major events, or unexpected surges in usage, are no longer just a test for end-user organizations. They are also a direct test of the partners and providers responsible for keeping critical systems running.

When things go wrong during these high-pressure periods, the impact can be significant. The 2025 cyber attack on Marks & Spencer reportedly affected its H1 2025 performance, while the effects of the Jaguar Land Rover attack rippled across its supply chain.

As IT environments become more distributed and interconnected, the responsibility for maintaining uptime, performance, and security increasingly sits with Managed Service Providers (MSPs), network providers, and other channel partners. When outages or cyber incidents occur, customers expect immediate response, rapid recovery, and minimal disruption.

From service provider to operational owner

Rapid digital transformation is sweeping across businesses, with 94% of private sector IT leaders being familiar with smart spaces. Modern environments rely on a complex mix of cloud platforms, IoT devices, remote endpoints, and real-time applications.

These systems are deeply interconnected, with network performance underpinning every business function – so the need for strong, secure connectivity has grown in parallel.

MSPs are no longer just providers; they are embedded within customer operations. When something fails, they are expected to respond, coordinate, and resolve issues as part of a broader operational responsibility.

Complexity at scale

Today’s IT environments are more complex - and more fragile - than ever. During peak trading periods, network traffic can surge unpredictably, cloud services must scale in real time, distributed workforces require secure, reliable access, and connected devices continuously exchange data across environments.

These connected systems also magnify the consequences of any network failure. When connectivity falters, the impact is immediate and widespread, affecting productivity, service delivery, and customer experience.

At the same time, every endpoint and connection increases the potential attack surface. Security and networking have become interdependent challenges that must be addressed together.

For MSPs and connectivity providers, managing this complexity is now a core part of the value they deliver.

Network connectivity to power resilience

Traditional approaches to networking, which focused primarily on uptime and capacity, are no longer sufficient. Environments must now be designed with disruption and dynamic responses in mind.

Most organizations require dependable networks to keep operations running smoothly, particularly during high-demand seasons when every minute of downtime can impact revenue and customer experience.

High-capacity 5G connectivity offers the speed, reliability, and scalability needed to support IoT devices, mobile (POS) systems, digital signage, and other emerging technologies. Its adoption can also help organizations consolidate network providers, simplifying management and freeing up budgets that can be reinvested into innovation.

Hybrid working has reshaped operations across both the public and private sectors. Head office teams, customer service staff, and fulfilment managers often operate from different locations, all relying on cloud-based systems to access sensitive information. This makes secure access an essential requirement. Robust, always-on networks ensure that all parts of the business remain operational.

Investing in network infrastructure capable of supporting emerging technologies such as AI and IoT gives businesses the flexibility to scale operations and adapt quickly during trading peaks while preparing for future innovation.

Mapping your network

To survive busy periods, leaders must treat connectivity not as a technical upgrade but as a strategic investment. Infrastructure resilience begins with understanding where weaknesses exist, whether through outdated networks, insufficient bandwidth, or fragmented security policies.

Scalable connectivity technologies, such as SD-WAN and high-capacity 5G, allow networks to adjust dynamically when demand spikes or primary circuits fail.

At the same time, managing the growing number of connected devices is critical. Platforms that centralise IoT and smart devices reduce operational complexity and prevent issues being overlooked that could lead to unpatched vulnerabilities.

Security is an essential part of resilience. Secure Access Service Edge (SASE) integrates networking and security into a unified framework, providing centralised control over access, identity, and data protection.

Combined with scalable connectivity, SASE ensures that IT teams can maintain operations, safeguard sensitive data, and respond rapidly to emerging threats, keeping systems online and customer experiences seamless, even during peak trading periods.

The power of networks

Busy periods place extraordinary pressure on networks at precisely the time when cyber threats and IT faults pose the greatest risk.

What was once considered background infrastructure is now a direct driver of revenue, brand reputation, and operational continuity.

By investing in strong network foundations today, organizations of all sizes can safeguard their most critical trading periods and build the resilience needed for long-term innovation in an increasingly digital future.

Managed Service Providers (MSPs) are operating in a tougher market where customers are more price sensitive, competition is intensifying, and winning new business is becoming harder.

But according to Kaseya’s SVP of MSP enablement, Greg Jones, the top-performing MSPs are responding very differently from the rest of the market.

Speaking at the MSP Show 2026 in London last week, Jones said the sector is facing growing pressure despite strong demand from SMEs.

“We’ve never seen SME spend outpace enterprise spend before,” he said. “However, it is harder to win new business out there. We’re being challenged on price.”

Rather than cutting prices to stay competitive, Jones argued MSPs need to become far more focused on value, outcomes, and operational maturity.

Here are the five biggest lessons Jones believes MSPs should focus on.

1. Run the business on data, not instinct

The exec said too many MSPs are still operating reactively rather than using meaningful operational metrics.

“A lot of MSPs run their businesses on gut feel anymore. That’s not good enough,” he said.

Top-performing MSPs are tracking detailed KPIs, including ticket volumes, technician utilisation, revenue per employee, and profitability thresholds.

According to Jones, some providers are now supporting between 400 and 500 endpoints per employee by using automation, AI, and machine learning more effectively.

“How are they doing that? AI, machine learning, model change,” he said.

2. Be more selective about customers

Jones warned MSPs against trying to appeal to every type of business.

While many providers define customers by company size or vertical market, he argued the best MSPs go further by identifying organisations with the right growth mindset.

“What you’re trying to do is weed out lifestyle businesses. We’re interested in businesses that want to grow and scale,” he said.

Those customers are more likely to see MSPs as strategic partners rather than simply a supplier to negotiate down on cost.

3. Treat sales and marketing as a growth engine

“The biggest players out there are the ones focusing on sales and marketing,” said Jones, who also argued many MSPs lack a clearly defined sales process.

“When I ask MSPs what their sales process looks like, they don’t actually know,” he said.

Instead, he urged providers to create structured sales playbooks tied to measurable activities, including outreach, events, and pipeline targets.

“You can never fully control the sales numbers. However, you can control your actions today,” he told attendees.

4. Capitalize on AI and compliance services

Jones identified AI and compliance-as-a-service as two of the biggest commercial opportunities currently available to MSPs.

“AI at the moment, and compliance-as-a-service, is the biggest offering at the moment,” he said.

He argued that growing regulatory and governance pressures are creating demand for MSPs that can help customers manage compliance more effectively.

At the same time, AI is helping top MSPs scale operations more efficiently and support larger customer estates without dramatically increasing headcount.

5. Focus on customer experience, not just SLAs

Traditional service desk metrics alone are no longer enough, said Jones. While operational KPIs still matter, customers increasingly expect consumer-style digital experiences, rapid responses and seamless service delivery.

That shift is being accelerated by younger business leaders who are digital natives and expect technology to work instantly and intuitively.

Jones said MSPs must stop focusing purely on tools and technical delivery, and instead concentrate on the business outcomes customers actually value.

“Nobody wakes up in the morning looking for your services,” he said. “Businesses wake up looking for the outcomes of working with an MSP.”

Small and medium-sized businesses (SMBs) are increasingly prioritizing operational challenges over cybersecurity concerns, new research shows, and this trend is creating new opportunities for MSPs.

That’s according to the CyberSmart MSP Survey 2026 report, which surveyed 350 MSPs across the UK and Ireland. The study found that 46% of MSP customers are now more concerned about issues such as inflation and rising costs than security risks, despite growing threat levels.

Meanwhile, MSPs themselves identified AI-driven threats as the number one security challenge facing the channel, with almost half (49%) ranking AI as their top concern for the second year running.

Inflation and increasing costs jumped from fifth to third place (38%), likely driven by geopolitical instability and economic uncertainty.

MSPs also remain a key target for cyber criminals, the study found, with three-quarters of respondents revealing that they were hit by at least one breach over the last twelve months.

Over half (54%), meanwhile, said they had experienced two or more breaches over the same period, while 32% reported three or more incidents.

CyberSmart said the findings highlight the growing disconnect between rising cyber risk and the day-to-day operational pressures being faced by SMB customers.

“Cyber risk and economic pressure are now inseparable,” commented Jamie Akhtar, CyberSmart’s CEO and co-founder. “MSPs can no longer sell cybersecurity in isolation when rising costs dominate customer priorities. The real challenge has moved beyond the tech stack into liability, compliance and accountability.”

Notably, more than half (59%) of MSPs believe their customers are now at a greater cyber risk than they were a year ago.

Despite this, 46% of respondents said inflation and spiraling costs are now viewed as a greater threat to customers than ransomware and malware (41%), emerging AI threats (37%), and supply chain risks (25%).

Increased demand for compliance support

According to CyberSmart, the role of MSPs appears to be evolving beyond traditional IT and security services amid growing customer expectations around compliance.

The survey found that 61% of customers now expect MSPs to assist with these requirements, with providers responding by increasing investment in compliance and regulatory services.

CyberSmart added that MSP spending in this area has increased from 64% to 72% year-on-year.

With this greater demand, many MSPs are now positioning themselves as managed compliance service providers alongside their cybersecurity and infrastructure roles – particularly as frameworks such as Cyber Essentials are adapting to keep companies accountable for security commitments.

Shifting MSP investment priorities

As SMBs increasingly seek support in maintaining compliance throughout the year, MSPs said they are prioritizing investments in staff training (51%), continuous monitoring capabilities (46%), as well as proactive risk management (44%) across the next one to three years.

Amid this shift, the study also revealed that scrutiny on MSPs may be beginning to stabilize, with 70% reporting increased customer oversight in 2026 – down slightly from 77% last year.

CyberSmart said this decrease suggests security and compliance expectations are becoming a more standardized element of procurement and vendor management processes.

“For SMEs, the key is embedding security into day-to-day operations and working with trusted partners to maintain resilience without adding unnecessary complexity or cost,” Akhtar added.

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Managed Service Providers (MSPs) face growing pressure from surging AI workloads in data centers in their network. They have grown accustomed to predictable planning and forecasting, delivering infrastructure as required.

But, if the current pressures from AI highlight anything, it is simply that data centers are unequipped and are operating with the wrong infrastructure.

This leaves MSPs at risk of losing revenue and also means that operators cannot confidently adapt to meet changing demands at the speed required.

Now, unpredictable demand and sudden spikes are creating a significant dilemma where MSPs who overcompensate could lose revenue, whereas those who have insufficiently prepared may jeopardise customer retention.

With AI infrastructure requiring operational expertise, it is difficult for MSPs to address gaps by solely optimizing capacity. This change further brings about the need for heavy asset models, such as advanced cooling and GPUs.

The MSPs who continue to employ older GPUs will experience reduced performance and processing power capacities, which in turn can adversely impact client retention and future revenue. Ultimately, high-density workloads are not merely testing whether data centers can meet the new demand; they are assessing whether existing models can survive this transition, and the next.

Where cooling comes into it

Traditional cooling approaches, in particular, are being pushed beyond their limit. Not only do AI workloads operate at extremely high-power densities, but they are characterized by their highly concentrated heat generation, which air cooling cannot handle.

Direct-to-chip cooling was once a niche engineering solution, but it’s now a key consideration for MSPs as GPU manufacturer demands grow and the need to increase processing density becomes more prevalent.

There are varying levels of compromise to make regarding making updates to existing cooling infrastructure, for example, Rear Door Cooling and Liquid-to-Chip 'Side Cars' are two different approaches, though both fall short of true direct-to-chip cooling.

Sidecars, for instance, deliver coolant directly to the chip via a cold plate, but reject that heat back into the room air to be handled by conventional CRAC or CRAH units, making them dependent on the very air-cooling infrastructure they are meant to supplement.

Cooling requirements now impact site selection and speed to deployment, so these decisions clearly go beyond the data center. MSPs can no longer consider direct-to-chip cooling as a mere technical upgrade for data centers, as it has undeniably become a strategic business decision that directly impacts growth and risk and promotes long-term viability.

On average, direct-to-chip can support 60-120+ kW per rack, which is far more effective than traditional air-cooling methods. The MSPs that don’t act and make this change will significantly limit their densities and overall output.

In basic terms, the direct-to-chip method delivers coolant directly to processors, removing heat more efficiently than traditional cooling frameworks. In the past, direct-to-chip cooling was exclusively used in high-performance computing and specialist environments.

The high up-front costs and complexity meant it wasn’t suitable for traditional data center environments. Even today, the decision to adopt direct-to-chip cooling is not straightforward, with in-rack and in-row CDU options to consider, as well as how the technical loop is configured, which directly affects integration risk.

AI workloads continue to make this cooling method an invaluable and core aspect of data center infrastructure and a resource MSPs must strive to be familiar with.

Traditional cooling methods cannot withstand increasing AI workloads due to airflow constraints that struggle to transfer the dramatic increase in heat effectively.

Air cooling was not designed to handle the high rack densities and energy consumption that come with today’s level of AI usage. Data center operators and MSPs that do not upgrade their cooling infrastructure put their facilities at risk of thermal throttling and failures, which not only reduce performance but also increase energy costs over time.

Addressing cooling throughout the lifecycle

Cooling decisions have a big impact across multiple stages of the data center lifecycle. The direct-to-chip cooling method eliminates common problems often felt by operators by providing stable thermal environments, reducing component degradation, and offering a range of sustainable benefits.

This cooling system not only operates at higher temperatures, which reduces the need for mechanical cooling systems, but it also maximizes free cooling and provides 'heat reuse' opportunities for integration and alignment with local communities. Poor cooling decisions can shape the financial lifespan of a data center, so it is paramount to make changes early on.

Aside from operational benefits, external factors are pushing MSPs towards adopting direct-to-chip cooling for their data centers. Regulatory pressures are intensifying reporting requirements concerning energy consumption, water usage, and carbon emissions, which highlights a clear strategic implication.

Across EMEA, the percentage market for direct-to-chip is much less than in the US, and there is still high demand for air-cooled densities up to 75kW per rack, so MSPs and operators need to have a 'Liquid Flex' or 'Hybrid' model to address customer demand. These cooling techniques feature direct-to-chip cooling alongside a mix of other cooling types, which can support different workloads and provide flexibility when scaling.

Cooling methods now also determine where centres are built in relation to power sources and water, meaning some regions will be unsuitable for AI workloads. Speed-to-market can be impacted by the complexity of cooling infrastructure, influencing build times and the suitability of retrofits.

Within the last few years, changing regulatory standards have raised questions regarding the environmental impact of cooling systems. When poor cooling choices are made, regulatory pressures for MSPs will inevitably increase.

Site selection is also driven by cost, energy, and carbon impact. Direct-to-chip cooling requires less white space, but more grey space to support the increase in capacity. So, it’s clear how cooling now impacts where and when centres can be built and whether they can meet demand.

Adapt to survive the shift

Cooling is becoming a pressing consideration, and there is no room for error. When advanced cooling is not considered or implemented, MSPs will struggle to operate their centers at full capacity and face thermal bottlenecks that compound operational instability.

However, risks extend far beyond performance alone. Inadequate cooling systems can introduce operational exposures, including downtime and chemical imbalance within the technical loop, which can cause damage in the long run.

Without robust isolation strategies, these risks can disrupt the entire data center lifecycle. From a business perspective, failure to adopt direct-to-chip cooling may not only impact revenue and capacity but also enhance the risks of system compromise and regulatory non-compliance. Now more than ever, cooling is a critical point for resilience, long-term viability, and growth.

As AI workloads inevitably increase, traditional cooling systems will fail to sustain performance, meet sustainable guidelines, and eradicate unnecessary risks.

The MSPs that make the right decision early on and adopt direct-to-chip cooling will remain competitive and ensure sustainable compliance and performance is upheld. Those leading the way will undoubtedly be best positioned to scale AI infrastructure efficiently and sustainably.

For several years, vendors have pushed Managed Service Providers (MSPs) and integrators to sell AI. Yet, while enterprise demand for platforms like Azure OpenAI and AWS Bedrock is surging, the resale economics remain fundamentally unbalanced.

Gartner forecasts global IT spending will hit $6.15 trillion in 2026, driven largely by an unprecedented appetite for AI infrastructure. However, this boom disproportionately benefits the hyperscalers and major SaaS vendors, capturing the direct licence revenue. Much of this growth is down to hyperscalers increasing their artificial intelligence (AI) compute capacity. John-David Lovelock, distinguished vice-president analyst at Gartner, said: “Demand from hyperscale cloud providers continues to drive investment in servers optimised for AI workloads.”

However, partners are left doing the heavy lifting of integration and day-to-day management for razor-thin margins. This dynamic is forcing a strategic rethink. Rather than chasing low-yield AI resell volumes, forward-thinking MSPs are turning the technology inward. By embedding AI to automate their own operations, service providers are transforming artificial intelligence from a low-margin SKU into a high-impact engine that drastically reduces their cost-to-serve.

The shift from selling AI to running on it

To reclaim profitability, the channel is shifting its focus inward. There is an argument that the industry is moving past generative chatbots and embracing "agentic AI", autonomous agents embedded directly into service desks, Network Operations Centres (NOC), and Remote Monitoring and Management (RMM) workflows.

Unlike basic query-answering bots, these systems are proactive. Agentic AI does not just answer questions; it takes actions, orchestrates workflows, and self-corrects. This represents a fundamental shift in the MSP business model. It turns AI from a low-margin resale product into an operational engine that drastically alters this cost-to-serve.

Early deployments, and recent IDC data on channel partners’ AI investments, suggest that automation and AI agents are already helping service providers handle more work per technician and improve response times. In this model, AI shifts the focus from chasing thin software resale margins to building higher‑value, AI‑enabled managed services.

Inside the AI-enabled service desk

The most immediate impact of agentic AI is felt at the service desk. At scale, AI copilots and agents are now capable of handling the majority of routine queries, from password resets and account changes to basic troubleshooting and Knowledge Base (KB) lookups.

This automation of Tier 1 support deflects ticket volumes significantly. McKinsey & Company reported that AI-enabled customer service transformations typically result in a 40% to 50% reduction in service interactions and a more than 20% reduction in the cost-to-serve.

AI-assisted workflows are elevating junior staff to "Tier 1.5" technicians. With virtual agents now resolving up to 65% of initial contacts without human intervention, the AI acts as a sophisticated copilot, automating triage and surfacing remediation steps. This shift is profound: as Forrester notes, staff previously tethered to routine ticket logging are upskilled to focus on root-cause analysis and complex exception handling, moving the service desk from a reactive cost centre to a proactive, consultative partner.

Self-healing networks and the AI-driven NOC

Beyond the helpdesk, AI is redefining infrastructure management. The traditional NOC relies heavily on reactive alerts, but AI and policy-driven automations are enabling an era of autonomous remediation.

Modern tools can detect anomalies, pre-empt incidents, and trigger self-healing actions, such as automatic server restarts, configuration drift corrections, and patch rollouts, before a client even notices an issue. AI-driven remediation playbooks allow MSPs to maintain 24/7 operations without proportionally scaling their headcount.

By trusting automation to handle repetitive infrastructure maintenance, smaller teams can cover far more endpoints. This dynamic allows MSPs to retain strategic control over critical incidents while allowing AI to absorb the operational load of constant monitoring.

How AI rewrites MSP margins

The financial implications of running an MSP on AI are profound. Automation targets the very core of the service provider economic model: the cost-to-serve.

By reducing the cost-per-ticket and increasing the number of endpoints an individual engineer can support, MSPs can realise tens of thousands of pounds in annual savings per service desk. However, capturing this value requires a shift in how services are sold. As technology budgets evolve through 2026, analysts argue that MSPs will need to move away from task‑based billing and hourly rates, instead adopting outcome‑based pricing tied to uptime guarantees, Mean Time to Resolution (MTTR), and security outcomes.

Automation gives MSPs the operational breathing room to repackage their value, and analysts note that AI‑driven managed services are shifting profit pools from simple cost‑cutting to differentiated, outcome‑based offerings that command premium margins.

The automation-first divide in a record-spend market

While global IT spending is hitting all-time highs, this capital is not distributed evenly. Spending growth is heavily concentrated in cloud platforms and AI infrastructure, totalling trillions over the next few years.

This creates a sharp divide in the channel. On one side are automation-first MSPs aggressively adopting agentic AI to scale operations without linear headcount growth. On the other are labour-heavy MSPs who remain tethered to traditional, ticket-heavy models.

The latter group faces mounting margin pressure as customer expectations for uptime and responsiveness increase faster than what manual processes can economically support. Providers that fail to modernise their service delivery and pricing models risk being undercut by more automated rivals that can handle more clients with the same workforce while protecting profitability.

Before taking AI to market, MSPs must get their own house in order. Apply automation internally first, across ticketing, documentation, and finance. Because AI relies on clean data and strict Standard Operating Procedures (SOPs), perfecting these internal workflows is a prerequisite.

Once mature, these tools cease to be a disparate line item. Instead, they become the foundation of high-value retainers, packaged to clients as AI-assisted help desks, self-healing networks, and continuous compliance monitoring.

Risks, limits, and the human factor

Over-automation is a costly trap. Hallucinations, misrouted tickets, or the perception of being "abandoned to a machine" will rapidly erode client trust. AI must augment, not replace. Human engineers remain essential for complex edge cases, critical infrastructure outages, and relationship-building.

Critically, MSPs must resist the urge to chase immediate, revolutionary gains at the risk of operational stability. Forrester principal analyst Julie Mohr emphasizes the need for a more pragmatic approach to AI integration, noting:

“The AI-centric service desk blueprint is sound, but successful execution requires patience, investment, and realistic expectations. Early adopters have proven the concept while providing the roadmap for sustainable implementation at scale.”

For service providers, this means the "human factor" is now a strategic investment. Forward-thinking MSPs are treating AI implementation as a long-term evolution rather than a quick fix. Success is found by upskilling staff who previously handled routine password resets to focus on root-cause analysis, process improvement, and complex exception handling.

This transition requires significant investment in training and a cultural shift, but it is the only way to ensure AI serves as a powerful lever for the business rather than a point of friction. Furthermore, robust governance, stringent data residency policies, and transparent client communication remain non-negotiable foundations for maintaining trust in this new operational paradigm.

As the Digital Operational Resilience Act (DORA) and the Network and Information Systems Directive 2 (NIS2) reshape Europe’s regulatory landscape, data sovereignty is no longer a legal abstraction - it is becoming a practical and operational responsibility for Managed Service Providers (MSPs).

Customers are no longer asking where their data is stored. Instead, they are asking who controls it, how quickly it can be recovered, and whether that recovery will stand up to regulatory scrutiny.

These questions are landing directly with service providers managing SaaS applications, backup, and critical data environments. Sovereignty is no longer a compliance checkbox; it needs to be a core part of the service MSPs are expected to deliver.

From infrastructure management to data custodianship

For many organizations, MSPs now sit at the center of the data protection strategy. They manage SaaS environments, oversee backup, and ensure continuity in the event of disruption. This shifts their role significantly, meaning MSPs are no longer just operators of infrastructure; they are custodians of data control.

The challenge is that sovereignty is still often framed as a question of jurisdiction. However, in reality, it is operational. It depends on whether data can be accessed, controlled, and recovered when systems fail or access is lost.

From uptime and capacity to resilience

Historically, MSP offerings have been built around availability, performance, and cost efficiency. Uptime and capacity defined value, and backup was often treated as a background function.

That model is changing.

Regulation and customer expectations are driving a move toward demonstrable resilience. It is no longer enough to say data is protected - MSPs must prove that it can be recovered, within defined timeframes, and under real-world conditions.

Operational data reinforces this shift. We recently published the Keepit Annual Data Report 2026, which shows that:

• Some 90% of restore actions are single-file recoveries, reflecting how frequently real-world data loss occurs
• Most restore activity happens during working hours, highlighting that recovery is an everyday operational need, not a rare event

Resilience is not theoretical; it is tested daily in small but critical ways, and MSPs are increasingly expected to support that reality.

The hidden dependency risk in SaaS

The widespread adoption of SaaS has made recovery more complex.

Most organizations rely on multiple SaaS platforms to run critical parts of their business, often assuming those platforms provide comprehensive data protection. In reality, responsibility is shared.

SaaS providers ensure availability, but long-term data protection and recoverability often sit elsewhere. For MSPs, this introduces a dependency risk that is not always visible to customers.

If access to a SaaS platform is disrupted, whether by cyber incident, misconfiguration, or outage, recovery may be constrained by the platform itself. This creates the sovereignty gap: the difference between having data stored somewhere and having meaningful control over it when it matters most.

A maturity gap and an opportunity to guide readiness

Restore behavior scales with organization size, according to our research. Indeed, 28% of SMBs restore regularly, versus 91% of commercial and 95% of enterprise organizations.

This is often a natural outcome of resourcing - larger firms have more dedicated IT capacity, while SMBs may treat restores as an “as-needed” task. Even major outage events didn’t produce a measurable increase in restore testing, showing that awareness alone doesn’t create routine readiness.

That’s where MSPs and vendors can make the difference: lightweight, guided recovery checks that build confidence quickly and raise maturity over time - supported by assistance that helps admins take the right steps when it matters.

Designing services for sovereignty

Closing the sovereignty gap requires a rethink of service design.

Sovereignty cannot be addressed through policy alone. It must be embedded into how services are built and delivered. That means:

• Ensuring data can be recovered independently of the primary SaaS environment
• Reducing reliance on single vendors or platforms
• Regularly testing recovery processes
• Providing customers with clear visibility into recovery capabilities

It also means answering increasingly detailed questions. What happens if access to a SaaS platform is lost? How quickly can data be restored? Where are the dependencies in the recovery chain?

These are no longer theoretical scenarios. They are becoming part of standard due diligence, particularly in regulated industries.

From service provision to assurance

As expectations evolve, so too does the role of the MSP. Providers are moving beyond managing infrastructure to delivering assurance.

Customers are no longer simply buying services; they are seeking confidence that their data is protected, operations remain uninterrupted, and regulatory obligations are met.

Conversations that once centered on speed, capacity, and cost are now shifting toward reliability, governance, and responsibility.

For MSPs, this shift presents a clear opportunity. Those who can define and deliver a credible sovereignty strategy will stand out in a market where performance alone is no longer enough. The ability to demonstrate ownership, stability, and continuity is becoming the differentiator.

Sovereignty redefined

Data sovereignty is evolving. It is no longer defined solely by where data resides, but by whether organizations can truly manage and restore it when it matters most.

For MSPs, this is a turning point. Those that move beyond uptime and capacity, and instead design for continuity, autonomy, and restoration, will be best placed to meet both regulatory demands and rising customer expectations.

Partner accreditation didn’t used to mean that much. Badges signaled little more than alignment with a particular vendor. But the landscape has changed.

Today, accreditation from Microsoft isn’t just about positioning, but rather a meaningful indicator of capability. It shows not just that you work with a brand, but you are invested in and trusted by it. So, when you earn Microsoft Modern Work, Azure Infrastructure, and Security designations, it’s something worth talking about. It requires operational, technical, and commercial buy-in, and few people really understand what goes into the process.

The shift from accreditation to differentiation

Microsoft’s partner model has evolved enormously in recent years. It began as something that was at least theoretically accessible to all Managed Service Providers (MSPs), but has become closer to an elitist framework.

Certification is performance-driven. It prioritizes measurable outcomes over theoretical expertise. And this is entirely in keeping with wider changes within the wider tech environment. Customers are no longer interested in credentials; demonstrable results matter more.

So, while Microsoft solution partner accreditation used to be awarded based on headcount - the number of team members who had passed Microsoft exams - a lot more is now required. Instead, businesses must exhibit a blend of technical skilling, customer adoption metrics, and verified project success. And that makes accreditation significantly harder. But, on the plus side, it also lets customers know that the accreditation is worth having. Which for MSPs, makes it into a competitive differentiation.

Holding one designation may indicate competence in a specific domain. Holding all three suggests an ability to deliver more. Integrated, end-to-end solutions that span productivity, infrastructure, and security.

So when an MSP can claim triple Microsoft designation, they stop being a simple service provider and can claim the role of strategic partner. A company able to support its clients through complete digital transformation, and every associated challenge.

The real cost of capability

Achieving triple designation demands substantial internal investment across people, processes, and platforms.

The first step is a commitment to continuous skilling. All members of the team - engineers, architects, and consultants - must maintain up-to-date certifications across multiple Microsoft technologies. And this must be further supported through the development of robust operational frameworks that live up to Microsoft’s expectations around compliance, governance, and security.

The security designation can be particularly challenging. Businesses must be able to demonstrate capabilities in a whole range of areas, from identity management and endpoint protection to threat detection and response. This can mean a lot of investment for many MSPs, ensuring that they not only have the security operations capabilities, but also the tools and teams to manage them.

Commercial alignment also has to be considered. Microsoft’s model is intended to reward partners who drive customer consumption of its cloud services. That’s the whole point of the system. So for businesses seeking accreditation, that can mean a complete change of go-to-market strategies, including pricing and customer engagement.

Ultimately, all of this is a significant commitment for any company.

Metrics that matter

The focus on performance metrics has come to define Microsoft’s partner program in recent years. MSPs must meet specific thresholds across skilling, customer success, and performance to attain and maintain partnership status.

While skilling metrics are relatively straightforward, focusing on the number and level of certified professionals within the organization, customer success metrics are more nuanced. They require partners to demonstrate real-world impact through customer deployments and usage.

Performance metrics tie these elements together by measuring overall business growth within Microsoft’s ecosystem. What makes this more difficult to achieve is that Microsoft regularly updates its criteria to reflect market trends and strategic priorities. This means that achieving designation requires ongoing optimization and improvement.

Beyond the badget

For MSPs willing to invest, the benefits of triple designation are manifold. Internally, the process drives greater discipline, consistency, and alignment, helping businesses to initiate and maintain best practices. While externally, it enhances trust. Customers increasingly look for partners who can demonstrate proven expertise across multiple domains. Triple designation provides a clear signal that an MSP has met rigorous standards and delivered measurable outcomes.

Perhaps most importantly, it positions MSPs to compete in a market that is becoming more demanding. Customers want and expect more. In meeting the exacting standards of Microsoft, businesses are positioning themselves to be able to deliver it.

The rise of triple Microsoft designation as a benchmark reflects a broader development of the managed services industry. Customers expect more, technologies are more complex, and the stakes are higher. In this environment, superficial credentials are no longer enough.

By tying accreditation to real-world performance, Microsoft has effectively raised standards for all MSPs. It’s down to the individual companies whether they wish to accept that challenge and maximize the potential of the business, or to take the easy path and consistently underachieve.

“Unprecedented. I’ve never seen anything like it in my entire IT career.”

That’s how Ian Thompson, senior vice president of business delivery at Managed Service Provider (MSP) Assured Data Protection, sums up the current state of server pricing.

“Prices are incredibly volatile and only going upwards, while availability is also unpredictable,” he said.

“Quotes that used to be valid for weeks are now valid for days, and sometimes even hours…it’s definitely far more chaotic than normal market fluctuation.”

Across the channel, partners are grappling with a perfect storm of memory shortages, AI-driven demand, and dynamic pricing models that are upending how infrastructure deals are usually priced, quoted, and delivered.

While pricing cycles are nothing new in IT, many in the channel believe this time is different. Rene Klein, executive vice president for Europe at distributor Westcon-Comstor, said volatility is no longer a temporary disruption – it’s becoming embedded in how the market operates.

“What we’re seeing goes beyond the usual market cycles. Price volatility looks set to become a structural feature of the market… fundamentally changing how the channel operates,” he said.

At the heart of the issue is price uncertainty, he added.

“Quotes no longer represent certainty in the way they once did. Partners have to think earlier about deal structure, timing, and commercial exposure.”

Dynamic pricing reshapes the deal cycle

A major contributor to that uncertainty is the rise of dynamic pricing, where hardware costs can change right up to the point of shipment. Vendors, including Cisco and HPE, have already updated partner terms to allow price adjustments between quote and delivery. Indeed, HPE has explicitly reserved the right to reprice orders up to the day of shipment and shortening quote validity windows to as little as two weeks.

For partners, that creates a real challenge: how do you quote a deal when the underlying cost base is moving?

Chris Gilmore, CTO at Axians UK, says the result is a shift in how resellers approach commercial risk.

“Resellers are increasingly backing their own quotes off to vendor terms, rather than absorbing risk themselves. That means shorter quote validity, explicit pass-through pricing, and tighter alignment to vendor conditions,” he explained.

The traditional model, where partners could confidently quote weeks in advance, is quickly disappearing.

“The net effect is a more cautious commercial model replacing the stability we’d historically expect,” said Gilmore.

Margins under pressure

That shift is putting margins under strain, particularly for partners reliant on hardware resale.

“Margin pressure is a real challenge when pricing changes after a customer quote has been issued. We increasingly see partners exposed to repricing late in the process,” said Klein.

Analyst data suggests this pressure is unlikely to ease soon. Omdia has warned that ongoing memory shortages are squeezing margins across the supply chain and forcing vendors to pass on higher component costs.

Some partners are mitigating risk through planning. Thompson said his firm has taken the unusual step of ordering hardware up to 12 months in advance, compared with about one month historically.

“That agility gives us confidence we’ll be ahead of many organizations,” he explained.

Memory shortages at the core

Gilmore points out that the biggest driver of volatility is strong demand for DRAM and high-bandwidth memory as AI workloads consume global manufacturing capacity. That pressure is cascading across the stack.

“It flows into storage, especially SSDs, and into networking and security platforms that rely on the same supply chains,” he said.

Thompson agrees, describing memory and flash as the “major pinch points” affecting server builds.

“Memory is used in everything – smartphones, tablets, set-top boxes. Demand is huge.”

Analysts warn that continued AI-driven demand for DRAM, NAND, and high-bandwidth memory is outstripping supply, creating sustained pricing pressure across server and storage markets.

Customers caught off guard

For many end users, however, the scale of the disruption hasn’t fully landed.

“There’s still a lot of denial in the market. Customers assume they can order a server and have it delivered within a few weeks at a predictable price,” said Thompson. “When they discover the real lead times or fluctuating costs, it comes as a shock.”

That disconnect is making it harder for partners to advise customers and lock down project budgets.

“Customers still want predictable outcomes, but the market is delivering variable inputs. That’s why transparency matters more than ever,” said Klein.

The impact is already being felt more broadly. Omdia forecasts a 12% drop in global PC shipments in 2026 as rising memory and storage costs force vendors to prioritize higher-margin systems.

Adapting to a new reality

Faced with ongoing uncertainty, partners are being forced to adapt. Some are stockpiling equipment, others are broadening supplier options, or turning to refurbished hardware. But the bigger shift may be strategic.

“We’re continuing to move toward services-led models,” said Gilmore, who noted that managed services, lifecycle support, and automation are areas less exposed to hardware volatility.

Klein sees a similar trend at the market level, with early signs that customers are shifting away from hardware-heavy models toward software, cloud, and consumption-based approaches.

“Forward-thinking partners will focus on evolving their commercial models rather than waiting for stability to return,” he said.

No quick return to normal

If partners are hoping for a return to calmer conditions, they may be waiting some time. Omdia expects memory pricing to remain elevated through 2026 and into 2027, indicating that volatility is not a short-term spike but a longer-term shift.

“I don’t think we’ve even reached the peak of it yet,” said Thompson, predicting continued price increases through 2026 and potentially beyond.

Gilmore is slightly more measured, suggesting the market will eventually rebalance – but not quickly.

“This is demand-led, driven by global AI investment. The market will stabilize, just not immediately,” he said.

For now, however, the old rules no longer apply. In a market where prices can change overnight and quotes expire in hours, the ability to manage commercial volatility as much as technical risk is becoming one of the channel’s most valuable skills.

Kaseya has officially moved beyond AI advisory tools, unveiling its new "agentic" IT management platform.

The new platform, which is powered by a proprietary engine dubbed Kaseya Intelligence, was announced at the firm’s Connect event in Las Vegas. It is designed to break the cycle of "alert fatigue" by moving from AI that simply surfaces recommendations to a system that autonomously executes tasks across IT operations, security, and backup.

The company is positioning this as a fundamental architectural shift. It says that many vendors have spent the last year bolting AI features onto disconnected tools. This, Kaseya argues, is an approach that often backfires, providing inaccurate recommendations based on partial data.

"The industry doesn’t need another AI feature bolted onto a disconnected tool," said Rania Succar, CEO of Kaseya, in a statement.

"What MSPs and IT teams need is a platform that runs their operations, one that sees across every system, understands context, and acts autonomously. That’s what we’ve built. Kaseya Intelligence is the engine. The platform is the operating system. And the outcome is IT that manages itself."

Closing the loop with ‘agentic’ AI

The platform’s core differentiator is its scale. Kaseya Intelligence is trained on a massive, purpose-built dataset including over 1 billion help desk tickets, 3 exabytes of backup data, and 17 million managed endpoints.

Unlike standard AI layers that hand off tasks to humans, Kaseya’s new agentic approach seeks to "close the loop." By executing actions, such as triaging a ticket or containing a threat, and then validating the outcome, the platform aims to function as an operating system for IT rather than a collection of utilities.

Dermot McCann, executive vice president and general manager for Kaseya in APAC and EMEA, talked about the need for this shift at the firm’s event in London earlier this month.

"You don't have time to be messing around with stuff,” he said. “Everybody is under pressure. Everybody's busy. Our role is to make sure that the technologies that you pay us for work in the way that you need them to work."

Three pillars of the new platform

The vision for this autonomous platform is being delivered through three major product launches unveiled today. First, the company introduced Agentic Digital Specialists, who are designed to handle high-volume, repetitive tasks. The first of these focuses on "Ticket Triage," a feature that automatically categorizes and routes incoming tickets, a process Kaseya claims can reduce downstream billing and routing errors by up to 80 percent.

The second pillar is Unified Cyber Resilience, a release that consolidates on-premises, SaaS, endpoint, and cloud backup into a single portal. By eliminating tool sprawl, the platform aims to provide AI-driven screenshot verification with 99.9 percent accuracy while offering expanded support for Azure and Hyper-V environments.

Rounding out the trio is Kaseya Security Information and Event Management (SIEM), which targets the complexity and high cost typically associated with traditional security operations.

This new SIEM correlates signals from more than 60 data sources to provide full-surface attack visibility. It is designed to be accessible to standard IT teams without requiring a dedicated staff of security engineers, featuring automated threat containment and 400-day out-of-the-box log retention to assist with compliance requirements.

An operating system, not a tool

The release signals a clear attempt by Kaseya to commoditize feature-based AI while claiming the high ground of autonomous operations.

"This is the difference between AI as a feature and AI as an operating system," the company stated in a press release detailing the launch. By embedding this intelligence across its entire portfolio, Kaseya aims to enable MSPs to scale their service delivery without the proportional need for human headcount.

"We notice that it wasn't architecturally possible to keep up with this emerging threat [using traditional tools]," McCann added. "We have to capture [innovation], then we have to put some structure around it, governance around it, and determine whether they fit in our roadmap. These types of technologies are really driven because our customers are saying, 'Can you do this for us?'"

With this launch, Kaseya is betting that the future of the MSP market won't be won by those who provide the best AI suggestions, but by those whose platforms can actually do the work.

Integris has announced plans to acquire Australian MSP First Focus, as the US-based provider looks to expand its international footprint and broaden its services for small and medium-sized business (SMB) customers.

The proposed deal, which is subject to regulatory approval, will see Integris gain a significant presence across Australia, New Zealand, and the Philippines, further extending its reach into key growth markets.

Founded in 2003, First Focus serves more than 800 organizations across the region, providing expertise spanning managed IT, cybersecurity, cloud, AI, and software development, alongside support services for mid-market customers.

The company employs almost 400 staff across its operations.

In an announcement, Integris said the acquisition will bolster its ability to deliver enterprise-grade technology to SMB clients operating across multiple regions as demand for secure, scalable AI capabilities continues to grow. Financial terms of the deal were not disclosed.

"This acquisition represents a meaningful step forward in our long-term vision to build a truly global platform MSP focused on the needs of small and midsize businesses," explained Rashaad Bajwa, chief executive officer at Integris.

"As AI rapidly reshapes how organizations operate, scale, and compete, this combination allows us to accelerate our ability to deliver secure, governed AI capabilities globally—while maintaining the high-quality, people-first service our customers expect."

Integris eyes international growth

Headquartered in New Jersey, Integris provides managed AI and IT services to SMBs, helping organizations modernize their technology environments and improve operational performance.

The company’s planned purchase of First Focus marks its first international acquisition as part of its broader plans to scale its AI and IT services offering globally.

Integris said the move will position it as one of the largest global MSPs focused on the SMB segment, with the additional resources expected to improve its ability to support clients operating across multiple regions while continuing to deliver localized service and expertise.

Upon completion of the deal, First Focus will continue to operate under its existing leadership team, with both companies planning to focus on integration efforts such as knowledge sharing and operational alignment while maintaining continuity for customers.

"We see this partnership as a strong cultural and strategic fit," commented First Focus CEO Ross Sardi. "Integris brings the scale and long-term approach to help us grow our people and deliver even more value to our clients—while staying true to who we are."

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The IT channel has spent the last year riding the unpredictable wave of artificial intelligence hype. But according to Dermot McCann, EVP and GM, EMEA and APAC of Kaseya, the industry is now firmly crossing the threshold from speculation into a living, breathing reality.

Speaking to a room of managed service providers (MSPs) and channel partners at Kaseya's Local Connect event in the London Docklands Museum on Tuesday, McCann delivered a keynote that looked past the daily grind of ticketing and patch management. Urging the channel to prepare for a profound shift driven by AI, data unification, and evolving customer demands.

While acknowledging the vital importance of day-to-day operational quality, McCann spent much of his address focusing on what will dictate the future survival and profitability of the MSP business model.

Evolving business models picture the MSP as an AI consultant

The traditional role of the MSP is fracturing and expanding. According to McCann, the conversations channel partners are having with their end-users have fundamentally changed.

"Nine out of ten of the customer conversations I'm having at the moment are about your end customers asking you for your opinion on AI," McCann noted. "How are you going to deliver AI to the services?"

This shifting dynamic presents both a challenge and an opportunity. McCann pointed out that many partners are pivoting heavily into professional and consulting services to meet this demand for AI guidance. However, he warned against abandoning the foundational recurring revenue model. The true opportunity, he argued, lies in leveraging AI internally to expand margins, eliminate "day-to-day friction," and deliver consulting services without losing sight of the core managed services that differentiate an MSP's business.

Fighting fire with fire

With innovation comes immense risk. McCann was frank about the dark side of the AI revolution, noting that the threat landscape is currently a "hacker's dream."

"We know that AI is perpetrating significant threats and significant attacks on our partners," McCann explained, highlighting the dramatic increase in automated brute-force attacks. To combat this, the channel must fundamentally change its defensive posture. "We're having to fight fire with fire."

However, McCann cautioned MSPs against hastily bolting AI capabilities onto their existing tech stacks or trying to build homegrown AI tools without the proper foundation. The biggest risk in the AI era isn't a lack of innovation, it's a lack of compliance.

"Unless you've got the security posture in place to support it, it comes with a lot of risk," McCann warned. He stressed that any AI integration must be underpinned by rigorous security and compliance frameworks, such as SOC 2, GDPR, and PCI compliance, noting that playing fast and loose with AI data "scares the living hell out of me."

The power of the connected data layer

So, how do MSPs safely harness this technology to automate at scale? For McCann, the answer lies not in isolated niche products, but in a holistic, API-first ecosystem.

Kaseya's overarching philosophy for the future relies on a "connected data layer." By moving away from fragmented, multi-vendor environments and embracing a unified platform, the channel can tap into an unprecedented wealth of anonymized telemetry.

"If you can measure that data that you have, then you can manage it," McCann explained. "Once you can manage it, then you can make decisions on it, and that decision is made through an automation. And that's really the final piece of how we think about this AI during the future."

This interconnectedness is what will allow MSPs to move from reactive break-fix cycles to proactive, automated service delivery. When an RMM, documentation tool, and cybersecurity suite share a single brain, the economies of scale multiply.

An aggressive but secure adaptation to AI for MSPs

Ultimately, McCann's message to the channel was one of aggressive, yet secure, adaptation. The AI revolution is no longer a buzzword reserved for future-facing whitepapers; it is an everyday reality dictating the pace of the market.

For MSPs willing to embrace a unified platform approach, prioritize robust security compliance, and step into the role of AI consultants for their clients, the future could be a bright one.

Being a Managed Service Provider (MSP) no longer means providing reactive support or solving clients’ technology issues as they happen. Instead, the most successful MSPs now mine the data available to them to anticipate their clients’ requirements, optimize their business operations, and proactively address challenges before they arise.

More business processes than ever before live on digital platforms. Remote Monitoring and Management (RMM) and Professional Services and Automation (PSA) platforms are ubiquitous in the MSP space, while some providers also have separate Customer Relationship Management (CRM) systems, billing, accounting, and finance software, and IT documentation solutions.

Together, these tools hold a wealth of data that can give MSPs valuable insights into how well their business is doing, as well as crucial information on how to improve client acquisition, service delivery, profitability, help desk productivity, and more.

Too many MSPs, however, never find the time to review these metrics. Overloaded with the day-to-day running of their business, they miss the chance to take a step back, analyze the data available to them, and act on it. Business decisions are made on gut instinct rather than hard evidence. This reliance on guesswork comes with risks and lost opportunities.

Flying blind

A lack of insight is particularly evident in sales and marketing, traditionally a weak spot for many MSPs. Although most have a CRM system or use CRM features in their PSA platform, only a few actually study the data. As a result, an MSP may not know where their sales come from, whether their marketing campaigns have been successful, or whether their lead generation activity is delivering. They may be able to track the number of leads but have no insight into lead quality, or no understanding of the cost of acquiring new customers.

Without this awareness, much of an MSP’s growth planning will be based on speculation rather than hard facts.

The issue is not a lack of data. It’s about not taking the time or making it a priority to query it. The data could, for example, reveal that it would make more business sense to outsource certain lead-generation activities or automate marketing processes.

Equally, analyzing support tickets alongside customer retention and satisfaction could flag risks of customers churning as well as upsell opportunities, allowing MSPs to act early on these to strengthen customer relationships and protect or even boost their bottom line.

How to become more data-driven

Mining the data already available to them to understand strengths and weaknesses and pinpoint areas for improvement could be a game-changer for many MSPs. Better business insights lead to better decisions, more predictable growth, and ultimately, a healthier business. Plus, keeping a close eye on service desk activity can help predict and pre-empt client needs and measure outcomes, in turn allowing the MSP to prove the value they deliver.The most data-driven providers already use analytics, automation, and AI insights, as well as industry benchmarking, to be smarter. They base business decisions on verified facts, regularly review key performance metrics, and hold their teams accountable to agreed standards.

Deciding what data to track will depend on each MSP’s individual circumstances. A good starting point is focusing on metrics related to business health, such as growth rates and monthly recurring revenue (MRR) projections, productivity statistics such as ticket close rates and time spent on each ticket, sales and marketing success figures, and financial data.

Evaluating these numbers and comparing them to how peers perform will allow an MSP to identify areas for improvement, verify perceived issues, and review whether their current business strategy needs adapting.

Where the data displays unexpected patterns or unusual trends, such as a spike in help desk requests or recurring service tickets, it is worth carrying out root cause analysis. Does the data suggest bigger problems down the line? Are there bottlenecks that could be removed? Does a client’s infrastructure need an upgrade? And, are there any recurring tasks that can be automated or supported by artificial intelligence (AI)? By using data insights to anticipate issues and optimize labor-intensive tasks, MSPs can free up valuable engineer time.

Adding AI to the mix

For MSPs who are not sure where to begin, AI can be helpful in deciding which KPIs to monitor. From reconciling and modelling financial data to summarizing support ticket histories and finding patterns in the smallest details, GenAI tools are powerful at analyzing and making sense of data. By interrogating AI, MSPs can review where their challenges are, diagnose business symptoms such as cash flow shortages or declining profit margins, and determine relevant statistics to track.

Many MSP tools already have built-in AI capabilities as well as consoles for monitoring vital metrics and creating automated alerts. In addition, customizable dashboard solutions help aggregate data from multiple sources. Once established, keeping an eye on the important business parameters won’t take much time. The key is to decide where to focus – and to keep the desired business outcomes in mind.

The question should never be whether there is time for data analysis, but how to make it a priority. Those MSPs that invest the time and resources will gain a significant competitive advantage by using the insights to operate more efficiently, fuel growth, and deliver a better service.

Over the past year, organizations in the UK have faced several high-profile cybersecurity incidents costing millions in lost revenue and operational disruption. While the headlines focus on the scale of the breach or the sophistication of the attackers, the real lesson tends to emerge later. Many organizations were simply unprepared for what happened after the intrusion occurred. 

The impact of these incidents has rarely come down to a single failure. Instead, it is usually a combination of factors: attackers gaining access to systems more easily than expected, threat actors using technology to accelerate their activity, and organizations lacking the resilience needed to contain and recover from an attack. 

For managed service providers and channel partners, this changing landscape has shifted expectations. Preventing cyber incidents will always remain a priority, but customers are increasingly judging their partners on something else as well - their ability to guide organizations through a breach when prevention fails. Incident response has moved from being a “nice to have” to a core operational capability

Accepting the reality of the threat landscape

Security teams often talk about tools, such as endpoint protection and monitoring platforms, but tools are only one part of the picture. In practice, cybersecurity relies on three broad categories of risk controls: physical, technical, and procedural. When those layers work together, organizations build what’s commonly referred to as a defence-in-depth strategy.

Technical controls usually receive the most investment. Identity and access management systems, multi-factor authentication, cryptographic protection, and immutable backups all play a critical role in protecting sensitive data.

However, technology rarely determines the outcome of a cyber incident on its own. What often makes the difference is the third layer: procedural controls. These include policies for business continuity, disaster recovery, and, most importantly, incident response. When those procedures are missing or poorly defined, even strong technical controls can’t prevent chaos once an attack unfolds.

The gaps MSPs repeatedly overlook

In conversations with service providers, the same weaknesses appear again and again. The first is treating incident response as documentation rather than an operational process. Many organisations have an incident response policy sitting in a compliance folder somewhere, but the people responsible for executing it have never walked through the plan step by step. 

The second gap is unclear escalation. When suspicious activity appears in logs or monitoring systems, teams may not have clear thresholds for when an event becomes an incident. That hesitation can waste critical hours.

The third issue is communication planning. During an incident, organizations often need to coordinate between IT teams, executives, legal advisors, regulators, and sometimes customers. Without predefined communication roles, technical teams can find themselves answering questions they weren’t prepared for while trying to investigate the attack itself.

Finally, many MSPs underestimate the importance of testing the plan. Incident response documents are written, approved, and filed away, but never exercised. When a real incident occurs, teams discover too late that the procedures don’t reflect how their systems or responsibilities actually work.

Frameworks help, but they aren’t a shortcut

Every organization needs an incident response plan, regardless of whether it is a small business, a global enterprise or a managed service provider. However, the effectiveness of the plan depends on how well it reflects the realities of the organization it is designed to protect. 

Frameworks such as those provided by the National Institute of Standards and Technology (NIST) or the UK National Cyber Security Centre (NCSC) offer valuable guidance for building structured incident response processes. They provide proven frameworks that organizations can use as a foundation. What they are not intended to be is a template that can simply be copied and pasted.

Effective incident response planning requires a detailed understanding of the organization’s environmental operations priorities and regulatory obligations. For MSPs, that also means understanding the businesses they support and the risks those clients face. Attempting to shortcut that process with generic templates of automated prompts may produce documentation, but it rarely produces a plan that will stand up to the pressures of a real incident. 

What an effective incident response plan includes

While every organization’s response plan will look slightly different, effective incident response strategies tend to share several core components. 

Clear escalation procedures are essential. Teams need to know when an event becomes an incident and who has the authority to make critical decisions as the situation evolves. Equally important is a well-defined communications strategy. Cyber incidents often require coordination across technical teams, leadership, legal advisors, regulators and sometimes customers. Without a structured communication plan, confusion can escalate quickly.

Operational guidance also plays a major role. Detailed runbooks, checklists, and response templates help ensure that key steps are not missed in the pressure of an unfolding incident. These structured processes also make it easier for teams to respond consistently and efficiently. 

Finally, incident response planning must be treated as an ongoing process rather than a static document. The threat landscape evolves rapidly, and response strategies must evolve alongside it. Modern frameworks from both NIST and the NCSC emphasize continual improvement, encouraging organizations to refine and adapt their plans as news risks emerge.

The MSP opportunity in incident readiness

For MSPs and channel partners, strengthening incident response capabilities is not just about risk reduction.

Customers already assume their provider has the technical controls in place. What they increasingly want to know is whether their partner has a clear, tested plan for the moment those controls fail. That is where real differentiation lies, and where the most forward-thinking partners are already pulling ahead.

The organizations that invest seriously in incident response preparation today will be the ones clients trust, retain, and recommend when the pressure is on.

Since artificial intelligence (AI) first hit the mainstream, its capabilities have evolved significantly. AI adoption has become widespread; a McKinsey report found that 62% of organizations are experimenting with AI agents to boost productivity, increase efficiency, and enable creativity across multiple businesses.

This boost in AI adoption presents a significant opportunity for Managed Service Providers (MSPs) to position themselves as trusted experts guiding customers through the AI adoption process. With proper preparation, this can help them stand out from the competition and foster long-term relationships with their clients.

Why AI is crucial for MSPs

As AI becomes commonplace, customer expectations are growing. Almost three quarters of small-to-medium businesses (SMBs) are currently experimenting with AI, with 83% of high-growth SMBs in the adoption stage. Ninety-two per cent of MSPs say that their own business has expanded as a result of an increased interest in AI.

In today’s surging market, MSPs must be prepared with an AI-ready offering to maintain their market position. Failing to do so significantly limits potential revenue and the risk of losing clients to competitors that are ahead of the curve.

Legal challenges in the AI era

Many companies are overlooking the risks of shadow AI, the unauthorized use of AI tools or applications within an organization.

The spike in interest in AI among SMBs indicates that MSPs’ customers are likely beginning to use AI tools at work. The downside of this trend is that it raises concerns about the security of sensitive information that they neither own nor have the right to share.

An accidental confidentiality breach is often the result of human error. For example, users may be unaware of the risks of integrating personal AI tools into their work, or of the sensitive content they are dealing with.

Businesses must ensure that privacy and HR policies are airtight and well communicated across departments. Alongside this, the ever-changing landscape of AI regulation means legislation and guidance must be regularly reviewed to stay on track.

The benefits and challenges of Copilot

Designed specifically for business use, Copilot operates as an isolated instance of AI per business, enabling secure access to internal data with a reduced risk of data leaks compared to traditional OpenAI sources.

As part of the Microsoft 365 suite, Copilot ensures secure access to all data and environments, streamlining administrative tasks and saving businesses valuable time and resources.

Despite its advantages, Copilot comes with its own security challenges. Given that it draws upon an organization’s internal data to form answers, there is always a risk that sensitive data could end up in the wrong hands.

Such breaches leave data vulnerable to being maliciously exploited, potentially leading to devastating consequences. To mitigate risk, MSPs must ensure their customers’ Microsoft 365 tenants are secured in accordance with best practices before rollout. Microsoft data must be properly secured and strengthened access controls to prevent unauthorized users from accessing sensitive information.

Once deployed, education is paramount. MSPs should properly educate customers on how to integrate Copilot into existing processes to optimize efficiency effectively. For instance, customers should understand how Copilot integrates with the rest of their business data in Microsoft 365.

How to become AI-ready

MSPs must understand their customers’ business goals, analyse their data environment, and develop an intuitive deployment plan for Copilot ahead of selling AI services. MSPs that succeed with AI services won’t be those who rush to roll out subpar agentic enablement services. Assessing what drives business value and defining use cases is key.

Proactively reviewing customers’ data environments to ensure that their security posture is up to scratch is critical. They should have established content management practices and data governance and carry out a thorough audit of security policies, including data access controls, retention policies, and sensitivity labels.

These are managed and updated centrally and rolled out across their Microsoft 365 environment. If security gaps are uncovered, this is a good opportunity to upsell the benefits of a Microsoft 365 Business Premium license, which provides access to Entra ID for identity access management, Purview for data security, and Defender for ransomware and device protection.

Once it’s established that the tenant is secured, MSPs can help customers to identify champions for Copilot within their organization. A small group of employees can initially test Copilot, demonstrating its potential and driving wider adoption by becoming advocates. This approach enables the organization to make the most of its 365 licenses.

Today, MSPs must not only be prepared to leverage AI internally within their operations, but also ensure they are well-equipped to support and guide customers as Copilot becomes central to their technology stack.

Managed Service Providers (MSPs) are increasingly being asked to do more than shift products. Clients want complete development stacks, and rely on their channel partners to provide a secure, reliable, and fully-featured line-up of systems. In the age of AI-assisted development, that expectation is becoming a liability.

The speed at which AI code generation tools have been adopted is remarkable. The risk embedded in that adoption is not yet well understood, particularly by the channel partners who are recommending and deploying those tools on behalf of clients.

Buy efficiency, inherit vulnerabilities

A study by CSET found that nearly half of AI-generated code snippets contained security vulnerabilities. That alone should give channel partners reason to pause. The problem compounds when you consider how few organisations are asking the right questions before deploying AI tools: the World Economic Forum found that 67% of organisations fail to assess the security of AI tools before deployment.

The AI tools generate flawed code, and most organisations are deploying them without proper validation. For the channel, there creates double exposure: you recommend the stack, and in many cases, manage it too. So if it’s not trustworthy and reliable, you’re left carrying the risk.

The reasonable assumption has been that, as AI models grow more sophisticated, the quality of their outputs will improve accordingly. That assumption doesn’t hold in practice OpenAI's own reasoning models found that hallucination rates more than doubled between o1 and o3, rising from 16% to 33% on factual accuracy benchmarks. The smaller o4-mini model hallucinated at 48%. More capability has consistently led to more errors.

In code generation, a hallucination is not immediately clear. It looks like a complete, well-structured function that compiles and often passes a surface-level review. Then it fails in production, or introduces a security vulnerability that sits quietly until someone finds it. By then, the damage is often already done.

AI Risk: What it means for channel partners

MSPs now operate in a market where clients expect AI to be embedded in the development toolchain, and the partners who can deliver a coherent, integrated AI development stack will win the business.

But partners who win business without understanding what they are recommending are creating a problem for themselves. When a client's code is compromised because an AI tool produced insecure authentication logic, the conversation about responsibility will ultimately come back to whoever put the stack together.

This is an avoidable problem, and with the right approach, creates a commercial opportunity. MSPs who get ahead of AI governance can both protect themselves and strengthen their market position. Clients are increasingly aware that AI tools carry risk; most simply don’t know what to do about it. A partner who can articulate that risk clearly and demonstrate a clear way to manage it becomes indispensable.

Building a secure AI development stack: what it takes

The key is a strong foundation. Before recommending any AI development tool, MSPs should ensure clients have deployed strong pipelines to catch risks early, clear policies that limit exposure, and have trained their teams to work securely with AI.

Modern Continuous Integration and Continuous Delivery (C/CD) platforms must be a part of any development stack, providing the infrastructure that reliably detects issues before they reach production. These should be supported by strong DevOps practices that map workflows, standardise processes, and bring code generation and integration into one system.

Shadow AI (use of AI tools without approval or insight) is an immediate threat to this foundation. Development teams routinely adopt tools without procurement or security review. Code written with unapproved tools ends up in production, and the intellectual property generated with those tools may not be fully owned by the client. MSPs managing development environments need tooling that surfaces which AI tools are actually in use, not just what’s approved, but what developers run day to day.

The pipeline itself also needs to be designed for AI use. Modern CI/CD platforms can apply specific scrutiny to AI-generated code: automated vulnerability scanning calibrated for LLM failure modes, static analysis capable of detecting AI-generated patterns, and mandatory review checkpoints before AI-assisted code reaches production. These are capabilities that already exist; they just need to be configured with AI outputs in mind.

The crucial point is that AI-generated code tends to look clean, which is precisely what makes it so dangerous. Developer-written code, with its visible imperfections, prompts scrutiny. AI-generated code does not, and teams unconsciously extend the trust it has not earned. Building structured review into the pipeline is not a drag on velocity. It is the only way to deploy AI at speed without accumulating technical and security debt that compounds with every release.

Building differentiation through security

MSPs are well placed to turn AI governance and validation into a core service offering. Clients are already aware that AI tools carry risk. What they lack is a partner who can quantify it and help them manage it without slowing things down.

Partners that provide that clarity will own the client relationship in the future, setting a high standard of trust that no product margin can buy.

Managed Service Providers (MSPs) are seeing operational sprawl across nearly every client environment. Duplicate SaaS tools, unused licenses, shadow contracts, and unmanaged permissions create waste and hidden security gaps.

Making vendor sprawl one of the biggest destroyers of margin in managed services. It’s time to step back from the impetus to keep acquiring more tools and lean towards a more controlled, optimized ecosystem.

Why is vendor sprawl happening?

For years, the MSP value proposition was the ability to solve problems quickly. And tools were the answer. Whatever the issue, whether it was the need for better endpoint protection or problems with collaboration gaps, SaaS platforms and access tools were the go-to solution.

Individually, these decisions made sense. But collectively, over time, they created environments that are expensive to manage and hard to secure. Funds are being drained through disused and forgotten contracts, while neglected permissions are creating extensive gaps in both security and compliance standards. And with budgets tightening, IT leaders and MSPs need to regain control.

The real impact of vendor sprawl on MSPs

The obvious impact of vendor sprawl is inflated software bills, but that’s just the start; mismanagement of SaaS tools, licences, and permissions directly affects MSP operations. Each additional platform introduces complexity: separate consoles, unique update cycles, inconsistent APIs, and different support models.

For service desks, this translates into longer resolution times and higher ticket volumes. Technicians need broader (and shallower) expertise across dozens of tools instead of deep mastery of a focused stack. And for security teams, it creates blind spots. Unused or forgotten SaaS licenses often retain active user accounts. Departed employees keep access. And permissions drift over time, with users accumulating privileges they no longer need. Every unmanaged app becomes a potential entry point for attackers, and a liability for the MSP responsible for the environment.

There’s also the commercial impact. Managing too many vendors erodes partner margins through fragmented billing, lost volume discounts, and increased administrative overhead. Instead of predictable, scalable operations, MSPs end up spending valuable time reconciling invoices, tracking renewals, and responding to audits.

Consolidation is now essential

Consolidation sounds like a lot of hard work that can result in a loss of functionality or in making do with one-size-fits-all solutions. But it’s actually about deliberate design; using fewer, strategically chosen vendors to create tighter integrations, simpler management, and better outcomes for both MSPs and their customers.

A consolidated tech stack reduces context switching for technicians, which lowers operational friction and improves service quality. Integrated platforms share data, automate workflows, and provide a more complete view of the environment. And from a security standpoint, fewer identity silos and better visibility into access and usage make it easier to enforce least privilege and detect anomalies.

There’s also the financial benefit; when you consolidate, your margins grow. MSPs can negotiate better partner agreements, standardize deployments, and reduce the hidden costs of supporting niche or redundant tools. Customers benefit too, with clearer value, fewer surprise renewals, and a more transparent technology roadmap.

Frameworks MSPs can use to regain control

Controlled structure is the antithesis of vendor sprawl. Successful consolidation typically starts with three foundational frameworks:

• SaaS visibility: You can’t optimize what you can’t see. MSPs need a clear inventory of every application in use, licensed or not, across each client. This includes usage data, ownership, and integration points. Visibility often reveals surprising redundancies and tools that no one remembers approving.
• Contract and license rationalization: Once you know what you’re dealing with, MSPs can assess which tools deliver real value and which simply persist due to a lack of action. Are multiple products doing the same job? Are licenses consistently underutilized? The point of rationalization is to ensure that the business has the tools it needs - and no more.
• Permission audits: Sprawl isn’t just tech-related, but use-related too, and they tend to be interlinked. Regular audits help MSPs reduce unnecessary privileges, remove unused accounts, and enforce consistent access policies across platforms and across the business. This not only improves security but also simplifies compliance.

The strategic advantage of consolidation

Consolidation isn’t just about the benefits it brings to your company, but how it impacts your customers. When MSPs focus on optimization instead of deploying endless tools, they move from being tied to the provision of reactive support to providers of strategic guidance.

The emphasis moves from “which tool?” or “what outcome?” as MSPs begin to help clients in far more qualitatively valuable ways. And that builds loyalty.

It’s a tough business climate right now, making differentiation more important than ever. Consolidation is a step towards MSP maturity; a move away from the “pile it on” mindset, towards efficiency, security, and long-term value.

Autonomous systems in businesses are no longer experimental. Robots now work alongside humans in warehouses, retail stores, and hospitals, frequently deployed and supported by channel partners who never expected to become advisors on fleet operations and security inside customer networks.

This evolution is creating both opportunity and exposure. Robotics-as-a-Service and robotic deployments are becoming part of the managed service stack, yet few frameworks exist to guide customers on how they should be secured, audited, or governed. This raises new questions in the adoption process: “Should I trust it?”, “Who has access to the data?”, and “What happens when something goes wrong?”

When scale expands the attack surface

Automation within customer environments blurs the line between IT and OT (operational technology) responsibilities. Treating them as a shared domain is key to preventing blind spots.

Deploying a robot at one site is manageable for IT and OT groups to handle. Managing hundreds of thousands of robots across multiple retail or logistics locations -- that’s the challenge that customers are facing today. Each new machine deployed adds potential risk to a customer's infrastructure: misconfigured WiFi, outdated firmware, and insecure update channels all present meaningful vulnerabilities.

This is where Robotics-as-a-Service offerings can cover this gap and minimize risk. The robotic provider’s fleet management services should provide each channel partner and customer with:

• Visibility: A complete inventory of devices, software versions, and configurations.
• Integrity: The Robotics-as-a-Service offering should perform updates and end-to-end monitoring for any unauthorized access.
• Agility: Ability to monitor performance and make remote edits for operating within dynamic environments.

Each robot should be designed to operate fully autonomously without requiring broad or sensitive access to customer networks, and should maintain strict security boundaries. This ensures the robot does not introduce new risks for IT and OT teams.

Data governance in motion

Robots challenge traditional data governance programs because of the sheer diversity of information they collect while operating in public and dynamic spaces. A single robot may generate events, points of interest, telemetry, imagery, and mapping data — each with different formats, storage needs, retention profiles, and compliance considerations. Data is often distributed across multiple locations, aggregated at different layers, and subject to handling rules that depend on the specific sensor or subsystem producing it.

For channel partners and customers, the ability to understand these data types, sources, locations, and flows — and ensure they align with relevant frameworks — is foundational to creating trust at scale. Documenting the full data flow is the first step in governing any autonomous system. If you can draw the flow, you can govern and secure it effectively.

From there, security-by-design and least-privilege architecture form the backbone of good governance. These practices ensure that data access is intentional, restricted, and auditable, supporting data quality, transparency, and accountability across the fleet. In robotics, a few principles consistently reinforce trust:

• Purpose limitation: Sensors capture only what is required for navigation, safety, task validation, or product improvement.
• Image anonymization: Any incidental human imagery is blurred before presentation or review.
• Encryption and access control: All data is encrypted in transit and at rest, with access tightly governed by role-based permissions.
• Retention discipline: Data is kept only as long as it provides operational or product value, then securely deleted.

When providers explain these safeguards in plain language, governance becomes an enabler rather than a constraint — and trust becomes a differentiator rather than a hurdle.

Governance as a service differentiator

As deployments grow, governance becomes a competitive edge. Customers no longer just want performance metrics; they want assurance that their automation ecosystem behaves predictably and securely.

Robotics providers should demonstrate that assurance by:

• Providing access to a centralized data governance, privacy, and compliance repository for documentation, such as a Trust Center, that includes diagrams, security controls, and other architectural insights.
• Sharing third-party attestations or certifications, such as UL safety certification for key components and SOC 2 for cloud-based robotic services.
• Offering whitepapers or other published documents that clarify the robotic data flow, including how and where data is processed, transmitted, stored, and for how long.

When you can show exactly how data moves and who can access it, trust stops being an abstract goal and becomes part of your value proposition.

Preparing for a new regulatory landscape

The regulatory landscape for AI and robotics is maturing quickly. In the EU, new risk-based frameworks are formalizing documentation, transparency, and post-deployment monitoring. In the US, state-level obligations for companies using AI and autonomous systems continue to expand, with similar patterns emerging globally.

The best way to stay ahead is to engineer for principles, not headlines. Autonomous systems that already minimize, anonymize, encrypt, and evidence their decisions will adapt naturally as new laws arrive. Document how the system makes decisions—even reactive ones like obstacle avoidance—and make audits part of routine operations rather than exceptional events.

For the channel, readiness isn’t about predicting every regulation. It’s about building around stable, durable principles: minimization, encryption, explainability, and documented accountability. Autonomous systems grounded in these fundamentals can shift with whatever policy comes next.

Trust as operational ROI

Every managed service provider (MSP) knows the cost of an outage or breach. The same applies to autonomous systems. The more predictable and transparent your deployments are, the faster customers will adopt and renew.

Trust reduces friction across every stage of a partnership: procurement, onboarding, compliance, and support. In practice, that makes trust a measurable form of ROI.

The future of robotics in the channel won’t be defined by who moves fastest, but by who moves most responsibly.

Artificial Intelligence (AI) is now perceived as essential for the success of modern enterprises. However, we are seeing a disconnect between what many of these tools promise for enterprises and what they actually achieve.

Rather than identifying specific business problems that AI can realistically solve, enterprises are getting caught up in the hype - or suffering from ‘Shiny Object Syndrome’. Our 2024 study found that fear of missing out (FOMO) played a big part in AI investment, with 63% of respondents reporting they are worried their company will be left behind if they don’t use it.

With a wealth of providers out there promising the world, clients are looking to Managed Service Providers (MSPs) and channel partners for guidance on evaluating and securing AI tools.

Before advising whether to buy or build AI, channel partners must first help clients answer a far more important question: what problem is AI expected to solve?

The level of customization

Buying AI tools means fast set-up and deployment, but customization is limited to what the vendor offers or allows. How much the solution fits a business’s unique needs, or integrates with their current IT, is limited.

Building tools themselves offers more flexibility to enterprises, and each feature of the tools can be tailored to match specific needs. However, this level of customization demands in-house skills, more development time, strong security and compliance knowledge, and continuous maintenance.

In many cases, for the AI use cases enterprises actually need, buying AI tools is a practical choice. For better customization, you can combine tools to get the best out of them - often, the best compromise is using an existing foundation building on top of existing “developer-friendly” AI APIs, tools, and models. This delivers differentiation where it matters — without burdening the business with full model ownership.

Combining tools can be the answer to many of the problems enterprises are facing. Our study found that nearly a third (31%) of businesses are struggling to train Generative AI models, and 21% report that staff are misusing the tools. However, it adds that leaders are already taking steps in the right direction by looking at closing these gaps by incorporating other technologies – for example, process intelligence, document AI, and retrieval-augmented generation (RAG).

Organizations that adopted this integrated approach reported higher consistency in outputs, stronger governance, and clearer cost control. In fact, 98% of businesses using blended AI stacks reported satisfaction with their generative AI initiatives — underscoring that orchestration and context, not raw model power, are now the primary drivers of success.

The cost

Cost is often positioned as a simple build-versus-buy calculation, but in reality, it is a balance of upfront investment, time-to-value, operational risk, and long-term scalability. There’s no one answer when it comes to the cost of these tools. Building AI is more expensive at the start, as enterprises will need to invest significantly in talent that can handle the job. Over time, security, compliance, and maintenance are ongoing considerations, but generally owning their own AI technology can help enterprises create new, bespoke ways to keep earning.

Buying AI tools usually costs less at the start and helps you get results quickly. You also don’t have to invest in a big in-house specialist team or infrastructure, as security, compliance, and maintenance are taken care of on your behalf. As enterprises’ needs grow, however, monthly subscription fees and add-ons can start to add up.

It might not seem intuitive, but blending AI tools for a more purposeful, problem-focused strategy can cut costs. A good example of this is for Know Your Customer (KYC) compliance. Financial services firms might use one AI provider for document scanning and data extraction, and another for ongoing checks and workflow. One delivers the “brains” for understanding documents, while the other provides the industry expertise and regulatory context. Combined, they create a KYC process that’s better than either tool on its own.

Using purpose-built AI tools like these will mean less manual input and fewer errors in customer onboarding, speeding up the process and reducing the need for large compliance teams. The downstream impact is measurable: lower labor costs, fewer re-checks, faster revenue realization, and improved regulatory confidence.

Channel partners are the glue

The success of an AI project that is bought, built, or combined hinges on the importance of channel partners and MSPs for translating what’s possible with AI into what’s right for each client’s context.

Their value no longer lies in simply reselling licenses — it lies in architecting outcomes, rather than just selling a one-size-fits-all solution that won’t give clients what they actually need. Good partners don’t just resell, they curate, bridging the gap and working with clients to develop the AI solution that works best for them.

MSPs play an important role in helping clients decide whether to buy or build AI tools. They help weigh up the pros and cons based on the client’s goals, budget, and skills, and can advise when it makes sense to mix off-the-shelf solutions with custom-built ones.

Sure, if they have the capital and skill, enterprises can build their own AI stack. However, many are unknowingly reinventing capabilities that already exist in mature, battle-tested platforms, when tools already exist that will do the job. It’s just a case of knowing who to partner with, or which complementary tools will solve problems.

More often than not, guiding clients toward a carefully selected ecosystem of complementary AI partners will deliver outcomes that are faster, more secure, and far more sustainable than any standalone solution.

Ransomware is accelerating at an alarming pace. Europe is headed toward its worst year yet for ransomware incidents, up about 80% in 2024, with 2025 already breaking previous records. 

Downtime caused by these attacks, not just the ransom, is one of the most damaging outcomes. For many mid-market organizations, losing access to critical systems for days or weeks can mean disastrous financial, operational, and reputational damage. This also causes a strong impact on IT staff mental health, where 84% of IT professionals feel uncomfortably stressed at work amid rising cybersecurity threats.

Immutable backup storage is one of the most effective defenses. Once data is written, it can’t be altered or deleted, meaning it’s always available for recovery after an attack. However, enterprise-grade backup storage has sometimes been seen as out of reach for small and medium-sized organizations, with unnecessarily high entry points in terms of capacity and capital expenditure.

Now, that’s changing. Consumption-based subscription models are breaking down these barriers by bringing enterprise-level protection to a much wider range of businesses. By offering predictable monthly billing, these models are levelling the playing field, and for channel partners, they’re also creating new recurring-revenue opportunities.

Ransomware protection for all

Ransomware is no longer a problem that only hits large enterprises. With Ransomware-as-a-service platforms, attackers can automate campaigns and scale their reach across businesses of every size. Mid-market firms, often seen as ‘too small’ to attract sophisticated attacks, are increasingly in cyber criminals’ crosshairs because they may have fewer protections in place.

This shift puts the channel in a pivotal position. Managed service providers (MSPs), value-added resellers (VARs), and system integrators (SIs) can now deliver enterprise-grade ransomware protection to clients of any size through consumption-based models.

Instead of large capital outlays, partners can help customers deploy immutable backup solutions on a predictable, subscription basis, reducing friction and speeding up adoption.

It's not just about affordability. This model fosters long-term recurring revenue and deeper customer relationships built on ongoing protection rather than one-off sales. In an era where trust and resilience are critical, that combination of commercial and security value is compelling.

Backup as the new frontline

The modern approach to ransomware must start with the assumption that a breach will inevitably happen. Preventative tools remain crucial, but downtime from attacks, even when ransoms aren’t paid, can be devastating.

Immutable backups shift the focus from reactive recovery to proactive assurance. By ensuring data cannot be encrypted, deleted, or modified once written, businesses can be confident they have a reliable way to recover after an attack. That confidence is invaluable for IT teams facing today’s constant threat pressure.

For the channel, it’s also an opportunity to evolve conversations from “prevention” to resilience. Partners can help customers build recovery strategies that meet compliance and insurance requirements, while delivering managed backup and recovery services that generate recurring revenue. Immutability, once a niche enterprise feature, is fast becoming a core managed service differentiator.

Immutability for the mid-market

Smaller firms face a unique challenge; just like larger enterprises, they handle sensitive data, but often lack the resources to secure it to the same standard. Attackers are aware of this and increasingly target mid-market organizations because they often represent ‘low-hanging fruit’.

Consumption-based subscription models help partners close that gap. They can offer mid-market customers enterprise-grade immutability and recovery to remove any budget hurdles that originally slowed down adoption.

For MSPs and VARs, this flexibility creates tangible business advantages. They can scale protection alongside their customers’ growth, adjust resources, and bundle ransomware protection into broader management. The result is therefore a sustainable service model where customers gain accessibility and partners gain reliability in revenue.

Insurance-ready backups

Cyber-insurance requirements are tightening, which means insurers increasingly demand verifiable, immutable backups and tested Recovery Time Objectives (RTOs) as conditions for coverage. Firms that cannot demonstrate these capabilities may face higher premiums or denied claims.

Immutable backup solutions simplify this process and position partners to help customers meet insurer expectations. By offering managed immutable backup and recovery services, MSPs can support compliance reporting while delivering faster recovery and strengthening clients’ security.

This builds trust with customers and insurers, enhancing both protection and revenue for the channel.

A model built for resilience

Today, ransomware does not discriminate. Every organization and every partner serving them faces the same pressure to recover quickly. In the fight against ransomware, no partner or customer stands alone. Resilience grows from collaboration, a connected ecosystem where vendors, MSPs, and customers all play their part.

Consumption-based immutability makes resilience achievable for all. It allows partners to deliver enterprise-grade backup protection to any customer, regardless of size, while building steady revenue streams. Predictable billing supports long-term customer relationships and margin planning, turning security into a shared business goal between provider and client.

As ransomware threats continue to rise, recovery readiness will define resilience, not just prevention alone. Immutable backups delivered through flexible, subscription-based models ensure that every organization can restore operations swiftly whilst protecting reputation and maintaining trust. For the channel, that’s not just a service opportunity; it’s also a chance to lead in shaping the next era of cyber resilience.