The real role of UK channel partners in turning AI ambition into action
UK channel partners are the missing link between AI ambition and AI execution
Leave it to the Office for National Statistics (ONS) to give the UK’s AI ambitions a reality check. In a July data release, the UK stats body found that take-up of AI tools among British businesses has risen a meagre 0.2% since 2023. Between now and then there’s been significant advances in the reliability of LLM outputs and the protocols that hook models into the apps and platforms that hold business data. It would therefore be fair to argue that AI adoption among UK firms hasn’t yet reflected these advances, and a major uptick is on the cards.
That’s the thinking of many UK channel partners. In shallow adoption of AI lie opportunities to consult many new customers through the process of entrenching AI into core operations. At the current moment, UK business leaders are trying to make sense of new agentic AI products from AI labs and best practices for their rollout. Channel partners can help, acting as the connective tissue between platforms and customer intent.
‘Can’ is the crucial word. While partners possess the technical and change management skills to rise to the moment – a deliberate shift in partner skills and proposition framing is also required. Deliberate data and analytics advisory has the right answers to break businesses out of stop-start AI pilot stasis. Partners need to position themselves to hold those answers.
The ambition-execution gap
The state of the UK’s AI rollout is similar to other countries in that it’s plagued by a failure to scale beyond pilots. Indeed, in February our research found that more than 80% of AI projects fail to operationalise into production.
The bottleneck is not AI access (which is increasingly commoditized), but what AI systems are built with. Fragmented, and often inaccurate, data spread across business units is the norm for many UK firms. Business logic - the rules, calculations, and decision-making definitions that determine how work gets done - is locked in spreadsheets, legacy systems, and employees' heads.
There’s a clear risk of deploying AI over broken data foundations, a risk that compounds as the application of AI gets more sophisticated - like the deployment of AI agents.
This is not a problem IT can fix in isolation. Reckoning with this state of affairs requires honesty on the operating system refresh required to make AI work. Wrangling with data is one aspect - but so too is addressing the trust and culture issues that slow AI rollouts down. The conundrum is a business transformation challenge, which requires both technical capability and contextual advisory. That is exactly where channel partners are comfortable.
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How channel partners can think about the operating system refresh
For many organizations, AI is seen as a line-item purchase. Business users are set up on the software with perhaps some training. The conditions aren’t set for pilot testing of AI to scale up into a transformational reimagining of business processes.
Channel partners can address that gap with a neat proposition: a pre-agent layer. This is an analytics platform that sits underneath AI systems with pre-processed proprietary data, defined rules, and business context. Models connected to the layer are rooted in how the business actually runs and, therefore, more easily integrated into day-to-day work.
Channel partners should know that such propositions are most effective when built around platforms that offer visual-in-nature, intuitive-to-build analytics workflows that make AI visible, understandable, repeatable, and auditable.
Managers can quickly establish the inputs into AI workflows and validate the presence of governance steps and role-based access controls to extract the confidence they need to sign an AI workflow off. With a pre-agent layer proposition, channel partners come with workable solutions to help UK firms evolve beyond AI pilots and the hesitations that fuel the standstill.
Channel partners are well positioned to build pre-agent layers for UK firms as they already sit inside customer data infrastructure. They have the familiarity with these environments - the legacy systems, the data flows, the processes that run under the surface - to hit the ground running in a solution-oriented way.
The new blueprint for channel success
AI services are widely expected to be the single biggest driver of revenue growth for IT service providers over the next 24 months. That is a significant opportunity - but only if partners evolve "AI services" with the wants and needs of customers.
Wrapping a managed service around a generic AI model is not the same as genuine AI delivery. The skillset that generates commercial growth is the ability to translate a customer's business context into governed AI processes. Partners who build that capability can immediately move into selling AI readiness - helping customers make their data and analytics estate fit for agents and their most impactful use cases.
From my conversations with UK MSPs and GSIs, this evolution is already happening. UK partners, in my experience, are showing innovation that - despite the naysayers - surpasses US counterparts. That’s because the deep customer relationships and genuine sector-specific specialisations that make up the UK IT channel’s strengths are precisely the assets that inform the thinking for a successful customer AI operating system refresh.
The gap is the opportunity
The gap between the state of UK AI rollout today and where it could be is not a failure story. It’s an opportunity for the UK channel. The partners who ready their propositions - vendor relationships, skills, service design - will be the ones positioned to benefit.
Rishi Kapoor is head of worldwide partner sales engineering and solutions at Alteryx, where he works with global partners to help organizations translate data and AI capabilities into tangible business outcomes.
He has built his career at the intersection of enterprise analytics and commercial delivery - evolving from hands-on data and modelling roles into AI and data leadership and has held previous roles at Morgan Stanley and JP Morgan.
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