Why ‘buy vs build’ Is the wrong question for AI strategy

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.