Build vs Buy: A False Dichotomy

Conversations we’ve had with consulting leaders recently reveal an ongoing debate: AI is transforming businesses, but when it comes to internal tools, do we build or do we buy? Some consulting firms invest in building from scratch, while others buy external solutions. The most effective firms, however, are letting go of this binary approach.

The Problem with the “Build vs. Buy” Debate

The Build vs. Buy dilemma usually feels like a choice between two extremes: build something custom in-house or buy a ready-made solution. Making this decision can be frustrating, with products that fall short of expectations.

Why is this so? The answer is simple: most tools that can be bought aren’t made for you. They are designed to be general-purpose solutions—“one-size-fits-all.” But in the consulting world, where client needs vary and the stakes are high, you need something specific, tailored, and adaptable to your unique requirements.

So, while buying may seem like a faster, more efficient choice, it usually leaves gaps in functionality and scalability. This is where the building comes into play. Instead of buying software and hoping for the best, you can build to understand your needs, experiment, and explore what’s possible. However, there’s only so far one can go with experimentation.

Here’s a framework we have for thinking about it.

Building Go-Karts

Think of building your internal AI solution like assembling a go-kart. It’s cost-effective, and you have complete control over its mechanics. You can fine-tune different settings, test configurations, and refine your understanding of the core challenges you’re trying to solve.

But a go-kart isn’t designed to compete in a Formula 1 race. While internal development can be valuable, most firms don’t want to take on the complexity of deploying enterprise-wide AI platforms. Managing data access controls, staying ahead of the latest foundation model advancements, and ensuring seamless integration across workflows can quickly become a full-time job that diverts your focus from the firm’s core business.

That said, building a proof of concept in-house has real strategic value. It allows firms to test ideas, measure early signs of ROI, and gauge impact at a manageable scale. A small team of 5 might build and deploy an internal AI tool and increase productivity by 30% in a month. The challenge then shifts: how do you scale these results to 5,000 employees? Developing internally lays the foundation, but scaling and optimizing for enterprise-wide adoption usually requires partnering with a focused team.

Buying off the Assembly Line

Buying, on its merit, feels like opting for a factory-built car. It’s reliable, efficient, and designed for everyone. These off-the-shelf solutions are seen everywhere as “the tool to use” thanks to large marketing budgets. One could consider them justifiable because they’re selling to everyone.

But if they’re built for everyone, they are not built for you.

A generic solution will fall short for complex, consulting-specific use cases such as automating RFP responses, slide generation, insight synthesis from past work, etc.

This is not to say that these solutions have no value. They can be a good starting point for young firms, especially in industries they are implicitly optimized for, like product-led organizations, which much of today’s enterprise software has been built around (we discussed this in our essay last week), but there are limits.

Buying-to-Build: The F1 Engineering Team

Buying to build means you partner with a focused team to co-create solutions tailored to your unique business needs. This way, you get access to the specialized knowledge, expertise, and resources to develop AI solutions that can handle complex challenges at scale. You get first-class solutions by working with these providers who understand your firm's requirements and can seamlessly integrate AI systems into your operations.

This is where the F1 Engineering team comes in. In these engagements, you don't just get “the car”—you get the engineering team with it. While you begin at the starting line with a solution above off-the-shelf offerings, the true value lies in their ongoing commitment to refining and optimizing the system for your firm's unique needs.

The most agile firms are showing us that the future of AI in consulting is not about choosing sides. It’s about understanding the balance between internal innovation and partnerships in pursuit of their core mission— delivering exceptional value to their clients.

Till next time, Kenley Team

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