What 'Build vs. Buy' Misses About AI Decisions

When companies consider AI, the conversation often starts with “should we build or buy?” It’s a reasonable question, but it’s the wrong framing. It assumes the main variable is technology acquisition, when the main variable is usually something else entirely.

 

The Hidden Question

Build vs. buy is really asking: “Should we create this capability ourselves or acquire it from someone else?”

But that question hides a more important one: “What kind of capability are we actually trying to develop?”

Consider the difference between:

• “We need an AI that can classify customer inquiries”
• “We need to understand our customers well enough to serve them better”

The first is a technical requirement. You can build it or buy it. The second is an organizational capability. You might use AI to develop it, but the AI isn’t the capability—the understanding is.

This distinction matters because it changes what “success” looks like and what you’re actually investing in.

 

Three Types of AI Decisions

Commodity capabilities: These are well-understood problems with mature solutions. Spam filtering. Basic image recognition. Standard language translation. For these, buying almost always makes sense. The technology is commoditized, the integration is straightforward, and there’s no competitive advantage in doing it yourself.

 

Differentiating capabilities: These are problems where your specific context matters. How you serve your particular customers, with your particular products, in your particular market. Here, the answer is more nuanced. You might buy a foundation and build on top of it. You might build from scratch if the capability is truly central to your value proposition. The key question is: does doing this well give us an advantage that competitors can’t easily copy?

 

Learning capabilities: These are less about solving today’s problem and more about building organizational muscle for the future. Even if you could buy a solution, building it yourself might be worthwhile because of what you learn in the process. The teams that build AI systems develop intuitions about what’s possible, what’s hard, and what questions to ask next. That organizational knowledge compounds over time.

 

What Companies Get Wrong

Treating all AI decisions the same way. Some AI is infrastructure (buy it). Some AI is product (maybe build it). Some AI is a bet on future capability (definitely think carefully). Using the same framework for all three leads to bad decisions.

 

Underestimating integration complexity. Buying is never just buying. Every tool needs to connect to your data, fit into your workflows, and be maintained over time. The purchase price is often the smallest part of the total cost.

 

Overestimating build complexity. Sometimes building is simpler than it looks—especially if you have clear requirements, good data, and realistic expectations. The explosion of AI tooling has made many problems dramatically easier to solve than they were even a year ago.

 

Ignoring the capability question entirely. The biggest mistake is treating AI as purely a technology decision. The technology matters, but what matters more is whether you’re developing the organizational capability to use it well.

 

A Better Framework

Instead of “build vs. buy,” try asking:

 

What capability are we actually trying to develop? Not what tool do we need, but what do we want to be able to do that we can’t do now?

 

How central is this to our competitive position? If it’s central, you probably need more control. If it’s peripheral, optimize for speed and cost.

 

What do we need to learn? Some projects are worth doing even if a vendor could do them better, because the learning matters.

 

What’s our realistic capacity? Be honest about what your team can actually take on. Building when you don’t have capacity leads to abandoned projects and burned-out people.

 

What’s the total cost of each path? Include integration, maintenance, training, and the opportunity cost of what else you could be doing.

 

The Real Answer

The best AI decisions come from understanding your situation clearly—your goals, your constraints, your organizational reality—and choosing accordingly. Sometimes that means building. Sometimes that means buying. Sometimes it means waiting, because the right answer is to solve a different problem first.

The framework matters less than the clarity of thinking behind it.