Build vs buy AI - why your leadership does not understand either choice

Your VP of Engineering wants to build. Your CFO wants to buy.

Both are wrong, but not for the reasons they think.

I keep watching companies burn through decision frameworks trying to figure out whether to build custom AI or buy off-the-shelf. They create weighted scoring models. They compare total cost of ownership. They analyze time-to-value. Then they pick one, invest heavily, and watch 30% of their AI projects get abandoned after proof of concept.

The problem is not the technology choice. The problem is that your middle managers have no idea how to use AI, and they are the ones who determine whether anything you build or buy actually gets used.

The part everyone skips

Here’s what actually happens in most companies. The C-suite gets excited about AI. They read analyst reports. They attend conferences. They mandate adoption.

Entry-level employees start experimenting immediately because they have nothing to lose and everything to gain.

But the middle layer - the people who actually run your operations - they’re stuck. Nearly half face pressure from above to deliver on initiatives they don’t fully understand while reassuring those below about job security. These are the managers who make or break your AI investment, regardless of whether you built it or bought it.

McKinsey found that organizations most commonly cite a lack of clear AI strategy as the biggest barrier to adoption. But dig deeper and you find the real issue: middle management doesn’t understand how AI works well enough to integrate it into their daily operations. They’re being asked to lead a transformation they don’t comprehend.

So they do what makes sense to them. They maintain the status quo.

Why build vs buy frameworks fail

The typical build vs buy analysis looks at cost, time, customization needs, and competitive advantage. All rational criteria.

What it doesn’t look at: whether anyone in your organization can actually explain to a new employee how the AI tool helps them do their job better.

I watched a mid-size company spend eight months on a build vs buy decision. They created a comprehensive scoring model. They interviewed vendors. They mapped their technical requirements. They chose build, invested significantly in custom development, and produced something technically impressive.

Six months after launch, adoption sat at 11%.

The AI worked perfectly. The problem was that managers didn’t trust it because they didn’t understand it. When employees asked questions, managers couldn’t answer them. When edge cases appeared, managers defaulted to the old manual process. The custom AI sat there, performing flawlessly for the tiny fraction of work anyone would send its way.

They could have bought an off-the-shelf solution and hit exactly the same adoption rate for a fraction of the cost.

What actually changes the equation

P&G figured something out that most companies miss. They created an ai mentorship program where junior employees - the ones who grew up with this technology - teach senior leaders how it actually works.

Not in a formal training session. Not in a webinar. Through structured reverse mentoring where a 24-year-old data analyst sits down with a VP and shows them, hands-on, what AI can and cannot do.

Their results: reporting time dropped from hours to minutes. But more importantly, the executives understood why and how the AI reached its conclusions. They could answer questions from their teams. They could make informed decisions about when to use the AI and when not to.

AXA Insurance ran a similar program starting in 2014. After six reverse mentoring sessions, 97% of participants recommended the program. Why? Because senior leaders finally understood the technology well enough to champion its adoption across their teams.

Linklaters, the law firm, reported that 100% of participants endorsed their reverse mentoring program as an effective way to create a more inclusive culture around new technology. When your managers understand the tools, they use the tools.

The framework that actually works

Stop asking build or buy first. Start by asking whether your management layer understands AI well enough to successfully deploy anything.

If they don’t, start an ai mentorship program immediately. Pair your junior employees who use AI naturally with your middle and senior managers who make adoption decisions. Give them three months of structured learning.

Then make your build vs buy decision.

Because here’s what changes: When your VP of Operations understands how AI works, they can tell you whether an off-the-shelf solution will actually fit your workflow. When your Director of Customer Success has hands-on experience with AI limitations, they can specify what custom features would actually deliver value versus what sounds good in a requirements document.

The build vs buy decision becomes dramatically clearer when the people making it actually understand the technology.

Research on middle management AI adoption confirms this. Give middle managers time and tools to become confident AI users themselves before asking them to lead others to adopt AI. Otherwise you get what you’d expect: resistance masquerading as legitimate concerns about the technology.

What this means for your decision

If you’re choosing between build and buy right now, add one more criterion to your framework: Which option includes a plan to make your managers competent AI users?

A custom-built solution with no adoption plan will fail. An off-the-shelf platform with no internal champions will fail. Both cost you money. Both waste your time.

The companies succeeding with AI - whether built or bought - are the ones that invested in reverse mentoring first. They created an ai mentorship program that gave their decision-makers actual hands-on experience before asking them to mandate adoption.

The spreadsheet comparison between building and buying matters less than you think. What matters is whether the people running your operations can confidently use AI in their daily work and teach others to do the same.

Fix that first. Then the build vs buy decision becomes obvious.