Why Smart Consulting Firms Buy AI Instead Of Building It

"Well, we thought we could give you a system to do XYZ but in fact we can only do ABC."

I've heard that conversation more times than I can count. A consulting firm promises their team an AI solution. Months pass. Budgets balloon. Then comes the walk-back.

The pattern is always the same.

Firms assume building AI internally gives them competitive advantage and control. They assign their best people. They invest in the latest approaches. Six months later, they're explaining why the system can't do what they promised.

Here's what actually happens.

The Obsolescence Problem

In AI, approaches become obsolete in weeks or months, not years.

Early on, consulting firms wanted to enhance their search process. Simple, right? Just run a RAG process on the dataset and let AI work its magic.

They didn't realize the complexity. RAG search has context window limitations. The system couldn't parse through 10,000 documents to find relationships and reason about them. And the hallucinations surprised everyone.

Most firms doubled down. They tried to patch it. They reset user expectations. This explains why MIT research found 95% of AI pilots fail to deliver measurable business impact.

The Cost Nobody Counts

When you pull your best consultant off a client project to work on internal AI, something invisible happens.

You bill that consultant at $300/hour to clients. Their internal cost is $100/hour. But you can't bill internal projects at $300/hour. The accounting doesn't work that way.

That $200/hour difference vanishes from the calculation.

And there's always urgency from the client side. Internal projects get paused for weeks. Your best people stay focused on billable work. The AI project becomes what everyone does when they have spare time.

Which means never.

Knowledge Amnesia Gets Worse

Employees leave. Knowledge vanishes. New people take weeks or months to get up to speed.

When clients ask for experience documentation, it takes weeks to answer simple questions. People ask other people who ask other people. They search, find partial answers, then get back to client work.

Building AI internally doesn't solve this problem. It makes it worse. You're now maintaining complex technical infrastructure while trying to serve clients.

The Data Tells The Story

Consulting firms that purchase AI from specialized vendors succeed 67% of the time. Internal builds succeed only 33% as often.

Vendors succeed twice as often.

Why? They have both industry domain expertise and deep technical know-how. They can constantly pivot and adjust as AI evolves. That's their full-time job.

For consulting firms, AI is a side project competing with client work. The mission is serving customers. Energy goes to executing projects. Internal projects take lower priority.

There isn't always a way to account for this properly. But the opportunity cost is real.

What's Coming Next

Agentic AI will replace manual workflows entirely. Specific processes will run autonomously. The speed of evolution will accelerate.

Consulting firms building internally right now are unprepared for this. They're learning yesterday's approaches while tomorrow's solutions are already being deployed.

The question isn't whether to adopt AI. It's whether you can afford to build it yourself.

After 30 years in consulting, I've learned this: consulting firms are notorious for not solving their own problems successfully. The market demands vertical solutions built by specialists who understand both the industry and the technology.

The commonality between consulting firms is striking. Knowledge amnesia affects everyone. The solution requires specialized focus.

Smart firms recognize this. They partner with vendors who've already solved the hard problems. They deploy in months instead of years. They avoid the $200/hour opportunity cost bleeding.

And they don't have to explain why XYZ became ABC.