They Built It. Nobody Used It.

PERSONAL BRAND BLOG · AISHA ARIEL DAVIS

The real story behind the build vs. buy AI conversation — and what happens after organizations make the wrong call for the wrong reasons.

Aisha Ariel Davis

AI Workforce Transformation Specialist

June 2026

I published a LinkedIn article this week on the build vs. buy AI question — the framework, the two questions that clarify everything, and what organizations need to have in place before they make that decision.

But the article doesn't tell you what I've actually seen happen.

That's what this is for.

The Pattern That Keeps Repeating

Here's what the conversation usually sounds like.

An organization is evaluating AI solutions. Someone looks at the cost of an enterprise tool and says: "We have developers. We have data. Why are we paying someone else for this?"

Leadership agrees. The decision gets made. The team starts building.

Months later, the tool is live. The announcement is made. And then the silence starts.

Employees aren't using it. Not because it doesn't work. Not because people don't want to use AI. But because nobody thought about adoption before the build started. Nobody asked: how will we train our people? Who will champion this internally? What does good usage even look like for someone in this role?

WHAT ACTUALLY HAPPENED

I've seen organizations get to this point and do the logical next thing — bring in outside consultants to drive adoption.

Here's the problem nobody anticipated: the consultants didn't know how to use the tool either.

It was custom built. It wasn't in the market. There was no training material, no user community, no shared knowledge base. The consultants were being onboarded onto something that only existed inside that one organization.

So now you've paid to build it. And you're paying consultants to figure out how to get people to use something the consultants themselves are learning in real time.

The cost of the decision just doubled. And adoption still wasn't where it needed to be.

The Pressure Nobody Talks About

I want to be honest about something — because I think it's important context.

A lot of these decisions aren't made irrationally. They're made under pressure.

User enthusiasm around AI is real and it's high. Employees are already using consumer AI tools on their own. Boards are asking questions. Competitors are making announcements. And so leaders feel the urgency to move — to have something, to show something, to be somewhere.

That pressure leads to what I call place making — launching AI initiatives not because the problem is defined and the organization is ready, but because it feels necessary to be in the game.

"Place making is launching an AI tool and hoping your employees figure out what to do with it. Transformation is changing how work actually gets done."

Place making looks like: running a pilot without defining what success means. Distributing licenses without concentrating them in any one team. Measuring time saved without knowing how much time the task took before AI. Launching without a baseline — so when leadership asks "did this work?" nobody can honestly answer.

Real transformation looks different. It looks like a sales team that now has an AI agent giving them real-time data to make decisions in the field. An HR team where a new employee gets answers in minutes instead of waiting days. A process that didn't exist before that now generates revenue or saves cost in a way you can prove.

The difference isn't the technology. It's the intention behind the deployment.

The Data Conversation Nobody Wants to Have

I had a conversation recently — not in a boardroom, not on a call — just someone I ran into casually who mentioned they used their company's AI tool.

"It's frustrating," they said. "I only get a few useful responses because apparently it can only access a handful of our SharePoint sites."

That's a data problem. And it's one of the most common silent killers of AI adoption I see.

When employees hit the wall of what AI can access — when the answers are incomplete, when the tool can't find what they need — they stop trusting it. And once trust is broken with a tool, it's very hard to rebuild. The perception of AI inside that organization takes a hit that has nothing to do with the technology itself.

What I'd Tell Any Organization Starting This Conversation

Start with a security and compliance assessment before you make any AI investment decision. Know your data footprint. Understand what you actually have and what state it's in.

Then ask the two questions from my LinkedIn article — honestly, at the leadership level. Not as a formality. As a real conversation about readiness.

And before you launch anything — define what success looks like. Not "we want to save time." How much time? On which task? How will you measure it before and after? Who owns that measurement?

The organizations that get AI right aren't the ones who moved fastest. They're the ones who moved intentionally — who took the time to build the foundation before they built the thing on top of it.

That patience is hard to maintain when the pressure is real. But it's the only thing that makes the investment last.

This is the companion piece to my LinkedIn article: "Should You Build Your Own AI or Buy a Solution? Two Questions That Change Everything." The article covers the framework. This is what I've actually seen.

Aisha Ariel Davis

AI Workforce Transformation Specialist with nearly two decades partnered with Microsoft — beginning as a DigiGirl in 2008 through Senior AI Solutions Specialist today. Published author of six books. I share what it actually takes to transform an organization with AI. No hype. Just what works.