AI Adoption Isn't Failing Because of the Technology. It's Failing Because of Us.

By Leah Clelland Jochim | Convergence Technology Solutions

Inspired by: "Why Your Best Employees Quit" by Nate — watch it here

I was in a discovery session with a mid-market PE firm last quarter. Sharp team. Serious operators. They'd rolled out AI tools six months earlier with genuine intention — executive sponsorship, training sessions, the whole playbook.

When I asked how many people were still using the tools daily, the room went quiet.

That silence had nothing to do with the technology.

It had everything to do with change.

"Adoption is the biggest barrier to unlocking the promised benefits of any major transformation. The bigger the change, the more vital it is to have a solid change management approach."

The Three-Week Cliff Nobody Talks About

Nate's video "Why Your Best Employees Quit" makes a sharp and uncomfortable argument: a significant portion of employees stop using enterprise AI tools within roughly three weeks of adoption. Not because they lack access. Not even because they lack training. But because early wins don't translate into reliable, day-to-day performance. The tool disappoints them at a critical moment — and they quietly move on.

That's not a product failure. That's a change management failure. And it's one I've watched play out at organizations of every size.

Change Management Isn't Optional — It's the Whole Game

Every major transformation in the history of enterprise technology has lived or died on adoption. ERP rollouts. CRM implementations. Cloud migrations. The pattern is painfully consistent: organizations invest heavily in the technology and underinvest dramatically in the human side of change.

AI is not different. It is harder. Because unlike a new CRM, AI asks people to change not just what they do, but how they think. It challenges the expertise they've spent years building. It introduces uncertainty into workflows that have always felt controlled. That kind of change requires a structured, sustained, human-centered approach — not a launch event and a Slack channel.

Kotter got this right decades ago: you don't manage your way through transformation. You lead people through it — with urgency, coalition, vision, and the patience to remove the real barriers, not the assumed ones.

"The bigger the change, the more vital it is to hold the human dimension at the center. Technology doesn't transform organizations. People do."

This Is a Systems Problem, Not a Skills Problem

Nate's video is precise on this point, and it's worth sitting with: adoption failure is most often a systems failure. The work design doesn't support AI in the loop. There are no quality controls for AI-assisted outputs. Managers aren't equipped to set expectations or catch confidently wrong answers. The incentives still quietly reward the old way.

We built a judgment gap and called it a prompting gap. They are not the same thing. And no amount of additional training fixes a broken operating model.

Marty Cagan draws a sharp distinction between empowered teams and feature factories. The same line exists in AI transformation: organizations that deploy features — "here's your Copilot license" — are running feature factories. Organizations that invest in building a judgment layer — how we work with AI here, when to trust it, how to verify it, what to do when it's wrong — those are the ones that actually transform.

What I See in High-Stakes Environments Like M&A

In private equity due diligence work, this dynamic is particularly acute. Deal teams operate under extraordinary time and quality pressure. When an AI tool surfaces a flawed comparable or misses a material risk — even once, at the wrong moment — the analyst who got burned will not reach for that tool again. Especially not on a deal that matters.

That's why trust architecture matters as much as technical architecture. With ARIA, we're building Information Barriers and auditability into the foundation, not bolting them on later. Every feature has to earn trust incrementally — verifiable sources, transparent reasoning, workflow patterns that give practitioners a quality floor, not just acceleration.

Speed without reliability isn't a feature. In M&A, it's a liability.

Three Things Worth Acting On Now

If you're leading a transformation, here's where I'd focus.

First, treat AI as a change program, not a software rollout. That means standing up governance, behavior change, and an operating model alongside the technology — not after it stalls.

Second, measure beyond logins. Track daily active use by workflow, retention at weeks one, three, and six, and rework rates. Logins tell you about access. Those metrics tell you about trust and adoption.

Third, train your managers before you train your teams. Not on prompting — on recognizing where AI is reliable and where it isn't, setting verification expectations, and coaching their people through the discomfort of changing how they work. The organizations that win this transformation won't be the ones who moved fastest. They'll be the ones who held the human dimension at the center, consistently, all the way through.

A Question Worth Sitting With

If you asked your team today — not the leaders, the actual practitioners doing the work — whether they trust the AI tools in their workflow, what would they say?

And if the honest answer is "it depends," what change management infrastructure do you have in place to close that gap?

I'd genuinely love to hear what you're seeing — especially if you're navigating this in a high-stakes environment. Drop a comment or reach out directly.

#ChangeManagement #AIAdoption #ProductLeadership #EnterpriseAI #PrivateEquity

Leah Clelland Jochim is a Product & Engineering leader at Convergence Technology Solutions (CTS Partners), where she leads development of ARIA — an AI-powered M&A due diligence platform for mid-market private equity. With 20+ years transforming technology investments into scalable solutions, she works at the intersection of PE, product, and enterprise AI.

Watch Nate's original video: "Why Your Best Employees Quit" — https://youtu.be/EZ4EjJ0iDDQ