From Availability to Necessity: What Actually Drives AI Adoption

A portfolio company was running five-plus AI tools across different functions. On paper, they were ahead of the curve. In reality, usage was close to zero.

Every team had access. No one had a reason to use it consistently.

The problem wasn’t capability — it was integration. So we made one change: instead of layering AI on top of existing workflows, we embedded it directly into them. Tasks couldn’t be completed without it. Outputs depended on it. Managers reinforced it.

Within weeks, behavior changed — not because the tools got better, but because the environment did.

That’s the real lesson. AI adoption doesn’t come from availability. It comes from necessity. Make the tool the path of least resistance for getting real work done, and usage takes care of itself.

Most AI Initiatives Don't Fail in the Pilot — They Fail Right After

The pilot almost always looks good. The tool works, the use case checks out, leadership is aligned. On paper, everything is promising.

Then nothing happens. Adoption stalls, usage drops, and the momentum quietly disappears.

This isn’t a technology problem. It’s an execution problem. AI only creates value when it becomes part of how teams actually operate — embedded into the workflow, tied to incentives, and reinforced by management. If it stays optional, it gets ignored. If it stays disconnected from the real work, it gets abandoned.

Most firms spend their energy on what AI can do. Very few spend it on how it gets used. That gap — between a validated pilot and a changed operating rhythm — is where most initiatives break.

AI in Portfolio Companies Is a Coordination Problem, Not a Tooling Problem

Most firms approach AI in their portfolio companies as a tooling problem: pick the right software, get the licenses in place, and assume value follows.

It doesn’t work that way.

AI is a coordination problem across three things that rarely move together: operators, workflows, and incentives. You can put the best tools in the building and still see zero impact, because value isn’t created at the tool level. It’s created at the execution layer — where decisions get made, processes get followed, and teams operate day to day.

A strategy that stops at which tool to buy is just theory. The detail that matters is how the work actually changes — who does what differently on Monday morning, and why it’s worth their time. That’s the part most initiatives skip, and it’s the reason most of them stall.

AI Adoption Isn't Blocked by the Frontline — It's Blocked in the Middle

Most AI strategies assume the hard part is getting frontline teams to use new tools. In practice, that’s rarely where adoption breaks.

It breaks in the middle.

Middle management decides what actually gets used, what quietly gets ignored, and what becomes standard practice. If that layer doesn’t trust a tool, doesn’t reinforce it, or doesn’t use it themselves, it never scales — no matter how good the technology is.

Most rollout plans skip this layer entirely. They focus on procurement and training and treat managers as a pass-through. But managers are the transmission mechanism for any operating change. When they’re bought in, behavior shifts. When they’re not, the initiative stalls no matter how strong the pilot looked.

If you want AI to stick in a portfolio company, start with the people who set the daily standard — not the ones at the top who approved it, and not only the ones at the front who are asked to use it.