Trust Was Always the Rollout

In the past four decades in technology, trust has always been assumed as part of the conversation.

We talked about adoption curves, change management, training programs, rollout timelines, features, interfaces. Our biggest concern was whether the system was intuitive enough for people to pick up quickly. You built something that worked, you showed people how to use it, and eventually they used it.

Dana Houston Jackson's new book, The AI Adoption Traps, puts language around something practitioners have been running into for two years without a clean way to name it. Her core argument is that organizations have confused AI activity with AI adoption, and AI adoption with actual capability. Licenses bought, pilots launched, training delivered, usage dashboards climbing. All of it looks like progress. None of it reveals whether AI was valuable or something the organization could depend on.

The report that took four hours now takes twenty minutes, and then it sits in the same review queue, waiting on the same approver, moving at the same old pace. Nothing was gained. The bottleneck simply relocated. Jackson's point is that productivity has to be measured across the whole system rather than the isolated task.

Jackson doesn't define trust as enthusiasm for a tool or confidence that it works. She defines it as appropriate reliance. Trust comes from knowing where AI can be depended on, where human judgment has to prevail, what needs verification, and who's accountable when something goes wrong.

Governance in Jackson's framing is a fence line rather than handcuffs. That distinction gets sharper as organizations move from generative to agentic AI, where the question stops being what a system can produce and becomes what it should be permitted to do.

In our view, appropriate reliance is a two-sided arrangement. It asks people to rely on a system, which is a design problem. It also asks them to rely on the organization that put the system in front of them, which is a trust problem, and it didn't start with AI.

What we've learned watching AI rollouts that others have run is that the stalled ones share a shape with technology rollouts going back four decades. The tools are fine. The use cases are real. The math works. Adoption is flat because somewhere upstream, people never got a real answer to the question underneath the question, which is what does this mean for me. Leadership moved to execution before building the foundation execution requires.

The stakes are almost never about the technology. They're about whether people believe leadership is being straight with them. Most organizations skip that step because it's uncomfortable and it doesn't show up on a project plan.

Jackson gives executives a vocabulary for this, which matters because vocabulary shapes what gets prioritized. When you can name the Absorption Gap out loud in a room, you stop treating adoption friction as a training problem and start treating it as a design problem.

AI suffers from all of the same hurdles of what people believe, what they've been told, what they were promised before, and whether any of it turned out to be true.

The work that comes after the trust conversation is a shared map of what changes, what stays, and who owns what. That map is what the tools get built on.