When Faster Isn’t Better: A Review of The AI Adoption Traps

Every leader I talk with these days is under some version of the same pressure, to show the board, the investors, or the market that the organization is doing something meaningful with Artificial Intelligence (AI). Dana Houston Jackson’s new book, The AI Adoption Traps: How Leaders Build Trust, Value, and Lasting Capability in the Age of AI, is written directly to that pressure, and to the leaders who feel it most acutely. Her argument, developed patiently across fourteen chapters, is that organizations have confused AI activity with AI adoption, and AI adoption with genuine organizational capability. Licenses purchased, pilots launched, employees trained, and usage dashboards trending upward all look like progress. None of them, on their own, tell you whether AI has become something the organization can actually depend on.

One idea that stayed with me long after I finished the manuscript is what Houston Jackson calls the Absorption Gap. It captures something leaders intuitively sense but rarely articulate. A task can become dramatically faster without making the organization itself any faster at all. If a report that once took four hours now takes twenty minutes, but the people who review it, approve it, and act on it are still working at their original pace, the organization has not gained speed. It has simply relocated the bottleneck. Houston Jackson’s point is not that productivity gains are illusory, but that they must be measured at the level of the whole system rather than the isolated task. That reframing alone is worth the price of the book for any executive who has funded an AI initiative on the promise of time saved.

She builds toward that idea through a set of earlier traps that are, in their own right, some of the most useful diagnostic tools in the book. The Speed Trap describes leaders who move faster than either their own understanding or their organization’s readiness, mistaking visible activity for genuine progress. The Savings Trap addresses the familiar and costly assumption that because AI can perform a visible task, the full cost of that task simply disappears, when in fact much of its value lay in judgment, exception handling, and coordination that never appeared on an org chart. And the Rollout Fallacy draws a sharp, necessary line between giving people access to a tool and actually redesigning the work around it. Taken together, these ideas give leaders language for a pattern many of us have watched play out without quite being able to name it.

Houston Jackson’s treatment of trust is, to my mind, one of her more original contributions. Rather than framing trust as enthusiasm for a tool or confidence in its reliability, she defines it as appropriate reliance, the ability to know precisely where AI can be depended upon, where human judgment must prevail, what requires verification, and who remains accountable when something goes wrong. That distinction becomes increasingly important as she moves from generative AI toward agentic AI, where the operative question shifts from what a system can produce to what it should be permitted to do. Her insistence that governance function as, in her words, a fence line rather than a set of handcuffs is a genuinely helpful reframe, and one I expect to see borrowed widely.

The book is equally attentive to how adoption actually spreads through an organization, which is rarely through enterprise communication alone. Houston Jackson draws on research in social learning and diffusion to make the case that employees take their cues from their direct managers and from credible peers who are already using AI well. She pairs this with what she calls an Evidence Ladder, moving from activity to behavior to performance to capability, as a corrective to the tendency to scale AI simply because a dashboard shows heavy use. Both ideas push leaders toward a more honest accounting of what their organizations have actually learned, rather than what they have merely deployed.

Where the book ultimately arrives is at a destination larger than AI adoption itself, what Houston Jackson calls an AI-capable organization, one that can continually identify worthwhile uses, redesign work around them, govern responsibly, and learn from evidence rather than instinct. Much of the underlying thinking, participatory design, manager enablement, learning from evidence, has deep roots in organizational change work, and readers familiar with that discipline will recognize the lineage even as Houston Jackson extends it thoughtfully into AI-specific territory.

I recommend this book warmly to any leader accountable for the return on an AI investment, and particularly to those whose programs have been measured more by deployment than by durable results. It does not attempt to replace the technical, legal, or risk work already underway in most organizations, nor should it. What it offers instead is a clear, well-reasoned vocabulary for a set of problems many leaders have sensed but not yet had the language to name, and the Absorption Gap is only the beginning of what makes this book worth your time.