AI is moving fast. Most organizations are moving fast with it. Yet somewhere between the pilot, the rollout, and the promise of transformation, adoption starts to wobble. Employees experiment quietly. Leaders push for scale before the work is ready. Use cases multiply, but trust, clarity, and real business value remain harder to find. The problem usually isn’t the technology. It’s the way organizations try to absorb it. The AI Adoption Traps explains why traditional rollout thinking breaks down with AI—and what leaders must do differently. Drawing on decades of organizational change experience, Dana Houston Jackson explores the hidden traps that undermine adoption: speed without readiness, savings without redesign, literacy without judgment, trust without proof, and scale without a stable foundation. This is not a book about slowing AI down. It’s a practical guide for helping organizations catch up to the technology they are already buying. Through real-world examples, clear models, and plainspoken advice—leaders will learn how to choose better use cases, build trustworthy guardrails, redesign work, strengthen human judgment, measure what matters and move from scattered AI activity to lasting organizational capability. Because successful AI adoption is not about getting more people to use the tools; it’s about creating the conditions where responsible use becomes useful, trusted, and worth sustaining.