Adopting AI

The case for radical optimism — and radical caution. What AI could mean for the UN Sustainable Development Goals, education, creativity, and human flourishing. And what could go wrong.

From rule-based games to transformer architectures — a clear, non-technical history of AI for leaders who need to understand the technology without becoming engineers.

Frontier models, agents, multimodality, and the agentic bottleneck. What's coming next and why it matters for your organization right now.

Why most AI strategies fail the people test. A framework for building AI strategy that starts with humans — their fears, skills, and potential — not the technology.

The Adaptive Adoption™ model — how to deploy AI in complex organizations using principles from complexity theory and behavioral science, not traditional change management.

The five dimensions of AI literacy. Why training budgets are being wasted, what the WEF data says about GenAI skills demand, and how to find the human-AI sweet spot.

The three foundational ethical problems of AI: you can't explain it, you can't predict it, and it was trained on other people's work.

Why "Responsible AI" isn't responsible enough. The gap between corporate AI ethics statements and actual deployment decisions — and a framework for closing it.

Job displacement, bias, deepfakes, data privacy, and surveillance — the five ethical flashpoints every organization will face. With frameworks for navigating each.

The global regulatory landscape, AI Centers of Excellence, governance processes — and a final chapter on navigating what comes next with a people-first compass.