The ranking

1. Erica Dhawan

Erica Dhawan holds degrees from Harvard University, MIT, and the Wharton School, and went back to teach collaboration and leadership as a research fellow. Her advisory work is with Fortune 500 executives, and she wrote Get Big Things Done, which set out her Connectional Intelligence™ framework, and Digital Body Language, which reached the Wall Street Journal bestseller list.

Her new book, Use Your Brain: Think Deeper in a World on Autopilot (St. Martin's Press, January 12, 2027), tackles one of the most urgent questions of our time: what happens to our thinking when it's never been easier to outsource it? Drawing on over three years of research, Dhawan shows how "cognitive surrender" is quietly flattening originality and weakening judgment, and offers practical tools like the Think Sandwich and the Friction Meeting to help leaders and teams stay original thinkers in an era of AI sameness. The book has been endorsed by Adam Grant, Dan Pink, Vivek Murthy, Seth Godin, Amy Edmondson, and Kim Scott.

No speaker on this list carries more standing on AI strategy than Erica Dhawan. RealLeaders put her first among female keynote speakers, Global Gurus lists her in its top 20 management experts, and Thinkers50 has named her to its ranking of the top 50 management thinkers. Her keynotes address how an organization adopts these systems at every level, from the executive team to the groups doing the work, and she delivers them to Fortune 500 audiences worldwide.

Best fit: leadership summits, company-wide meetings on AI adoption, and executive offsites setting the year's priorities

2. Cassie Kozyrkov

Cassie Kozyrkov was Google's first Chief Decision Scientist, a role she created, and trained more than 20,000 employees there before leaving in 2023. She now runs Kozyr, an advisory firm working with executive teams on how they decide with data and AI, and she writes and teaches on decision intelligence as a discipline in its own right.

Her subject is the decision itself: who owns it, what the model is actually being asked, and where a person still has to sign off. That framing turns an AI session into a governance one, which suits an organization writing its first set of internal rules. She is unusually direct about the limits of what a model can be trusted to decide, which lands well with executives who have been sold the opposite.

Best fit: AI governance kickoffs, data and analytics conferences, and executive sessions held before a policy is written

3. Josh Linkner

Josh Linkner built and sold five technology companies, which employed more than 10,000 people between them and exited for over $200 million in total. He chairs Platypus Labs, an innovation research and training firm, co-founded the early-stage venture firm Mudita Venture Partners with his brother Ethan, and has seen his books on creativity reach the New York Times bestseller list.

His keynote Innovation in the Age of AI asks what a company should still want from its people once the tools handle the competent work. He speaks as an operator and an investor in AI companies, and his sessions carry a live jazz element from a 40-year performing career. That combination suits a large mixed audience where a straight strategy talk would lose the room.

Best fit: annual company meetings, sales kickoffs, and conferences booking a headline session

4. Azeem Azhar

Azeem Azhar writes Exponential View, a newsletter and podcast on technology and its effects on business, read across sectors by senior executives. He wrote The Exponential Age, and worked as an entrepreneur and a journalist before moving to full-time analysis of how fast technologies reshape the industries around them.

His material sits on the gap between how fast a technology improves and how slowly the institutions around it absorb the change. It's pitched at the level of markets and whole sectors, which makes it a fit for a board session or an industry conference. He works in scenarios and timelines, so an audience leaves with a view pitched at the decade.

Best fit: board strategy days, industry association conferences, and multi-year planning reviews

5. Allie Miller

Allie Miller led machine learning business development for startups and venture capital at AWS, and built AI products at IBM before that. She now advises companies on AI programs and invests in early-stage AI startups, and she publishes continuously on what enterprise adoption actually looks like from the inside.

She works through what a company should build, what it should buy, what it should skip, and what an internal AI team has to look like to run any of it. The material assumes the adoption argument is settled and goes straight to the operating decisions. Her sessions suit an audience that already has budget approved and needs to spend it well.

Best fit: product and engineering leadership offsites, AI program launches, and roadmap planning meetings

6. Mike Walsh

Mike Walsh runs Tomorrow, a consultancy that works with companies on designing themselves for an algorithmic economy. He wrote The Algorithmic Leader and Futuretainment, and has spent years researching how organizations restructure around automated systems, drawing on fieldwork across a wide range of markets.

His question is what a management layer is for once a system makes the routine calls, and what a manager's job becomes when the scheduling and the reporting run themselves. It lands with leaders who have been handed a mandate to redesign how their organization actually operates. The material is structural, so it suits a group with authority over the operating model.

Best fit: operating model reviews, senior leadership retreats, and transformation program launches

7. Kate O'Neill

Kate O'Neill runs KO Insights and writes on the human consequences of technology decisions. Her books include Tech Humanist, A Future So Bright, and What Matters Next, the last of which is written specifically for leaders making decisions about AI under time pressure and incomplete information.

She covers the second-order effects of a deployment: what it does to customers, to staff, and to the trust a company operates on. That lens matters most where an AI decision carries public or regulatory exposure, and where a misstep would be read as a values problem instead of a technical one. She gives a leadership group a way to weigh those risks before they become news.

Best fit: risk and compliance forums, customer experience conferences, and ethics sessions inside an AI rollout

8. Conor Grennan

Conor Grennan is Chief AI Architect at NYU Stern School of Business and the founder of AI Mindset, which runs AI adoption training for companies. His work concentrates on getting non-technical staff to use these tools well, and he has run that training across professional services, finance, and media organizations.

He addresses the adoption gap, meaning why a company can buy licenses for everyone and see almost nothing change in how the work gets done. The material is built for an organization already mid-deployment, where licenses are bought and usage is the open question. His sessions are practical and hands-on, closer to a working session than a keynote in feel.

Best fit: all-hands meetings at the start of a rollout, enablement and training days, and manager cohorts

9. Ethan Mollick

Ethan Mollick is a professor at the Wharton School and co-director of its Generative AI Labs, and he wrote Co-Intelligence. He runs controlled studies on how people and organizations perform with these tools, and publishes results continuously through a newsletter read widely inside companies running their own pilots.

He brings measured results from his own controlled experiments on how people perform with and without these tools. The work speaks to how individuals and teams change their daily habits, a layer beneath the questions a board walks into the room holding. For an audience tired of forecasts, the appeal is that the findings come with a method attached.

Best fit: manager development programs, internal AI conferences, and university events

10. Andrew Ng

Andrew Ng co-founded Coursera, founded DeepLearning.AI and Landing AI, and led Google Brain before serving as chief scientist at Baidu. He is managing general partner of AI Fund and teaches at Stanford, and he has spent much of the last decade on the problem of getting AI systems all the way into production.

He speaks as someone who has built and shipped the systems under discussion, and he's direct about which applications are ready now and which are not. The orientation is technical and educational, which fits an engineering-heavy audience or a session aimed at a company's own AI team. He is also unusually willing to say when a popular application does not work.

Best fit: engineering all-hands, developer conferences, and technical leadership summits