Gregg Semenza
2019 Nobel Laureate in Physiology or Medicine | Discoverer of HIF-1 | C. Michael Armstrong Professor, Johns Hopkins University
Former Head of Autonomous AI Co-Innovation, Microsoft Research | Author of Autonomous Transformation | Founder & CEO, The Future Solving Company | Thinkers50 Radar 2025
Brian Evergreen led Autonomous AI Co-Innovation at Microsoft Research before founding The Future Solving Company, where he advises Fortune 500 leaders on AI strategy. His book Autonomous Transformation was a Thinkers50 Top 10 management book, and Forbes named him one of the Top 30 Thinkers Redefining Leadership in 2025. His argument on stage: AI is not your strategy, and vision has to come first.
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Brian Evergreen spent years inside Microsoft helping Fortune 500 executive teams build their AI strategies, and came away with an uncomfortable conclusion: the playbook business inherited from the industrial revolution, and then from the era of digital transformation, does not work for the era of AI. His response was a different method entirely, one that starts not with the technology but with a clear-eyed answer to a harder question: what future is actually worth building?
AI speaker Brian Evergreen is the former Global Head of Autonomous AI Co-Innovation at Microsoft Research, with earlier roles at AWS and Accenture, and is now founder and CEO of The Future Solving Company. He advises more than a dozen Fortune 500 organizations, and the methodology he introduced in his book has been adopted by more than fifty Fortune 500 companies as well as NASA. His central argument is deliberately contrarian: AI is not your strategy. Vision comes first, and AI is one of several ways to get there.
His book, Autonomous Transformation: Creating a More Human Future in the Era of Artificial Intelligence, was named a Next Big Idea Club “Must-Read” and one of the Thinkers50 Top 10 Best New Management Books, and it introduced the distinction at the heart of his work: problem solving is the craft of getting rid of what you don’t want, while future solving is the craft of getting what you do want. He is a Thinkers50 Radar honoree, a Senior Fellow at The Conference Board, a guest lecturer at Northwestern’s Kellogg School of Management and Purdue University, and was named by Forbes one of the Top 30 Thinkers Redefining Leadership. His LinkedIn Learning course on leadership in the era of AI is among the platform’s highest-rated.
As a speaker, Brian Evergreen is the rare AI voice who spends more time on judgment than on tools, and he is unusually willing to tell an audience which applications will not work for them. He speaks on why vision must precede AI strategy, the future solving method for setting and reaching an ambitious future, where AI agents genuinely fit within a specific industry, and how leadership and work change when tasks are decoupled from jobs. Audiences leave with a way of thinking rather than a list of tools, and with considerably better questions to bring to their own AI investments.
Evergreen makes the case that most organizations have the sequence backwards, starting with the technology and working outward, which produces pilots that never scale and investments nobody can defend. Drawing on years developing AI strategies with Fortune 500 executive teams at Microsoft, he shows how to set a vision first, then treat AI as one instrument among several for reaching it, and why this ordering is what separates companies compounding real advantage from those accumulating expensive experiments.
This is the method at the center of his book and his practice. Problem solving, Evergreen argues, is the craft of eliminating what you don't want, which is useful but inherently limited. Future solving is the craft of getting what you do want, and it requires whole-systems thinking plus a willingness to re-examine assumptions about which parts of a market or organization are genuinely fixed. He walks audiences through the method and how leaders apply it to set an ambitious but achievable direction.
Agentic AI is the subject of enormous enthusiasm and very little discrimination. Evergreen brings a practitioner's skepticism, working through where autonomous agents create real value in a given industry, where they quietly fail, and what governance and accountability structures have to exist first. This talk is customized to the sector in the room, and its usefulness comes as much from what he rules out as from what he recommends.
Evergreen argues that a job is accountability for an outcome, and that AI cannot hold accountability, which makes wholesale job replacement both a category error and a costly mistake. He points to companies that cut human teams and then scrambled to rebuild them. His alternative is to separate tasks from jobs, route the repetitive work to machines, and concentrate people on judgment and relationships, and he explores what that means for how leaders organize, measure, and develop their teams.
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