Thinkers & Ideas

In AI Native: The Mandate to Transform Your Company, Kai-Fu Lee argues that real competitive advantage from AI does not come from buying access to the most powerful model, but from rebuilding the company itself around AI.

Lee is the chairman and CEO of Sinovation Ventures, the founder and CEO of 01.AI, the former president of Google China, and the bestselling author of AI Superpowers. In his new book, he contends that AI is commoditizing human intelligence, making it the first and last intelligence revolution. He shows leaders how to move past common misconceptions, tap the dormant knowledge inside their organizations, and restructure teams so that small groups of people direct fleets of AI agents.

In his conversation with Nikolaus Lang, global leader of the BCG Institute, he discusses the four flaws in how companies approach AI, why off-the-shelf models fail to solve a company’s hardest problems, the rise of the directly responsible individual, how leaders should think about AI costs, the uniquely human skills of future leaders, and whether AI poses an existential risk.

Key topics discussed:

01:36 | Why AI is more fundamental than electricity or the internet

03:12 | The four flaws in how companies approach AI

06:20 | Why off-the-shelf AI won’t solve your hardest problems

11:03 | Tapping dormant data inside the company

13:32 | The directly responsible individual (DRI) and fleets of AI agents

18:15 | The end of traditional middle management

19:15 | How leaders should think about AI costs

22:49 | Taste, foresight, conviction, and courage: what remains human

26:46 | The elevator pitch for becoming AI native

27:43 | Is AI an existential risk?

Additional inspirations from Kai-Fu Lee:

• AI Superpowers: China, Silicon Valley, and the New World Order (Harper Business, 2018)

In The CEO: The Role, the Reality, the Responsibility, Nitin Nohria argues that the CEO’s real work is not making the big decisions personally but building the conditions under which thousands of good decisions get made by everyone else.

Nohria is the former dean of Harvard Business School, where he is currently the George F. Baker Professor of Administration, and for nearly 30 years he has run the invitation-only New CEO Workshop for recently appointed chief executives of the world’s largest companies. His latest book draws on that work, on empirical research into how much CEOs actually affect company performance, and on roughly 70,000 hours of data from real CEO calendars, to replace the myth of the heroic chief executive with a clear-eyed account of what the job involves: setting direction, allocating resources, shaping culture, managing stakeholders, and absorbing relentless scrutiny.

In his conversation with Adam Job, senior director at the BCG Institute, Nohria discusses how much the person in the seat really matters, why new CEOs have to stop being COOs, where the hours in a CEO’s day actually go, how to tell a genuine crisis from daily noise, the structurally induced narcissism the role invites, what AI and geopolitics demand of this generation of leaders, and the one piece of advice he gives every CEO.

Key topics discussed:

01:00 | How much the CEO in the seat actually matters

03:51 | Making decisions versus ensuring good decisions get made

06:19 | Why new CEOs struggle to stop being the COO

08:15 | The unexpected challenges of the job

10:19 | Inside 70,000 hours of CEO calendar data

13:18 | Whisper warnings, siren songs, and daily noise

16:21 | What CEOs must protect in their calendar, and what to cut

19:44 | Loneliness and structurally induced narcissism

23:59 | What AI and geopolitics demand of CEOs

[26:42] How CEOs use AI, and the problem of AI slop

[28:58] Why anyone would want the job

[29:49] The single most important piece of advice

Additional inspirations from Nitin Nohria:

• In Their Time: The Greatest Business Leaders of the Twentieth Century (Harvard Business Review Press, 2005)

In How Great Ideas Happen: The Hidden Steps Behind Breakthrough Success, George Newman argues that great ideas are not conjured from within but discovered outside of us, already present in the world, and that finding them is a methodical search anyone can run rather than a flash of genius reserved for a gifted few.

Newman is a cognitive scientist and psychologist, and an associate professor of organizational behavior and human resource management at the Rotman School of Management at the University of Toronto, having previously held joint appointments in management, psychology, and cognitive science at Yale. In his first book, he draws on research across the arts, science, and business to recast creativity as an archeological dig: survey the terrain for fertile ground, grid the site, dig for volume, and only then sift for what is worth keeping.

In his conversation with Adam Job, senior director at the BCG Institute, he discusses why organizations "turtle up" under pressure when they should be looking outward, why our best ideas arrive after we think we have run out, where AI genuinely helps the creative process and where it flattens it, the roughly 95/5 balance between proven ground and original twist, and why breakthrough ideas often feel uncomfortable at first.

Key topics discussed:

01:01 | Reframing creativity as discovery rather than inspiration

02:38 | The archeology method: survey, grid, dig, sift

04:33 | Why organizations "turtle up," and how to stay outward-looking

06:13 | The creative cliff illusion: why the best ideas come last

08:35 | AI as an excavator: when it helps and when it doesn't

10:49 | Transplanting ideas from other domains

13:08 | The 95/5 balance of proven ground and original twist

16:13 | Ideas versus execution, and Steve Jobs' rock tumbler

19:36 | Creative hot streaks, from Jackson Pollock to Mondrian

25:14 | Why good ideas often feel uncomfortable at first

27:28 | How Newman applies his own research to his work

In Manage the Machine: How to Harness Human-AI Collaboration at Work, Paula Goldman argues that AI has crossed over from being a tool to being a partner, and that the companies that win will not be the ones automating the most or the fastest, but the ones that deliberately design around where humans still need to lead.

Goldman is the first-ever chief ethical and humane use officer at Salesforce, where she is also EVP of Product, and previously led the Tech and Society Solutions Lab and impact investing at Omidyar Network. She has served on the US National AI Advisory Committee, chairing its generative AI working group. Her new book is a practical guide to integrating AI across functions from sales to HR to legal, setting out how to decide which tasks can safely be delegated to AI, which should stay with people, and what guardrails keep either from going astray.

In her conversation with Adam Job, senior director at the BCG Institute, she discusses her "human at the helm" model of AI adoption, the role customer emotion plays in deciding where AI belongs, whether AI erodes the very human judgment it depends on, how to measure AI ROI beyond time saved, and what it takes to move from pilots to enterprise-wide rollout.

Key topics discussed:

01:01 | What changes for leaders once AI shifts from tool to partner

02:28 | The "human at the helm" model in practice

07:09 | How the chief ethical and humane use officer role fits into AI governance

09:43 | Customer and employee emotion as a design input

12:39 | Skill decay: does the model erode itself over time?

15:54 | The jagged technological frontier and where to deploy AI

18:32 | Calculating AI ROI: cost savings versus new products

22:14 | Navigating two moving frontiers, better and cheaper

27:09 | Getting from successful pilot to enterprise rollout

In Global by Design: How to Create Innovations That Scale, Travel, and Transform, Vijay Govindarajan argues that the most powerful innovations are designed from the ground up for emerging markets like rural China or India, and then travel back to disrupt wealthy ones.

Govindarajan is the Coxe Distinguished Professor at Dartmouth College’s Tuck School of Business and one of the world’s foremost experts on strategy and innovation, most known for the New York Times and Wall Street Journal bestseller Reverse Innovation. In his new book, co-authored with MIT engineer Amos Winter, he fuses business strategy with engineering science to show how high-value, low-cost products built for poorer countries can win there first and then upend established markets abroad, offering a roadmap for innovators across engineering, business, NGOs, and government.

In his conversation with Nikolaus Lang, global leader of the BCG Henderson Institute, he discusses how GE built a $15,000 ultrasound machine for rural China; why most multinationals chase only the thin top slice of emerging markets; the three phases of his innovation framework; the difference between serial and parallel reverse innovation; and why globalization behaves like gravity even in a fragmented, multipolar world.

Key topics discussed:

03:29 | How reverse innovation works: GE’s ultrasound machine for rural China

06:39 | Why most multinationals aren’t global by design

08:01 | The mistake of serving only the thin top slice of emerging markets

10:00 | Phase 1, elucidating requirements: P&G’s 3,000 hours redesigning a razor

13:00 | Phase 2, closing the innovation gap: GE’s low-cost CAT scanner

16:48 | Phase 3, serial vs. parallel reverse innovation

20:20 | Value-driven innovation, the Segway cautionary tale, and where the biggest opportunities lie

24:14 | Global by design in a geopolitically fragmented world

Additional inspirations from Vijay Govindarajan:

• Reverse Innovation: Create Far From Home, Win Everywhere (Harvard Business Review Press, 2012)
• LinkedIn: Three Box Solution Weekly Column

In Blue Ocean Strategy: How to Create Uncontested Market Space and Make the Competition Irrelevant, Renée Mauborgne argues that lasting growth does not come from winning market share in crowded existing industries, but from creating entirely new market space that no one is competing in yet.

Mauborgne is a professor of strategy at INSEAD and co-director of the INSEAD Blue Ocean Strategy Institute. Blue Ocean Strategy, co-written with W. Chan Kim has sold more than 4 million copies and is recognized as one of the most iconic and impactful strategy books ever written. The new 20th anniversary edition tests the original theory against today’s market realities and is enhanced with fresh cases, including NVIDIA’s three successive market-creating moves, a new preface that highlights the book’s ongoing global impact, and a curated list of over fifty blue ocean cases across diverse industries that expand far beyond those in the original version.

In her conversation with Adam Job, senior director at the BCG Institute, Mauborgne discusses when a company should stop competing and start creating, why the search for new demand begins with non-customers rather than customers, how NVIDIA chases “zero-billion-dollar markets,” why differentiation and low cost need not be a trade-off, whether AI’s commoditizing effect makes advantage harder to defend, and how to reconcile bold commitment with the case for keeping options open.

Key topics discussed:

01:12 | Why beating the competition is the wrong goal

02:34 | How to know when it’s time to shift from competing to creating

05:26 | Finding a blue ocean: value innovation and the role of non-customers

07:02 | Where the process starts inside an organization

09:25 | NVIDIA and the pursuit of zero-billion-dollar markets

12:46 | Breaking the trade-off between differentiation and low cost

16:21 | What prompted the new edition

18:33 | Will blue oceans stay defensible as AI commoditizes capabilities?

24:17 | Why using AI only for speed and efficiency is a trap

25:36 | Reconciling bold commitment with the need for optionality

Additional inspirations from Renée Mauborgne:

• Blue Ocean Shift: Beyond Competing—Proven Steps to Inspire Confidence and Seize New Growth (Grand Central Publishing, 2017)

As uncertainty continues to define the business landscape, we’re revisiting our conversation with economist and complexity scientist Doyne Farmer on why conventional forecasting often falls short in a world shaped by interconnected, dynamic systems. Drawing on decades of research, he explains how complexity science offers a more realistic way to think about prediction, risk, and decision-making—and how leaders can navigate uncertainty with greater confidence.

Whether you’re hearing this conversation for the first time or revisiting it with fresh perspective, Doyne’s insights remain as relevant today as ever.

In Crisis Engineering: Time-Tested Tools for Turning Chaos Into Clarity, Matthew Weaver and Mikey Dickerson argue that organizational crises are not problems to be survived but are rare windows in which rapid, directed transformation becomes possible within days rather than years.

Weaver was a founding Site Reliability Engineer (SRE) at Google and later established the Defense Digital Service at the U.S. Department of Defense. Dickerson, also a founding SRE at Google, led the rescue of healthcare.gov, an effort that landed him on the cover of Time magazine. President Barack Obama subsequently appointed him deputy chief information officer of the United States, where he created the United States Digital Service. Together with their co-author Marina Nitze, the three are partners at the crisis engineering firm Layer Aleph. Drawing on decades of experience inside some of the most complex systems in government and industry—from the healthcare.gov rescue to wildfire response and pandemic logistics—the writers deliver a practical framework for identifying the five signals of a genuine crisis, and how to use that moment to drive lasting change across tech, government, healthcare, and beyond.

In their conversation with Nikolaus Lang, global leader of the BCG Henderson Institute, they discuss why breakdowns in sensemaking cause well-trained operators to make situations worse, what the healthcare.gov rescue reveals about co-location and shared urgency, how crisis disrupts normal incentive structures to surface the organization’s real map, the concept of bracketing as a tool for directing team attention, and how AI integrations are creating the conditions for larger and harder-to-manage crises ahead.

Key topics discussed:

01:24 | What “crisis engineering” means and what conditions define a true crisis

05:28 | Sensemaking: the invisible force driving decisions under pressure

10:38 | The 2013 healthcare.gov collapse: arriving on the crisis scene

14:05 | Why physical co-location is essential in a crisis

17:18 | Using a crisis to remap how the organization actually works

20:17 | Bracketing: deliberately defining the beginning and end of a crisis

25:33 | Why organizations struggle to define an exit condition

27:34 | Capturing crisis learning before returning to normal operations

30:22 | How AI is changing (and likely multiplying) crisis situations

35:51 | Advice for business leaders on building crisis readiness before a crisis hits

In The Irrational Decision: How We Gave Computers the Power to Choose for Us, Benjamin Recht argues that the optimization and mathematical rationality we apply to every corner of modern life—from dieting to hiring to strategy—often fails when encountering the messy realities of life.

Recht is a professor of electrical engineering and computer science at UC Berkeley. In his new book, he traces how a narrow conception of rationality, born from 1940s wartime computing, came to dominate decision-making across society—and shows that this approach works brilliantly in closed, controlled systems like microchip design but breaks down in the complex, unpredictable domains where most real decisions are made.

In his conversation with Adam Job, senior director at the BCG Henderson Institute, he discusses the origins of mathematical rationality, why optimization works for microchips but not for diets, why game theory fails to describe how humans actually behave, and how leaders should think about the boundary between human and machine intelligence in the age of AI.

Key topics discussed:

01:02 | What is mathematical rationality and where do we encounter it?

02:49 | The origins of rational thinking in the 1940s

07:18 | Where optimization works: microchips, logistics, controlled systems

09:13 | Where it fails: Chernobyl, Waymo, and the limits of control

13:17 | When human “qualitative irrationality” is the right answer

14:59 | A framework for assigning decisions to machines vs. humans

17:45 | How the boundary between human and machine decision-making will evolve

19:14 | Why game theory fails to describe how humans actually behave

22:07 | Kahneman vs. Klein: two views on human decision-making

24:30 | What we risk losing as we outsource more decisions to AI

In AI Needs You: How We Can Change AI’s Future and Save Our Own, Verity Harding argues that AI governance is too important to be left to technologists alone—and that the rest of us need to join the conversation to shape this technology’s future.

Harding is the director of the AI and Geopolitics Project at the Bennett School of Public Policy at the University of Cambridge and the founder of Formation Advisory. She spent more than a decade at Alphabet, first as head of Security Policy at Google, then as DeepMind’s first global head of Policy. In her book, she draws on historical case studies to show that democratic societies have successfully governed transformative technologies in the past.

In her conversation with Nikolaus Lang, global leader of the BCG Henderson Institute, she discusses why the nuclear arms race is the wrong analogy for AI, what the 1967 Outer Space Treaty can teach us about cooperation between rivals, how Britain’s regulation of IVF became a gold standard by depoliticizing the technology, and what business leaders get wrong about their own role in shaping AI governance.

Key topics discussed:

01:56 | Why the framing of AI as “too complex for nonexperts" is harmful

07:46 | Why the nuclear arms control analogy is counterproductive for AI

12:25 | The Space Race and the 1967 Outer Space Treaty as a model for cooperation

17:11 | IVF, the Warnock Committee, and why a philosopher led the regulation effort

20:38 | The internet: from open ideals to commercialization and surveillance

26:41 | What business leaders can do to shape AI governance

30:50 | Four principles for AI: peaceful intent, embrace limitations, purpose over profit, societal trust

35:25 | If you could mandate one thing for global AI governance, what would it be?