Prof Gianvito Lanzolla: Strategy in the World of Data and AI

AI is changing the economics of business. But it is not rewriting the fundamentals of strategy.

In this episode of Scouting for Growth, I sit down with Professor Gianvito Lanzolla, Professor of Strategy at Bayes Business School, advisor to boards and CEOs, and co-author of Diversification in the World of Data and AI, to examine what actually creates competitive advantage when access to intelligence is rapidly becoming ubiquitous.

The uncomfortable truth is that better technology alone does not create a better strategy. Being early to AI can accelerate learning, but scaling before you understand how the technology creates value, how the organization captures that value, and what customer problem it solves can simply mean buying expensive learning. Gianvito makes an important distinction between being early for learning and being early for scaling.

We then explore why data in context matters more than data volume. Tesla's understanding of driving behavior can translate naturally into insurance because the data is relevant to risk. Tesco's knowledge of what customers buy did not automatically reveal what they wanted to watch. The strategic question is not how much data you hold, but what the data describes, how it was generated, and which decisions it can improve.

That same discipline applies to diversification and governance. Companies still need a credible right to win, supported by continuous data in context, domain expertise, and the ability to orchestrate an ecosystem. And as AI increasingly influences or makes decisions, governance cannot remain a sequence of static gates. Gianvito argues for adaptive governance built around monitoring, feedback loops, escalation mechanisms, and the ability to revisit decisions as the technology evolves.

Most importantly, the conversation brings strategy back to fundamentals. If AI ultimately becomes more like electricity than a source of differentiation in itself, advantage will come from the business model built around it. Gianvito leaves leaders with three questions: Value, Visibility, and Viability. What new customer problem can you solve? What can you see or do now that was previously impossible? And what can you organize and execute economically today that you could not before?

Because AI may change the context, the economics, and even the architecture of decision-making. But competitive advantage still depends on understanding where value is created, why you have the right to capture it, and how you build an organization capable of doing so.

KEY TAKEAWAYS

I want to challenge the assumption that moving first on AI automatically creates an advantage. Gianvito's distinction between being early for learning and early for scaling is particularly important. Organizations should experiment early because they need to build the capacity to understand and implement the technology. Scaling is different. That decision requires a credible hypothesis about the business model: how does AI create value, how will the company capture it, and what becomes possible economically that was not possible before? Access to increasingly powerful models will spread. The differentiation comes from what the organization builds around them.

This is also why the conversation around data needs to become more precise. It is not enough to say that a company has enormous amounts of data. What matters is data in context: what the data describes, how it was generated, and which decisions it can inform. Gianvito identifies three capabilities behind AI-enabled diversification: generating a continuous flow of relevant data, combining AI with domain expertise, and orchestrating the ecosystem required to deliver the proposition. Even then, traditional strategic questions remain. Do you genuinely know something your competitors do not? Do you have a right to win? And is the opportunity economically large enough to justify pursuing?

For me, the governance implications are just as significant. As AI evolves and takes on greater decision authority, we cannot govern it solely through fixed approval gates designed for relatively stable technologies. Governance itself needs feedback loops, monitoring, escalation, and the ability to revisit decisions. The same principle matters for founders selling into enterprises: do not lead with how much AI sits inside the product. Lead with the bottleneck you remove and the outcome you enable. As operational activities become easier to automate, validation and trust may become increasingly scarce sources of value. The strategic question remains remarkably familiar: what customer problem can you solve, what can you now see or do, and can you execute it in a way that is economically viable?

BEST MOMENTS

“First mover advantage or advantages, they've never been about being first.” – Professor Gianvito Lanzolla [06:48]

“I would separate early for learning from early for scaling.” – Professor Gianvito Lanzolla [09:13]

“It's not data, it's data in context.” – Professor Gianvito Lanzolla [22:06]

“There is a conceptual shift from governance as gates to governance as a continuous learning system.” – Professor Gianvito Lanzolla [43:25]

“Access to AI is not a competitive advantage in itself.” – Professor Gianvito Lanzolla [01:02:41]

“AI strategy doesn't start with AI, starts with the three V, value, visibility, viability.” – Professor Gianvito Lanzolla [01:05:44]

ABOUT THE GUEST

Professor Gianvito Lanzolla is Professor of Strategy at Bayes Business School, City St George’s, University of London, Founding Director of the Digital Leadership Research Center, and an advisor to boards and CEOs.

Originally trained as a mechanical engineer, Gianvito's work has evolved around a fundamental question: if technology matters but is not enough, what else determines who creates and captures value? His research and advisory work spans strategy, digital transformation, AI, diversification, business models, governance, and organizational change.

He is co-author, with Constantinos Markides, of Diversification in the World of Data and AI and has researched how organizations can scale AI through adaptive governance. His work connects enduring principles of strategy with the rapidly changing economics of data, AI, and digitally orchestrated ecosystems.

You can buy the book on Amazon wherever you are in the world: Diversification in the World of Data and AI

ABOUT THE HOST

Sabine VanderLinden is a corporate strategist turned entrepreneur and the CEO of Alchemy Crew Ventures. She leads venture-client labs that help Fortune 500 companies adopt and scale cutting-edge technologies from global tech ventures. A builder of accelerators, investor, and co-editor of the bestseller The INSURTECH Book, Sabine is known for asking the uncomfortable questions—about AI governance, risk, and trust. On Scouting for Growth, she decodes how real growth happens—where capital, collaboration, and courage meet.

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