Artificial Intelligence & Generative AI
Speakers who decode the real-world impact of machine intelligence on industries, workforces and competitive advantage
Boards are asked to commit capital to AI before the returns are visible, and to do so while regulators, sovereign governments and a small group of US infrastructure companies redraw the rules around them. Most leadership teams do not have an internal source who covers all three at once. The gap shows up as exposure: investments made on vendor narratives, strategy decks built on last quarter’s headlines, and a quiet sense that the people in the room do not actually know who controls what.
Most large companies still treat innovation as a creative event rather than a managed discipline. The teams shipping new products lack the metrics, governance, and decision rules that the core business takes for granted, so good ideas stall and bad ones consume capital for too long. Growth then depends on individual heroics instead of a repeatable system.
Boards are being asked to make calls on artificial intelligence and health technology before the evidence base has settled. Most senior teams have a strong grasp of the hype cycle and a weak grasp of what the science actually supports, where the ethical exposure sits, and which innovations will reach customers and workforces inside the planning horizon. The gap between confident vendor pitches and defensible internal judgement is widening.
Frontier technology now arrives faster than corporate strategy, regulatory frameworks, or supply chains can absorb it. Boards face decisions about immersive platforms, defence-adjacent tools, and contested AI applications with no precedent to draw on. The cost of waiting is ceded ground. The cost of moving without judgement is reputational and ethical exposure that does not unwind.
Most enterprise AI programmes are stuck between an executive mandate to deploy and an operating reality that cannot absorb the change. Boards want commercial returns. Workforces want to know what happens to them. Risk and compliance want to know how the model decides. The leaders running these programmes need someone who has actually shipped AI inside large companies, not someone describing the journey from outside.
Most boards now treat AI as a strategic priority without a grounded view of how the systems setting that pace are actually built. Executive advice tends to swing between technical detail no operator needs and speculation no fiduciary can act on. The view from inside a frontier lab is rarely in the room with the people who most need it.
Incumbents in the Middle East are no longer being disrupted only by Silicon Valley. The threat now comes from regionally funded, regulator-aware digital challengers that understand local payments, language and consumer behaviour better than any global entrant. Most regional boards still treat innovation as a corporate venturing line item, not as an operating decision about where the business will compete in five years.
Building a venture-backed business is hard. Building one in a regulated industry, as a non-technical founder, from outside the usual networks, is a different problem. Most founder talks skip the part where capital, regulation, and category timing decide whether the company survives. Operators who have lived that arc, and who can name what actually broke, are rare.
Most enterprises now have an AI strategy on paper and very little operating advantage to show for it. Pilots stall, governance is improvised, and the gap between board ambition and frontline deployment keeps widening. Leaders need a credible operator who has built AI inside a Fortune 500 and shaped it inside the United Nations, not another commentator describing the trend.
Healthcare systems, employer health plans, and public health institutions keep designing for populations they do not include in the room. The result is wasted spend, poor outcomes for the communities that need the service most, and a widening gap between what leaders say about equity and what their operations actually deliver. Closing that gap takes an operator who can move between boardroom strategy, clinical reality, and the lived experience of the patients being served.
Most enterprises have bought into generative AI in principle and stalled in practice. Pilots multiply, demos impress, but very few make the jump to operating on proprietary data inside real workflows. The hard question for boards is no longer whether to adopt AI, but how to make it useful at scale without losing control of accessibility, governance and the workforce alongside it.
Every executive team is being asked to deploy AI faster than their governance can keep up. The harder question, which boards now own, is which use cases should be refused. Bias inside the models is not the only risk; the bigger one is shipping systems into contexts where the cost of being wrong is borne by people the organisation cannot see.