Artificial Intelligence & Generative AI
Speakers who decode the real-world impact of machine intelligence on industries, workforces and competitive advantage
Most organisations have already run AI pilots. The harder question is what happens after the proof of concept ends. Procurement standards stay unclear, accountability for AI-assisted decisions is unassigned, and the governance frameworks people quote in slides do not survive contact with real workflows. Leadership teams cannot say with confidence which decisions AI should be trusted with and which it should not.
Most strategy functions are not built for exponential change. They forecast from the past and plan in quarters. When AI, energy transition, and geopolitical realignment compress decades of disruption into months, the system stops working.
Every organisation can now use the same AI tools, so the work increasingly looks the same. Leaders are starting to ask a different question: what can their people do that an algorithm cannot. Most companies have not answered with anything more specific than slogans.
Most organisations face a contradiction they have not solved. Boards now demand faster innovation and faster AI adoption than the structures, talent and risk appetite below them were ever built to handle. Without the language to name that tension, leadership teams produce noise, burnout and bold-sounding decisions that quietly damage the business.
Most companies treat customer experience as a stated priority while routinely delivering something that contradicts it. The gap between the language used in board decks and what customers actually receive keeps widening, even as technology budgets grow. The real question for leaders is how to turn CX from a yearly aspiration into a daily operational decision.
The operating assumptions most organisations still use for strategic planning come from a more predictable century. Leaders are running multi-year capital plans, technology roadmaps and workforce strategies against scenarios that are now changing inside the planning cycle. The real discipline is no longer long-range forecasting; it is anticipation, antifragility and agility, and most leadership teams are not yet trained to reason that way.
Most leadership teams know they are behind on consumer technology, but cannot tell which trends will reshape their category and which will fade in eighteen months. The cost of guessing wrong is real: misjudged AI rollouts, security gaps, retail experiences that miss the customer, product roadmaps built on yesterday’s behaviour. Senior teams need a working filter, not another vendor pitch.
Most large organisations have run AI pilots. Far fewer have moved them into operating reality. The gap is not the technology, it is the absence of an internal innovation discipline that translates promising experiments into measurable change inside a workforce that is, in many cases, quietly resisting it.
Marketing budgets are getting bigger while the proof that any of it works is getting weaker. Viewability metrics inherited from a decade ago tell buyers an ad was technically on screen; they say nothing about whether a human noticed it. The gap between paid impressions and commercial outcome is now the single largest unmanaged risk on the marketing P&L.
Boards and executive teams now make decisions about AI, data, and digital infrastructure that touch every part of the business. The technical case is well rehearsed. The harder questions, what these systems do to customer trust, to employee agency, to the meaning of the work, get pushed to ethics committees or deferred indefinitely. Leaders need a way to think clearly about technology that is neither uncritical adoption nor reflexive fear.
Most organisations do not fail because they cannot think of new ideas. They fail because they cannot stop doing the old ones. The harder problem for senior teams is not generating innovation but dismantling the legacy practices, narratives, and habits that absorb every new initiative and quietly neutralise it.