Artificial Intelligence & Generative AI
Speakers who decode the real-world impact of machine intelligence on industries, workforces and competitive advantage
Productivity has not recovered. Engagement scores have flatlined, HR technology budgets have grown, and yet the link between what people do and what the business produces has weakened. The question for the people function is no longer whether to invest in workforce experience, analytics or AI, but how to connect those investments to measurable performance.
Most large organisations have run AI pilots. Very few have moved them into operating reality. The gap is rarely about the technology. It is about governance, internal capability, legacy stacks and the absence of senior leaders who can credibly translate AI from a vendor pitch into a portfolio of operational bets.
Boards now make commercial decisions inside a state-shaped landscape. Sanctions, export controls, AI rivalry and severed supply corridors are no longer background context, they are the terms on which growth, capital allocation and market access are negotiated. Most leadership teams have no internal capability to read these moves before they become balance-sheet events.
Retail and consumer businesses are running two clocks at once. The five-year horizon is being rewritten by AI, automation, and a generation of consumers who expect physical and digital to behave as one channel. Most leadership teams are deciding capital allocation and store strategy without a clear read on what the next three to five years actually look like on the ground.
Retail leadership teams are running two organisations at once: a legacy operation built around store footprint, seasonal buying and broadcast marketing, and an emerging one shaped by AI personalisation, gamified loyalty and immersive commerce. The capital is flowing into the second, the revenue still sits in the first, and most boards cannot tell which experiments are worth scaling and which are theatre. The question is not whether AI changes retail. It is which bets pay back inside the planning cycle.
Most boards now have an AI strategy on paper and very little shared understanding underneath it. The gap between what executives say about emerging technology and what they actually grasp about it is widening, and it shows up in every investment decision, vendor conversation and workforce question that follows. Closing that gap, in language a senior audience will trust, is the work.
Most large companies still confuse digital activity with commercial reinvention. They run pilots, refresh apps and back venture funds, then wonder why challengers keep eating their margin. Building genuinely new business models inside a corporate envelope requires founder instinct that almost no executive team has on its bench.
Most organisations have run their AI and digital pilots. The hard part now is operating advantage: building products, teams and cultures that hold up when the underlying technology shifts every quarter. Boards want practical innovation discipline, not another futurist preview.
Senior leaders are being asked to commit capital and strategy to technologies whose second-order effects are still being written. The gap is not a shortage of information about AI, cybersecurity or platform shifts. It is the absence of a sober, editorially disciplined read on which signals matter, which are noise, and what the next eighteen months look like for the companies making the bets.
Most leadership teams have an AI strategy. Far fewer have changed how the business runs. The gap between stated intent and operating-model impact is where executive teams stall, and where the investment case quietly unravels.
Generative AI has collapsed the cost of producing content, code, and creative output, and most leadership teams still cannot say where it changes their economics. The conversation moves between executive workshop demos and abstract policy debate, with little useful ground in between. Boards need a translator who has run a production business, taught the technology at MBA level, and can describe what changes in the operating model and what does not.
Organisations deploying AI in high-stakes decisions typically believe their governance frameworks are adequate. The evidence says otherwise: most widely used bias detection tools do not satisfy the legal standards they are meant to address, and explainability is frequently promised but rarely delivered in a form that holds up to regulatory scrutiny. Boards are making accountability commitments about AI that the technical systems underneath those commitments cannot actually keep.