Artificial Intelligence & Generative AI
Speakers who decode the real-world impact of machine intelligence on industries, workforces and competitive advantage
Boards are being asked to make large, irreversible bets on AI while the rules governing it are still being written. The people drafting those rules, and the people deploying the technology, rarely sit in the same room. Without a translator between Westminster, Silicon Roundabout and the executive committee, firms either over-invest in the wrong guardrails or under-invest and wait for enforcement to find them.
Financial services firms are expected to adopt new technology faster than their regulators, risk teams or cultures are built to absorb. Innovation programmes stall not on the technology itself but on the gap between what executives announce in public and what their organisations are actually able to execute. Closing that gap requires someone who has lived inside both the trading floor and the startup, and can speak credibly to each.
Hybrid working has hardened into a structural problem rather than a temporary arrangement. Leaders are being asked to hold productivity, culture and connection together while their people work in places, patterns and rhythms the old office was never built for. The instinct to issue mandates rarely survives contact with the workforce, and the cost of getting it wrong shows up in attrition, engagement and trust.
Most organisations deploying AI have optimised for capability, not accountability. Algorithms now shape hiring, lending, clinical diagnosis, and criminal justice at scale – but the governance structures to challenge them barely exist. The gap between what a model optimises for and what an organisation is actually accountable for is where the real risk lives.
Most leaders now agree that AI will reshape their workforce. Fewer can say what that looks like on a Monday morning for a marketing coordinator, a finance analyst or a field engineer. The distance between boardroom AI strategy and the person being asked to use the tools is where adoption stalls, budgets leak and cultural resistance hardens.
AI now drafts the email, summarises the meeting and proposes the decision before anyone has finished thinking. The danger for most organisations has flipped. Speed used to be the constraint. The new risk is moving fast on autopilot, quietly handing judgment to tools built only to assist it. What senior leaders want is for their people to keep thinking and deciding well as the tools accelerate.
Boards are now accountable for AI decisions they do not fully understand. Regulators, customers, and employees expect defensible governance, but most companies still treat ethics as a slide at the end of the deck. The gap between AI ambition and AI accountability is where reputational, legal, and operational risk now compounds fastest.
Most organisations have AI budgets. Most are still running pilots. The problem is not investment – it is that AI has been framed as a strategy in its own right, which turns a deployment decision into an open-ended design problem. Meanwhile, the gap between AI experimentation and scaled competitive advantage is narrowing fast. Organisations that cannot move AI into production – aligned to business goals they already have – will cede ground to those that already have.
Most retail and consumer businesses can list the trends shaping their category. Few can turn that awareness into operational change before competitors do. The gap is not insight, it is the discipline to test, adapt, and scale what works while leaving the theatre of innovation behind.
Most organisations still treat technology as something the user picks up, looks at and puts down. That model is breaking. Sensors, haptics and ambient computing are moving the interface into the body, the garment and the room, and the businesses building for that shift need product leaders who can think across hardware, software and human design at once.
Organisations are deploying AI in hiring, healthcare, and operations before they understand whose assumptions are encoded in those systems. AI bias is not a data problem – it is a design problem, and it traces directly to the homogeneity of the teams building the tools. The second risk is less visible: research shows that humans routinely defer to automated systems in ways that go well beyond the reliability of those systems, including in high-stakes scenarios. Boards that have approved AI adoption have often not reckoned with either problem.
Executive conversations on markets, policy and geopolitics rarely fail for lack of material. They fail when the person in the chair cannot press a CFO, a central banker and a trade minister with the same confidence, or hold a room when the news changes between rehearsal and showtime. The cost is a flagship event that reads as polite rather than sharp, and a leadership team whose message never lands.