Innovation & Disruption
Speakers who examine how industries are reshaped — and how organisations can lead rather than follow change
Most organisations sit on more data than ever and communicate less clearly than they used to. Boards, customers, and employees are drowning in dashboards, decks, and statistics that fail to land. The gap is not analytical capacity. It is the discipline of turning numbers into a story people actually act on.
Most technology products fail not because the technology stops working, but because people won’t use them. Organisations pour investment into building capability and almost nothing into understanding adoption. The psychology of why users reject genuinely useful innovations is a problem most corporate innovation teams are not equipped to see – let alone solve.
Banking, payments and customer trust are being rewritten by code, and most incumbent institutions are still organising around branches, products and quarterly earnings. Boards know the platform players, embedded finance and AI agents are reshaping the economics of the industry. The strategic question is how far to push, how fast, and what kind of institution remains on the other side.
Generative AI has moved from novelty to infrastructure inside most large organisations, but the operating question has shifted. The risk is no longer falling behind on tooling, it is letting the technology replace the human judgement and creative instinct that made the business worth building. Leaders need a working theory of where AI accelerates people and where it quietly hollows them out.
Most leadership teams have run their generative AI pilots and now face a harder question: where does the technology actually sit inside the operating model, and which categories of work change shape entirely. The answer is rarely visible from the inside, where vendors pitch tools and consultants pitch frameworks. It comes from people who have built original commercial product with these systems and watched the next layer of human-machine technology arrive in a hospital bed.
Most innovation programmes stall in the gap between idea generation and operational adoption. Stakeholders are consulted late, ownership stays with a small central team, and the resulting initiatives lose energy before they touch the customer. The harder question is how to design an innovation process that the people responsible for executing it actually feel they built.
Most leadership teams consume far more futures content than they can act on. The problem is not a shortage of prediction. It is the absence of a structured method for connecting macro change to the specific decisions an organisation is already under pressure to make. Without that connection, strategic planning is reactive, investment decisions trail the market, and the wrong questions dominate the board’s time.
Most large companies have an innovation programme that produces activity but not commercial outcomes. Pilots multiply, hackathons run, idea portals fill up, and the operating model still rewards what worked last year. The harder question is how to make innovation a managed discipline that allocates real capital to the right problems, not a creativity theatre that the executive committee tolerates.
Most large companies have an innovation problem they cannot solve internally. They have signed memoranda with startups, run accelerators, opened innovation labs, and still struggle to convert any of it into operating advantage. The gap is not strategic intent. It is the practical discipline of partnering across a size and culture asymmetry that defeats most corporate teams.
Boards have approved AI strategies they cannot fully explain, govern, or defend. Pilots multiply, ethical frameworks lag, and the human side of the operating model erodes faster than anyone planned. The question is no longer whether to deploy AI, but how to do it without losing the judgement, trust, and accountability that hold the enterprise together.
Most senior teams have run their first generative AI pilots and stalled. The technology is general-purpose, but the operating decisions are not: which workflows to redesign, which tools to standardise on, where hallucination is tolerable and where it is not. The question is no longer whether to adopt, but how to convert curiosity into measurable operating advantage without ceding judgement to the model.
Most organisations are now running AI through their creative, design and brand functions without a clear view of what humans should still own and what machines should do. The result is output that looks generative but feels generic, and teams that cannot articulate where their craft adds value. The harder question, what creative judgement actually contributes once the machine can produce a draft, rarely gets answered.