Innovation & Disruption
Speakers who examine how industries are reshaped — and how organisations can lead rather than follow change
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.
Most products, messages and change initiatives fail not because the idea is wrong, but because it does not move through people. Buyers know they need word of mouth, persuasion that lands, and customers and employees who actually shift behaviour. What they lack is a tested model for which specific levers cause that to happen.
Most organisations treat innovation as a technology question and culture as a brand question. The two functions report separately, fund separately, and rarely produce anything a customer can actually use. The leaders who build durable advantage are the ones who can run cultural intuition and product engineering as a single discipline.
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.
Most boards now treat AI as a strategic line item, but few know how to translate it into operating advantage without tripping the regulators, the workforce, or the customer. The gap between AI ambition and AI deployment is widening, not closing. Leaders need someone who has sat on both sides: the commercial side that has to ship, and the governance side that decides what shipping looks like.
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.