Innovation & Disruption
Speakers who examine how industries are reshaped — and how organisations can lead rather than follow change
Most growth capital still flows through the same networks it always has, leaving credible founders outside those networks structurally underfunded. Senior teams know the talent exists. The harder question is how to source it, back it, and build the surrounding infrastructure that turns a fundable founder into a scaled company.
Every executive team is being asked to deploy AI faster than their governance can keep up. The harder question, which boards now own, is which use cases should be refused. Bias inside the models is not the only risk; the bigger one is shipping systems into contexts where the cost of being wrong is borne by people the organisation cannot see.
Most capital flows to founders who pattern-match to the people allocating it. The result is a structural blind spot: viable businesses, large markets, and disciplined operators get passed over because they do not fit a familiar template. Closing that gap is a commercial problem before it is a values one.
Most innovation strategies still assume one capital model, one growth curve and one definition of a winning company. That assumption now constrains where ideas come from, who gets funded, and which businesses survive their second decade. Boards backing the next generation of operators need a sharper view of what disciplined, purpose-aligned entrepreneurship actually looks like at scale.
Few business environments compress consequence the way Formula 1 does. Decisions are made in seconds and judged within laps. Leaders who want their teams to perform under that kind of pressure look to the sport for a vocabulary that their own organisations rarely produce.
Brand trust has collapsed faster than most marketing functions can rebuild it. Customers, employees and investors now treat corporate claims as suspect by default, and the playbooks that worked when trust was assumed produce diminishing returns. The harder question is what an authentic commercial proposition looks like when audiences arrive sceptical, and how to plan brand and innovation strategy when the operating environment keeps shifting underneath the plan.
Most large organisations have run AI pilots. Few have turned them into operating advantage at scale. The hard problem sits between proof-of-concept and production: legacy estate, unclear governance, talent gaps, and a board that wants commercial outcomes rather than experiments.
Boards are being asked to make capital and workforce decisions on AI without a shared map of where the technology is actually heading. Internal teams default to either pilot-by-pilot caution or unchecked enthusiasm, and neither produces a defensible long-range position. What is missing is a credible read of what the next decade looks like, grounded in technology history rather than vendor marketing.
Boards and executive teams know they need to act on AI, but most are stuck between vendor pitches, pilot fatigue and a regulatory picture that keeps moving. The harder question is not whether to invest, but which decisions belong in the boardroom, which belong with the operators, and how to govern the technology without stalling it. Few advisors have sat on all three sides of that table: building the technology, running it at scale, and writing the policy that shapes its limits.
Most organisations have run AI pilots. Almost none have rebuilt how work actually gets done. The gap between board ambition and operational reality is where competitive position is now being lost, and senior teams are running out of room to keep treating AI as an experiment rather than an operating model.
Downtowns are competing for residents, employers and investment against suburbs, other cities and the option of remote work. The decisions that determine whether they win, where streets go, how wide they are, what is built at ground level, are made one project at a time by people who rarely see them as a single strategy. The cost of getting that wrong shows up later in vacancy rates, carbon footprints, public health budgets and the talent that quietly leaves.
Boards know they need to convert AI and automation pilots into operating advantage, but the path between policy ambition, capital allocation and a working factory or service line keeps stalling. Megatrends are easy to name. Translating them into a sequenced bet that survives a budget cycle is not. Leaders need a frame of reference built from inside the policy and standards machinery, not above it.