Artificial Intelligence & Generative AI
Speakers who decode the real-world impact of machine intelligence on industries, workforces and competitive advantage
Speakers Associates represents 354 speakers on Artificial Intelligence & Generative AI, including Kemal Apaydin, Olivier Sibony, Rahaf Harfoush, Purna Virji, Itai Green, Limor Ziv, Tom Goodwin, Daniel Trabucchi & Tommaso Buganza, Katja Schipperheijn and Diana Verde Nieto.
Most leadership teams have formally committed to AI and data as strategic priorities. The harder problem is what comes next. Boards and executive committees that cannot interrogate vendor claims, distinguish genuine capability from hype, or set coherent data governance policy become dependent on specialists whose priorities may not align with theirs. Strategic intent without strategic fluency produces expensive, poorly governed technology programmes – and the gap is widening faster than internal capability is growing.
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 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.
Most companies treat customer experience as a stated priority while routinely delivering something that contradicts it. The gap between the language used in board decks and what customers actually receive keeps widening, even as technology budgets grow. The real question for leaders is how to turn CX from a yearly aspiration into a daily operational decision.
Autonomous systems, from self-driving vehicles to generative AI, are moving from lab to revenue faster than most boards can absorb. The strategic question is no longer whether the technology works. It is which timelines are real, which are marketing, and which regulatory and civil-liberties fights will decide who gets to deploy at scale.
Boards want the upside of founder-led growth without the chaos that usually comes with it. Most corporates cannot tell the difference between a genuine scaling business and one that simply spends fast. The gap between how operators build and how incumbents invest is where value is lost.
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 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.
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 large organisations are reacting to AI and digital disruption, not directing it. Leadership teams know the operating model needs to change but keep funding incremental programmes that preserve the status quo. The harder question is how to spot the shifts that matter, get the company aligned around them, and turn innovation from theatre into a measurable change in how the business runs.
Leaders assume that deploying AI leaves their own judgment intact, but that assumption has not been tested. Algorithmic systems shape beliefs and steer decisions from within organizations, through the architecture of information rather than through visible force. The organization that cannot distinguish its own conclusions from those it has been guided to reach has a governance risk without a name.
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.