IA agentique speakers
Conférenciers et ateliers sur l’IA agentique, les agents IA et les systèmes d’agents pour conférences et équipes de direction. Plusieurs options pour un brief.
Speakers Associates represents 13 speakers on IA agentique, including Kemal Apaydin, Purna Virji, Saakshar Duggal, Kieran Gilmurray, Andreas Welsch, Ashlea Atigolo, Elemi Atigolo, Oliver Leisse, Tim Cortinovis, et Sebastian Thrun.
Most enterprise AI programmes stall between pilot and operating advantage. Boards have approved the spend, vendors have shipped the tools, and the value is still trapped in slideware. The tension now is governance, accountability and workforce redesign at the speed agentic AI is moving, not whether to invest.
Most boards have approved AI strategies. Very few have AI in production at the heart of a regulated business. The gap between pilot enthusiasm and operating reality is where strategy stalls, governance gets nervous, and customer-facing teams quietly lose faith in the technology.
Most large banks know their operating model was not built for the speed of modern technology. The harder question is not whether to innovate but how: when to build, when to partner with a startup, when to buy, and how to make any of that stick inside a regulated balance sheet. Leaders need honest answers from people who have sat on both sides of that table.
Most boards are setting AI strategy from briefings that are already out of date. The pace of frontier development now exceeds the speed at which incumbent organisations can absorb it. Telling which shifts genuinely change the operating model from those that do not has become a core test of senior leadership.
Financial firms are under pressure to put generative and agentic AI into regulated work without breaching rules, losing trust, or building tools advisers ignore. Most boards can describe the opportunity; far fewer can describe the operating model, the controls, or where an agent stops helping and becomes a liability. The gap between AI ambition and deployment that creates value without eroding the business model is where most programmes stall.
Autonomous AI agents have started acting on people’s behalf, sending messages, booking, buying, and making decisions without a human in the loop. Organisations now have to decide how much of that autonomy to hand over, and who answers for it when an agent gets something wrong. The technology is moving faster than the controls and oversight meant to govern it.
Boards have approved AI strategies and run pilots. Few have moved beyond them into operating advantage. Most leadership teams still cannot answer a basic question: which decisions, processes, and roles should an AI agent now own, and how do we govern that shift without breaking the business?
Most AI investment is still trapped in pilots, demos and isolated tools. The harder problem is redesigning how the organisation actually decides, staffs and operates once machines do meaningful work. Senior teams need a way to move from AI as a project portfolio to AI as the operating model.
Leadership teams are being asked to plan three to five years ahead while AI agents, automation and consumer behaviour shift faster than annual strategy cycles can absorb. The instinct is to wait for clarity. By the time clarity arrives, the operating model is already behind.
Most AI investments stall after the demo. The model works, the pilot impresses, but customer behaviour does not change and the board sees no return. The hard problem is not building the capability. It is closing the distance between what the technology can do and what a market will actually adopt, trust, and pay for.
When an AI system causes harm, most organisations cannot say who is accountable for it. Agentic AI sharpens the problem, because software now takes actions no one explicitly authorised. The legal and governance structures most companies rely on were built for tools that wait to be told what to do.
Most boards have approved an AI strategy and seen very little of it reach operations. The gap is not ambition or model choice. It is the absence of a workforce that can build, govern and run AI systems inside the business, and a leadership team that knows what production AI actually looks like.