IA agêntica speakers
Palestrantes e workshops sobre IA agêntica, agentes autónomos e sistemas de agentes para conferências e equipas de liderança. Várias opções para um brief.
Speakers Associates represents 17 speakers on IA agêntica, including Purna Virji, Ashlea Atigolo, Saakshar Duggal, Kemal Apaydin, Kieran Gilmurray, Andreas Welsch, Cassie Kozyrkov, Elemi Atigolo, Kate Ancketill e Oliver Leisse.
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.
AI has moved from answering questions to taking actions, and most operating models were not built for that. Leaders now have to say which judgements stay human and who is accountable when a system acts on its own. The pilots keep multiplying while nobody can name the business metric any of them improved.
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.
Generative AI arrived as a tool that people use. Agentic systems plan and act on their own, and most operating models were not built for that. Boards are being asked how much autonomy to hand to software, usually before anyone internally can model the failure modes.
Most boards are setting AI strategy from briefings that are already out of date.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.
Boards have signed off on AI ambitions that the operating business has no idea how to execute. Pilots multiply, vendor decks pile up, and the gap between strategy slides and what customers actually experience keeps widening. The job leaders need help with is choosing where AI changes the commercial model, and where it is noise.
Most organisations now accept that AI will reshape how their customers behave. Acceptance is where it stops. Budgets stay on three-year cycles, and the people inside who could move first spend their energy persuading everyone else.
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.