Inteligência Artificial e IA Generativa
Oradores que descodificam o impacto real da inteligência artificial em indústrias, força de trabalho e vantagem competitiva
Speakers Associates represents 355 speakers on Inteligência Artificial e IA Generativa, including Kemal Apaydin, Olivier Sibony, Rahaf Harfoush, Purna Virji, Itai Green, Limor Ziv, Tom Goodwin, Daniel Trabucchi & Tommaso Buganza, Katja Schipperheijn e Diana Verde Nieto.
Most organisations are deploying AI into environments designed for people, then expecting the people to adapt. The result is friction that looks like a technology problem and is actually a collaboration problem: badly timed hand-offs, brittle trust, staff working around the system rather than with it. The buyers who feel this most acutely are the ones who have passed the pilot stage and are now trying to make human and machine teams productive at scale.
Boards have approved AI investment. Most have not yet decided what good looks like. The question is no longer whether to deploy AI, but how to deploy it without inheriting failure modes that legal, regulatory and reputational teams cannot defend later.
Most organisations deploying AI have optimised for capability, not accountability. Algorithms now shape hiring, lending, clinical diagnosis, and criminal justice at scale – but the governance structures to challenge them barely exist. The gap between what a model optimises for and what an organisation is actually accountable for is where the real risk lives.
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.
AI systems are going into production faster than anyone is checking whether they can be attacked. A model can be poisoned during training or manipulated after deployment, and most security functions have no test for either. That risk sits between the data science team and the CISO, and neither owns it.
Marketing budgets are under sharper scrutiny than at any point in a decade, and the old assumptions about how brands earn attention have stopped holding. AI has reset what creative, media and customer experience teams are expected to produce, and most organisations are still reasoning about it as a tool rather than a structural change to how brands compete. The commercial question is which parts of the marketing operation get rebuilt around AI, and which parts get protected because they still depend on human judgement.
Most inclusion programmes never reach the decisions that matter. Hiring, promotion and performance calls keep producing the same outcomes, and those decisions are increasingly made by AI. The hard question for a leadership team is what to do differently in the next cycle, and what to check before trusting the algorithm that runs it.
People do not stop being people when they walk into work. They carry cognitive bias, fatigue, threat responses and habit into every decision a leader asks them to make. Organisations that treat behaviour as a performance issue, rather than a biology issue, keep running the same change programmes and getting the same results.
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.
Customer expectations now move faster than most innovation pipelines can absorb. Strategy teams see the shifts in the data, but by the time a proposition reaches market, the reference point has moved again. The real question is not which trend to chase, but how to build a repeatable method for turning early signals into commercial bets that leaders will back.
Boards now expect HR to defend operating decisions, not narrate them. CHROs are being asked to govern AI, restructure talent models, and hold culture together through IPOs, take-privates, and multi-country integrations. Most organisations do not have a people leader who can sit credibly in the boardroom on all three at once.
Leaders of banks, central banks and other regulated institutions know their organisations are being rewired by AI, platforms and new regulation. What they struggle with is translating that awareness into sequenced decisions about capability, talent and operating model. The gap is not vision. It is a practitioner view of which AI moves build durable advantage and which ones become stranded pilots.