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.
Every organisation now has a digital transformation strategy. Very few have the executive fluency to decide which emerging technologies actually deserve investment, which are years away from being usable, and which belong on the regulator’s desk rather than the roadmap. The cost of getting that distinction wrong, in smart-city programmes, public-sector IT and corporate digital strategy, is quietly absorbed as failed projects and stranded spend.
Artificial intelligence is moving from pilot to protocol inside hospitals, space agencies, and infrastructure programmes, and most leadership teams are still arguing about what is real and what is theatre. The cost of getting this wrong is not slower innovation. It is patient harm, missed regulation, and capital deployed against the wrong assumptions. Boards want a translator who has actually built and deployed clinical AI, not a commentator describing it from the outside.
Hybrid working has hardened into a structural problem rather than a temporary arrangement. Leaders are being asked to hold productivity, culture and connection together while their people work in places, patterns and rhythms the old office was never built for. The instinct to issue mandates rarely survives contact with the workforce, and the cost of getting it wrong shows up in attrition, engagement and trust.
Financial services firms are expected to adopt new technology faster than their regulators, risk teams or cultures are built to absorb. Innovation programmes stall not on the technology itself but on the gap between what executives announce in public and what their organisations are actually able to execute. Closing that gap requires someone who has lived inside both the trading floor and the startup, and can speak credibly to each.
AI is raising the floor for every company at once. The same models, the same speed, the same outputs are now available to every competitor in a category. The danger is no longer falling behind on adoption. It is spending heavily to arrive at the same place as everyone else, faster but indistinguishable.
AI investment is running ahead of any defensible view of what the workforce, the operating model, or the regulatory environment will actually look like in five years. Most boards are committing capital to technology decisions without a method for thinking systematically about the futures those decisions produce. Foresight is treated as a creative exercise, not a discipline.
Sustainable competitive advantage has stopped behaving like it used to. Incumbents with strong positions, talent, and capital still lose share to entrants who reframe the question rather than win on the answer. The work is no longer protecting a moat; it is detecting where the moat has already moved.
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.
Retail and consumer businesses are running two clocks at once. The five-year horizon is being rewritten by AI, automation, and a generation of consumers who expect physical and digital to behave as one channel. Most leadership teams are deciding capital allocation and store strategy without a clear read on what the next three to five years actually look like on the ground.
Most leadership audiences are told that AI, mixed reality and the next wave of consumer technology will reshape their business, but few of them follow the field closely enough to separate signal from noise. The result is a workforce that hears the headlines and a leadership team that struggles to translate them into a position the rest of the organisation can act on. Bringing the technology story into a room of non-specialists, without dumbing it down or hyping it up, is a specific craft.
Boards are asked to commit capital to AI before the returns are visible, and to do so while regulators, sovereign governments and a small group of US infrastructure companies redraw the rules around them. Most leadership teams do not have an internal source who covers all three at once. The gap shows up as exposure: investments made on vendor narratives, strategy decks built on last quarter’s headlines, and a quiet sense that the people in the room do not actually know who controls what.
AI now makes decisions that once belonged to people, and it is absorbing more of the work. Leaders still have to keep human judgement and accountability in place while that happens. Most operating models were built for stability, and offer no map for the ground between what worked before and what comes next.