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.
Digital transformation programmes still stall in the gap between the boardroom slide and the operating reality. Most leadership teams have the strategy. Few have run the messy work of converting telecoms, media and SaaS businesses from old revenue models into new ones, through acquisitions, restructurings and capital constraints. That is where the value is now decided.
Most organisations describe innovation as a value, then run it as a series of disconnected pilots. The result is activity without compounding advantage, and customer experiences that are designed by accident rather than intent. Boards are now expected to show that innovation produces measurable growth, not slide decks.
Boards are being asked to make irreversible bets on AI, quantum, and biotech without a credible internal voice on where these technologies are actually heading. The instinct is to delegate the question to consultants who repeat last year’s consensus. That leaves the most consequential decisions with leaders who lack the horizon to judge them.
Boards are being asked to make consequential decisions about AI systems they do not fully understand, on timelines set by competitors, regulators and the technology itself. The vocabulary used inside these conversations, alignment, capability, existential risk, governance under uncertainty, was largely built by a small group of thinkers before the commercial AI race began. Without that vocabulary, leaders end up either dismissing the risk or capitulating to it.
The rules that govern AI, data, and global platforms are being rewritten in Washington, Brussels and Beijing at the same time, and rarely in the same direction. Boards now have to make capital and product decisions inside a regulatory environment that no single jurisdiction controls. Reading that landscape, and acting on it before it forces your hand, is now a core leadership task.
Senior leaders are asked to lead change, AI transition, and transformation continuously, often while still recovering from the last cycle. Most leadership development equips them analytically and leaves the harder part untouched: under pressure, the brain protects rather than adapts. The gap between leaders who can articulate the change and leaders who can land it is a human biology problem, not a strategy problem.
Most organisations understand that AI and digital transformation are not optional. The problem is the gap between acknowledging this and making irreversible decisions about infrastructure, talent, and operating models: particularly in industries built around physical assets and long capital cycles. Leaders in real estate, construction, financial services, and retail are being asked to future-proof portfolios before the technology landscape has stabilised. The consequence of moving too slowly and too fast look equally costly from a boardroom.
Most strategy functions are not built for exponential change. They forecast from the past and plan in quarters. When AI, energy transition, and geopolitical realignment compress decades of disruption into months, the system stops working.
Most boards still treat AI as a software question their CIO will solve. The story is bigger than that. The contest is over compute, fabs, energy supply, and the sovereign infrastructure that will decide which companies and which countries hold the next decade of pricing power. Leaders who frame AI as a productivity tool are already a strategy cycle behind.
Boards are being asked to make capital and governance decisions inside a global order that no longer behaves the way the post-Cold War playbook assumed. Geopolitical fracture, AI moving faster than policy, and a younger workforce and customer base that distrust traditional institutions are now operating constraints, not background context. The leadership question is no longer how to read the change, but how to govern through it.
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.
Boards approve strategies that look rigorous on the deck and fail in the market. The same executives, looking at the same evidence, reach different conclusions on different days, and nobody notices. Most decision processes are built to confirm what senior leaders already believe, not to surface where their judgment is wrong.