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 leadership teams now have an AI policy, a metaverse deck and a digital roadmap, and still cannot tell which of these will move revenue inside twelve months. The gap is rarely technical. It sits between the C-suite and the teams running marketing, product and customer experience, where theory has to become a shipped campaign, a working interface, a measurable result.
Most organisations talk about innovation and ship incremental product. The gap shows up in how invention is governed: which problems get resourced, how patents become products, and how a founder or intrapreneur converts a research prototype into a funded, regulated, commercial business. Boards want operators who have done both sides, scaled invention inside a multinational and built a venture from nothing.
Most large brands are running metaverse and avatar projects inside the same marketing teams that built their websites. The output is decorative, not commercial. Companies that want a serious return from digital worlds need to decide whether to retrofit existing functions or stand up a dedicated avatar-native business, and they need a credible view on which categories of revenue, audience, and intellectual property warrant the second route.
Boards are being asked to make capital and risk decisions on AI while the rules around it are still being written. The pressure is no longer whether to deploy, but how to deploy defensibly when regulators in Brussels, Washington and Beijing are pulling in different directions. Most executive teams do not yet have a clear view of who is setting those rules, on what timetable, and what compliance, data and infrastructure choices will look like on the other side.
Retail leadership teams are running two organisations at once: a legacy operation built around store footprint, seasonal buying and broadcast marketing, and an emerging one shaped by AI personalisation, gamified loyalty and immersive commerce. The capital is flowing into the second, the revenue still sits in the first, and most boards cannot tell which experiments are worth scaling and which are theatre. The question is not whether AI changes retail. It is which bets pay back inside the planning cycle.
Most strategy processes treat the future as uncertain and respond by hedging. That posture costs time and investment while competitors move on signals that were knowable in advance. Leadership teams need a disciplined way to separate the parts of the future that are already decided from the parts that are still open, and to act on each differently.
Most large organisations have run AI pilots. Few have moved AI into operating reality at scale, with clear lines on governance, accountability and where it is allowed to make decisions. Boards now need a sharper read on what AI can actually do for their business, what it should not do, and how to deploy it without inheriting risks they cannot defend in front of regulators or customers.
Established companies are being disrupted by platform businesses built on assets those companies already own. Legacy structures, customer relationships, and proprietary data are competitive advantages, but only if the organisation knows how to activate them as platforms. Most do not.
Most large organisations have run AI pilots. Very few have turned them into an operating model that moves revenue, cost or risk at the scale of the business. The gap is not the technology. It is leadership conviction, governance design and the discipline to industrialise what works before the next cycle of tools arrives.
Most organisations invest in technology to do the same work faster. That gap – between efficiency and genuine effectiveness – is where digital transformation programmes stall and where competitive advantage quietly disappears. As generative AI accelerates the pressure to adopt, leaders face the same trap at greater speed: automate the existing, rather than reinvent what is possible.
Most boards are now briefed on AI, but few have thought seriously about what happens when AI has a face. Customer service, healthcare, education and hospitality are all heading towards interactions with machines that look back at you, recognise you, and hold a conversation. The strategic question is no longer whether the technology works. It is how organisations design for trust, responsibility and emotional register when the interface is a humanoid.
Customer expectations don’t shift gradually – they reset when a leading business makes a move that becomes the new standard. Most organisations track their own customers too closely and the forces reshaping those customers not closely enough. The arrival of AI has made the problem acute: more signals, faster change, and a greater penalty for placing bets on the wrong ones.