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.
Large organisations want the speed and originality of a founder-led startup, but the operating system inside them rewards the opposite behaviours. Boards approve innovation budgets and then watch promising pilots stall in legal, brand and procurement reviews. The harder question is how to design a venture inside a corporate parent so that it survives long enough to learn something useful.
The organisations leaders were trained to run are not the organisations they are now being asked to lead. Employees, customers, regulators and activists all expect participation, transparency and speed that the command-and-control playbook cannot deliver. The leaders who thrive in this environment are not the ones with the loudest brand or the biggest advertising budget; they are the ones who understand how influence actually flows in a hyperconnected system, and who can build the models that work with that grain rather than against it.
Boards are being asked to make consequential bets on generative AI without a stable read on what the technology can actually do, what it cannot, and what its deployment will mean for the workforce. Most executive briefings collapse into either hype or alarm. Leaders need a sober technical interpreter who can separate marketing from mechanism, and tell them which decisions matter now.
Leaders are being asked to make consequential bets on quantum, AI, and biotech without the tools to separate genuine scientific progress from marketing. Boards over-index on confident vendors and under-index on the slower, harder question of what the science can actually do. The cost of getting this wrong is years of misallocated capital and credibility lost when claims fail to land.
Most executive teams have run AI pilots. Few have moved AI into the operating core, where it changes margins, headcount and customer experience at scale. The gap between experimentation and operational advantage is where competitive position is being decided right now, and most leadership teams cannot see clearly across it.
Most large companies have run AI pilots. Few have moved them into operating advantage. The tension is no longer whether to invest, but how to convert experimentation into revenue, new business units, and customer interfaces that legacy organisations can actually run.
Service organisations are being asked to deploy AI agents and intelligent automation faster than their operating models can absorb them. Leaders know the productivity case, but the harder question is what the customer relationship, the workforce, and the cost-to-serve actually look like once agents handle the work front-line teams used to own. Most transformation programmes underestimate that redesign and end up automating the old service blueprint instead of rebuilding it.
Boards are signing off on AI deployments faster than their organisations can govern them. Privacy, consent, and data lineage have moved from compliance topics to live commercial risks tied to model training, customer trust, and regulatory exposure. Most leadership teams have no shared language for deciding which uses of data are defensible and which are not.
Most large companies have spent a decade investing in digital, data and AI, and the commercial return is still uneven. The hard question is no longer whether to transform, but how to convert that investment into customer experiences, brands and business models that actually grow revenue. The answer sits at the intersection of strategy, culture and data, and very few leadership teams have a coherent view across all three.
Legacy businesses with strong brands and weakening unit economics keep asking the same question: how do you charge directly for what used to be paid for by advertisers, without losing reach. The answer is rarely a pricing tweak. It usually requires rebuilding the relationship with the customer, the product, and the data underneath, against an internal culture that was not designed for any of it.
The workforce has been reset by remote work, AI, generational change, and contested politics inside the workplace. Boards now expect HR and the executive team to deliver culture, engagement, and skills as commercial outcomes, not as soft functions. Most leadership teams are still working from talent assumptions that no longer hold.
Trust between brands and the people they sell to has eroded faster than marketing functions can rebuild it. Generative AI now writes the copy, targets the audience and shapes the campaign, and consumers know it. The commercial question is no longer how to be seen, but how to be believed.