Intelligenza artificiale e IA generativa
Relatori che decodificano l’impatto reale dell’intelligenza artificiale su industrie, forze lavoro e vantaggio competitivo
Speakers Associates represents 354 speakers on Intelligenza artificiale 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.
Biology is moving from something organisations observe to something they can write. Pharma, agriculture, materials, energy and insurance leaders now face an industry that behaves like software, with the same compounding curves, platform dynamics and governance risks. Most executive teams have no clear view of what is already possible, what is five years out, and where their own business model is exposed.
Most organisations have now invested significantly in digital infrastructure. Most are still not performing like digital organisations. The companies consistently outcompeting established players are not winning on technology budget – they are winning on operating model, decision-making speed, and cultural norms that established businesses have not yet diagnosed, let alone changed. Leaders are under pressure to demonstrate digital transformation outcomes without a clear account of what actually separates digital investment from digital performance.
Most organisations want the upside of AI but cannot share the data that would make their models useful. Regulators, customers, and competitors all push in opposite directions, and the standard answer is to slow down. The harder question is how to use sensitive data across institutional boundaries without giving it up, and that question is now sitting on the desk of every senior leader running an AI programme.
Executive conversations on markets, policy and geopolitics rarely fail for lack of material. They fail when the person in the chair cannot press a CFO, a central banker and a trade minister with the same confidence, or hold a room when the news changes between rehearsal and showtime. The cost is a flagship event that reads as polite rather than sharp, and a leadership team whose message never lands.
Most organisations say they want diverse technology teams and stronger digital talent pipelines, yet keep recruiting from the same narrow funnel and wondering why the numbers do not shift. The gap between stated intent and hiring reality is now a strategic risk, not a values conversation. Leaders need a practical read on what actually moves representation, retention and product quality in technical functions, without defaulting to training budgets and pledges.
Most boards still treat AI, automation and connected mobility as a technology programme. The harder question is what they do to the operating model, the workforce, the customer relationship, and the social contract a company sits inside. Leaders need a way to think about exponential change that is sharper than scenario decks and more useful than another keynote about disruption.
Generative AI is trained on what people have already created, then competes with them using it. Boards now face a question with no settled answer: who owns the human capability a machine has absorbed, and what does the company owe the workforce it displaces? Most AI strategy stops at deployment and ignores the legal and economic claims forming underneath it.
Most boards have approved AI strategies. Very few have AI in production at the heart of a regulated business. The gap between pilot enthusiasm and operating reality is where strategy stalls, governance gets nervous, and customer-facing teams quietly lose faith in the technology.
Boards are being asked to deploy AI faster than they can govern it. The question is no longer whether to adopt the technology but how to make decisions about it that hold up under scrutiny from regulators, employees, and the public. Most organisations have no working model for that, only policies that lag the systems they are meant to oversee.
Most marketing organisations collect more data than they act on and run more campaigns than they can defend. The gap between dashboards and decisions has widened with generative AI, not closed. Senior leaders need a way to connect customer intent, measurement and commercial outcomes without handing the argument to the loudest vendor in the room.
Organisations are deploying AI in hiring, healthcare, and operations before they understand whose assumptions are encoded in those systems. AI bias is not a data problem – it is a design problem, and it traces directly to the homogeneity of the teams building the tools. The second risk is less visible: research shows that humans routinely defer to automated systems in ways that go well beyond the reliability of those systems, including in high-stakes scenarios. Boards that have approved AI adoption have often not reckoned with either problem.
Most large organisations have funded AI programmes and run pilots. Most of those pilots never reach production. The gap is not technical capability. It is the absence of an outcome architecture that connects experimentation to structural change. Meanwhile, boards are approving AI investment without the governance frameworks to manage the risks that sit inside AI agents and automated decision-making systems.