Inteligencia artificial e IA generativa
Oradores que descodifican el impacto real de la inteligencia artificial en industrias, fuerzas laborales y ventaja competitiva
Speakers Associates represents 354 speakers on Inteligencia 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 y Diana Verde Nieto.
Smart cities, precision agriculture and environmental programmes all run on the same commitment: that data will be used to improve institutional decisions, not to weaken accountability. Most IoT conversations at board level treat the technology as purely operational. They rarely grapple with the governance question underneath. The CEOs who deploy the hardware at scale are usually the ones with the sharpest view of that question.
Most AI deployments produce pilots, not capability. Tools land in the organisation faster than people can absorb them, and leaders default to vendor narratives because they lack a vocabulary for the human variables that decide whether productivity actually moves. The bottleneck is rarely the model. It is the gap between what AI can do and how the workforce learns to think with it.
Workforces have absorbed wave after wave of restructure, system migration and AI rollout. Engagement is flat, change initiatives stall on adoption, and the people expected to deliver the next transformation are visibly tired of the last one. Leaders need a credible way to rebuild appetite for change without another corporate culture programme that lands as noise.
Most organizations are running AI somewhere. Getting it to run everywhere, consistently, strategically, at scale, is where senior leadership investment consistently stalls. The gap between a working pilot and an embedded enterprise capability is not a technology gap. It is a strategic and structural one: the wrong organizational design, insufficient data foundations, and a leadership layer that cannot distinguish between AI as a point tool and AI as a new operating logic.
Strategy cycles run on three-year horizons. The technologies reshaping markets operate on ten-year ones. Without a methodology for reading early-stage signals, organisations discover the future after competitors have already acted on it.
Most organisations are now deploying AI and IoT faster than they are building the governance, culture and decision rights that decide whether those deployments will work. The technology gap is closing; the leadership-and-ethics gap is widening. Audiences want a speaker who has written the technical manuals and also spent years inside the rooms where large companies argue about whether to proceed.
Leaders now have access to more knowledge than at any point in history – and less clarity about what to do with it. Most strategic frameworks for navigating AI and exponential technology were designed for a world that no longer exists. The gap is not information; it is understanding: the capacity to anticipate what comes next, make decisions with philosophical coherence, and preserve human agency in organisations that are accelerating faster than their leadership thinking can follow.
Most organisations have pilots running, copilots deployed, and a roadmap deck. Few have a clear answer to what their managers and frontline teams should actually do differently when AI is sitting next to them. The gap between AI capability and human capability is now the binding constraint on commercial value.
Most enterprise AI programmes stall between pilot and operating advantage. Boards have approved the spend, vendors have shipped the tools, and the value is still trapped in slideware. The tension now is governance, accountability and workforce redesign at the speed agentic AI is moving, not whether to invest.
Most leadership development assumes the leader is already steady. They often are not. Senior people are being asked to lead through restructure, AI disruption, and team fatigue at the same time, and the gap between what they expect of themselves and what they can sustain is widening. The organisations that close that gap treat self-leadership as a capability to be built, not a personality trait to be assumed.
Most organisations face a contradiction they have not solved. Boards now demand faster innovation and faster AI adoption than the structures, talent and risk appetite below them were ever built to handle. Without the language to name that tension, leadership teams produce noise, burnout and bold-sounding decisions that quietly damage the business.
Most executive teams can describe what generative AI is. Far fewer can tell you which specific decisions inside their business should change because of it. The gap between surface-level fluency and operational judgement is where transformation stalls, budgets drift, and boards lose patience.