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 customer experience programmes stall in the gap between brand promise and frontline behaviour. Leaders fund the technology, redraw the journey maps, and find that nothing material changes in what the customer actually receives. The harder problem is moving an organisation from compliance with policy to ownership of outcome, at the scale where it shows up in retention and growth numbers.
Most leadership teams still make their biggest calls inside a small room of senior people who broadly agree with each other. The cost is slow decisions, narrow options, and innovation programmes that surface the same ideas the company already has. The harder question is how to widen the input set, employees, customers, partners, networks, without losing speed or accountability.
Most breaches do not come through the firewall. They come through a tired employee, a shared password, a click on a convincing email, a process that nobody reviewed. Boards have spent a decade buying technology, and the human layer is still where attackers walk in.
Most organisations have run out of patience with culture work that does not change anything. Engagement surveys plateau, hybrid policies are contested, and five generations now sit on the same teams with conflicting expectations about trust, communication and what work is actually for. The cost of getting this wrong shows up in attrition, manager burnout and quietly stalled change programmes.
Senior teams know the AI race rewards speed and punishes caution, even when caution is what their own risk function is asking for. Coordination across competitors looks naive; unilateral restraint looks like ceding ground. The question is how to operate, and govern, inside that pressure without sleepwalking into outcomes no one in the room actually wants.
Fashion businesses run on a development model that was already strained before AI changed what was possible. A typical garment moves from sketch to production through six to eight weeks of manual pattern work, multiple physical samples, and inventory commitments made months before a customer is asked anything. The operational question is no longer whether to automate. It is whether the leadership team understands which parts of the cycle can now be compressed, what the supply chain looks like when production becomes on-demand, and how to integrate digital and physical product lines without losing brand identity.
Most organisations have AI governance policies. Very few have a principled account of what those policies are actually trying to govern. The result is compliance frameworks that cannot answer the questions boards now face: when AI acts, who is responsible, and why.
Most organisations plan as if the future is a continuation of the present, only faster. The future they actually face is shaped by turning points, unexpected shocks, and ideas that arrive from outside the industry. Long-range thinking is rarely a discipline inside the leadership team, which leaves strategy exposed to events that were predictable to almost no one in the room.
Most large organisations talk about innovation as culture and end up funding pilots that never reach the P&L. The gap is not ideas, it is process: how a bank, telco or pharma company moves a creative concept through the same operational rigour it applies to risk, finance and supply. Without a repeatable method, innovation stays personality-led and stops when the sponsor leaves.
Most corporate sustainability programmes are eco-efficiency exercises dressed as transformation. They reduce harm at the margin while the underlying business model still depends on extraction, waste, and single-use materials. Leaders increasingly sense the gap between their ESG narrative and the operating reality of their supply chains, and they need a credible framework for what comes next.
Boards are being asked to make long-horizon capital decisions while the rules-based order they relied on for thirty years is coming apart. Sanctions regimes, technology controls, and great-power rivalry now sit inside ordinary commercial decisions about supply chains, AI investment, and market access. Leadership teams need a serious framework for reading geopolitical change, not headlines.
Most enterprises now have AI on the agenda but no method for getting it into the operating model. Pilots stall, design teams default to features instead of customer problems, and the organisation cannot tell the difference between a real innovation portfolio and a list of experiments. The gap is not ambition. It is discipline.