Künstliche Intelligenz & Generative KI
Experten erklären die realen Auswirkungen von Machine Intelligence auf Industrien, Arbeitskräfte und Wettbewerbsvorteil
Speakers Associates represents 354 speakers on Künstliche Intelligenz & Generative KI, including Kemal Apaydin, Olivier Sibony, Rahaf Harfoush, Purna Virji, Itai Green, Limor Ziv, Tom Goodwin, Daniel Trabucchi & Tommaso Buganza, Katja Schipperheijn und Diana Verde Nieto.
Most boards now accept that AI will change their business. Few have a defensible view on what it changes first, what it changes structurally, and what it does to the labour model their P&L assumes. The gap between accepting AI as a trend and treating it as a strategic variable is where serious organisations are exposed.
Organisations are deploying AI faster than they are rethinking what their workforces should do. The gap between automation investment and workforce strategy is not a technical problem – it is an institutional one. Every historical wave of technological disruption has produced the same error: treating short-term labour displacement as permanent decline, or resisting disruption until the window for adaptation has closed.
Most leadership teams treat digital risk as a technical problem they can delegate. The real exposure is power: who controls the information, the platforms, and the narratives that now decide a company’s reputation, a market’s direction, and an election’s outcome. By the time that shift is visible on a balance sheet, the advantage has already moved.
Global supply networks were built for a world of open trade, cheap logistics, and predictable demand. None of those conditions hold any longer. Boards now face a live question: how do you keep cost discipline, meet customer commitments, and re-engineer operations for a fragmented tariff environment, all at the same time, and without stalling growth?
Most executives have mapped their AI technology landscape; far fewer have mapped the governance architecture being built around it. The EU AI Act now sets binding constraints on which AI applications can be deployed, which require conformity assessments, and which are prohibited entirely. Parallel frameworks at UN level will extend these obligations globally.
Most organisations are spending heavily on AI without a clear view of which decisions the technology is actually supposed to improve. Models get shipped, dashboards proliferate, and senior leaders still cannot tell whether any of it is changing the quality of the choices the business makes. The missing layer is not more data or better algorithms, it is a disciplined way to connect AI outputs to the decisions a company is trying to get right.
Most retail and consumer businesses can list the trends shaping their category. Few can turn that awareness into operational change before competitors do. The gap is not insight, it is the discipline to test, adapt, and scale what works while leaving the theatre of innovation behind.
Most organisations have AI budgets. Most are still running pilots. The problem is not investment – it is that AI has been framed as a strategy in its own right, which turns a deployment decision into an open-ended design problem. Meanwhile, the gap between AI experimentation and scaled competitive advantage is narrowing fast. Organisations that cannot move AI into production – aligned to business goals they already have – will cede ground to those that already have.
Most organisations say they want breakthrough innovation but design approval processes that guarantee safe outcomes. The ideas most likely to create new categories are also the ones expert consensus will most reliably reject. Getting something genuinely new to market requires a method for staying in motion when the evidence argues against you.
Most B2B businesses sell something genuinely different, then describe it in language that sounds like everyone else. Sameness feels safe, but it quietly erodes pricing power and gives buyers no real reason to choose. The harder task is finding the difference a company already holds and making a market actually feel it.
Incumbent banks are facing increasing competition from challenger institutions that now match them on product and user experience. The more complex question is how banking will evolve as money and data become increasingly programmable, and who will control the underlying infrastructure.
Most organisations treat creativity as a personality trait held by a few people, rather than a process a team can run. The result is innovation that depends on whoever is in the room on a given day, ideas that never convert into commercial decisions, and leadership teams that confuse brainstorming with problem solving. What is missing is a repeatable method for turning ambiguous business problems into defensible answers.