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 large organisations now claim an AI strategy and an innovation function. Few can show what either has produced in the last twelve months. Pilots multiply, capability stalls, and the question of how to move from experimentation to operating advantage stays open.
Boards are being asked to make large, irreversible bets on AI while the rules governing it are still being written. The people drafting those rules, and the people deploying the technology, rarely sit in the same room. Without a translator between Westminster, Silicon Roundabout and the executive committee, firms either over-invest in the wrong guardrails or under-invest and wait for enforcement to find them.
Most leadership teams are reacting to AI and Web3 from outside the rooms where capital is being deployed. They cannot tell which companies, products, and behaviours will define the next cycle, and they cannot tell which are noise. Without a credible view of where venture money is going, and why, strategic decisions on partnerships, acquisitions, and product bets are guesses dressed as strategy.
Most bank digital transformation programmes are redesigning customer interfaces, not the structural model underneath. The real question is whether a bank retains a meaningful role when AI manages financial decisions autonomously on the customer’s behalf. Boards that cannot answer that question are investing in the wrong conversation.
Most large banks know their operating model was not built for the speed of modern technology. The harder question is not whether to innovate but how: when to build, when to partner with a startup, when to buy, and how to make any of that stick inside a regulated balance sheet. Leaders need honest answers from people who have sat on both sides of that table.
Most organisations treat creativity as a personality trait of a few staff and a slogan for everyone else. The result is innovation that depends on individual heroics, breaks under pressure, and does not survive restructure. The shift is from creative culture as an atmosphere to creative output as a trainable team capability with measurable behaviours.
Every organisation now sits on more customer signal than it can read. The question is no longer whether to listen to social and behavioural data, but how to turn it into a decision a marketing director, a customer service lead, or a board can actually act on. The gap between “we have the data” and “we changed what we do because of it” is where most programmes stall.
Most leadership teams treat AI as an efficiency question rather than a question of identity. When algorithms absorb cognitive work, the traits that actually differentiate an organisation become both more valuable and harder to preserve. The strategic question is not whether to adopt AI but what a business chooses to remain unmistakably human about as AI reshapes the default.
Boards have approved AI pilots, signed responsible-AI principles, and named ethics committees, and still cannot answer whether their deployed systems would survive a regulator’s audit or a serious public failure. The gap is not awareness. It is the operating distance between governance language and the decisions engineers, product leads and procurement teams actually make every week.
Most large organisations have more knowledge than they can use and less curiosity than they need. Process discipline, accumulated expertise and AI tooling do not by themselves produce the next product, the next category, or the next reason for a customer to choose. Leaders are being asked to defend creative capacity inside companies that have spent two decades engineering it out.
Senior leaders are running ever larger events on AI, transformation and the energy transition, with regulators, investors and operators in the same room. The quality of the conversation, on stage and in the recording, decides whether the day reads as strategic clarity or as a logo parade. The chair has to be fluent in the subject and confident enough to interrupt a CEO when the answer is evasive.
AI has moved faster than the institutions it is reshaping. Leaders now face a version of the problem that universities are confronting first: when the tools students, employees, and customers use can produce plausible work in seconds, the old boundaries around expertise, integrity, and credentialing stop holding. The question is no longer whether to adopt AI, but which parts of the institution it quietly dismantles if you do.