From experimentation to
industry transformation.

In the past few years most enterprises have used AI to automate: faster reporting, cheaper support, better drafts. The gains are real, and available to anyone with a budget, competitors included. The value that changes a company's position comes from autonomy, when AI is allowed to decide and act inside a perimeter that leaders define. It starts with software agents and is moving into machines and robots that work in the physical world, where the question of trust stops being abstract. The stakes rise as AI stops advising and starts acting, in software agents today and in machines and robots next. This is where the real value lies, because autonomy changes a company's position in ways automation never did. The models that will run this autonomy are built in the United States and China, and the rules of trust that govern them are still unwritten. Europe, and this region with it, can import those rules as defaults or write its own.

Many people are turning to AI for advice that shapes their decisions. Yet, they cannot see the incentives behind such advice and typically assume it aligns with their interests. But what happens when an advisor has hidden objectives that push toward inferior alternatives? In a randomized experiment spanning financial and emotional contexts, we found that covertly misaligned advisors shifted participants’ preferences toward inferior alternatives, yet were still rated as helpful. We found no reliable evidence that adding explicit influence strategies increased this effect beyond covert misalignment alone. Our results highlight a behavioral vulnerability with implications for AI governance: When AI advice is covertly misaligned, people may shift their preferences toward inferior alternatives while continuing to perceive the advisor positively.