The European Commission has launched a formal antitrust investigation into whether Meta is unfairly limiting third-party artificial intelligence providers’ access to WhatsApp, potentially shutting out competitors to its own “Meta AI” service.
Italy’s competition regulator is tightening the screws on Meta, warning that the company’s latest changes to WhatsApp could choke off competition in the fast-moving AI chatbot market just as it begins to take shape.
In a recent article, Norman Marks asks a pointed question that’s becoming increasingly urgent across boardrooms, risk teams, and C-suites alike—are organizations truly leveraging the potential of AI, or are they still circling the runway while competitors take off? Drawing on new insights from Google AI and McKinsey’s latest 2025 survey, Marks explores whether companies are moving fast enough, cautiously enough, or strategically enough to turn AI from hype into real enterprise value, and what it means for practitioners who risk being left behind.
The California Privacy Protection Agency isn’t staying quiet as new federal proposals surface that could sideline state-level protections governing artificial intelligence and automated decisionmaking technology. This week, the agency came out firmly against two separate federal efforts, one in Congress and one inside the executive branch, that would make it harder for states to enforce their own guardrails on emerging technologies.
Singapore’s financial watchdog is moving to tighten oversight of artificial intelligence across the financial sector, issuing a new consultation paper that lays out supervisory expectations for how firms should manage the risks of increasingly powerful AI systems.
As artificial intelligence races deeper into the enterprise, a new global survey from EY suggests the real winners aren’t just those investing the most in AI, they’re the ones governing it best.
For years, AI governance has been built around preventing bad decisions before they happen. Organizations assess training data, test accuracy, evaluate bias, write principles, and sign off on models before they go live. That made sense when AI produced insights and humans made the choices that followed.