Donna Dror is CEO at Usercentrics.
An AI agent is about to book your customer’s next flight, renew their subscription and share their data with three platforms they’ve never heard of. It will do all of this on the basis of a consent decision they made in 2019, in less than three seconds, without reading a single word.
That consent decision almost certainly happened on a cookie banner. And it almost certainly wasn’t a real decision. For more than a decade, consent interfaces were designed with one goal: getting past the legal requirement. Not to inform, not to build understanding or trust, but to minimize friction and maximize throughput. The result was a consent layer that reflects that intent, technically valid, but never actually meaningful.
For years, this was a manageable problem. The worst consequence was a suboptimal ad targeting profile. However, that calculus has changed. Agentic AI systems are beginning to act on behalf of users at scale, booking travel, managing subscriptions, interacting with services and making data-sharing decisions in real time. These agents inherit whatever consent posture the user established. And in most cases, that posture was never a deliberate choice. It was a reflex built by a decade of compliance-first design. BCG reports that CEOs have already committed over 30% of their AI budgets specifically to autonomous agents. The deployment is no longer a pilot. The consent foundation it’s running on is.
The European Data Protection Supervisor has already flagged the structural risk: Agentic systems may combine personal data from diverse sources in ways users never anticipated, potentially without meaningful consent, reducing individuals to data points rather than preserving their role as decision-makers. The implications extend well beyond data protection. They land on CEOs’ desks.
The Reflex We Built Into The Internet
The numbers bear this out. Research from Ruhr University Bochum found that most users spend only a few seconds on a cookie banner decision, not because they don’t care about their privacy, but because the banner was never designed to help them exercise it.
A Syrenis survey found that 87% of U.S. consumers say they would be more likely to opt in if they could easily opt out, meaning consent design has been actively suppressing the very data quality it was supposed to produce. The cookie banner didn’t fail because users disengaged. It failed because it was built to be ignored.
That failure is now structural. The organizations best positioned for this moment are not necessarily the ones moving fastest on AI. They are the ones that built consent infrastructure with intention, designed to capture genuine understanding rather than reflexive clicks and maintained as living infrastructure rather than a one-time checkbox. That foundation now determines what their AI agents are permitted to do and how defensible those decisions are when a regulator or a customer asks.
Three Questions To Ask Before Your Next AI Investment
The Cisco 2026 Privacy Benchmark Study found that 75% of organizations now have an AI governance committee. Only 12% describe it as mature. That gap between the governance that exists on paper and the readiness that exists in practice is where the exposure lives.
For those who don’t have strong governance, the rebuild is harder than it looks. The data that AI models are trained on and operating with today reflects years of consent decisions that were never genuinely meaningful. Changing that means changing what data exists, what it reflects and how much of it can actually be trusted. Companies that treated consent as a legal formality are discovering that the debt compounds, and that it shows up somewhere they didn’t expect.
The right response is not to slow down AI deployment. It is to connect two conversations that have been running in parallel, AI investment and data governance, and recognize them as the same conversation. Here are three questions every leadership team should be asking now:
First: When an AI agent acts on a user’s behalf, what consent is it actually inheriting, and would that consent hold up under scrutiny? Not in terms of legal compliance, but in terms of what the user actually understood when they clicked accept.
Second: Who in your organization is accountable for the quality of consent your systems produce, not just its existence? If the answer is the legal team, you have a legal asset and a business liability. The U.K.’s Information Commissioner’s Office (ICO) and the IAPP have both flagged that organizations deploying agents need strong guardrails around purpose limitation and data minimization beyond what current frameworks can provide.
Third: Is your organization treating consent as a one-time moment, or as infrastructure that compounds in value over time? The companies getting this right have moved consent out of the compliance function and into the operational core, measuring it the way they measure data quality, because in an agentic world, it is data quality. The difference between agents operating on assumptions and agents operating on trust is going to matter more, not less, as deployment accelerates.
The consent moment, as we have known it, is disappearing. Agentic AI is making data decisions faster than any disclosure mechanism was designed to handle. The companies that built their consent layer as a genuine reflection of user understanding will find that foundation transfers directly into AI readiness. The ones that didn’t will face that reckoning on someone else’s timeline.
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