It helps to be concrete about where that value shows up first. Across very different industries, it is the same kind of work: High-volume, rules-clear, with a clear definition of “good.” In a bank, that is fraud triage and reconciliation. In a health plan, it is first-pass claims and prior-authorization routing. In retail, it is service-case deflection and returns. In a federal agency, it is eligibility screening and case intake. None of these are moonshots. They are the unglamorous, high-friction workflows where an agent under supervision takes out cost and cycle time without betting the business and where the supervision muscle gets built for the harder, higher-stakes work that follows. Start where the value is obvious and the blast radius is small.

The cost of skipping that is now quantified. Gartner projects that more than 40 percent of agentic AI projects will be canceled by the end of 2027, not because the models fail, but because of escalating costs, unclear business value and inadequate risk controls. The market muddies the picture further through what Gartner calls “agent washing”: Of the thousands of vendors claiming agentic capability. CIO.com’s own reporting finds the same pattern inside enterprises — pilots that demo beautifully stall the moment they meet production, where documents vary, exceptions multiply and someone has to be accountable when an agent acts. What I see most often is that the teams that struggle aren’t the ones with the weakest platform. They’re the ones with the vaguest intent. AI amplifies ambiguity as efficiently as it amplifies capability.

The leadership response is not a bigger bake-off among platforms. Naming vendors tells you where the market is heading; it doesn’t tell you what to do. The work is to design the roles around the agents. People move up the value chain — from doing the task, to steering it, to supervising and handling the exceptions the agent can’t. Autonomy follows a ladder, not a switch: Assistant, then participant, then genuine team member, with human supervision tightening as the stakes rise. Start with a bounded use case, build the supervision muscle and only then widen the boundary. In a federal modernization program I advised, the teams that pulled ahead began with a single high-volume, rules-clear workflow, proved the audit trail and human sign-off, and widened autonomy only once the supervision held. The goal was never more agents. It is agents that belong to a team someone actually leads.