The theme of an AI summit held last week in Samsung‑dong, Seoul, has stayed with me. “After AI adoption comes operations.” A CAIO on stage summed it up: “Organizations that use AI tools must become organizations where AI works.” It’s a spot‑on diagnosis. But instead of applauding those words, a different question came to mind first.
In the past two to three years, almost every company has raced to “adopt” AI—running pilots, showcasing demos, issuing press releases. Yet tangible results remain scarce. Over nearly three decades working in IT strategy, CTO roles, and consulting, I’ve seen this pattern repeat. The problem was never the model or the tool; it was the next phase—“operations”—that no one talks about earnestly.
Adoption Is a Demo; Operations Is a Product
Adoption is actually easy. Today, create a single account and you can spin up a convincing demo in half a day—often earning applause from the executive suite. The challenge begins the moment that demo is thrust into real work. Requests change shape each time, edge cases surface, and the glued‑together tool starts misbehaving. What was invisible in the demo becomes fully exposed in production.
I call this the “valley between adoption and operations.” Every time a new technology arrives, this valley reappears—think ERP, think cloud migration. Adoption ends as a project; operations never ends. Most failures aren’t due to a botched adoption but to leaping across the valley without the necessary preparation.
Agents Deepen the Valley
Legacy systems required a human to press a button, so when something went wrong a person could intervene. Agents are different. They decide on their own, invoke tools autonomously, and choose the next action without human input. The convenience comes with real‑time responsibility, cost, and security implications.
What we hear on the ground all too often is: we attached an agent, and suddenly the API bill exploded to several times the forecast, or it performed actions no one had approved. This isn’t a model‑performance issue; it’s a question of whether the organization has a framework to handle something that moves autonomously. The smarter the tool, the more critical the governance that runs it.
In the End There’s One Question: Who Owns Operations?
I call this “operational ownership.” Who is accountable for the quality of an agent’s output? Who monitors cost leakage? Who draws the line on which data may be touched? Organizations that can assign names and faces to these responsibilities transition to true operations. Those that can’t stay stuck in a perpetual adoption loop—the graveyard of pilots.
Therefore, I urge anyone evaluating AI adoption to flip the order: instead of asking “Which model should we use?” start with “Who will run it and under what governance?” Adoption without an operational blueprint, no matter how flashy, stalls at the edge of the valley.
If your organization is still stuck in the demo phase,