The scenario isn’t hypothetical: Some of the companies that went furthest in replacing people with AI have had to backtrack.
For example, in 2024 Klarna became a European benchmark for what AI could do for a company. Its AI assistant handled two-thirds of customer service chats in its first month, performing the equivalent of 700 full-time agents. As a result, company leadership decided to freeze hiring, and the workforce shrank from around 5,000 to 3,800 employees. Just a year later, Klarna’s CEO admitted the company had gone too far in replacing people with agents, which had negatively impacted both the service and the product. In fact, the company reversed course, rehiring human agents to ensure customers could always speak to a person.
The interesting point here isn’t that AI failed. The problem was something else: understanding the customer service function solely in terms of productivity and costs, without considering the bigger picture.
If measured by response times and equivalent FTEs, automation was optimal. Measured by satisfaction, perceived quality, and the ability to resolve complex cases, the result was different — and ultimately forced a reversal.
For CIOs, this disconnect presents a leadership opportunity: Management and other departments need precisely the comprehensive technical and business process perspective CIOs can bring to the table.
It’s tempting to read Klarna’s AI journey (and back) as a customer service story. But the pattern affects every business function. Introducing AI agents isn’t just adding another tool: It reshapes decision-making, day-to-day learning, and ultimately, how service is delivered.
If you only think in terms of productivity (what’s automated, how much is saved, how many equivalent FTEs are freed up), it’s easy to lose sight of the deeper implications. It’s easy to discover too late that what’s being delivered is no longer the same, even if on paper more is being produced. This is difficult to see at first. A function can perform worse and still show better operational metrics for months. The consequences appear in other areas, far removed from the automated function: in reputation, lost customers, or poor decisions.
CIOs see this pattern earlier and more strongly. When an agent used by IT — often among the earliest adopters — ceases to be a helpful assistant, the changes have quick and significant impact. They influence which alerts reach the operations team, which code modifications are proposed to developers, which incidents are prioritized by security personnel. This goes beyond simply speeding up work: It determines what the team sees and doesn’t see, and it shifts the decision-making environment.
Agents don’t just execute. They change how they detect problems, how they respond, and even how they learn. If this phenomenon is evaluated solely with performance metrics, it runs the exact same risk Klarna faced internally: gaining speed and losing perspective.
Many IT managers are beginning to notice the paradox inherent in AI agent use. The organization can act faster, deliver more volume, and automate more decisions, but at the same time lose touch with the complexity of reality.
Previously, a support team learned not only by resolving incidents, but also by identifying where integrations failed or what user behaviors revealed a deeper problem. If that work is now automated, the organization can continue to resolve issues, but employees lose valuable learning opportunities.
The risk the team faces is that AI will work well enough to push knowledge and capabilities about how a business unit should operate out of the foreground.
This is where the CIO’s role needs to change. CIOs must move beyond being those who simply automate processes to become those who provide, both within and outside their department, a comprehensive understanding of how AI impacts a business function. This means going beyond productivity gains and contributing other, less visible aspects, such as enhanced experience, business perspective, and changes in service delivery, whether for employees or customers.
This perspective is invaluable both at the senior management level and in other areas such as operations, customer service, and, of course, human resources. In the current climate, with its constant announcements of workforce reductions, the conversation tends to focus on cost and time savings. The CIO is well-positioned to provide the other side of the coin: where strong oversight is necessary, what can be delegated to AI, and where it’s essential to plan for the reversal of automation that, on paper, appears to be working.
That ability to recover is, in fact, one that the organization cannot afford to lose. Not all organizations can regain capabilities as quickly as they are lost.
The CIO’s mission, therefore, is to help clarify what can be delegated to AI and what should not be relinquished without losing the capacity to intervene. In some cases, the answer will be clear: repetitive tasks, initial classification, draft generation, or technical searches. In others, the boundary may be more delicate: prioritizing risks, deciding on exceptions, changing legacy systems, or acting on processes without sufficient oversight.
This will be one of the most important services in the CIO’s role over the next few years. Beyond advancing the adoption of agents, they will have to provide, both within and outside of IT, the necessary understanding of the impact of agents on a business function. And, finally, they must retain the ability to reverse course when the expected results aren’t being delivered, no matter how good the metrics look.