Don’t let your company be fooled by AI efficiency. The scenario isn’t hypothetical, because some of the companies that went furthest in replacing people with AI have had to backtrack. 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 and rehired human agents to ensure customers could always speak to a person. The key observation is that AI did not fail. The problem was something else: understanding the customer service operation solely in terms of productivity and costs, without considering the bigger picture.
Related: AI Needs New Operating System
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. This disconnect presents a leadership opportunity for CIOs, who possess a complete technical and business process perspective that other departments might lack.
Beyond the metrics
It’s tempting to read Klarna’s AI journey (and back) as a customer service story. But the pattern affects every business unit. Introducing AI agents isn’t just adding another tool; it reshapes decision-making, day-to-day learning, and ultimately how service is delivered. Thinking solely in terms of productivity makes it easy to lose sight of deeper implications. One might discover too late that what is being delivered is no longer the same, even if on paper more is being produced.
A function can perform worse and still show better operational metrics for months. Reputation suffers. The consequences appear in other areas, far removed from the automated process: in reputation, lost customers, or poor decisions. CIOs see this pattern earlier and more strongly. When an agent used by IT 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, and which incidents are prioritized by security personnel.
Related: AI safety claims face executive scrutiny
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. There is a distinct risk that as AI automates the resolution of errors, the organization forgets how those errors occurred in the first place, leaving the organization blind when the model encounters a novel situation it was not explicitly trained to handle.
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 team faces the risk that AI will work well enough to push knowledge and capabilities about how a business unit should operate out of the foreground.
The CIO’s new responsibility
This is where the CIO’s role needs to change. CIOs must move beyond being those who simply automate processes to become those who offer a total understanding of how AI impacts a business operation. This means going beyond productivity gains and contributing other, less visible aspects, such as enhanced experience, business perspective, and changes in service delivery.
Related: Phone wallpapers reveal hidden judgments, survey finds
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 planning for the reversal of automation is essential, even when the numbers look good.
Losing this ability is a risk no organization can afford. 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.
