MIT research shows most corporate AI initiatives fail, and many erode engagement and cognitive capabilities.

Approaching AI as an automation tool isn't returning value, and in many cases it's robbing workers of meaning.

There are three ways to take a more effective, human-centered approach: optimize, elevate, and innovate.

With intentional design, AI can unlock our highest human capabilities.

In early 2024, Klarna, the Swedish fintech company, announced that its AI-powered chatbot had assumed the workload equivalent of 700 customer service agents, managing millions of conversations across dozens of languages. The company reduced its workforce by roughly 40 percent. An IPO filing followed and, for a brief moment, Klarna appeared to be winning the AI-driven future of work (Haun, 2025).

But the story did not end there. As months passed, challenges emerged that efficiency metrics had obscured. Customers dealing with nuanced or emotionally charged situations encountered responses that felt formulaic and hollow. Satisfaction scores declined. The quality of service, particularly for complex cases requiring human empathy and judgment, deteriorated in ways that the gains in efficiency could not offset. By mid-2025, Klarna began bringing back human agents. CEO Sebastian Siemiatkowski acknowledged that the company had prioritized cost reduction over quality, and that approach proved unsustainable (Haun, 2025).

Klarna’s story is not unique. A growing body of evidence suggests that a similar tale is playing out, in quieter ways, across thousands of organizations and impacting millions of employees.

The Returns That Never Came

The gap between AI investment and AI results was one of the defining business stories of 2025. A major MIT study concluded that roughly 95 percent of enterprise generative AI pilots produced no measurable impact on profitability (Challapally et al., 2025). McKinsey’s QuantumBlack research unit arrived at a similar conclusion from a different angle. Their analysis found that while adoption of generative AI had become nearly universal, most companies reported no meaningful financial benefit (Sukharevsky et al., 2025). One analysis of 2025 enterprise data estimated that the average large company lost $7.2 million per failed AI initiative, with most abandoning more than two projects over the course of the year (Pertama Partners, 2026).

The financial shortfalls, however, seem to be masking a deeper problem. A growing body of research suggests that efficiency-focused AI deployment is changing the experience of work in ways that erode the very things that keep employees engaged, effective, and motivated.

In a series of experiments involving more than 3,500 participants, researchers found that working alongside generative AI on professional tasks like writing and brainstorming did improve output quality—but it came at a psychological cost. When participants transitioned to subsequent tasks without AI assistance, their intrinsic motivation dropped measurably, and their reported boredom increased (Liu et al., 2025). The researchers attributed this to a diminished sense of personal control. When AI handles the cognitive load, the human experience of authorship and agency fades, and with it the internal drive that sustains engagement over time.

In a longitudinal study at MIT, EEG scans revealed that those relying on AI exhibited progressively weaker neural connectivity compared to a control group working without technological assistance (Kosmyna et........

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