The Operational Excellence Tools Series | #37: CEOs Say AI Boosts Productivity.

Employees Are Experiencing a Different Reality

Welcome to the unique weekend article for the Loyal Fan subscribers-only edition.

This is the #37 article of The Operational Excellence Tools Series.

Outlines and Key Takeaways

Part 1 – Official Announcement

Part 2 – Background and Meaning

Part 3 – Analysis Through the Lens of Operational Excellence

Part 4 – Lessons for Businesses

Part 5 – Conclusion

PART 1: OFFICIAL INFORMATION

During the period from 2024 to early 2026, artificial intelligence (AI)—especially generative AI—has been viewed by many business leaders as a key tool to improve operational efficiency, increase knowledge-worker productivity, and reduce indirect costs. However, recent reports and investigations from authoritative U.S. sources reveal a significant gap between executive-level claims and the day-to-day experiences of employees using AI in their daily work.

A notable analysis published by The Wall Street Journal in early 2026 compiled perspectives from numerous CEOs, senior executives, and employees across a wide range of U.S. industries. According to the article, the majority of interviewed CEOs believe that AI is making work more efficient, particularly for tasks such as document drafting, data analysis, report preparation, customer support, and decision assistance. Many leaders emphasized that AI shortens task completion time, reduces manual workload, and frees employees from repetitive tasks.

This viewpoint is also consistent with official statements from several major consulting organizations. According to the McKinsey Global Institute, generative AI has the potential to generate trillions of U.S. dollars in economic value annually, primarily through increased knowledge-worker productivity. BCG and Deloitte, in reports released during 2024–2025, likewise argued that AI can improve office productivity by 10% to 30%, depending on the level of integration and the nature of the work.

However, when examining the actual experiences of employees, the picture becomes far more complex. Based on employee surveys and interviews cited by The Wall Street Journal, many workers report that the time savings delivered by AI in practice are significantly lower than expected. Rather than achieving clear time reductions, employees often have to spend additional effort reviewing, editing, and correcting errors in AI-generated outputs. This is particularly common in roles that require high accuracy, deep contextual understanding, or strict regulatory compliance.

An independent survey conducted by the Pew Research Center in the United States reflects a similar trend. According to Pew, although many workers have been required or encouraged to use AI at work, only a small proportion believe that AI has substantially reduced their workload. Most respondents stated that AI provides only limited support, and in some cases actually increases pressure, as employees remain responsible for controlling and validating AI outputs.

A concept frequently cited by U.S. researchers and media in this context is the “AI tax.” This term does not refer to a financial tax, but rather to the time cost, effort, and mental strain employees bear when using AI—ranging from rewriting responses, verifying information, and adjusting tone, to ensuring that content does not violate internal policies or legal regulations. According to The Wall Street Journal, many employees feel that AI does not eliminate work, but instead shifts work into a different form—one that is more subtle, harder to measure, and less visible.

Labor research organizations such as MIT Sloan Management Review and Harvard Business School have also offered cautious assessments. In studies on AI-supported productivity, MIT found that AI accelerates simple tasks, but that its benefits diminish as tasks become more complex and require judgment, experience, and accountability. In such cases, humans cannot fully delegate responsibility to AI, but must maintain close supervision.

Another important point emphasized by official sources is the difference in how efficiency is measured. At the executive level, AI effectiveness is often evaluated through aggregated metrics, such as the number of documents produced, average processing time, or automation rates. At the employee level, however, effectiveness is experienced through personal factors such as fatigue, responsibility pressure, and the frequency of exception handling. This difference in reference frames contributes significantly to the perceptual gap between CEOs and employees.

According to Gartner, one of the greatest risks in deploying AI in enterprise operations is equating “having AI” with “being more efficient,” while overlooking critical factors such as process design, decision rights, and change-management capability. Gartner warns that if AI is layered onto existing operating models without redesigning how work is actually done, the realized benefits will be far lower than initial expectations.

Taken together, official information from The Wall Street Journal, Pew Research Center, McKinsey, BCG, Deloitte, MIT Sloan, and Gartner presents a consistent data-level picture: business leaders hold very high expectations for AI-driven efficiency, while the operational reality experienced by employees still shows substantial gaps. AI has not failed, but it also has not yet delivered a uniform productivity leap comparable to many strategic-level claims. This gap is becoming a central operational issue in 2025–2026, laying the groundwork for deeper OPEX analyses in the sections that follow.