Global executives predict massive AI productivity surge despite looming job losses and slow start

A comprehensive international study of nearly 6,000 verified executives has provided a pivotal reality check on the actual business impact of artificial intelligence.[1][2][3][4][5] Published as a working paper by the National Bureau of Economic Research and involving researchers from the Federal Reserve Bank of Atlanta, the Bank of England, the Deutsche Bundesbank, and Macquarie University, the report offers the most rigorous assessment to date of how AI is being integrated across firms in the United States, the United Kingdom, Germany, and Australia. While the initial findings suggest that AI has yet to drastically alter productivity or employment on an aggregate scale, the data reveals a profound undercurrent of executive optimism.[5][3][1][6][4] Rather than viewing the modest results of the last three years as a failure of the technology, corporate leaders interpret this period as the necessary groundwork for an imminent and significant acceleration in economic performance.

The current state of AI adoption is characterized by widespread experimentation that has not yet translated into bottom-line shifts.[2][1][3] According to the study, approximately 70 percent of firms are now actively using some form of artificial intelligence, with 69 percent of businesses utilizing it for core functions such as large language model-based text generation, data processing via machine learning, and visual content creation.[7] Despite this high rate of adoption, nearly 90 percent of the surveyed executives reported no measurable change in their total headcount or productivity levels attributable to AI over the past three years.[7] This disconnect between usage and measurable output has been termed a modern productivity paradox, where the technology is visible everywhere except in the official economic statistics. However, analysts argue that this reflects the early phases of deployment typical of general-purpose technologies, which often require years of organizational restructuring before their full benefits are realized.

The modest historical impact stands in sharp contrast to the aggressive projections executives have for the next three years.[7][2] On average, the surveyed leaders expect AI to drive a 1.4 percent increase in productivity and a 0.8 percent rise in total output across their organizations by 2028. These figures vary significantly by geography, with U.S. executives projecting the highest productivity gains at 2.25 percent, followed by their counterparts in the United Kingdom at 1.86 percent.[1] In economies that have struggled with stagnant productivity growth for over a decade, gains of this magnitude represent a potential structural shift. This optimism is fueled by the transition from simple task-based automation to more complex agentic workflows, where AI systems are beginning to handle end-to-end business processes rather than isolated functions.

Labor market implications remain a focal point of the study, revealing a notable divide between executive strategy and employee expectations.[2][7][8] While over 90 percent of managers reported no AI-driven change in headcount over the last three years, the forecast for the future is more disruptive. Executives across the four surveyed nations predict a net employment reduction of 0.7 percent by 2028 as a direct result of AI integration, a figure that translates to roughly 1.75 million job losses in those markets alone.[2] Interestingly, individual employees surveyed in the same study expressed a more optimistic view of their own job security, predicting a 0.5 percent increase in employment opportunities over the same period. This suggests a potential communication gap within organizations regarding how AI will be used to either augment human labor or replace specific roles entirely.[9][5][4][8]

The study further highlights that the primary barriers to realizing AI’s potential are structural and human, rather than purely technical. Although 87 percent of executives report using AI tools personally, their average usage remains low at just 1.5 hours per week, with a significant quarter of top leaders reporting no direct interaction with the tools at all. This lack of hands-on familiarity at the top may explain why 68 percent of executives expressed concern that their AI initiatives could fail due to a lack of integration with core business operations. Furthermore, the report notes that while 82 percent of leaders believe companies should reskill workers impacted by AI, only 17 percent of organizations currently have robust transition programs in place. The data suggests that the firms seeing the highest early returns are those that combine AI investment with intensive workforce training, which the study notes can amplify productivity benefits by nearly six percentage points.

Ultimately, the research frames AI as a slow-burn revolution that is currently traversing the trough of a classic J-curve of technology adoption. This curve occurs when initial investments in a new technology actually lead to a temporary dip or stagnation in productivity as resources are diverted toward training, infrastructure, and experimentation. The constructive headline of the study lies in its evidence that the lack of immediate results is a standard historical pattern for technologies as transformative as the steam engine or the internet. As firms move past the pilot phase and begin to weave AI into the fabric of their decision-making and innovation strategies, the modest aggregate shifts observed today are expected to give way to the more substantial gains predicted by the C-suite.

The long-term outlook for the AI industry remains bullish, provided that the current gap between executive ambition and operational preparedness can be closed. While the market has recently seen volatility driven by fears that massive capital expenditure on AI is not yielding immediate returns, this study suggests that the underlying business case is still being built.[5][3] The move from efficiency-focused AI use cases toward those driven by innovation is already underway; by 2030, executives expect over 60 percent of their AI spending to be directed toward creating new products and revenue streams rather than just optimizing existing costs.[9] For the global economy, the true measure of AI’s success will not be found in the hype of the last three years, but in the structural improvements that executives are now positioning their companies to capture over the remainder of the decade.