Artificial intelligence is moving quickly from an experimental tool to a core business capability, with measurable effects already visible across several sectors. A Morgan Stanley survey of companies that had used AI for at least one year found an average net productivity increase of 11.5% over the past 12 months, alongside a 4% net decline in headcount. The findings covered five sectors identified as most likely to face significant near-term effects from AI adoption: consumer staples distribution and retail, real estate management and development, transportation, healthcare equipment and services, and automobiles and components. These figures point to a dual shift in how businesses operate and compete.
AI adoption is linked not only to efficiency gains, but also to changes in hiring, redeployment and workforce size. The business impact is no longer theoretical: it is already influencing staffing decisions, operational performance and investment priorities across countries, sectors and company sizes.
Workforce Effects Differ by Region, Sector and Company Size
AI adoption was associated with substantial workforce change over the last 12 months. Survey respondents reported that 11% of jobs were eliminated and another 12% were left unfilled. At the same time, 18% new hires partly offset those changes, resulting in a 4% net job loss globally. That outcome differed from earlier expectations that AI would support employment growth rather than reduce headcount.
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The pattern was not uniform across markets. U.S. companies reported a 2% net gain in jobs, with AI-related hiring exceeding both eliminated roles and positions that were not backfilled. Europe was expected to see the biggest negative impact. These differences indicate that the employment effects of AI are developing unevenly rather than producing the same result in every geography.
Sector variation was also clear. Automotive companies recorded the largest net loss of positions at 10%, while real estate showed a 1% net gain. Company size shaped the outcome as well. Smaller businesses with fewer than 49 employees reported the strongest staff retention and a 4% net gain in positions. By contrast, firms with 501 to 1,000 employees recorded the highest net loss, cutting 15% of positions.
The impact within the workforce was not evenly distributed by experience level. Early-career roles faced the highest combined levels of elimination and non-replacement. Employees with two to 10 years of experience showed the highest rates of retraining or redeployment, a pattern that was consistent across sectors and regions.
Productivity Gains Reach Across Countries and Industries
While staffing effects were mixed, productivity gains appeared across countries and sectors. Nearly half of surveyed companies said AI adoption increased productivity by 1% to 10%. Around a third reported gains of 11% to 20%, and 14% said productivity rose by more than 20%. These results indicate that the operational benefits of AI are already broad-based, even if the scale of improvement differs between companies.
Country-level results showed that productivity gains were strongest in Australia and weakest in Germany. Even with that variation, the overall pattern remained positive. AI adoption was linked to measurable productivity improvement across all countries in the survey, reinforcing the view that the technology is delivering practical business effects rather than isolated pilot results.
Differences across sectors were also notable. Healthcare recorded the highest AI-driven productivity gains, while real estate recorded the lowest. That contrast matters because real estate was also the only sector to show a net gain in positions, while healthcare stood out more for efficiency improvement than workforce expansion. The sector comparisons suggest that AI can shape outcomes in different ways, with some industries seeing stronger labour effects and others stronger productivity effects.
Taken together, the findings show that AI is not generating a single standard business outcome. Instead, it is producing a combination of operational gains and workforce adjustments shaped by geography, sector exposure and organisational scale.
Investor Focus Turns to Earnings and Reskilling
The findings point to several areas investors may need to watch more closely in 2026. One is sector selection. At full adoption levels, some industries could achieve savings worth more than 50% of estimated 2026 consensus pre-tax earnings. Consumer staples distribution and retail, real estate management and development, and transportation were identified as the sectors with the largest potential benefits, with possible gains exceeding 100% of earnings.
Another shift concerns the main focus of investor debate. Attention in 2026 may move away from pricing pressure and towards how effectively companies convert AI-driven efficiency into financial performance. The question is no longer only whether AI adoption is spreading, but how strongly those gains appear in bottom-line results.
Portfolio assessment is another priority. The findings suggest that many investors may still be underestimating the impact of AI adoption. If AI capabilities continue to improve at a non-linear rate, the scale of value creation could exceed current expectations. That possibility increases the importance of evaluating how exposed companies are to AI adoption and how far implementation has progressed.
Workforce adaptation is also becoming a more material issue for investors. Survey respondents said that 27% of employees had been retrained in the previous 12 months. As organisations expand reskilling programmes to meet changing needs, staffing and education providers could benefit.
AI adoption is already producing measurable business effects across multiple sectors, with companies reporting strong productivity gains alongside net workforce reductions. The impact is uneven by country, sector, company size and career stage, but the overall direction is clear. Early-career positions appear most exposed, while retraining and redeployment are playing a larger role for workers with some experience. At the same time, productivity gains were reported across all surveyed countries and sectors, with especially strong results in healthcare. The findings point to a changing competitive landscape in which AI affects operating models, labour structures and earnings potential at the same time.
Source: Morgan Stanley
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