Is AI at work actually helping? Major survey claims many firms see no obvious benefit despite billions in investment
- More than two out of three companies now use AI
- 89% have not seen an increase in productivity and 60% do not expect any other impact
- Yet 90% have not experienced major job losses and 63% expect this to stay the same.
A major study of around 6,000 CEOs, CFOs and other senior leaders in the US, UK, Germany and Australia has quantified how many companies are actually using AI – and claims many companies are still not seeing the benefits previously promised.
According to data from the National Bureau of Economic Research (NBER), more than two-thirds (69%) of businesses are currently using AI tools, with further growth expected over the next three years.
However, 89% of them reported no impact on productivity over the past three years. Yet with so many (90%) noting any impact on employment over the same three-year period, it’s clear that businesses are willing to keep trying.
Are we really using AI to its full potential?
The most common use cases highlighted in the report include text generation (41%), data processing (30%), visual content creation (30%), and image processing (20%). But even though it is not as commonly used in robotics (9%) and autonomous vehicles (3%), where implementation is likely more complex, total usage will increase by about 50% in 2025.
Still, the outlook is not overwhelmingly positive. While 25% expect small productivity gains and 12% expect large gains, 60% expect to see no impact. The picture is largely the same in terms of employment, with 63% expecting no impact, and only 26% expecting job cuts, which brings at least some good news.
“Over the next three years, companies expect AI adoption to increase productivity by about 1.4% on average, while reducing employment by about 0.7%,” the researchers write.
Although the authors acknowledge that the study in isolation does not provide sufficient meaningful information, they call for further studies to be conducted using standardized methodology to ensure more reliable comparisons: “We further emphasize the importance of consistency in survey design and timing. »
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