Economic uncertainties and disappointing returns are tempering corporate AI investment enthusiasm, according to a new IBM survey of 2,000 global CEOs. While executives plan to increase AI spending by 31% over the next two years—double last year’s rate—only one in four report that their current AI initiatives have delivered expected returns. This cautious approach reflects broader economic concerns, with trade policy specifically identified as creating a “wait-and-see” environment that’s restraining technology investments.
The big picture: CEOs are increasing AI investments despite underwhelming initial results, driven primarily by competitive pressure rather than proven returns.
- IBM’s global survey found executives expect their AI spending to grow 31% over the next two years, compared to approximately 15% growth reported for the past year.
- Only 25% of CEOs report their AI initiatives have delivered the expected return on investment to date.
Why this matters: Despite disappointing initial returns, business leaders remain optimistic about AI’s future value, with 85% expecting positive returns within two years.
Behind the numbers: Gary Cohn, IBM vice chairman and former Trump administration economic advisor, attributes the investment hesitation to macroeconomic uncertainty.
- “There are very few companies in the world that are spending more today than they were a year ago, on anything,” Cohn noted in an interview.
- Half of surveyed CEOs report having disconnected technology systems across their organizations, creating integration challenges.
What they’re saying: Cohn, who resigned from the Trump administration in 2018 over tariff disagreements, identified trade policy specifically as creating business uncertainty.
- The former Goldman Sachs president described the current business climate as an “environment of uncertainties” holding back technology investment.
Key recommendations: Cohn advises CEOs to focus on three areas for successful AI implementation:
- Continually adapt AI strategies as technology evolves
- Ensure high-quality data inputs for AI systems
- Provide better resources for machines to access organizational data
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