What happened
A McKinsey survey of 1,719 professionals and business leaders found that while 80% say AI has improved their individual productivity, only 37% report it has a meaningful impact on earnings before interest and taxes (EBIT)—unchanged from a year ago.
Why it matters
Companies are spending more on AI, with 60% of respondents expecting to increase AI investment next year, yet the financial payoff remains elusive for most. The survey highlights that "AI high performers"—the 6% of organizations attributing at least 5% of EBIT to AI—are more likely to have redesigned workflows (nearly three-quarters, up from 55% last year) rather than just adding AI to existing processes.
What to watch
The gap between large and small companies is widening. Among firms with over $1 billion in revenue, 54% are scaling AI enterprise-wide, and the share scaling AI agents in at least one function rose from 27% to 40% in a year, while smaller companies remained at 22%.
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The survey data suggests that simply pouring more money into AI does not guarantee financial returns. While individual productivity gains are widely reported, the translation to corporate profitability is rare, with only 37% of firms seeing meaningful EBIT impact. This stagnation, despite increased investment, indicates that many companies may be using AI in ways that don't fundamentally change their operations.
A key differentiator appears to be workflow redesign. The 'AI high performers' are those that have rethought their processes rather than layering AI onto existing ones. This insight, coupled with the statistic that 20% of respondents say AI-related operating costs are already constraining use, suggests that the next phase of AI investment may need to focus on process innovation and cost efficiency, not just technology adoption.
Furthermore, the divergence between large and small companies is notable. Larger firms are scaling AI more aggressively, likely due to greater resources, which could widen competitive gaps. The rise in AI agent adoption among large firms indicates a strategic shift towards more autonomous systems, which may require even more substantial workflow changes to realize value.
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