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Ganesh Sivakumar
Ganesh Sivakumar

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Three in four executives see AI returns. Only one in twenty see real value.

#ai #leadership #roi #management

Two big enterprise AI surveys landed this year with findings that look like a contradiction. The Wharton GBK 2025 AI Adoption Report - the third annual wave of a tracking study of roughly 800 senior decision makers at US enterprises with 1,000+ employees and more than $50 million in revenue, fielded in June and July 2025 - reports that nearly three out of four leaders already see positive returns from generative AI. Meanwhile, BCG's Build for the Future 2025 Global Study, covering more than 1,250 firms worldwide, finds that only 5% of companies generate substantial bottom-line value from AI, while 60% report minimal revenue and cost gains at all.

Both numbers can be true at once. Reading them together is more useful than quoting either one alone.

The perception ledger and the P&L ledger

Wharton measures perception, and it says so. Leaders were asked whether they see positive returns, and roughly three-quarters do; four in five expect positive returns within two to three years. Notably, the optimism rises with altitude: 81% of VP-and-above respondents believe ROI is positive, versus 69% of mid-managers, who are presumably closer to the actual workflows. The same study finds real momentum underneath the perception: 82% of leaders use generative AI at least weekly (up 10 points year over year), 46% daily, and 72% now formally track ROI with structured, business-linked metrics like profitability and throughput.

BCG measures something harder: substantial value in the form of revenue and cash flow. By that standard, only 5% of firms - BCG calls them "future-built" - qualify. Another 35% are scaling and seeing some returns, and the remaining 60% (14% stagnating, 46% emerging) report minimal gains. The future-built 5% are pulling away: BCG reports 1.7x revenue growth, 1.6x EBIT margin, and 3.6x three-year total shareholder return compared with the lagging groups. One caveat from BCG's own methodology note: results reflect the business area each respondent knows best, not always the whole company.

A third data point sits between the two. In Google Cloud's 2026 ROI of AI survey of 2,403 senior executives, 86% agreed that AI has let them scale revenue or output without proportional increases in operating costs. That is again an agree/disagree perception question - and the survey is published by a company that sells AI - but it shows how broadly the "AI pays" belief now holds among executives.

Why the gap is organizational, not technical

If three-quarters of leaders see returns but only 5% see substantial value, the binding constraint is not model quality or access to tools. Everyone has the tools. BCG's comparison of what future-built companies do differently reads like an org chart critique, not a technology shopping list:

  • Leadership owns it. Nearly 100% of future-built organizations report deeply engaged C-suites, versus 8% of laggards - a 12x difference. They are 3x more likely to have appointed a chief AI officer and run AI as a board- and CEO-sponsored multiyear program rather than a collection of pilots.
  • They redesign workflows instead of sprinkling AI on old ones. Future-built firms have 5x more AI workflows deployed or scaled, with value-based prioritization and rigorous tracking of results.
  • They aim at the core of the business. About 70% of AI's value sits in core functions - sales, marketing, operations - not support functions, up from 62% in BCG's 2024 study.
  • They invest in people, not just platforms. The five strategies in BCG's playbook include broad-based upskilling and an AI-first operating model built on human-machine collaboration, alongside the data and platform foundation.

There is one more number in the BCG study worth sitting with: agentic AI, barely mentioned in 2024, already accounts for about 17% of total AI value in 2025. The value pool is shifting toward systems that act, and those systems demand exactly the workflow redesign and governance that the 60% have not done.

My read

The Wharton optimism is not wrong; it is measuring an earlier point on the curve. A team that saves an hour a day with a drafting assistant is genuinely "seeing positive returns." But that kind of win is cheap, widespread, and easy for competitors to copy, which is why it barely registers in BCG's revenue and cash-flow ledger. The uncomfortable implication of the VP-plus-versus-middle-manager gap (81% versus 69%) is that confidence about AI value is strongest furthest from the work. If you lead an AI program, the question worth asking in your next review is not "are we seeing returns?" - everyone says yes to that - but "which P&L line moved, and by how much?" BCG's data suggests the honest answer, for most companies, is still "none of them, yet." The fix is unglamorous: executive ownership, workflow redesign, tracking against a small number of core-business outcomes. None of it requires waiting for a better model.

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