TL;DR: Your board will ask what the AI spending bought, and most companies can no longer answer, because nobody measured the work the deployments were about to change. The lag in returns is real, and it sorts the buyers from the beneficiaries. Six sectors out of sixty took 99% of the 1990s gain, and the heaviest spenders missed it. You cannot locate yourself in a distribution without a "before", and almost nobody took one. Engineering and support kept timestamps, so those baselines can still be recovered.
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Your board is on your back, asking what the AI money bought. The answer went missing in 2023, when the first deployments went in and nobody wrote down how the work ran.
The comfort on offer is that returns lag, and it is well founded. Robert Solow wrote, in the New York Times Book Review in July 1987: "You can see the computer age everywhere but in the productivity statistics." The statistics caught up about a decade later. A general-purpose technology pays off only once processes are redesigned around it and the people retrained, and the accounts book all of that as cost until it pays. Re-tooling alone changes the tool and leaves the process where it was. The average company that read Solow as a promise spent the decade buying computers and worked out later what to do with them.
The catch-up was narrower than almost anyone remembers. Six sectors out of sixty produced 99% of the gains. The sectors that bought the bulk of the new computing grew productivity 0.3% a year. Retail was one of the six, and inside it the acceleration happened because Wal-Mart forced it: rivals copied the big-box format and the barcode scanning after a decade of losing share. So the sorting ran between companies inside one industry, as well as between industries. No competitor announces that it has finished reorganizing. The market share moves first, and that is the notice.
So the lag is real, and it sorts the buyers from the beneficiaries. Productivity is rising while the share of executives who credit AI with 5% or more of their company's earnings, and call the effect significant, holds at about 6%. An average can rise while the group capturing the gain stays the same size.
Which side are you on? The answer changes your next two years, and you will have a hard time getting it. AI went in at scale from 2023 onward, and I still haven't seen a company measure the work it was about to change. Not one. The freed hours get eaten by approval steps and handoffs that are still sitting there, and the gain never reaches the P&L. The most expensive thing companies did with AI wasn't the spend. It was deploying without a "before".
Engineering and support are the exceptions. Count resolved tickets per support person, and lead time from commit to production in engineering, with median time to resolution beside the ticket count. The tracking systems were already stamping times when the first models arrived, so you can recover those baselines instead of starting from scratch. Everywhere else, the record is gone. Start the count now; in 2028 you will be arguing from data instead of from memory.
One objection I can already hear loud and clear. Baselines on knowledge work are gameable, and the two I just named are as gameable as any: cycle time moves for six reasons a quarter, and none of them is the model you deployed. Good leaders have judged a process change by whether the work got visibly better, for a century, and it has served them. It never once told them whether they were gaining on the field or losing to it.
Solow's paradox resolved itself in the end. The sectors that had paid for the computers watched the gains land somewhere else. Will you re-tool or re-think your processes with AI? Only one of them earns anything back.
All the images were generated with AI (ChatGPT Images, Gemini Nano Banana, Claude Opus) by Gérard Métrailler.
Sources
Brynjolfsson, Erik, Daniel Rock, and Chad Syverson. "The Productivity J-Curve: How Intangibles Complement General Purpose Technologies." American Economic Journal: Macroeconomics 13, no. 1 (January 2021): 333-72. https://www.aeaweb.org/articles?id=10.1257/mac.20180386
McKinsey Global Institute. US Productivity Growth 1995-2000. October 2001. https://www.mckinsey.com/~/media/McKinsey/Featured%20Insights/Americas/US%20productivity%20growth%201995%202000/usprod.pdf Accessed 2026-09-08
Solow, Robert M. "We'd Better Watch Out." New York Times Book Review, 12 July 1987, 36.
Tinkoff, Dan, Lieven Van der Veken, and Michael Chui. "The state of AI in 2026: On the road to ROI." McKinsey Global Survey, 25 August 2026. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai Accessed 2026-09-08
US Bureau of Labor Statistics. Productivity and Costs, Second Quarter 2026, Revised. Released 3 September 2026. https://www.bls.gov/news.release/archives/prod2_09032026.htm Accessed 2026-09-08
