Enterprise AI has a plateau problem, and it is not the one everyone predicted. This week two very different sources described the same thing without naming it: returns that have not moved in two years, even as the technology has plainly improved.

Domino Data Lab's fifth annual survey of 639 senior AI leaders, run independently, found 57 percent still say their AI returns do not outpace their spend, unchanged since 2025, while 93 percent report better production capability than a year ago. Capability up, returns flat. That is not a technology problem. It is a measurement problem.

Meanwhile OpenAI's chief financial officer, Sarah Friar, published a scorecard proposing a new unit, "useful intelligence per dollar," and in doing so named the trap: for years software success was measured through adoption, seats and active users and renewals, and AI breaks that proxy completely. A thousand lit seats can produce nothing you would put in front of a board.

Stephen Forte on why the unit you count AI in is the wrong unit, the four questions to put to your largest AI investment today, why productivity felt is not revenue banked, and why the number on your AI dashboard you trust the most is probably the one measuring the least.

Almost every conversation about AI and work assumes your employees are on the receiving end of your decisions: leadership decides, the organisation adapts, and the only question is how kindly you manage it. In much of Europe that assumption is simply false.

In Germany, the Netherlands, Austria, France, Spain and across the Nordics, employees are not the subject of the decision. Through their representatives they are a party to it, by law. Germany's Works Constitution Act gives a works council co-determination over "the introduction and use of technical devices designed to monitor the behaviour or performance of employees," and the Federal Labour Court reads that to cover systems merely capable of monitoring, not only those intended to. Most enterprise AI tools qualify almost incidentally. Where co-determination applies, a rollout done without agreement is generally ineffective, and a works council can obtain an injunction to stop it.

Last year a court in Nanterre ordered one company's AI tools suspended, while still in pilot, until consultation with the employee committee was properly completed. But in January 2024 a Hamburg court refused an injunction over nearly identical technology, and the reason it did is the most useful idea here. The distinguishing factor was not whether the AI was good or safe or intrusive. It was whether the company deployed it or merely permitted it. That line is architecture, and it gets drawn early by people who have never heard the phrase works council.

Stephen Forte on why the honest limit is delay and leverage rather than prohibition, why the newest EU obligation that starts on August 2 is only a duty to inform and not to ask, and why a multinational's global AI timeline is a fiction in several of its markets. Your AI timeline does not belong to your plan. It belongs to your most protected workforce.

Two software companies on opposite sides of the world have now done the same strange thing to themselves, and the reason they gave is not the one anyone expected.

On July 22, monday.com, the Israeli work-management company listed in New York, filed notice of a roughly twenty percent workforce reduction, about 620 people, with restructuring charges of forty-five to fifty-five million US dollars. In the same breath it reaffirmed full-year revenue guidance of about 1.47 billion US dollars and nineteen to twenty percent growth. Grow twenty percent, shrink twenty percent, same announcement.

Co-CEO Eran Zinman said the decision "was not made to reduce costs or replace people with AI," that "the organization we built for our previous chapter is not the organization that fits the new AI era," and that work which "could have been done in a few days" had instead been taking "many months with multiple meetings and endless friction." Then the line that makes the episode: "This wasn't people's fault." The fix he describes is a flatter organisation with fewer management layers and smaller, more autonomous teams. The constraint AI relieved, in his telling, was coordination. Not the cost of labour.

He is not alone. In March, Atlassian, the Australian equivalent, cut about 1,600 people, roughly ten percent, while growing thirty-two percent, explicitly to "self-fund further investment in AI and Enterprise Sales." Mike Cannon-Brookes was unusually straight about it: their approach is not that "AI replaces people," but "it would be disingenuous to pretend AI doesn't change the mix of skills we need or the number of roles required in certain areas."

Stephen Forte on why two companies that sell AI betting their own org charts is worth more than any vendor presentation, why Salesforce is the awkward third case that teaches the distinction between an AI-shaped decision and a cost cut wearing AI language, and why revenue per employee, a number sitting underneath headcount planning, peer benchmarking, board judgement and acquisition pricing, just moved about twenty-two percent at one company through nothing more than a redrawn structure. No action items in this one. One idea, and one number that stopped meaning what you think it means.