Common Intelligence

Newsroom02 / 03

The Newsroom

Vol. III · No. 1 · September edition

Value

McKinsey finds that eight in ten respondents feel more productive with AI, while reported enterprise EBIT impact remains unchanged from last year.

A shared workflow turns one person's useful method into capability the company can keep. Common Intelligence
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Common Intelligence
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Value

McKinsey's August survey contains two figures that belong together. Eighty per cent of respondents say AI improves their individual productivity, yet only 37 per cent report any enterprise-level EBIT contribution, almost unchanged from last year.

People are already finding ways to draft, analyse and decide faster. The company captures less of that benefit when each method remains personal, when systems are not connected and when nobody owns the change to the wider workflow.

From individual use to shared practice

The organisations reporting the strongest results redesign the work itself. They combine leadership attention with operational rigour, treat risk controls as part of the build and pursue growth or innovation alongside efficiency.

For a team, that means agreeing how individual techniques become shared practice without turning useful experimentation into bureaucracy. A repeatable workflow needs an owner, a set of real evaluation cases, clear permissions and a visible route for exceptions.

Measure the company outcome

For management, it means choosing a measure that the people doing the work can influence and the finance team can verify. The purpose of the system is not to make everyone busier with AI; it is to change a business outcome without obscuring who remains accountable.

The opportunity is to convert the gains employees already feel into capability the company can keep. That conversion happens through shared workflows, not through a larger licence count.

Sources

More from the newsroom

Talk it through

Leo Largillet

Leo LargilletChief executive

Available this week

Bring a process your team knows should work better. In forty-five minutes, we will listen to how it really runs, identify where AI could help, and be clear about what we would leave with people.

Frequently asked

  • Our work starts from the people already responsible for the operation. The aim is to remove repetitive search, comparison and drafting, while making their judgment easier to apply and keeping accountable decisions with named human owners.

  • They help map the real process, choose the difficult cases, test early versions and define where review or refusal is required. An internal group learns to operate and improve the system before handover.

  • One senior sponsor, access to the people and systems involved, and agreement on the business measure before building begins. Leadership also protects time for operators to participate, because their knowledge is part of the system.

  • It should be frequent, expensive, evidence-heavy and close to a measure the company already trusts. The two-week diagnostic compares candidate workflows and tests the strongest one on real company data.

  • That is why it is tested on real exceptions before production. Corrections become part of the evaluation set, and the system is designed to ask for review when evidence is weak rather than appearing certain.

  • We build in the client environment wherever possible, use the minimum permissions each action needs, and make activity auditable. Data boundaries, retention, human review and prohibited actions are agreed before a production release.

  • The engagement is designed to reduce that dependency. The client receives the source, prompts, evaluation cases, runbooks and controls, and proves the handover by running the next cycle with its own team.

  • We establish the current cost and operating baseline together, then review the same measure after each release. If the workflow does not produce a credible financial or operating improvement, we do not expand it.