Think about the manager everyone calls when a store has a problem the manual doesn’t cover.
She notices that a labor problem is really a training problem. She asks the question that changes how a customer complaint gets handled. She knows when a new manager needs coaching and when the issue needs to move up the chain.
The technology may be ready before the employment bargain is.
Now imagine asking her to help build an AI assistant so the other locations can get some of that judgment without calling her.
The business case is clear. The question worth asking is what changes for her.
If the system gets better because her knowledge travels further, but her role, recognition and workload stay exactly the same, the technology may be ready before the employment bargain is.
Who Benefits From The Knowledge?
One of the strongest workplace studies makes this question concrete. Economists Erik Brynjolfsson, Danielle Li and Lindsey Raymond studied an AI assistant used by 5,172 customer-support agents; the published study found a 15% average increase in issues resolved per hour. The Quarterly Journal of Economics
The average hides the important part: the lowest-skilled group improved by 36%, while the highest-skilled agents had no significant increase in issues resolved per hour and small declines in resolution rates and customer satisfaction. The Quarterly Journal of Economics
The system had been trained on customer conversations, with greater weight given to conversations handled by top performers. The Quarterly Journal of Economics
In other words, the people whose work helped make the assistant useful were not necessarily the people who benefited most from using it. The Quarterly Journal of Economics
That does not prove a chatbot can reproduce a great general manager’s judgment. Customer-support conversations are not store leadership. It does establish a mechanism worth testing: examples of strong performance can help less-experienced people handle situations better.
The authors also raise the incentive question directly: “compensation policies that provide incentives for people to contribute to model training could be important.” The Quarterly Journal of Economics
For a multi-unit operator, that turns “How do we capture her knowledge?” into a second question: “Why would she want to help us keep improving it?”
Capture Decisions, Not Personality
The opportunity isn’t to upload every document the manager has written or record everything she does. It is to identify decisions other managers struggle with and make the reasoning behind good decisions easier to use.
Take an illustrative field question: “Sales are down. Should we cut the schedule?”
A useful answer needs context. Is traffic down, or are fewer visitors buying? Which hours are affected? Are the people on the floor trained? Would cutting coverage make conversion worse?
The valuable knowledge is not simply “keep two people on Saturday.” It is what to check before changing coverage, what conditions would change the answer, and when the decision requires approval.
That gives you a practical capture method. Start with an actual case and ask:
- What did you notice first?
- What explanations did you rule out?
- What information would have changed your decision?
- What did you do, and what happened afterward?
- Where would another manager need to stop and ask for help?
Then have another experienced manager review the reasoning. One vendor guide to capturing expertise recommends asking “why” questions against real cases rather than abstract ones, and validating the result with two or three experts so personal habit is not mistaken for operating logic (Verti). Treat that as vendor advice, but it matches common sense: a successful store’s habit is a candidate for the standard, not proof of it.
AI can help organize those cases, retrieve the relevant example and explain an approved approach. That does not require building a custom model. A small, reviewed case library may be the right starting point. The test is whether another manager makes a better decision—not whether the answer sounds like the original expert.
The Incentive Is Operating Work
Willingness to share cannot be assumed. In an August 2025 survey of 4,000 knowledge workers in four countries, published by technology consultancy Adaptavist, 35% said they were hoarding knowledge for fear of being replaced. It was a self-reported survey, not a study of store managers. Adaptavist
A September 2026 IESE analysis makes the same organizational point: expertise transfer can meet resistance when employees see it as taking away what made them valuable. IESE Insight
Calling that resistance “a bad attitude” would miss the design problem.
If the manager contributes cases during a scheduled working session, someone has to cover her store. If she reviews disputed answers, that is work. If her contribution helps other locations improve, the review should recognize more than her own store’s results.
A sensible arrangement could include protected time, an explicit coaching responsibility, recognition for validated contributions, and an incentive tied to demonstrated use—not to the number of documents produced.
Be equally clear about what gets recorded, who can access it, whether it leaves the company’s approved environment, and whether the project changes anyone’s role. Don’t promise job security you cannot guarantee. Don’t hide the purpose behind a vague request to “help us with AI.”
Where The Pattern Can Reverse
The “AI lifts the bottom” result is not a law. In a Berkeley Haas field experiment with Kenyan entrepreneurs, access to a GPT-4 business assistant produced no detectable average effect on revenue or profit, while low performers did nearly 10% worse and high performers may have benefited by over 15% (Berkeley Haas). The researchers said the outcome depended on which tasks people chose for the AI: stronger firms asked for help with straightforward tasks, while weaker firms asked about difficult ones (Haas Magazine).
Store leadership looks more like open-ended business problems than a scripted support chat. That is why the pilot should start with a narrow, repeatable decision, not a general “ask the best manager” assistant.
The Owner's Case Is Fair
The strongest opposing argument is straightforward: knowledge sharing is already part of a manager’s job.
The company pays for the work, supplies the setting in which experience develops, and carries the consequences when knowledge stays with one person. It should not have to create a separate royalty arrangement for every useful answer. A manager who deliberately remains indispensable by keeping everyone else dependent is not building a healthy business.
There is also a plausible benefit to the expert. If newer managers learn faster, the best manager takes fewer routine calls and has more time for difficult problems and coaching. The support-agent study found evidence that AI assistance facilitated learning among less-experienced workers, which is the mechanism that would make that true. Stanford GSB
All of that is fair. The obligation to share knowledge does not disappear because AI enters the picture.
Nor does every capture project need a new compensation scheme. A modest pilot may need nothing more than scheduled time, clear expectations and honest recognition.
The distinction is between expecting ordinary collaboration and quietly adding an ongoing system-building job. If the business wants the latter, it should define the responsibility and make it workable.
Test One Transfer
Before buying a platform, run a small transfer test:
- Choose one recurring field question and one experienced manager willing to help.
- Review three resolved cases, including one where the normal answer would have been wrong.
- Turn the reasoning into an approved guide. Remove private employee and customer information.
- Let a newer manager use it on fresh cases. An experienced reviewer checks the decisions before action.
- Record answer quality, review time, follow-up calls and errors—not just how quickly a response appeared.
Use AI only if it makes that guide easier to find, understand or apply. If a one-page decision card works just as well, use the card.
Your best manager’s knowledge may be an AI opportunity. The larger opportunity is building a business in which helping other managers succeed is part of how the best manager succeeds too.