AI and Copilot
Information architecture is the work that decides whether Copilot succeeds
Generative AI answers are only as good as the content behind them. The preparation that determines quality happens before any licence is assigned.
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Article ยท AI and Copilot
Licensing is rarely the constraint. Five things stop deployments in practice, and four of them are decided before any technical work starts.
Organisations rarely stall on Copilot because the technology does not work. They stall because a decision nobody owns has not been made, and the programme quietly waits for it.
Legal, risk and security each hold a partial view of what employees may enter into an AI tool, and none has been asked to write it down. Without a position, every scenario is escalated individually and the programme moves at the speed of the slowest committee. A one-page statement, agreed once, removes weeks of recurring debate.
Copilot surfaces what a user can already open. When nobody is confident what that includes, the safe response is delay. The fix is narrow: review sharing and membership in the specific locations the pilot scenarios touch, and evidence the result. A full-estate review is rarely fundable and rarely necessary to start.
Summarise documents is not a scenario; it is a feature. Produce the first draft of a monthly service report from the case export and last month report is a scenario, because you can tell whether it worked and how long it took before. Programmes that cannot express scenarios at task level cannot measure them either.
Licences allocated to leadership produce enthusiastic anecdotes and no usable evidence. The cohort should be the people whose daily work matches the scenarios being tested, whatever their grade.
Scenario libraries go stale, content decays, and prompts that worked stop working as documents change. Without a named owner and a small standing budget, usage declines from the third month regardless of how well the launch went.
Written by
Nadia leads Avanteria work on Copilot, agents and retrieval. She spends most of her time on the part of generative AI that decides whether it succeeds: the content it is grounded on, the permissions it inherits, the review points around it, and whether people still use it three months after launch.
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