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Putting language models to work inside an organisation: deciding whether a process needs an agent, choosing and measuring internal copilot pilots, grounding answers in company documents with permission-aware RAG, and adding an LLM to legacy systems through a clear architecture and a staged rollout.
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Choose internal AI copilot use cases by four properties of the workflow: maintained sources with owners, permissions you can enforce, an error cost you can absorb or catch, and a natural human review before the output takes effect.
Judge an internal AI copilot pilot by comparing accepted outputs and total cost against a baseline measured before launch. Usage counts do not prove value, and no return should be promised before the pilot has measured one.
Adding an LLM to a legacy application is mostly a boundary problem: where the model runs, whose identity it uses, which tools it may call and how changes reach your records. This reference architecture makes each boundary explicit.
Add an LLM to a legacy system in controlled stages: inventory touchpoints and data owners, pilot a read-only feature, run it in shadow mode, widen a routed rollout with rollback ready, and add write-back last.
Enterprise RAG searches your own documents when a question arrives and gives the most relevant passages to a language model as context. Built well, it checks permissions before the model sees anything, cites what it used and declines when nothing it may use supports an answer.
Most business processes do not need a fully autonomous AI agent. This playbook helps CTOs decide when model-directed autonomy is justified, how to bound it with tool permissions and human approval, and which outcomes to measure before scaling.
