What applied AI means in my work

Requests for AI features usually show up as a capability, like summarise these documents or classify these messages. The first thing I do is find the decision or action that would use the output. "Summarise this document" could mean helping someone decide whether to open it, pulling out obligations for a project plan, or briefing someone who hasn't seen the source. A fluent paragraph might be fine for getting your bearings and useless for tracking obligations, where you need dates, who's responsible and a link back to the exact clause.

So I try to describe the work without naming a model. Who gets the input? What do they do with it now? Which part takes the time, where does judgement come in, and what record has to be left behind? If I can't explain the workflow in plain operational terms, adding AI tends to make the confusion harder to see. I also want a concrete list of the sources available at that point, who can access each one and how current it is. A model can't fix a workflow that doesn't have the information it needs, and it'll often give a plausible answer anyway.

That source work sometimes shows ordinary software is enough. If the decision follows stable rules over structured fields, a query or rules engine is easier to test and explain, so I'd compare that with a model on the same cases first. I also split errors by consequence instead of looking at one accuracy number. A wrong category on an internal queue might cause a small delay. A wrong instruction sent to a customer can cause rework or a commitment nobody meant to make. Could it expose private information, move money or change access? That's where review and approval go.

Review only works if the reviewer has the source, enough time and a screen that shows where the system is unsure. If the feature saves two minutes of drafting and adds three minutes of checking, how fast it generates doesn't matter. A model call that looks cheap in a demo adds up across retries, long prompts, repeated retrieval and reviewer time, so I'd cost the whole task, checking and correcting included.

The AI doesn't have to do the whole job to be useful. It might pull out candidate fields for someone to confirm, rank a queue or draft a reply that stays clearly unapproved. With one action like that you can name the input, who receives the output and what happens next, and you get something you can test. "Did it get the invoice date right?" is a lot easier to check than "was the assistant helpful?"

When a request for an AI feature turns up as "summarise these documents", applied AI in my work starts with the decision that output feeds, the sources available at that point and what a wrong answer would cost. Before I pick a model or design a chat screen, I'd build a set of real cases (routine ones, hard ones, incomplete inputs and ones where the right move is to decline) and write down what an acceptable result has in it and what would make it unsafe. Keeping the source and expected outcome beside each output means I can rerun them whenever the prompt, model or retrieval changes. Then I'd check the workflow around it, making sure the user can see the source and correct a result, the correction is stored, and an operator can find failed runs without a retry duplicating the action.