A practical guide to AI agents for small teams
Most teams do not need an AI strategy. They need two or three repetitive jobs handled reliably, and a clear account of what happens when the model is wrong.
A practical guide to AI agents for small teams
Start with a task that is high-volume, low-stakes and well-defined: triaging enquiries, drafting first-pass replies, extracting fields from documents. Keep a person on the outcome, not on the keystrokes. Measure the time saved against the time spent correcting it, because that ratio is the whole business case.
Key takeaways.
- Pick the boring job first The unglamorous tasks are the ones with a measurable baseline.
- Design for being wrong An agent without a review step is an agent nobody can trust twice.
- Measure correction time Time saved minus time spent fixing it is the only number that matters.
- Keep the data question separate What the agent may read is a governance decision, not a technical one.
Sample content, created for layout review. Replace or remove it before this site goes live.
Where to start
The best first candidate is a task somebody already does the same way every time, where a mistake is visible and cheap. Enquiry triage fits. Contract interpretation does not.
Write down how long the task takes today before anything is automated. Without that number, the project can only be argued about.
What a good implementation looks like
A narrow scope, an explicit prompt held in version control, a review step where a person accepts or corrects the output, and a log of both. The log is what turns the next iteration into engineering rather than guesswork.
What usually goes wrong
Scope creeps from one task to a department. The review step is dropped once it seems to be working. Nobody owns the prompt, so it drifts. All three are organisational failures rather than technical ones, which is why they are worth naming up front.
Sources
- Sample source, replace before publishing Placeholder citation, present so the sources block renders.
Turning this into a decision?
Bring us the situation behind the question. A discovery conversation is where the general answer becomes a specific one.