Be clear about the outcome you want
Before you add any AI tool, write down the simple outcome you expect: what question should it answer, who will use that answer, and what a usable result looks like. Small teams benefit from a single, well-defined aim rather than a long wish list.
Also note the acceptable error and the cost of getting it wrong. If a suggested action needs human approval, design the flow so people can spot and correct common mistakes without hunting through several systems.
Tidy the source systems that feed the model
- Identify the true source of each data field (who edits it and where the canonical copy lives).
- Make identifiers consistent (customer IDs, invoice numbers) so records link reliably across systems.
- Standardise common formats (dates, addresses, product SKUs) and remove obvious duplicates.
- Record who can access personal data and confirm you have necessary consent to use it with third-party tools.
- Add basic timestamps or version notes so you can trace when data changed.
Validate, monitor and keep humans in the loop
Start with a small, controlled pilot: feed the model a limited dataset and compare outputs against known good answers. Have the team evaluate usefulness for a week or two and capture where it fails.
Put simple monitoring in place (sample checks, error logs, and a rollback plan) and make approval a human step for actions with customer impact. If you want a pragmatic hand to prioritise which systems to tidy and run a pilot, Optira can help.