Start from the task, not the technology
AI automation creates value when it replaces a specific, repeatable manual task — not when it's added as a feature because the technology is available. The starting point should always be: what task currently takes staff time that follows a consistent enough pattern to automate reliably?
Good candidates share a pattern
The tasks that automate well tend to be repetitive, rule-based or pattern-based, and have a clear definition of correct output — data entry from structured sources, routing and triage, drafting first-pass responses, reconciling records across systems. Tasks that require significant judgment or handle high-stakes decisions need a different approach.
Human review isn't a limitation — it's the design
For anything with real consequences if it goes wrong, keeping a human review step isn't a compromise on the automation — it's the right architecture. The value of automation in those cases is doing the first 90% of the work reliably and fast, not removing the person entirely.
Integration matters more than the AI model
Automation is only as useful as its connection to the systems it needs to read from and write to. A well-integrated automation using a simpler model consistently outperforms a more advanced model that can't actually reach the data or systems it needs to act on.