Automation is useful when a repeated workflow has enough volume, clear rules and a measurable outcome. It is not a cure for an unclear offer, broken process or unreliable data.
1. The same task repeats frequently
The workflow happens often enough to document and follows a recognisable sequence. If every case requires different judgement, automation may add complexity rather than remove it.
2. The problem is measurable
You can record the current response time, admin hours, missed calls, no-shows, quote acceptance or another relevant baseline. Without a baseline, a vendor can claim success from activity alone.
3. The rules and exceptions are clear
The business knows what the system may say or do, when it must stop and who owns escalation. Safety, health, legal, financial and sensitive customer matters need stronger human control.
4. The source data is reliable
Calendars, customer records, stock, services and prices are accurate enough to use. Automation amplifies bad data just as quickly as good data.
5. A person owns the workflow
Someone will review errors, messages, exceptions and the commercial result. “Set and forget” is not a responsible operating model.
Run a narrow trial
Choose one workflow, define the success and stop conditions, test with realistic edge cases and compare the same measures after launch. Expand only when the evidence supports it.
Use the AI readiness calculator, review AI automation Adelaide, or discuss the smallest useful test.