Process Automation Opportunity Finder

Even if volume is low, high automation scores (>70) can be worth automating if: (1) error impact is high, (2) implementation is simple/cheap, or (3) the process will likely grow. Our calculator weights volume at 20%, not 100%, so one strong factor (like "error-critical") can still justify automation. If volume is genuinely low and error impact is zero, deprioritize.

This is why we recommend a pre-automation checklist - document all decision rules and edge cases before automating. Many processes seem rule-based (80% rules, 20% exceptions), but those exceptions can derail automation. Start with a 2-week process mapping exercise to identify all exceptions. This improves automation score accuracy.

Frequently changing processes are poor automation candidates. Rule of thumb: if a process changes more than 25% per year, delay automation until it stabilizes. Use this audit to identify which processes need standardization BEFORE automation.

Our estimates assume successful implementation, team adoption, and no major rework. Actual results vary by ±20%. Many projects deliver more savings because: the team becomes more efficient, additional processes are added, and error reduction is higher than predicted. Review actual vs. projected monthly.

Depends on opportunity cost. If a process scores 35 (low) but costs only $500 and 2 weeks to automate, and you have excess capacity, it's fine. But if you have limited bandwidth, focus on high-score processes first. The priority matrix helps balance: Quick Wins (high score, low effort) are the best use of time.

A data availability score below 5 suggests the process isn't automation-ready yet. First digitize and integrate your systems, clean and validate data, then set up API access. Revisit automation after. This is a prerequisite, not a permanent blocker - many companies must do this anyway for other reasons.

Processes requiring judgment (rule-based score below 6) can be partially automated: automate the clear-cut 70% of cases and route exceptions to humans. This still reduces workload significantly. Some AI approaches (machine learning, NLP) can handle some judgment, but they're more complex and expensive to implement.