Score by impact
TODO: Replace with the scoring model and example impact metrics.
Clarify how to quantify speed, quality, and revenue impact.
- Estimate hours saved, error reduction, and speed gains. If you cannot quantify, keep it small.
- Look for compound value: tasks that unblock downstream work or improve data quality.
- Track customer-facing impact so automation supports experience, not just efficiency.
Score by effort
TODO: Replace with a breakdown of effort assumptions and tool dependencies.
Explain how to avoid brittle automations early in the roadmap.
- Consider data availability, edge cases, and required approvals. Avoid brittle automations first.
- Prefer changes inside existing tools to reduce adoption friction.
- Factor in training time and change management for frontline teams.
Pick the first three
TODO: Replace with how to sequence quick wins, medium lifts, and foundational fixes.
Add guidance on creating a repeatable backlog review cadence.
- Choose one quick win, one medium lift, and one foundational fix (like clean data inputs).
- Ship them in order and reassess your backlog as you learn.
- Record wins and outcomes to keep leadership aligned on ROI.