Automation Readiness Checklist
Forge RPA — ~10 minutes. Mark R / P / N per row. Qualitative only — no scores or ROI. Save via Print → Save as PDF.
R = Ready (documented, trusted day-to-day) |
P = Partial (exists but inconsistent) |
N = Not ready (missing or blockers known)
VP/Director IT, Finance Ops, or transformation lead — patterns matter more than perfection.
Book a discovery call
Bring this checklist to orient the conversation.
Book a Discovery Call
https://forgerpa.com/book
1. Governance & ownership
| # | Statement | R/P/N | Note |
|---|
| 1 | Executive sponsor named for automation investments | | |
| 2 | Clear owner for cross-system process outcomes (not only IT tickets) | | |
| 3 | Documented change control for finance/ops-impacting workflows | | |
2. Process clarity
| # | Statement | R/P/N | Note |
|---|
| 4 | Top manual workflows named (not only generic efficiency goals) | | |
| 5 | Happy-path steps written or demo-able for at least one workflow | | |
| 6 | Exception paths acknowledged honestly | | |
3. Data & access
| # | Statement | R/P/N | Note |
|---|
| 7 | Source systems identified for the workflows you care about | | |
| 8 | Path to read data exists beyond one-off manual exports only | | |
| 9 | Security / privacy constraints understood for those datasets | | |
4. Tooling & integration
| # | Statement | R/P/N | Note |
|---|
| 10 | Inventory of core systems (ERP, CRM, ITSM, banking) is current | | |
| 11 | Sandbox or non-prod available for validation | | |
| 12 | Identity model understood (SSO, service accounts, least-privilege intent) | | |
5. Operations mindset
| # | Statement | R/P/N | Note |
|---|
| 13 | Team can tolerate pilot → refine (not only big-bang) | | |
| 14 | Runbooks or on-call expectations exist for production-adjacent automation | | |
| 15 | Definition of evidence exists for audits / controls where relevant | | |
Quick read (non-prescriptive): Mostly R → strong candidate for structured discovery. Mix of R/P → common; tighten docs and access for 1–2 workflows. Several N on governance or data → shore up foundations before scaling spend.