From RPA to Governed AI Automation

Map a service process with its actual inputs, decisions and handoffs.

Evidence: A map identifies bottlenecks and missing ownership.

Score steps for rules, APIs, screen automation or AI assistance.

Evidence: Each choice has a reason and a manual fallback.

Estimate cycle time, error rate and reviewer effort from sample cases.

Evidence: A baseline distinguishes measured data from assumptions.

Extract structured fields from synthetic documents with validation.

Evidence: Missing or uncertain fields go to review rather than being guessed.

Demonstrate a controlled mock interface flow and detect changed selectors.

Evidence: A changed page fails safely without unintended submissions.

Separate policy rules from model-generated suggestions.

Evidence: Policy violations remain blocked even if the model suggests an action.

Design assignment, escalation and approval capture.

Evidence: Reviewers see evidence and can reject or request revision.

Record who approved each consequential action.

Evidence: An audit reconstructs the exact inputs and approval decision.

Handle duplicate files, unavailable services and ambiguous instructions.

Evidence: Each fault has a tested recovery or escalation route.

Compare automation with the baseline on a held-out case set.

Evidence: Results include rework and reviewer time rather than only model speed.

Write training, communication and operational ownership plans.

Evidence: A plan identifies affected roles and a feedback mechanism.

Present evidence for expand, revise or stop.

Evidence: The recommendation follows predeclared success and stop criteria.