AI Workflow Automation Checklist

Use this checklist with the people who do the work, the process owner, and the teams responsible for data, security, and operations. Work through one workflow at a time. The aim is to improve an outcome people care about, with controls that match the consequences of an error.

Mark each item, write down evidence, and assign an owner to anything unresolved. A No or Not sure is a discovery task; it is not a reason to buy a tool or begin a production pilot.

Quick fit check

• We can name the process owner and describe who benefits from a better outcome.
• We know where the workflow starts, what “done” means, and which steps are in scope.
• The process is stable enough to map, including common exceptions and handoffs.
• We know whether the work follows explicit rules or needs interpretation of unstructured information.
• Required data can be used for this purpose and accessed from a reliable source.
• We can compare a pilot with a current baseline and define what success and failure look like.
• A person can catch a consequential error, stop the automation, and use a fallback process.

If any answer is No or Not sure, start with process discovery, data cleanup, or a smaller assistive step. Do not automate an unclear workflow end to end.

1. Map the work as it happens

• Name the workflow, its trigger, its final outcome, and the customer or team that depends on it.
• Set a clear boundary: what is included in this candidate and what is out of scope?
• Name the process owner and the people who perform, receive, review, or support the work.
• Map the normal path from trigger to completion, including systems, decisions, handoffs, and sources of truth.
• Check a representative sample of real cases or event records; note how often the actual path differs from the documented path.
• Record volume, frequency, seasonality, wait time, hands-on time, rework, and common delays.
• List exceptions: missing or conflicting information, policy questions, unusual requests, duplicates, and cases that need judgment.
• Identify undocumented knowledge or workarounds that currently live with individual staff.
• Decide whether to improve or standardize any step before automating it.

Use this small map to make gaps visible:

2. Choose the smallest suitable approach

Use the simplest approach that meets the need. A workflow can combine these patterns, but keep interpretation separate from the systems that enforce business rules and make changes.

• We can explain why the candidate needs AI instead of ordinary rules or process improvement.
• We have identified which steps interpret ambiguous information and which steps apply known business rules.
• Any AI output that leads to a system change passes through the system’s normal validation and permission controls.
• We have not given an agent broad access just because it may need one narrow action.
• The proposed first step is small enough to test and easy to reverse or review.

Prefer rules for predictable work. Consider AI when interpretation of text, images, or other variable inputs is part of the task. Consider a multi-step agent only when its tools, allowed actions, stopping point, and human escalation path can all be bounded.

3. Establish a baseline and intended value

Use a defined time period and representative cases. If reliable metrics do not exist, collect a small manual sample before the pilot and record how it was collected.

• Record the baseline period, data source, case count, and any known gaps.
• Measure current cycle time and hands-on time separately where possible.
• Record volume, completion rate, first-pass quality, errors, rework, escalations, and missed service targets where relevant.
• Estimate the time people spend reviewing, correcting, and recovering from exceptions.
• Describe the expected benefit in terms of quality, turnaround, capacity, customer or employee experience, or risk reduction.
• Include integration, model or platform, human review, support, and error-recovery costs in the value estimate.
• Agree which measures must improve and which must not get worse.
• Set acceptable error, escalation, and review-load limits before seeing pilot results.

Do not rely on “time saved” alone. A faster workflow that creates more rework, more review, or worse outcomes has not improved the process.

4. Set data, permission, and review boundaries

• List every data source the workflow needs and identify the authoritative source for each field.
• Check that data is sufficiently accurate, current, and complete for the proposed use.
• Classify sensitive or restricted information and confirm where it may be processed and retained.
• Ask the relevant privacy, security, procurement, or compliance owner to resolve requirements that apply to this use case.
• Grant only the minimum data access and actions required; separate read access from write access where possible.
• Record what the system may read, prepare, recommend, change, or send.
• Require a named person to approve actions that are consequential, external, difficult to reverse, or outside agreed limits.
• Decide how uncertain, conflicting, incomplete, or out-of-scope cases reach a qualified person.
• Give reviewers enough context to check a result and a clear way to correct, reject, or stop it.
• Define a manual fallback for service outages, denied permissions, and cases the system cannot handle.
• Decide what evidence must be logged to investigate a result, while avoiding unnecessary retention of sensitive inputs.

For each action, complete this boundary table:

5. Design and evaluate a bounded pilot

• Limit the pilot to a named team, workflow slice, case type, or other clearly defined boundary.
• Decide what stays manual and what the pilot will automate or assist.
• Build a test set from representative normal cases and known difficult cases before rollout.
• Include missing, stale, contradictory, unexpected, and out-of-scope inputs in evaluation.
• Test failure paths such as timeouts, unavailable systems, permission denials, duplicate triggers, and changed fields or schemas.
• Have a qualified reviewer compare outputs with expected results and document errors by type and consequence.
• Measure quality, cycle time, completion, rework, cost, escalations, and reviewer effort against the baseline.
• Set pass, revise, and stop criteria in advance; include who can approve expansion.
• Gather feedback from the people doing and receiving the work, including whether the review step is practical.
• Choose a pilot period or case count that exposes normal variation and meaningful exceptions.

Avoid a single “accuracy” number. Track errors that have different consequences separately, and count the human effort needed to find and fix them.

6. Prepare for day-to-day operation

• Name a business owner, a technical owner, a reviewer or escalation owner, and an operations contact.
• Record the automation, its purpose, connected systems, data categories, allowed actions, and current versions or configurations in the team’s approved inventory.
• Keep a traceable record of triggers, relevant input references, system or model version, decisions or outputs, actions, reviewer changes, and failures.
• Monitor trigger failures, schema or API changes, output quality, exception rates, review backlog, and running cost.
• Assign someone to review alerts and define how staff report a suspected error.
• Decide who can pause the workflow and how that person is reached during operating hours.
• Test that queued work returns to the manual path when the automation is paused or unavailable.
• Document how to undo or reconcile a bad write, prevent duplicate actions, and notify affected people when needed.
• Set a review date and revisit the workflow after material changes to policy, inputs, integrations, or model configuration.

Pause the pilot and route work back to the existing process if it takes an unauthorized action, creates duplicate or harmful changes, exceeds the agreed quality or review limits, loses required data or permissions, or cannot be safely monitored and recovered.

Decision record

Complete this with the process owner before committing to a pilot.

When to pause

Do more discovery before automating if no one owns the process, staff cannot agree on what “done” means, the workflow changes unpredictably, the needed data cannot be used reliably, or there is no workable way to catch and correct a harmful error. A clearer process, a deterministic rule, or an AI-assisted draft may be a better first step than autonomous execution.

Your next step

Review the decision record with the process owner and the people responsible for data, security, and operations. Resolve the open items, choose the smallest useful pilot, and schedule the review that will decide whether to revise, expand, or stop.