Automate it, assist it, or keep it human?

Automate bounded, checkable steps

Good candidates have reliable inputs, explicit rules, detectable failures and a safe recovery path. Examples include routing a request, extracting a field for validation or creating a draft record.

Define the conditions under which a step may proceed. Put uncertain or out-of-policy cases into an exception queue. Start with a narrow permission set and record what happened.

Assist work that needs judgment

Drafting proposals, comparing contracts and preparing management analysis often benefit from AI without removing human review. The agent gathers and prepares; a responsible person checks the evidence and decides.

Make the review practical: highlight assumptions, show source links and separate confirmed facts from suggestions. A long unstructured answer can shift work to the reviewer instead of reducing it.

Keep authority with the right person

Irreversible, sensitive or consequential actions need controls appropriate to their impact. Payments, contractual commitments and decisions affecting people should follow the organization’s authorized process.

AI can prepare materials without receiving permission to execute the final action. A capable model is not a substitute for clear accountability.

Use a short decision checklist

• Can we state what a correct result looks like?
• Do we have current, permitted sources?
• Can the system detect an uncertain or invalid case?
• What happens if it is wrong?
• Who can approve, correct or stop the process?

Missing answers are design work to resolve before expanding autonomy.

Increase autonomy with evidence

Run a pilot with human review. Measure correct completions, time spent reviewing, exceptions and operating cost. Expand the allowed actions only when the evidence and process owner support it.

Our packages start with one task or one workflow. The scope should match the problem and the quality of the available information.