Skip to content
All guides

AI workflow automation

AI workflow automation uses models for tasks such as interpreting a request or drafting a response, while software controls the process and people retain the decisions that need judgment.

Discuss a workflow

From a customer request to a recorded outcome.

Consider an operations team receiving free-text requests. Today, someone reads each message, finds the relevant policy, drafts a response, and updates a record by hand.

  1. Receive and classify

    Validate the request and identify missing information. Use rules for structured fields; consider AI for varied language or document interpretation.

  2. Retrieve and prepare

    Find permitted, current information and draft a response or proposed update. Keep the evidence beside the recommendation.

  3. Review the action

    Show an authorized reviewer the recipient, exact change, and supporting information. Changed details require a fresh decision where approval is required.

  4. Execute and confirm

    Apply the approved update, verify the destination state, and record the outcome. Route failures to a visible exception queue.

Keep a route out of the happy path

Missing data, ambiguous requests, restricted records, declined approvals, and failed writes need a named owner and a way to continue manually.

Illustrative example. Not a client project or measured result.

Design the exception before the automation.

This sample review sheet tests whether the workflow can reach a correct outcome, including when it should stop. It is a starting point for a discussion with the people who handle the work.

Design the exception before the automation.
SituationExpected behaviorEvidence to check
Request is incompleteAsk for the missing information; do not guess required fields.The request remains open and the missing fields are visible.
Two policies conflictStop the proposed action and ask the policy owner.Both sources and the unresolved question reach the reviewer.
Approval is declinedRetain the request and decision; make no external change.The destination record and outgoing communications are unchanged.
Connection times outCheck whether the action completed before retrying.No duplicate record; an unresolved outcome remains visible.

Rules, AI assistance, or an agent?

  • Use rules for stable steps

    If the input and decision can be expressed reliably as explicit conditions, ordinary automation is often simpler to test and operate.

  • Add AI for interpretation

    Use a model where language or documents vary: classification, extraction, retrieval, or drafting. Validate its output before a later step relies on it.

  • Consider an agent for variable paths

    Let a model choose tools or steps only when the task needs that flexibility. Bound its authority, time, cost, and stopping conditions.

Choose the least complex approach that meets the job. Adding an agent can add delay, operating cost, and new failure paths; it needs to earn that complexity against a simpler baseline.

Scope one workflow you can evaluate.

AI & systems integration
  1. Map its trigger, inputs, owner, destination, and exceptions. Observe the manual process before changing it.
  2. Count eligible cases and measure handling, review, correction, and recovery time. Include provider, integration, and support costs.
  3. Define read and write permissions, consequential approval points, duplicate protection, and a manual fallback.
  4. Start with a bounded release and representative cases. Compare completed, correct outcomes with the original process before expanding.
Sources and further reading

Which handoff is worth changing first?

Discuss a workflow