How do you turn an AI pilot into everyday work?
Give the workflow an owner, teach the actual review and exception steps, and measure whether people get useful work done. Tool access and a successful demo are the start of the rollout.
Change the work, then support the people doing it.
| Decision | Make it concrete |
|---|---|
| Choose the job | Name the recurring case, current process, baseline and outcome. |
| Assign ownership | Name who accepts the result, handles exceptions and maintains the system. |
| Change the workflow | Specify what the agent prepares, what people check and who may act. |
| Practice by role | Use real representative cases, including missing data, errors and denied access. |
| Support ordinary work | Give operators a short runbook, an escalation contact and a fallback. |
| Review and expand | Compare quality, review effort, accepted volume and the business result. |
The business owner should help choose the acceptance criteria. Operators should test the cases. The technical maintainer should observe failures. Training cannot repair an inaccessible data source or an unreliable workflow; capture those problems as delivery work.
Follow a case from eligible to useful.
Count eligible cases, cases where the new process was used, results accepted after review, and results that led to useful action. Record why cases were skipped, corrected or escalated. This distinguishes access, use and actual value.
Compare time per accepted case, errors, review effort, cost and the outcome that justified the pilot. More activity can mean adoption, but it can also mean retries or extra work. Keep the definitions and case mix consistent with the original baseline.
If AI makes previously deferred work possible, measure useful findings and actions rather than claiming labor savings for work nobody was doing. See how to prioritize that opportunity.
A weekly account review needs a new operating routine.
Suppose AI prepares a draft of customer risks from approved account records. The account owner checks the evidence, resolves missing or conflicting facts, and chooses the follow-up. A technical maintainer owns broken connections and failed runs.
Training covers accepting a sound finding, rejecting a weak one and escalating missing access. The rollout review asks whether useful risks were found, whether they led to action, how much correction was needed and what it cost. This is a hypothetical workflow, not a claimed client result.
Reuse the foundation; test the next team's job.
A second team may reuse the model gateway, permissions system and evaluation tooling while needing different source data, review rules and success measures. Carry over the infrastructure contract, then validate the new workflow with its own owner.
Plan the shared infrastructure, assign implementation ownership, and make authority and approvals explicit.
Xero’s public AI Enablement hiring brief asks for repeatable workflow adoption, coaching and outcome baselines. That is an explicit example of this need, not proof of a market-wide trend or a client relationship.
If people are using unapproved AI to finish the job, build an approved route that works for them, with usable tools, clear data rules and an exception owner.