Design the human handoffs before the pilot starts: decide where AI work is reviewed, who handles exceptions, who can approve or override an action, and who can stop the pilot. A pilot with no defined handoffs is hard to measure, because you cannot tell whether time was saved or simply moved to someone else's desk.
What is a handoff in an AI pilot?
In this article, a handoff is any point where work passes between AI and a person, between two people, or between the AI and a business system. Examples include a person reviewing a draft before it is sent, an exception moving to a manager, or a completed case being written back to your CRM or accounting software. Each one needs a named owner and a clear rule for when it happens.
Which handoffs should you map before the pilot starts?
- Intake: what triggers the workflow, and which cases are in or out of scope.
- Review: which outputs a person checks before anything reaches a customer or a record.
- Exceptions: what the AI must pass to a person instead of attempting, and how fast that person must respond.
- Approval: which actions, such as refunds, price changes, or commitments to a customer, always need a human decision.
- Stop and rollback: who can pause the pilot and how the work returns to the previous process.
- Record keeping: where the final outcome is written so reporting is not rebuilt by hand.
The NIST AI Risk Management Framework Playbook is a useful reference for this step. It lists suggested actions under Govern, Map, Measure and Manage. Among them: define and differentiate roles and responsibilities for human-AI configurations and oversight (Govern 3.2), document how system output may be used and overseen by humans (Map 2.2), and define, assess and document processes for human oversight (Map 3.5). NIST states that the Playbook is voluntary, is not a checklist to be followed in its entirety, and will be updated after the AI RMF is revised. It is a reference point, not a certification or a guarantee of results.
What should the handoff record for each case contain?
If a person receives a case from an AI workflow, they should not have to reconstruct what happened. A short, consistent record keeps review fast and makes the pilot measurable. Consider capturing:
- The source record or message the case started from.
- What the AI proposed or did.
- Why the case was passed to a person, for example a rule it matched or a missing field.
- The named person responsible and the response deadline.
- The final decision and whether the person changed the AI's output.
- The time the case spent waiting for a person.
How do handoffs change what you measure?
Handoffs are where hidden cost lives. A pilot that drafts faster but sends more work to review can look successful on one metric and fail on the whole process. NIST's Playbook suggests measuring performance under conditions similar to the real deployment setting (Measure 2.3) and examining the costs, including non-monetary costs, of AI errors (Map 3.2). It also suggests considering viable non-AI alternatives when weighing resources (Manage 2.1), which is a fair question for any pilot.
- Share of cases completed with only a light review.
- Share of cases escalated to a person.
- Review and correction time per case, recorded against your pre-pilot baseline.
- Time cases wait in the handoff queue.
- Cases reopened or corrected after they were marked complete.
- Time from intake to completed outcome, compared with the old process.
- Owner or manager hours spent on oversight each week.
What does this look like in a hypothetical example?
The following is a hypothetical illustration with invented numbers. It is not an Omni Workforce customer result and not a prediction. Suppose a service business receives 200 inbound requests a month, and staff spend an average of 6 minutes routing each one. The baseline is 200 x 6 minutes = 1,200 minutes, or 20 hours.
Now suppose the pilot routes 140 requests with a 2-minute human check each, and passes 60 exceptions to a person who spends 10 minutes on each. That is 280 + 600 = 880 minutes, or about 14.7 hours. The apparent saving is about 5.3 hours a month, before setup, software, maintenance and oversight time are subtracted. If those exceptions also waited a full day for a reply, response time to the customer could get worse even as staff hours fall. The handoff design, not only the routing step, decides whether this pilot is a good trade.
What is the decision checklist before launch?
- Can you describe the workflow boundary in one sentence, including what is excluded?
- Has someone recorded the baseline for volume, handling time, errors, and response time?
- Is every handoff assigned to a named person, not a team or a shared inbox?
- Is the response time for exceptions written down?
- Is there a list of actions the AI may never take without approval?
- Can a person override or reverse an AI action, and is the override recorded?
- Does the final outcome land in the system where you already report results?
- Who can pause the pilot, and what conditions trigger that?
- What non-AI fix, such as a clearer form or a simpler process, would you compare against?
When should you pause or stop the pilot?
Decide this in advance, not after a bad week. NIST's Playbook suggests having mechanisms in place, with assigned and understood responsibilities, to supersede, disengage or deactivate an AI system that shows performance or outcomes inconsistent with its intended use (Manage 4.1 and Manage 2.4 address monitoring, override, and deactivation). Write down your own thresholds, such as an error rate you will not accept or a maximum exception wait time, and who makes the call.
Where can a consultation help?
The right answer for a workflow might be a process change, an integration, an automation, an AI agent, or no AI at all. A consultation is a way to test that choice against your own workflow and measures. Any capability for your specific systems would need to be confirmed for that case, not assumed from an article.
Sources: NIST AI RMF Playbook, https://airc.nist.gov/airmf-resources/playbook/, and NIST AI Risk Management Framework overview, https://www.nist.gov/itl/ai-risk-management-framework, both read October 18, 2026. The NIST material is cited for its published suggested actions, not as an endorsement of Omni Workforce or of any approach described here.
Book a consultation with Omni Workforce to map the handoffs in one workflow, define the baseline, and decide what a measurable pilot would need to prove.