AI and workflow automation
Human in the loop automation
Human in the loop automation deliberately keeps people at certain points in an automated process: approving actions before they happen, reviewing results the system is unsure about, or making the final call on decisions that matter. It combines the speed of automation with human judgement where judgement counts.
This page explains where to place people in an automated workflow, how to make their involvement meaningful rather than a rubber stamp, and how their corrections improve the system over time.
Illustrative diagram
- Input arrives
- AI suggests
- Route by rulesuncertainty, value, type
- Routine proceeds
- Person reviews the rest
- Corrections recorded
Where people belong in an automated workflow
| Point | Example | Why a person |
|---|---|---|
| Before external action | Approving an AI-drafted reply before it is sent | Tone, accuracy and accountability |
| On uncertain results | Reviewing documents the AI could not read confidently | Avoids guesses entering records |
| On high-value items | Confirming large refunds or orders | Limits the cost of errors |
| On decisions about people | Shortlisting, credit or eligibility | Legal safeguards and fairness |
| On samples | Spot-checking a share of automatic results | Detects drift and hidden errors |
Make involvement meaningful
A person who approves hundreds of AI suggestions an hour, with no time or information to question them, is not really in the loop. Meaningful involvement means the reviewer sees the source information, understands why the item reached them, has time to judge it and can change the outcome. The workflow should make disagreeing as easy as agreeing.
UK data protection law addresses this directly for significant decisions about individuals. The Data (Use and Access) Act 2025 replaced the previous Article 22 rules with new Articles 22A to 22D, in force from 5 February 2026, which apply where there is no meaningful human involvement in such a decision and require safeguards including the ability to contest it.
Designing a good review step
Information
- Original input shown beside the suggestion
- Reason the item was flagged
- Relevant history or context
Action
- Accept, edit or reject in one place
- Ability to escalate to a colleague
- No pressure from unrealistic queue targets
Learning
- Corrections recorded
- Patterns reviewed regularly
- Rules or prompts updated and re-tested
Setting thresholds
Start cautious
Send more items to review at first; reduce as confidence grows with evidence.
Use more than confidence scores
Value, customer type and validation results can all route items to people.
Review the thresholds
Check periodically whether the right items are reaching people.
Corrections make the system better
Every time a reviewer corrects an AI suggestion, the workflow learns something about where it struggles. Recording corrections, reviewing them regularly and adding difficult examples to the test set lets prompts and rules be improved and re-tested. Over time, fewer items may need review, but only once the evidence shows it is safe.
Plan the reviewers' workload
Review takes time, and that time must be planned. If the review queue grows faster than people can handle it, either items wait too long or reviewers start approving without checking. Estimating review volume during a pilot, and adjusting thresholds or staffing accordingly, keeps human involvement real.
Guarding against automatic agreement
People who review AI suggestions all day can start agreeing by default, especially when the suggestions are usually right. This tendency is natural, and it is why review design matters. Showing the source first, occasionally including items where the correct answer is known, varying the order of information and keeping queues manageable all help reviewers stay attentive. Measuring how often reviewers change suggestions also shows whether review is still active.
If reviewers almost never change anything, either the AI is very reliable for those items, or review has become a formality; the evidence should show which.
Telling customers when it matters
Where automated processing affects customers, such as eligibility checks or prioritising their requests, consider explaining how decisions are made and how they can ask a person to look again. Clear information builds trust and, for significant decisions, supports the legal safeguards that apply.
Questions about human in the loop automation
What is human in the loop automation?
Automation designed with people at specific points to approve, review or decide.
Where should people be involved?
Before external actions, on uncertain or high-value items, on decisions about people and on samples.
What makes human review meaningful?
The reviewer has the information, time and ability to change the outcome.
Is human review legally required?
For significant decisions about individuals, UK law sets specific rules. Take advice for your case.
Can review be reduced over time?
Yes, if evidence from corrections and samples shows it is safe.
What happens to reviewers' corrections?
They are recorded and used to improve prompts, rules and the test set.
Does review slow things down?
Somewhat, but only for the items that need it; the rest flow automatically.
How do we stop reviewers simply approving everything?
Show the source first, keep queues manageable and track how often suggestions are changed.
Sources referenced
Platform features, guidance and rules change. We check the current versions before committing to an approach. This page is general information, not legal advice.
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