“Have a human review it” is not a complete process
What should the person review? Every input, output, decision, action, or only exceptions? If employees completely redo the AI work, the business may have added technology without improving the process. The goal is to put human judgment where it adds value and where the consequences of getting something wrong justify it.
Start with what the AI is doing
AI may summarize, draft, extract, categorize, search, compare, identify exceptions, recommend actions, create tasks, update records, send communications, or trigger workflows. Those roles do not carry the same risk.
Think in levels of AI involvement
Assist: drafting, summarizing, searching approved information. Extract/Structure: turning information into usable fields or categories. Recommend: suggesting a response, category, priority, or action. Reversible Action: creating a draft task, tag, queue item, or low-risk internal update. Consequential Action: affecting customers, money, employees, sensitive information, contracts, or significant business decisions.
Risk should drive review
Ask: If this output is wrong and nobody notices, what happens next? The same reliability level can justify very different controls depending on consequence.
Consider reversibility and downstream effects
An internal tag can be changed. Confidential information sent to the wrong person cannot simply be undone. If an AI output feeds another automation, report, invoice, customer record, or decision, a small error can multiply downstream. Review may belong earlier in the chain.
Give human review a specific purpose
Do not say “double-check the AI.” Define what the reviewer validates: extracted amount, customer match, required information, classification, tone and facts, policy compliance, sensitive information, or another clear criterion.
Avoid rubber-stamp review
Hundreds of routine approvals can become automatic clicking. A human approval button does not automatically mean human judgment occurred. If review matters, give the reviewer source information, context, meaningful indicators, available actions, and escalation paths.
Use exception review and sampling where appropriate
For high-volume, lower-risk work, the business may review uncertain items, missing information, conflicting data, sensitive categories, or samples rather than every normal output. Sampling can help detect changing quality over time.
Treat corrections as process data
If employees repeatedly change the same AI category or output, track it. The cause may be weak instructions, missing context, unclear categories, outdated documentation, poor source data, or the underlying process.
Do not use human review to permanently compensate for bad inputs
If reviewers constantly correct the same field, fix the source. If AI uses outdated procedures, fix documentation governance. If reviewers always override the same rule, investigate the rule.
Control what AI can know and do
Access design determines what information AI can reach. Permissions determine whether it can read, draft, create, edit, delete, send, approve, change records, assign work, or trigger other systems. Greater authority deserves more careful control.
Separate drafting from sending when appropriate
AI can prepare an external communication while a person validates facts, tone, commitments, and recipient before sending. The business can expand autonomy later based on evidence rather than granting maximum authority on day one.
Review requirements can evolve
New workflows may begin with high review. After enough evidence, some low-risk outputs may move to exception review. Testing may also reveal categories that need stronger controls.
Measure the whole process
Track correction rate, error types, escalation, review time, rejected recommendations, false positives, and errors that escape review. If AI reduces a process from 20 hours to 8 but AI review and correction quietly grows to 8 hours, the business needs to see that too.
Put people where judgment matters
A sophisticated AI workflow is not necessarily one where AI does the most. It is one where responsibility is intentionally placed. Human review should be a control with a purpose - not a checkbox added because AI was involved.
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