Delegate tasks, not entire jobs. Use AI automation for repeatable, low-impact steps where errors are easy to detect and a person can review the result. Use AI assistance when a human must interpret context, make a judgment, or communicate a consequential decision. Keep goals, approvals, commitments, sensitive communications, and high-impact decisions human-led.
The practical dividing line is decision authority: an AI system may prepare, rank, draft, or execute within explicit limits, but a named person remains accountable for the outcome.
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Start by decomposing the job
“Should we automate this job?” is usually the wrong first question. Most jobs combine routine data handling, interpretation, coordination, and decisions. Treating the whole job as one unit either wastes useful automation or gives an AI more authority than the situation warrants.
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- the trigger and desired outcome;
- the inputs, including data sources and sensitivity;
- the action the system would take;
- the person who owns the result;
- what could go wrong and how quickly an error would be noticed;
- whether the action can be reversed; and
- where a review or approval must occur.
This activity-level view matches the human-centered approach in the National Institute of Standards and Technology’s AI Use Taxonomy: A Human-Centered Approach, published March 26, 2024. The taxonomy describes 16 AI-use activities and is useful as a prompt for asking what a person is actually trying to accomplish before selecting an autonomy level.
Example: a weekly customer-health report
The apparent “job” is producing a report. Its component activities might be collecting figures from approved trackers, calculating changes, drafting a standard narrative, identifying unusual movements, deciding what deserves escalation, and sending an update to a customer. The first three may be suitable for automation with review. Detecting the cause of an unusual movement and choosing what to tell a customer require assistance or direct human work. Sending the message may require a final named approval.
The four-axis delegation test
Evaluate every candidate activity on four questions: repeatability, impact, error detectability, and time sensitivity. The answers determine whether to automate with review, support a human with AI assistance, or keep the activity fully human-led.
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1. Repeatability
Automation is strongest when the trigger, inputs, steps, and output are stable. Recurring status reports, standard summaries, structured aggregation, dashboard updates, and routine reminders have a defined shape that a workflow can execute repeatedly.
Unique strategy, changing objectives, original thought leadership, and work whose success depends on an evolving understanding of people are poor candidates for unattended execution. An assistant can still help gather material or produce a draft, but the human sets direction and evaluates the result.
2. Impact
Ask what happens if the output is wrong or the action is taken prematurely. A low-risk internal draft can be generated and edited. A budget approval, contractual commitment, customer-facing proposal, employment decision, legal statement, or safety-related action has a materially higher impact and needs human ownership.
Rank #2
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Impact is not limited to money. A misleading explanation can damage trust; an incorrect eligibility decision can affect access to a service; an exposed private record can create security and regulatory consequences. High-impact activities should stop at an explicit approval gate.
3. Error detectability
Automation is safer when a reviewer can quickly compare the result with a reliable source. A total that does not reconcile, a missing field, or a reminder sent to the wrong list may be obvious. A subtle spreadsheet error, a misread customer insight, or a distorted research interpretation may look plausible and survive a casual check.
Do not treat a polished output as evidence of correctness. Define the checks a reviewer will perform, identify the authoritative source, and make exceptions visible rather than allowing the system to silently fill gaps.
4. Time sensitivity
Automation helps when speed matters and a realistic review window remains. If urgency removes any opportunity to inspect the output, keep accuracy-critical work human-led or narrow the system’s authority to reversible preparation. “There was no time to check” is a signal that the workflow needs a different design, not permission to remove accountability.
Assistance and automation are different operating modes
| Dimension | AI assistance | AI automation |
|---|---|---|
| Who leads? | A person sets the goal, evaluates suggestions, edits the work, and makes the decision. | A system executes a defined step or routine action inside specified boundaries. |
| Best fit | Brainstorming, outlining, exploratory analysis, research summaries, prioritization, and judgment-heavy drafts. | Recurring reports, structured aggregation, standard first drafts, routine reminders, and monitored updates. |
| Control | The human can question, revise, reject, or redirect each suggestion. | Control is provided through permissions, rules, monitoring, approval gates, and a stop mechanism. |
| Main risk | A person accepts a plausible but incorrect recommendation without checking it. | The system repeats an error or takes an unintended action at scale or without timely review. |
| Accountability | The operator owns the final interpretation and result. | A named owner remains responsible even though the system performs the step. |
Assistance accelerates preparation while the human remains the operator and editor. Automation is appropriate only when the step is sufficiently specified to run consistently and the organization has decided what the system may and may not do.
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What to automate, what to assist, and what to keep human-led
Automate with human review
- Compile a weekly status report from approved, structured data.
- Aggregate figures across project trackers and flag missing entries.
- Create a standard first draft of a document using a known template.
- Refresh a dashboard when the source data and calculations are defined.
- Send routine reminders after checking a fixed recipient list and schedule.
For each item, require a review that checks source freshness, totals, exceptions, recipients, and the intended action before release.
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Use assistance while humans lead
- Prioritize work against criteria that a manager must interpret.
- Prepare stakeholder updates whose tone and commitments need editing.
- Explore trends and propose hypotheses for a human analyst to test.
- Summarize research, with the reader verifying important claims in the original material.
- Draft a customer proposal that requires human positioning, pricing judgment, and approval.
Here the AI can organize options or expose patterns, but it should not silently choose the organization’s position or make an external commitment.
Keep these activities human-led
- Approve budgets, purchases, contractual commitments, or other binding decisions.
- Set an unclear goal when the stakeholders have not agreed on success.
- Resolve complex trade-offs involving competing values or incomplete information.
- Send legal, reputational, or otherwise consequential external communications.
- Make decisions affecting rights, safety, employment, eligibility, or access.
An assistant may help prepare evidence for these activities, but the decision and communication remain with an accountable person.
How to decide the right autonomy level
- Map the workflow. List each activity from trigger to outcome rather than labeling the whole job “automated.”
- Classify the inputs. Record which systems, files, personal data, and credentials are required. Remove access that is not essential.
- Score the four axes. Note whether the activity is repeatable, its potential impact, how visible errors are, and whether a review window exists.
- Choose an operating mode. Select automation with review for repeatable, lower-impact steps; assistance for interpretation and judgment; or human-led execution when consequences are high or goals are unclear.
- Define the boundary. Specify allowed tools, data, recipients, spending or change limits, and actions that always require approval.
- Design the review. Name the reviewer, list the checks, identify the authoritative data, and state what happens when a check fails.
- Run a limited pilot. Start with a narrow scope, observe normal and exceptional cases, and keep a human in the loop until the workflow demonstrates dependable behavior.
- Monitor and revise. Log outputs, corrections, exceptions, and near misses. Change the workflow when the data, policy, or consequences change.
When an agent can take actions
A chatbot that drafts text has a different risk profile from an agent that can edit records, send messages, change settings, or call external systems. Microsoft’s agent-risk guidance identifies task adherence, human oversight and control, system intelligibility, and disclosure as foundational design pillars.
Use least privilege
Give the agent the minimum tools, data, and permissions needed for the defined task. Read-only access is safer than write access. Separate test and production environments. Limit the records, folders, applications, and recipients the agent can reach, and set transaction or spending caps where applicable.
Require approval for consequential actions
Place an approval gate before irreversible or high-impact actions: publishing, deleting, sending an external commitment, changing a customer record, approving money, or altering access. The approval request should show the proposed action, relevant inputs, and any uncertainty or exception that triggered the gate.
Make actions visible
Show what the agent planned, which tools it called, what data it used, and what it completed. Keep logs that a reviewer can inspect after the event. Visibility lets an operator distinguish a bad instruction from a bad data source and makes incident investigation possible.
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Provide a reliable stop mechanism
An operator must be able to pause or stop execution without depending on the agent to agree. Test the stop path, define who can invoke it, and decide how in-flight actions are handled. A stop control that exists only in documentation is not an operational safeguard.
Disclose AI involvement appropriately
People affected by an automated interaction may need to know that an AI system participated, especially when they are relying on a recommendation or receiving a decision. Disclosure should be accurate about what the system did and which person remains responsible.
Designing an effective human review
“Human in the loop” is meaningful only when the reviewer has time, context, authority, and a practical way to reject or correct the output. A person who merely clicks approve on a long queue is not providing robust oversight.
- Set review triggers: route low-confidence results, missing data, policy exceptions, unusual values, and high-impact outputs to a named reviewer.
- Use source-linked checks: let the reviewer trace each important figure or claim to its authoritative input.
- Separate preparation from approval: the person who benefits from speed should not be the only person able to approve a consequential action when segregation is needed.
- Record decisions: retain the output, reviewer, time, corrections, and reason for approval or rejection.
- Sample routine work: even when every item is not checked, inspect a defined sample and investigate patterns in errors.
Common failure modes and fixes
Automating an ambiguous goal
If stakeholders disagree about what “good” means, an agent will optimize an unstable target. Hold a human decision session first, document the outcome, and automate only the agreed, measurable steps.
Granting broad access for convenience
Wide permissions increase the blast radius of a prompt mistake, compromised credential, or faulty workflow. Start read-only, scope access by function, and add write permissions one action at a time.
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Require calculations, source checks, reconciliations, and a reviewer who understands the subject. For research summaries, inspect the original sources before using a claim in a decision or publication.
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Removing review because the task is repetitive
Repetition improves consistency only when the rule and data remain valid. Keep exception handling and periodic sampling; update the workflow when policies, schemas, or business conditions change.
Allowing silent failure
Configure explicit error states for missing inputs, conflicting records, unavailable tools, and policy violations. A failed run should stop or route to a person, not produce a confident-looking substitute.
Measuring only speed
Track turnaround time alongside correction rates, exceptions, rejected outputs, unauthorized actions, review time, and downstream impact. No universal productivity percentage or accuracy figure applies to every organization; the right evidence comes from the specific workflow and its controls.
A practical delegation worksheet
Before enabling a workflow, complete one row for each activity:
| Field | Question to answer |
|---|---|
| Activity | What exact step is being performed? |
| Outcome | What does a correct result look like? |
| Repeatability | Are the trigger, inputs, and rules stable? |
| Impact | What is the worst plausible consequence? |
| Error check | How will a reviewer detect a wrong result? |
| Reversibility | Can the action be undone, and within what window? |
| Data | Which information and systems are necessary? |
| Permissions | What may the AI read, change, send, or delete? |
| Review gate | Who approves, what do they check, and when? |
| Stop path | How can execution be paused immediately? |
| Owner | Which named person remains accountable? |
If several fields are unknown, the activity is not ready for unattended automation. Use assistance to clarify the workflow or keep the step human-led until the missing decisions are made.
A useful reading example
Working with AI: Real Stories of Human-Machine Collaboration by Thomas H. Davenport and Steven M. Miller examines how people and AI divide work in insurance underwriting, telemedicine, train maintenance, and food preparation. The hardcover was published September 27, 2022, with ISBN 9780262047241. Its central practical idea is consistent with the task-level framework: let AI take selected activities while people concentrate on work that requires broader judgment and responsibility.
The accountability rule
Delegating work to AI does not transfer accountability. The organization still needs a named owner who can explain the goal, data, permissions, review decision, and outcome. Start with a bounded task, preserve visibility and interruption, and expand autonomy only when the evidence from that workflow supports it.
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