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Choose a task, not an entire job. The best first candidates are bounded, repeatable steps with clear inputs, checkable outputs, limited downside if wrong, and enough time for a qualified person to review the result. Keep a named human accountable for approval and use. Leave high-consequence, highly ambiguous, difficult-to-verify, or impossible-to-review work human-led.
This guide gives you a repeatable screen, examples, a human-ownership model, an AI-versus-rules decision, and a pilot plan that counts review, corrections, exceptions, privacy, and worker impact—not just the speed of the first draft.
Start with the task, not the job title
A job title bundles dozens of activities with different risks and patterns. “Accountant,” “support manager,” or “recruiter” is too broad a unit for an automation decision. Instead, isolate one step or a class of similar cases:
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- Input: what information arrives, in what format, and from whom?
- Process: what transformations, judgments, or lookups occur?
- Output: what exact artifact or action is expected?
- Decision points: where does someone choose, approve, reject, or escalate?
- Exceptions: which cases do not follow the normal path?
- Dependents: who will act on the result, and what happens next?
For example, “prepare the weekly operations update from approved metrics and notes” is a testable task. “Run operations” is not. Separating the work this way also prevents exposure statistics from being mistaken for predictions about whole occupations. The International Labour Organization explains that occupations contain a mix of tasks: few are wholly automatable with current generative AI, and nearly all retain activities requiring human input.
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A six-step method for selecting an AI candidate
1. Map the workflow before choosing a tool
Write the current process as a short sequence. Record the source systems, data owner, standard of acceptable quality, handoffs, and approval points. Note how often the task occurs and how long a competent worker takes, including waiting and rework. Capture a small sample of normal cases and exceptions; a process that looks uniform in a meeting may contain difficult edge cases in production.
Define the authoritative reference a reviewer will use. A summary can be checked against the meeting transcript; a financial classification can be checked against the chart of accounts; a customer reply may require the current policy and account record. If no reliable reference exists, “human review” may not be meaningful.
2. Score four task characteristics
Microsoft’s workplace guidance uses four practical questions: repeatability, impact, error detectability, and time sensitivity. Use a local low/medium/high rubric rather than treating the screen as a validated universal formula.
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|---|---|---|---|
| Repeatability | Stable inputs, steps, and output format | Each case needs novel interpretation | Can you describe a normal case with a consistent procedure? |
| Impact of error | Reversible inconvenience or minor rework | Safety, legal, financial, employment, eligibility, or reputation harm | Who could be harmed, and can the decision be undone? |
| Error detectability | Reviewer can compare output with an authoritative source | Errors are subtle, hidden, or require unavailable expertise | What evidence would expose a wrong answer? |
| Time sensitivity | There is time for deliberate checking | Output is acted on immediately or at high volume | Can a qualified person intervene before use? |
A task that is repeatable, low impact, easy to check, and not rushed is a strong pilot candidate. A task that is ambiguous, consequential, hard to verify, and immediate should remain human-led or receive only tightly controlled assistance.
3. Set the human-ownership level
Choose the operating mode before configuring automation. Accountability does not move to the software. Microsoft Support states: “When you automate a task or part of a workflow, you remain responsible for reviewing, validating, and approving how the work is used—and for the accuracy, tone, and impact of the final content.” See Microsoft’s task-selection guidance.
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| Ownership level | Appropriate use | Required control |
|---|---|---|
| Automate, then review | Repeatable, lower-impact work with visible errors | Named reviewer checks every output or a documented sample before release |
| AI prepares; person decides | Work needing context, judgment, or nuanced communication | Reviewer verifies evidence, assumptions, tone, permissions, and consequences |
| Human-led with optional assistance | High ambiguity, hidden-error risk, or defensibility requirements | AI may organize information, but the person owns analysis and decision |
| Human-only or strict control | High-consequence work with no realistic review window | No autonomous action; use segregation of duties, approvals, and audit records |
“Human in the loop” is not sufficient if the reviewer lacks expertise, time, access to source evidence, or authority to stop the process. Test whether the person can actually detect and correct errors, not merely click an approval button.
4. Compare AI with simpler alternatives
Do not assume an AI system is the right kind of automation. Compare the current manual process, deterministic rules, AI assistance, and a more autonomous agent against the same criteria. AWS Prescriptive Guidance summarizes the decision as understanding “the relationship between the task characteristics, risk tolerance, and operational approach.” Its agentic-AI economics guidance also emphasizes task assessment, risk impact, and return on investment.
| Approach | Best fit | Main limitation |
|---|---|---|
| Manual | Rare, variable, sensitive, or judgment-heavy cases | Labor cost and slower throughput |
| Rules or conventional automation | Stable, structured inputs with explicit rules | Breaks when inputs or policy change outside the rule set |
| AI assistance | Text, images, or data requiring flexible interpretation, drafting, extraction, or summarization | Can produce plausible errors; requires review and data controls |
| More autonomous agent | Multi-step work needing contextual decisions and tool use, where permissions and stop conditions are enforceable | Greater blast radius, monitoring burden, and exception complexity |
A fixed validation rule, template, database query, or workflow trigger is usually preferable when it can solve the problem reliably. Consider an agent only when contextual reasoning or adaptive decisions are genuinely required and the expected benefit exceeds setup, monitoring, review, and failure-recovery costs.
5. Run a bounded pilot
Limit the first deployment to one task, a defined population, approved data, and a short evaluation period. Before anyone uses it in live work, document:
- Allowed inputs, prohibited data, and the systems or folders the tool may access.
- The output format and the original quality standard.
- Who may approve, correct, escalate, pause, or shut down the process.
- What actions require a second person or a separate system approval.
- How prompts, source documents, outputs, edits, and decisions will be logged, subject to applicable privacy and labor rules.
- A baseline from the existing process, using comparable cases.
Start in draft or recommendation mode. Keep automatic sending, record changes, payments, permissions, and other irreversible actions disabled until the pilot demonstrates reliable controls. Use representative normal cases and deliberately include exceptions, ambiguous requests, and adversarial or malformed inputs.
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6. Decide from total outcomes
Expand only when the complete process is better, not merely the machine-generated portion. Compare pilot results with the baseline and record confidence intervals or case counts where practical. A useful decision asks whether quality, speed, cost, and risk remain acceptable after review and rework.
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Tasks that are often good first pilots
These are starting points, not automatic approvals. Verify your organization’s data, users, policies, and consequences before deployment.
| Task | Why it may fit | Review requirement |
|---|---|---|
| Draft a recurring internal status update from approved notes | Stable structure and known source material | Owner checks figures, omissions, and audience-appropriate wording |
| Format or classify routine documents | Repeated patterns and measurable categories | Sample against the taxonomy; route uncertain cases to a person |
| Summarize a meeting for a participant | Source transcript and a defined purpose | Participant verifies decisions, names, dates, and action owners |
| Extract action items | Output fields can be specified | Meeting owner confirms each action, deadline, and assignee |
| Prepare a first draft of a standard report | Recurring sections and approved data sources | Analyst reconciles every number and explains material changes |
| Produce a recurring sales or operations summary from known data | Regular cadence and structured metrics | Data owner checks freshness, definitions, and anomalies before distribution |
Keep the pilot narrow even when the task looks low risk. A summary can still leak confidential information, omit a critical exception, or attribute a statement to the wrong person.
When AI should support a human rather than decide
Use preparation mode when the work needs context or a defensible judgment. Examples include preparing a sales proposal, analyzing customer feedback, summarizing research used for a decision, and drafting communications that will leave the organization. The person responsible should check evidence, context, tone, recipient permissions, and likely consequences before use.
For these tasks, require the reviewer to inspect source passages rather than accepting a polished narrative. Ask the system to show uncertainty or missing information, preserve links to source records, and separate extracted facts from suggestions. A reviewer should be able to reject the draft without penalty for slowing the process when the evidence is weak.
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Work that should remain human-led or tightly controlled
Keep a person in charge, and add formal controls where appropriate, for:
- Approving budgets, payments, contracts, or other commitments.
- Employment, legal, medical, safety, credit, insurance, or eligibility decisions.
- Unusual, emotionally charged, or vulnerable-person cases.
- Decisions whose rationale must be explainable and defensible to a regulator, court, customer, employee, or affected individual.
- Publishing or sending content when there is no realistic chance to review it before release.
Applicable law and organizational policy may impose additional restrictions. Do not infer permission from an AI system’s ability to generate an answer. If the consequence is high and review cannot be timely and competent, do not automate the decision.
How to measure an AI pilot
Define measures before launch so enthusiasm or disappointment does not determine the result. Track the same task standard for baseline and pilot cases.
| Measure | What to record | Why it matters |
|---|---|---|
| End-to-end time | Generation, review, corrections, waiting, and escalation | Gross generation speed can hide a slower total process |
| Quality | Accuracy and completeness against the original acceptance criteria | Shows whether the output is usable, not merely fluent |
| Correction rate | Edits per output and material error frequency | Reveals hidden review burden |
| Exception and escalation rate | Cases diverted, stopped, or sent to specialists | Indicates whether the chosen scope is too broad |
| Downstream rework | Follow-up tickets, reversals, complaints, or duplicate work | Captures costs outside the immediate task |
| User and customer impact | Worker experience, service quality, accessibility, and trust signals | Prevents speed gains from shifting harm elsewhere |
| Risk events | Privacy, security, bias, inappropriate access, and policy violations | Identifies controls that must be fixed before expansion |
Calculate benefit using the total cost of ownership: configuration, integration, licenses or usage, training, monitoring, human review, corrections, exception handling, and change management. A task that saves ten minutes of drafting but adds fifteen minutes of checking is not faster.
Privacy, security, bias, and workforce safeguards
Limit data and permissions
Give the system only the information and access needed for the task. Follow approved-use and data-handling rules; evaluate retention, logging, access controls, and contractual terms before entering sensitive material. Separate test data from production data and remove unnecessary identifiers. Review permissions whenever the workflow or connected system changes.
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Test hidden effects, not just accuracy
Check whether outputs differ by language, demographic proxy, customer segment, or case complexity. Look for confident fabrications, missing context, unsafe recommendations, and systematic escalation of difficult cases to already-burdened workers. The UK Government’s Mitigating “Hidden” AI Risks Toolkit urges teams to anticipate risks beyond security and privacy compliance and warns that relying on oversight alone carries risk.
Protect worker agency
Explain what the system does, what is monitored, and how outputs affect work. Do not turn an experimental quality measure into an undisclosed performance score. Invite workers who perform the task to identify exceptions, unsafe shortcuts, and extra work created by the system. Provide a route to contest an output or pause the workflow without retaliation.
What productivity evidence can—and cannot—tell you
External studies are useful context, but they are not a substitute for your pilot. The ILO’s 20 May 2025 update assessed nearly 30,000 tasks using an updated task-level methodology. Its mean automation score was 0.29 in 2025, compared with 0.30 in 2023. Those figures describe exposure in the assessment; they are not the probability that a particular workplace task will be automated, nor a forecast of job loss.
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The OECD’s 15 March 2024 report found that four in five surveyed workers said AI improved their performance and three in five said it increased their enjoyment of work. These are survey findings from the employers and workers covered, not a universal current prevalence rate. Pair reported benefits with checks for workload, surveillance, bias, deskilling, and job quality.
A practical go/no-go checklist
- The task is defined as a repeatable step, not a whole job.
- Inputs, output format, acceptance criteria, and authoritative sources are documented.
- Impact and reversibility of errors are understood.
- A qualified reviewer has enough time, evidence, and authority to intervene.
- AI has been compared with manual work and deterministic automation.
- Data, permissions, retention, and approved-use boundaries are set.
- The pilot includes normal cases, exceptions, and failure tests.
- Baseline and pilot measures include review, correction, escalation, and downstream rework.
- Workers and affected customers have a way to report harm or contest results.
- Expansion criteria and a stop condition are written before launch.
If several answers are “no,” narrow the task, add controls, or keep it manual. If the task passes, run the smallest pilot that can produce trustworthy evidence and keep the accountable human visible throughout.
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