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This method helps you decide whether a task should be automated, supported by AI while a person decides, handled by conventional rules, or left human-led. It also gives you a defensible way to stop a pilot when the total result is worse than the current process.
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Start with the task, not the job title
A job usually combines dozens of activities with different levels of repetition, judgment and risk. Asking whether an entire role can be automated produces an answer too broad to operate. Instead, describe one step or one class of cases precisely enough that another person could observe it and identify when it is complete.
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Map the workflow before selecting a tool
Write down the task’s position in the workflow and record:
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- Inputs: the documents, data fields, messages or instructions the worker receives.
- Expected output: the exact artifact, decision recommendation, classification or action produced.
- Decision points: where someone interprets policy, chooses among options or approves an action.
- Exceptions: unusual, incomplete, contradictory or emotionally sensitive cases.
- Downstream users: who relies on the result and what they do next.
- Timing: how often the task occurs and how long is available before the result is used.
For example, “customer support” is too broad. “Create a draft reply for a password-reset request using the approved help article, for an agent to verify before sending” is a testable task. The latter has a known input, a bounded output, a review point and an identifiable exception path.
Separate the normal path from exceptions
Measure the routine case separately from the cases that require interpretation. An AI system may perform acceptably on a stable, well-documented request while failing on an account with conflicting records. Define what the system must do when required information is missing: ask for clarification, route the case to a person, or stop without making a recommendation.
Screen every task with four questions
Use four dimensions to make an initial low, medium or high assessment. Microsoft’s workplace guidance uses repeatability, impact, error detectability and time sensitivity as practical screening questions; it does not establish a universal numerical cutoff. Set the thresholds to match your organization’s risk tolerance.
| Question | What to examine | What a favorable answer looks like | Warning signs |
|---|---|---|---|
| Is it repeatable? | Inputs, steps and output format across ordinary cases. | A stable pattern, documented policy and limited variation. | Every case is bespoke, instructions change frequently or context is mostly tacit. |
| What is the impact of an error? | Financial, legal, safety, eligibility, customer and reputational consequences. | The mistake is reversible, contained and inexpensive to correct. | A wrong result could deny a right, harm a person, breach a duty or trigger an irreversible action. |
| Can a reviewer detect errors? | Whether an expert can compare the output with an authoritative source or observable facts. | There is a clear reference, test or reconciliation that exposes mistakes. | The output sounds plausible but cannot be independently checked. |
| Is there time to review? | The interval between generation and the action that uses the result. | A qualified person can inspect, correct and approve before use. | The output is sent, published or acted on immediately, or reviewers lack time and authority to intervene. |
A task with high repeatability, low or moderate impact, detectable errors and adequate review time is a strong pilot candidate. Low repeatability, high impact, hidden errors or no practical review should push the task toward human-led work, even if an AI demonstration looks impressive.
Choose the right level of human ownership
Automation changes who performs a step; it does not transfer accountability. 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.” Read the full guidance at Microsoft Support.
Automation followed by review
Use this mode for a narrow, repetitive step where a reviewer can quickly compare the result with a trusted source. The system may classify files, extract fields or draft a recurring update, but a named person checks the result and approves the next action. Define what must be checked rather than telling reviewers to “look it over.”
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AI preparation with a human decision-maker
Use AI to gather, summarize or draft information when the final decision depends on context, judgment or consequences that a model cannot reliably assess. The person should verify evidence, missing information, tone, permissions and the proposed action. A proposal, customer-feedback analysis or external communication commonly belongs here.
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Keep the person in charge when the task has high consequences, unusual facts, sensitive personal circumstances or a rationale that must be defensible. AI might help locate documents or suggest questions, but it should not decide or trigger the outcome. If no reviewer can meaningfully detect an error and intervene, do not treat a nominal approval button as a control.
Compare AI with simpler approaches
AI is not automatically the best form of automation. Compare the current manual process, deterministic rules, AI assistance and a more autonomous agent against the same operational requirements.
| Approach | Best fit | Strengths | Limits and controls |
|---|---|---|---|
| Manual work | High-judgment, rare or high-consequence cases. | Flexible context handling and direct accountability. | Can be slow or inconsistent; improve with checklists, training and workload design. |
| Deterministic automation | Stable rules, structured data and predictable exceptions. | Consistent, testable and easier to audit. | Breaks when inputs or policies vary; requires explicit exception handling. |
| AI assistance | Language-heavy or semi-structured work where a person decides. | Can summarize, draft, classify or transform varied inputs. | May invent or omit information; requires source checking, permissions and review. |
| More autonomous AI | Multi-step work needing contextual reasoning and bounded actions. | Can coordinate steps and adapt beyond fixed rules. | Greater permission, monitoring, failure-recovery and accountability demands. |
Amazon Web Services summarizes the choice this way: “This decision requires understanding the relationship between the task characteristics, risk tolerance, and operational approach.” Its guidance on agentic-AI economics also recommends assessing task fit, risk impact and return before building an agent: AWS Prescriptive Guidance. If a few explicit rules solve the problem, an AI model adds variability without adding value.
Good first pilots for workplace AI
These patterns are often suitable starting points when your own data, permissions and review process support them. Treat each as a task design, not a blanket approval for every version of the activity.
Recurring internal status updates
Provide approved notes, milestones and blockers, then have the owner verify dates, numbers and omissions before distributing the update. Keep the input set limited to information the author is already permitted to use.
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Formatting and classifying routine documents
Use AI to apply a known structure, label documents or route them to a queue. Test borderline examples and retain a way for a person to correct the classification without reprocessing the entire batch.
Meeting summaries and action-item extraction
Generate a draft for participants to verify. The owner should correct names, commitments, deadlines and statements that could be interpreted as decisions. Do not treat an unverified transcript as the official record.
First drafts of standard reports
Supply approved data and a fixed reporting template. A subject-matter reviewer checks calculations, source coverage, caveats and the reporting period before publication.
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AI can turn known, permissioned data into a readable summary. Validate totals against the system of record and check that the wording does not imply causes or forecasts that the data cannot support.
Work that should remain human-led or tightly constrained
Some tasks fail the screening test because the harm is high, the situation is ambiguous, or review cannot be done credibly.
- Approving budgets or releasing funds where an error could create material loss.
- Making employment, legal, medical, safety, credit or eligibility decisions.
- Handling unusual, vulnerable or emotionally charged cases that depend on personal context.
- Making a decision whose rationale must be defensible to a regulator, court, customer or affected worker.
- Publishing or sending content when there is no realistic opportunity to review it before action.
AI may still help a qualified person find relevant records or prepare questions in these settings, subject to applicable law, professional duties and organizational policy. Assistance is not permission to delegate the decision.
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Run a bounded pilot step by step
- Write a one-sentence task definition. State the trigger, allowed inputs, expected output, reviewer and completion condition. Exclude adjacent activities that have different risks.
- Record a baseline. For a representative period, measure current completion time, quality, correction and exception rates, volume and downstream rework. Include the people and systems involved.
- Set a narrow scope. Start with a limited team, document type, queue or time window. Keep sensitive data and permissions to the minimum necessary for the test.
- Define the review contract. Name who may approve, correct, escalate or stop the process. Specify the fields, claims and actions that must be checked and the maximum time available for review.
- Create a test set. Include ordinary cases, edge cases, incomplete inputs and known failure examples. Keep a versioned copy so results can be compared across changes.
- Run in shadow or draft mode first. Let the system produce outputs without allowing them to trigger external actions. Compare them with the baseline process and log every correction.
- Introduce controlled use. Permit only the approved output and action path. Keep audit records of inputs, generated content, reviewer edits, approval and any escalation.
- Review the results at a fixed gate. Decide in advance what would stop, revise, expand or abandon the pilot. Do not expand merely because users like the interface.
Measure the total outcome, not gross AI speed
An AI draft that takes 20 seconds to generate can still make the process slower if a reviewer spends five minutes correcting it. Compare the complete workflow with the baseline.
| Measure | How to calculate or inspect it | Why it matters |
|---|---|---|
| End-to-end time | Generation time plus review, edits, escalation, waiting and downstream correction. | Shows whether the process actually finishes sooner. |
| Correction rate | Outputs requiring a material edit divided by outputs reviewed; record the type of error. | Reveals hidden review burden and recurring failure modes. |
| Quality against the original standard | Use the same acceptance checklist or scoring method as the manual process. | Prevents speed from being treated as quality. |
| Exception and escalation rate | Count cases routed out of the normal path and why. | Shows whether the task boundary is realistic. |
| Downstream rework | Track later fixes, duplicate work, complaints and reversals. | Catches costs that do not appear at generation time. |
| User and customer impact | Monitor satisfaction, access, response quality, workload and complaints. | Identifies effects on people beyond the immediate operator. |
| Risk events | Log privacy, security, bias, policy and unauthorized-action incidents. | A single serious event can outweigh modest time savings. |
A simple net-benefit estimate can be expressed as: (baseline total cost − pilot total cost) − implementation and change cost. Include reviewer time, quality assurance, integration, monitoring, training, exception handling and remediation. State the period and assumptions; do not count unverified “hours saved” as cash savings.
What productivity evidence does—and does not—tell you
The International Labour Organization’s 2025 update assessed nearly 30,000 tasks using a task-level methodology that combines human and AI input. Its mean automation score was 0.29 in 2025, compared with 0.30 in 2023. Those figures describe an exposure assessment, not the probability that a particular task will be automated, a prediction of job loss or a profitability forecast: ILO, Generative AI and jobs: A 2025 update.
An ILO aggregation-paradox brief reports typical task-level productivity gains of 10–70 per cent in studies, with stronger effects for less experienced workers and well-defined, text-intensive tasks. It also says firm-level evidence is mixed and that gains have not clearly appeared in official sectoral or macroeconomic statistics. Treat the range as context for a pilot hypothesis, not a promised business result: ILO, The Aggregation Paradox of AI.
The ILO explains that exposure indicators estimate what systems may be capable of doing at the task level. They do not establish how work will be reorganized or what employment outcomes will follow, and most occupations contain both exposed and human-dependent tasks: ILO explainer on occupational impact.
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In an OECD survey of employers and workers, four in five workers said AI improved their performance and three in five said it increased their enjoyment of work. Those are reported findings from the surveyed population, not a universal current prevalence rate; the same policy work discusses workplace risks that should be checked in your own setting: OECD, Using AI in the workplace.
Build controls around the real failure modes
Prevent automation bias
People often accept fluent output too readily. Require reviewers to inspect source evidence, not merely the writing quality. Rotate or calibrate reviewers with known test cases, and make escalation easy when the output conflicts with policy or evidence.
Limit data and permissions
Give the system only the information and access needed for the defined task. Follow your organization’s approved-use and retention rules, and evaluate provider terms before entering confidential, personal or regulated material. Keep credentials, payment authority and irreversible actions outside the model unless a separate control system enforces them.
Test bias and unequal effects
Break results down by relevant customer, worker, language or case categories where lawful and appropriate. Look for systematic omissions, different error rates or tone changes. A high average accuracy can hide unacceptable performance for a smaller group.
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Check workload and monitoring effects
AI can increase work intensity if employees must monitor unreliable outputs while maintaining the old process. Measure review load, interruptions, discretion and worker feedback. Do not introduce hidden surveillance merely to collect pilot metrics.
Design a safe fallback
Document how to pause the system, restore the manual path, notify affected people and investigate an incident. The UK Government’s human-centred toolkit warns that risks can extend beyond security and privacy compliance and that relying on oversight alone carries risk: Mitigating Hidden AI Risks Toolkit.
Decide how much review is enough
Review depth should follow consequence, detectability and the time available to intervene.
- Low-impact, highly checkable output: use a defined checklist and consider sampling after the reviewer demonstrates consistent accuracy.
- Moderate-impact or mixed-quality output: review every case initially, require source verification and keep the reviewer authorized to reject or edit it.
- High-impact or difficult-to-check output: keep a qualified person responsible for the decision, require independent evidence and use a second review or formal approval where policy requires it.
- No meaningful review opportunity: do not automate the action. Change the timing or workflow first so intervention is possible.
Review is meaningful only when the person has enough expertise, information, time and authority to detect an error and stop the process. A name attached to an approval field is not sufficient.
Common mistakes and practical fixes
| Mistake | What it looks like | Fix |
|---|---|---|
| Automating a job instead of a task | A broad ambition with no defined input, output or exception path. | Decompose the workflow and pilot one repeatable step. |
| Choosing a flashy demonstration | Good-looking output on a few examples, with no baseline or edge cases. | Use a representative test set and the original quality standard. |
| Measuring only generation time | Reported savings exclude review and rework. | Measure end-to-end time and correction burden. |
| Using AI where rules are sufficient | Variable model output for a fixed policy or calculation. | Implement deterministic automation and reserve AI for genuine ambiguity. |
| Assuming a human in the loop solves risk | Reviewers approve everything because they lack time, evidence or authority. | Specify checks, train reviewers, audit decisions and narrow the task. |
| Expanding before controls work | More users or permissions are added after a small successful demo. | Pass a documented gate for quality, risk, workload and fallback readiness. |
Use a clear expansion gate
Expand only when the pilot meets its predefined quality and risk thresholds, the complete process is measurably better than baseline, reviewers can sustain the workload, exceptions have an owner, and privacy, security and policy controls remain workable at the larger volume. If one of those conditions fails, narrow the task, improve the controls or stop. A useful pilot is allowed to show that AI is not the right approach.
The practical rule
Automate bounded work that repeats, can be checked and has consequences you can contain. Use AI as preparation when judgment still matters, choose ordinary rules when the process is deterministic, and keep high-consequence or unreviewable decisions human-led. Make the decision from measured end-to-end results and explicit accountability, not from a model’s fluency or a job-level prediction.
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