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What makes a workflow task a good candidate?
A task is worth investigating when AI could improve a defined business outcome and the team can manage the risks of using its output. Suitability depends on the task and its place in the process: one workflow may contain low-risk drafting as well as consequential decisions that should remain with a person.
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Microsoft Support advises breaking a workflow into subtasks before deciding what to delegate, and notes, “Not every task in a workflow or content process should be automated—even if Microsoft Copilot can do it.” Microsoft’s task framework is a useful reminder to assess work rather than a product’s capabilities.
Screen tasks with four questions
1. Does the task repeat, and is its purpose clear?
Look for recurring work with a recognizable input and output: for example, preparing a first draft from supplied material or summarizing information from known sources. Define what the result is meant to accomplish before considering AI. A vague goal makes it difficult to judge whether an output is useful.
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2. What would happen if the result were wrong?
Consider the consequences for customers, employees, finances, compliance, and operations. A mistake in a draft that a colleague can edit is different from an incorrect approval or an external message sent without review. Higher-impact work calls for stronger human ownership and more rigorous controls.
3. Can someone detect and correct an error?
Ask whether a reviewer has reliable facts, rules, or other evidence against which to check the output. If errors are subtle, the work is ambiguous, or verification requires expertise that is unavailable, AI should not quietly become the final decision-maker.
4. Is there time to review before the output is used?
Even a checkable result may be a poor fit if it must be acted on immediately and no reviewer can intervene. Where time allows, a person can edit, reject, or approve an output. Where it does not, design the process so the system can stop or route uncertain cases rather than proceeding unchecked.
Identify the business need before choosing a tool
State the problem in operational terms: for example, reduce cycle time, lower handling cost, improve issue resolution, or raise quality. Establish how the team will measure the current process and what improvement would count as success. Google’s guidance on AI adoption recommends defining objectives and considering whether generative AI is the appropriate approach; Microsoft’s framework likewise advises grounding tool choices in the workflow need.
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Generative AI is not automatically the best answer. Compare it with a simpler process change, conventional automation, or other AI approaches. Google’s suggested measures include cost change, time to resolution, workload, satisfaction, and retention; these are possible measures to tailor, not promised outcomes or results for a particular organization.
Compare candidate tasks without inventing a universal score
When several tasks look promising, compare them using the same questions. The dimensions below synthesize guidance from Google, Microsoft, and Australia’s National AI Centre; they are not a validated weighted scorecard, so do not treat a summed score as an objective ranking.
| Dimension | What to assess |
|---|---|
| Business impact and strategic fit | Does the task address a meaningful organizational goal? |
| Frequency and repeatability | How often does it occur, and how much does it vary? |
| Error severity and detectability | What is the downside of a mistake, and how reliably can someone find it? |
| Potential time or quality improvement | What baseline will be used to assess change? |
| Reviewability and exceptions | Can a person review results, and how often do unusual cases arise? |
| Data and system readiness | Are the necessary inputs available, appropriate, and usable in the relevant systems? |
| Privacy, security, and compliance | What constraints govern the data and decisions involved? |
| People and workflow fit | Will employees and affected users accept the change, and what training or process changes will it require? |
| Measurement | Can the team define acceptance criteria and observe the intended outcome? |
Set baselines and success thresholds for the actual process rather than relying on a generic ROI cutoff. Readiness also includes whether the data and systems are appropriate and whether privacy, security, compliance, and user-experience constraints can be met.
Choose examples for a first pilot—and know what to keep human-led
Tasks that may suit a supervised pilot
- Summarizing recurring material from known sources.
- Producing a standard first draft for a person to edit.
- Extracting and structuring document information, with a human accuracy check.
- Sorting routine support tickets while routing exceptions to a person.
These are examples described in Microsoft guidance and by Australia’s National AI Centre, not universal recommendations. A task’s fit still depends on its inputs, error consequences, review process, and operating context.
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Tasks that need strong human involvement
- Approvals with significant budget consequences.
- Customer-facing proposals and other external communications.
- Analysis where errors are subtle or difficult to verify.
- Unique strategic work with substantial ambiguity.
- Time-critical work where review cannot happen before action.
AI may still help prepare information or a draft in these cases, but a person should retain the decision or final approval. As Microsoft Support puts it, “Delegating work to AI doesn’t transfer accountability.” Microsoft’s accountability guidance makes clear that using AI does not remove organizational responsibility.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Set the human-AI boundary before rollout
Specify which work the AI performs, who checks the result, who can act on it, and what happens when the system is uncertain or an exception occurs. Match oversight to impact and volume:
- High-impact outputs: require a named person to approve them before action.
- Medium-impact, high-volume work: use human monitoring with a clear way to intervene.
- Low-impact, very high-volume work: consider sampling outputs, with a plan to investigate problems.
- Predictable edge cases: route them to a person rather than forcing them through the standard path.
Define escalation triggers and stop conditions, including who has authority to pause or stop AI use. Document the process flow and AI touchpoints, role responsibilities, decisions and escalation paths, output standards, acceptance criteria, training needs, and technical systems and data sources. Track what employees change in generated outputs and why; those edits can reveal recurring failure modes or unclear instructions.
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Test with realistic cases and monitor results
Before committing to a build or rollout, walk through representative cases with the people who do the work and those affected by it. Include ordinary examples as well as exceptions, missing or incorrect inputs, stakeholder feedback, and peak-load conditions. Define acceptance criteria in advance so the team can decide whether the system is safe and useful enough to proceed.
After deployment, evaluate against the original business objective and monitor accuracy, speed, quality, user feedback, and negative impacts. Use errors and employee edits to improve the workflow—or pause it if it fails its criteria. Google’s guidance, the National Institute of Standards and Technology’s AI evaluation guidance, and Australia’s National AI Centre all emphasize evaluating AI in relation to its intended use and outcomes.
Use an existing tool if it meets the defined need. Consider customization only when available tools do not satisfy the workflow’s requirements; the starting point should remain the team’s problem, not a product pitch.
Do not mistake an illustrative estimate for a general result
Australia’s National AI Centre gives an illustrative client-onboarding scenario that expects onboarding time to fall from 5–7 hours to 2–3 hours and document-chasing rounds from 3 to 1. Those are scenario-specific expected benefits, not general measured results that another organization can assume. Set and test your own baseline and success criteria instead.
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