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Design AI workflows one task at a time: automate repeatable, low-impact work that people can verify; keep humans responsible for ambiguous or consequential decisions; and require approval before actions that affect people, money, compliance, or are difficult to reverse. A useful review point shows the proposed action and its supporting context, gives an authorized person a real chance to intervene, and remains part of the workflow after launch.
Classify each task before deciding what to automate
A workflow may combine routine information handling with decisions that need context or judgment. Assess each step separately using four questions from Microsoft’s task-selection guidance. These are decision aids, not a numeric scoring formula.
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- Repeatability: Does the task follow a consistent pattern, or is it novel and judgment-heavy?
- Impact: What could happen if the output or action is wrong?
- Error detectability: Can a reviewer check the result against source facts before it matters?
- Time sensitivity: Is there enough time for meaningful review? If not, Microsoft suggests keeping the task human-led.
Use the answers to choose an operating mode:
| Mode | When it fits | Human responsibility |
|---|---|---|
| Automate with review | Repeatable, lower-impact tasks with results that are straightforward to check, such as drafting an internal update or preparing a recurring summary. | Validate the output before sharing it or acting on it. |
| AI-supported, human-led | Work where impact is higher, errors may be subtle, or context matters. | Use AI to prepare analysis or options; a person makes the judgment or decision. |
| Fully human-led | Highly variable work that is difficult to verify, or where delegated execution would carry inappropriate consequences. | A person owns the decision. AI may still help with bounded preparation. |
As Microsoft puts it, “Delegating work to AI doesn’t transfer accountability.”
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A checkpoint belongs at the transition where an AI-generated result could cause a meaningful downstream effect—not merely at the end of the workflow. Microsoft recommends human approval for actions that are hard to reverse or affect people, money, or compliance. Its guidance also calls for review of exceptions, unclear priorities, and decisions affecting people or commitments.
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- Before sending an external communication or customer-facing proposal.
- Before approving a budget or taking another action involving money.
- Before a hard-to-reverse action or a decision that affects people, commitments, or compliance.
- When a case is sensitive, ambiguous, exceptional, or lacks necessary context.
- Before a recommendation or priority change is shared or used to trigger another action.
For each checkpoint, make the proposed action visible and give the reviewer enough context to judge it. A “reviewed” label without a meaningful opportunity to approve, correct, or stop the action is not an effective control. Make clear who is authorized to approve and how a user can interrupt autonomous behavior. See Microsoft’s human-oversight guidance.
Set the boundaries and reviewer context before building
Write down what the AI is meant to do, which information it may rely on, and where its authority ends. Microsoft’s Copilot planning guidance organizes these questions around Goal, Freshness, Context, Audience, and Checkpoints.
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- Goal: Define the task and what the AI should leave out.
- Freshness: Identify the current, approved sources; stale information can lead to inappropriate downstream action.
- Context: Specify what the AI and reviewer need to know, and how to handle missing context.
- Audience: Identify who will receive the output and whether it is suitable for them.
- Checkpoints: State what the AI may recommend, pass along, or trigger, and what requires review, approval, or escalation.
Also assign an owner for the system and identify who can pause or deactivate it. These decisions are easier to make during architecture design than after the workflow is built. Microsoft recommends assessing the system before production and scaling that assessment to the risk. Its boundary and checkpoint guidance offers an internal intake example: an agent can sort and route standard requests, while people handle judgment calls, unclear priorities, sensitive items, and decisions affecting commitments.
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Prelaunch review is only one point in the workflow’s life. Monitor for groundedness, safety issues, escalations, and user-reported problems. Revisit the design when the data, model, usage, workflow, or relevant regulation changes. Microsoft’s responsible AI policies guidance describes checkpoints during design, testing, and prelaunch, as well as ongoing audits and incident response.
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Decide in advance who receives incidents, who can stop or deactivate the system, and what safe alternative process takes over if it must stop. NIST’s Generative AI Profile includes suggested risk-management actions for incident response, recovery, and disengaging or deactivating systems when performance or outcomes conflict with intended use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use frameworks as guidance, not as a safety guarantee
The NIST AI Risk Management Framework (AI RMF) 1.0, released January 26, 2023, is voluntary guidance intended to help incorporate trustworthiness considerations into AI design, development, use, and evaluation. NIST’s AI RMF Playbook offers voluntary suggested actions and references organized around Govern, Map, Measure, and Manage; NIST announced its first complete version on March 30, 2023.
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NIST published the Generative Artificial Intelligence Profile (NIST AI 600-1) on July 26, 2024. As of October 7, 2026, NIST’s AI RMF page said the framework was being revised. These resources can help organize risk management, but adopting a framework—or adding a human approval gate—does not by itself prove a particular workflow is safe, lawful, or compliant.
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