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Set AI-at-work guidelines by documenting which tools and tasks are approved, what information employees may enter, how outputs must be checked, and who is accountable for decisions. Start with an inventory of actual uses, apply stronger review to uses that can affect people, and assign owners to train staff, handle reports, and keep the rules current.
Start with a use-case inventory
Before writing rules, find out what AI systems employees already use or want to use. Record the tool, the task, the information entered, and who may rely on the result. Include both organization-approved systems and unapproved or informal use disclosed by teams.
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Distinguish low-impact assistance, such as brainstorming or drafting internal text, from uses that affect customers, employees, candidates, or other people. The same tool may present different risks depending on the task, the data, and how much weight people give its output.
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Organize the policy around risk
NIST’s voluntary AI Risk Management Framework (AI RMF) offers a useful structure: Govern, Map, Measure, and Manage. Its Playbook provides suggestions for applying those functions, but it is not a mandatory checklist; NIST says it is “neither a checklist nor set of steps to be followed in its entirety.” Select controls that fit the team’s context and the proposed use.
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Assess each use against relevant dimensions, rather than treating “AI” as one uniform risk. NIST identifies validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy, and fairness with harmful bias managed. These qualities may involve tradeoffs, and their importance depends on the context.
- Govern: Assign decision-makers, approvers, operators, and oversight responsibilities.
- Map: Describe the task, users, affected people, data, and likely consequences.
- Measure: Decide how to test quality and identify relevant privacy, security, fairness, or safety concerns.
- Manage: Set controls, monitor results, respond to problems, and revise or stop the use when needed.
NIST released AI RMF 1.0 on January 26, 2023, and says it is being revised. Its Generative AI Profile was released July 26, 2024. Check NIST’s AI Risk Management Framework page and AI RMF Playbook for current material when creating or refreshing internal rules.
Define approved tools and tasks
Maintain an internal list that identifies approved systems and the tasks they may be used for. Approval should be tied to a specific use, not treated as blanket permission to use a tool for any purpose. Establish a route for requesting review of a new system or a higher-risk task; the appropriate reviewers and level of scrutiny will depend on the organization.
When comparing tools or proposed uses, consider the following together:
- Suitability for the task and the quality of results.
- What information the service collects, retains, or uses, and what configuration and terms apply.
- Security controls, access management, and the ability to audit activity.
- Whether outputs can be verified and, where needed, explained.
- Potential effects on workers, customers, and other affected people.
- Whether a human can review, override, or stop the system’s output from being used.
- Applicable obligations for the organization’s jurisdiction and sector.
- Cost and operational burden.
There is no universal scoring formula for these factors. Set a documented review method proportionate to the use and its consequences.
Set rules for information employees enter
Have the appropriate security, privacy, and legal owners determine what information may be entered into each approved system, taking account of its actual configuration and terms. Address confidential business information, personal information, regulated data, client material, and unreleased information as relevant to the organization. Avoid a policy that assumes every tool handles data in the same way.
Make the rule easy to follow: staff should know where to find the approved-use list, which data categories are restricted for each system, and whom to contact when a task involves information not covered by the policy. Privacy and security are recognized risk dimensions, but the exact categories and permitted handling depend on the organization, tool, and applicable requirements.
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AI output should not become final work merely because it looks confident or polished. Specify checks appropriate to the task: verify factual claims against reliable sources, recalculate important figures, test code, inspect citations, and review customer-facing text before it is sent. A team may need different checks for a brainstorming draft than for material used in a consequential decision.
Name the person or role responsible for accepting the final work, and define when a qualified human must make or review a decision. NIST’s Playbook recommends explicit human roles and responsibilities, risk tracking, proficiency standards, risk-management training, oversight procedures, and transparency policies. Build those into workflows rather than relying on a general instruction to “use judgment.”
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Apply extra scrutiny when people may be affected
Employment-related uses, worker monitoring, candidate evaluation, and other consequential decisions deserve careful review before deployment and while in use. Consider privacy, discrimination and fairness, labour rights, job quality, transparency, explainability, and accountability. OECD’s workplace analysis identifies these as important concerns; its guidance does not replace applicable law.
Rules differ by jurisdiction, sector, data type, and use. Have qualified local advisers assess the relevant requirements where necessary, especially before using AI in employment decisions or monitoring. This article offers general organizational guidance, not a legal determination for a particular workplace.
Train staff and provide a reporting route
Training should cover the approved tools and tasks, information-handling rules, output checks, and the human responsibilities attached to each workflow. Tell employees how to raise a question before using AI in an unlisted way and where to report an inaccurate output, data exposure, suspected misuse, or other incident.
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Assign someone to receive and route reports, and define who evaluates them and what action may follow. Clear reporting and oversight procedures help the organization identify recurring problems and decide whether a use needs tighter controls, a pause, or a policy change.
Assign an owner and review triggers
Give a named role or team responsibility for maintaining the guidelines, the approved-use list, and associated training. Set review triggers so the policy is revisited when a tool or vendor data practice changes, a new use case is proposed, a material incident occurs, or relevant law or guidance changes.
NIST describes the AI RMF as a living framework. Because its materials evolve and the framework is being revised, check official NIST guidance during policy reviews rather than assuming an older internal document remains current. The OECD’s Employment Outlook 2023, Chapter 6 also discusses workplace impacts and trustworthy AI, including respect for the rule of law, human rights, and democratic values throughout the AI system lifecycle.
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