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Why access and trust have to be addressed together
Workplace AI use can grow before an employer establishes an official program. In Microsoft and LinkedIn’s 2024 Work Trend Index, 75% of surveyed global knowledge workers said they used AI at work, and 78% of AI users said they brought their own AI tools to work. The survey covered 31,000 full-time employed or self-employed knowledge workers across 31 markets and was fielded February 15 to March 28, 2024. These are survey results for that group, not prevalence estimates for every workforce or proof that AI caused a particular workplace outcome. Microsoft and LinkedIn, 2024 Work Trend Index
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Access without guidance can leave employees guessing which tools are acceptable and what information is safe to share. Microsoft reported that 39% of people using AI at work had received company AI training in 2024. That figure describes training among AI users in the cited survey; it is not a measure of employee confidence or trust. Microsoft, 2024 Work Trend Index
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The practical aim is not to approve every tool or to prohibit experimentation across the board. It is to match safeguards to the task, information involved, possible harm, and opportunity to correct an error.
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Start with work employees want to improve
Identify repetitive tasks, bottlenecks, and frustrating processes before choosing a model or vendor. Ask the people who do the work where AI might help, what could go wrong, and what conditions would make a trial acceptable. This makes the use case—not the novelty of the technology—the starting point.
- Describe the task and the problem an AI tool is expected to solve.
- Ask affected employees what information the task uses and where errors would matter.
- Consider whether a person can check, correct, or reverse the result before it affects someone.
- Include worker representatives and relevant teams in pilots and later reviews.
Participation should be consequential, not ceremonial: explain what the system does and does not do, collect feedback, and tell employees how that feedback affected the decision to proceed, change, or stop.
Set proportionate safeguards for each use case
There is no universal risk score or approval threshold established by the cited guidance. Compare proposed uses by their likely consequences and reversibility, the sensitivity of the data and the tool’s limitations, and the degree of worker participation, transparency, and human accountability. NIST’s AI Risk Management Framework and Generative AI Profile offer voluntary resources for managing risk across the AI lifecycle; they are not substitutes for legal review applicable to the organization and its location. NIST AI Risk Management Framework NIST Generative AI Profile
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| Use pattern | Safeguards to consider |
|---|---|
| Low-consequence, reversible assistance, such as drafting or summarizing material that an employee reviews before use | Approved tool and data rules; training on verification; a clear way to report errors. |
| Work that uses sensitive information or produces outputs with material effects | Limit access and data exposure; examine known limitations; define who checks outputs and what happens when they are wrong. |
| Employment-related decisions, including recruitment, pay-setting, scheduling, or performance evaluation | Define the objective; examine data and system limitations; retain accountable human review; explain the process and provide a meaningful route to question outcomes. |
The table is a practical comparison, not a formal NIST classification. Apply controls to the actual system and workflow, and review them when models, data, vendors, or processes change.
Use particular care when AI touches employment decisions
AI used in recruitment, pay, scheduling, or performance evaluation can affect people’s opportunities and working conditions. The International Labour Organization highlights risks from unclear objectives, biased or incomplete data, and opaque processes, and calls for stronger worker participation, clearer governance, transparency, and social dialogue. That does not mean every HR system has identical risks; it means employers should scrutinize the specific purpose, inputs, process, and consequences. International Labour Organization, Generative AI and Jobs
As Janine Berg, Senior Economist in the ILO’s Research Department, put it: “Without a human-centred approach, AI can inadvertently undermine fairness, transparency and trust in the workplace.” Human review is meaningful only when a responsible person can examine the relevant information, question the output, and change the outcome—not merely approve an automated recommendation.
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Publish a short policy employees can actually use
A practical policy should answer everyday questions in direct language. Keep it easy to find, make responsibilities explicit, and update it as approved tools or workflows change.
- Approved tools: Name the services employees may use for work and explain how to request another tool for review.
- Data boundaries: Specify what must not be entered, such as confidential, personal, or regulated information, according to the organization’s obligations and tool configuration.
- Permitted and prohibited uses: Give concrete examples, including any uses that require prior review or are not allowed.
- Output checks: State when employees must verify accuracy, sources, and suitability before relying on or sharing AI output.
- Decision ownership: Identify who is responsible for final decisions and for correcting errors.
- Reporting route: Provide a named channel for reporting errors, unsafe outputs, suspected data exposure, or other harms, and explain what happens next.
Pair approved access with practical training
Offer tools through an approved route and train people on the tasks they actually perform. Training should cover safe data handling, output verification, and escalation, as well as how to use the tool. Explain both useful capabilities and known limitations; prompt-writing tips alone do not prepare someone to judge whether an answer is reliable or safe to act on.
Managers also need guidance. They should know which uses are permitted, when to seek review, how to handle a reported problem, and how to avoid treating AI output as a substitute for their own responsibility.
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Make oversight visible and responsive
Employees need to see that concerns can change how a system is used. Before and during a pilot, explain its purpose, what information it uses, what it does not decide, who reviews its output, and how employees can challenge or report a result. Share what was learned and what will change, while protecting personal and confidential information.
Trust is also shaped by how colleagues interpret AI use. A 2025 Microsoft Research synthesis describes studies in which suspected AI assistance could lead to negative judgments about competence or diligence, while also noting that disclosure effects can be complex. This is an emerging area of evidence, not a universal rule about how employees will react. Organizations can reduce avoidable ambiguity by clarifying when AI assistance is permitted, how employees should disclose it where relevant, and how work will be evaluated. Microsoft Research, 2025 synthesis
Check whether the program is useful and credible
Track both task outcomes and the quality of the safeguards. The following are proposed management measures, not metrics validated by the cited reports:
- Whether the intended task improves, and whether employees find the tool useful.
- Error and correction rates, including recurring failure patterns.
- Access to training and employee confidence in understanding the policy.
- Concerns raised, time to respond, and changes made as a result.
Use the results to adjust access, training, or controls. Reassess when the model, vendor, data, or workflow changes, and pay particular attention to new consequences or reduced opportunities for human correction.
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