The Tool Desk
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Start with a problem and the people affected
Describe the work or customer service you want to improve before evaluating AI systems. Be specific: for example, identify a recurring task, a service delay, or a type of information employees need to handle requests accurately. Avoid framing the goal simply as “use AI” or “increase productivity”; those do not say what should improve or how anyone will know.
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Map who does the work, who receives the service, and who may be affected indirectly. Write down employee outcomes and customer outcomes separately. An efficiency gain could come with lower service quality, added review work, or a less accessible customer experience, so one result should not stand in for all the others.
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NIST’s voluntary AI Risk Management Framework (AI RMF) uses four functions—Govern, Map, Measure, and Manage—to help organizations understand context, identify and measure risks, and manage them over time. Its structure is useful at the outset: establish the purpose and context, then identify plausible effects on people before settling on a system.
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Check readiness before choosing a system
A promising use case can still fail if the underlying process is inconsistent, the necessary data is unsuitable, or no one is responsible for operating and evaluating the system. Assess readiness across the workflow, technology, people, and governance—not just whether a model can produce a demonstration.
- Process: Is the workflow understood, sufficiently stable, and documented? Identify exceptions and decisions that require judgment.
- Data: Is relevant data available, accurate enough for the intended use, and handled in a way that respects privacy and security requirements?
- Technology: Can the proposed system work with existing tools and processes? What integration, access controls, and maintenance would it require?
- People: Do affected employees have the knowledge and time to use, review, and challenge the system? What training or role changes would be needed?
- Ownership: Is there an operational owner who can monitor results, respond to problems, and coordinate decisions across functions?
- Evaluation: Can the organization establish a meaningful baseline and continue checking performance after launch?
OECD adoption research recommends assessing an organization’s maturity and beginning proofs of concept with more straightforward problems and suitable available data. Its cited roadmap guidance also highlights planning for effects across business processes and departments and for how model performance will be maintained over time.
Co-design the workflow with employees
Bring affected employees into planning from the outset, including people who handle exceptions, review outputs, or explain decisions to customers. They can identify practical failure modes that a project team may overlook and help distinguish useful assistance from work that simply shifts effort onto staff.
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Before a pilot, make explicit:
- What the system may do, and which decisions remain with a person.
- Who reviews an output and what review is required before it affects a customer or employee.
- How staff can flag errors, unsafe or unfair results, and unexpected effects on workload.
- What happens when the system is unavailable, uncertain, or wrong.
- What training is required for users, reviewers, managers, and support teams.
Choose a bounded pilot and define its guardrails
Start with a manageable use case, suitable data, and a limited setting in which the organization can observe results. Set the pilot’s scope, duration, participants, and decision owner before it begins. Define both the conditions for success and the conditions that require pausing or stopping; do not wait for a serious incident to decide what counts as unacceptable.
Agree on measures that fit the task. Depending on the use case, these may include output quality, completion time, employee effort, error rates, accessibility, privacy, security, or fairness. Establish a baseline for comparison and document how results will be assessed. NIST describes testing and evaluation as evidence about whether a system meets individual or organizational goals while minimizing negative impacts; its Playbook is voluntary and should be tailored to the organization’s context.
Keep a record of the system and workflow tested, the data and people involved, the review process, issues found, changes made, and the decision at the end. This makes it easier to distinguish a genuine improvement from a result driven by a change in staffing, demand, or another part of the process.
Measure employee and customer outcomes separately
Choose measures that correspond to the intended benefit and the risks identified for this specific workflow. There is no single customer metric that applies to every AI deployment; explain why each measure is relevant and how it will be interpreted.
| Whose outcome | Questions to evaluate | Possible measures, where relevant |
|---|---|---|
| Employees | Does the system reduce or redistribute effort? Does it change work quality, autonomy, or the skills and training people need? | Task time or burden; quality of completed work; errors requiring correction; employee feedback; training needs; distribution of work. |
| Customers | Does the service become more accurate, accessible, or timely? Are some people more likely to encounter errors or obstacles? | Accuracy; successful completion; wait time; accessibility; complaint handling or resolution; differences in outcomes across affected groups. |
Use the measures together. A faster process is not a success if accuracy or accessibility falls below an acceptable level, and a higher-quality output may not be worthwhile if it creates unmanageable review burdens. Set thresholds and decision rules before examining pilot results to reduce the temptation to redefine success after the fact.
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Assign governance before scaling
Before expanding beyond the pilot, assign an accountable operational owner and establish a route for decisions that involve risk, legal or privacy concerns, security, workforce effects, and service quality. OECD due-diligence guidance emphasizes communicating policies and staff duties, using cross-functional groups, involving workers and their representatives, and preparing for incidents and system changes.
Document the controls that fit the use case, including human oversight, escalation, incident handling, and periodic review. Specify who can pause or change the system, how affected people can report a problem, and how the organization will investigate and respond. Record whether the decision is to proceed, revise the workflow, run another test, or stop.
Review the system after deployment as well as during testing. Changes in data, the workflow, the model, or the people using it can alter performance and risk. NIST encourages periodic evaluation of whether risk-management practices and expected outcomes are working; use reviews to check the actual service and employee experience, not only technical performance.
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Training should match people’s roles. Users need to understand the system’s intended use and limits; reviewers need to know how to assess outputs and escalate concerns; managers need to understand ownership and how work may change. OECD research on firm support identifies on-the-job training as one way to address skills bottlenecks.
Explain what responsibilities are changing and give employees a practical way to raise concerns or propose improvements. Involve worker representatives where appropriate, and include feedback in scheduled reviews. Where a system changes tasks, plan for the transition rather than treating training as an optional add-on after deployment.
The scale of potential workforce change is a reason to plan carefully, not a forecast for an individual organization. The 2025 OECD/ILO compendium reports ILO estimates that 6.5% of jobs in G7 countries—25 million jobs—are in a highly exposed category for generative AI, while a further 28% of G7 employment—109 million jobs—may be transformed as AI is incorporated into tasks. These estimates are specific to G7 countries and do not determine what will happen in a particular workplace.
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Worker experiences reported in the same compendium are also mixed rather than uniform: OECD surveys cited there found that around 80% of workers using AI reported improved performance, while 8% reported negative effects. Those survey findings do not guarantee an outcome for a new deployment; they reinforce the need to measure the experience of the employees affected by it.
Compare options using the same criteria
If more than one system or use case is under consideration, compare them against the same questions rather than allowing a compelling demonstration to dominate the decision. These criteria synthesize OECD adoption guidance with the NIST and OECD risk-management approaches; they are a practical comparison framework, not a scoring formula prescribed by one source.
- Problem fit: Does the option address the defined employee or customer problem?
- Expected benefit: What outcome should improve, and how will it be measured?
- Readiness: Are data and processes suitable, and what integration work is required?
- Impact and risk: What are the implications for people, privacy, fairness, security, and service quality?
- Oversight and recovery: Can people review decisions, correct errors, and return to a workable process if the system fails?
- Workforce fit: How will tasks, skills, training, and employee autonomy change?
- Operating effort: What ongoing work is needed for monitoring, review, support, and updates?
A practical decision gate for scaling
At the end of the pilot, make a documented decision using the criteria established at the start. Scale only when evidence supports the intended employee and customer outcomes, required safeguards are functioning, and operational ownership is clear. If results are mixed, revise the workflow or narrow the scope and test again. If harms cannot be controlled or benefits do not justify the operating burden, stop.
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