Build an AI adoption plan around a specific employee or customer problem—not a favored product. Assess whether your organization is ready, involve the people whose work will change, test a bounded use case, and measure employee and customer outcomes separately. Expand only when evidence, accountable ownership, and safeguards show the system is helping without creating unacceptable harm.
1. Define the problem and the people affected
Describe the workflow or service problem in concrete terms before discussing tools. For example, replace “use AI to improve support” with a question such as “Can staff resolve routine requests more accurately without increasing the time customers wait for complex cases?” That framing makes the intended change testable without assuming AI is the answer.
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Map who performs the work, who receives the service, and who could be affected indirectly. Separate the outcomes you want for employees from those you want for customers: a faster internal task does not necessarily mean a better customer experience, and a service improvement should not conceal an unreasonable increase in employee workload.
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- Customer outcomes: measures tied to the service, such as accuracy, completion, wait time, accessibility, or complaint resolution.
- Potential harms: errors, privacy or security exposure, unfair outcomes, reduced access, and effects on people who are not direct users.
Choose measures that fit the use case and explain why they indicate success. There is no universal customer metric that suits every deployment.
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2. Check readiness before choosing a system
A promising use case can still fail if its process, data, or ownership is not ready. Assess whether the organization can support the system in normal operations and evaluate its performance over time—not just run a convincing demonstration.
- Process: Is the workflow stable enough to test? Are exceptions and handoffs understood?
- Data: Is the data suitable, accessible, and of sufficient quality for the intended task? Are privacy and access controls clear?
- Technology: Can the system fit existing tools and processes? What integration and maintenance work would it require?
- People: Do employees have the skills and time to use, check, and report problems with it?
- Operations: Is there a named owner for outcomes, support, monitoring, and updates?
- Evaluation: Can you compare results with the current process and identify when performance changes?
OECD adoption research emphasizes maturity assessment and beginning proofs of concept with more straightforward problems and available data. Its roadmap guidance also calls for considering effects across business processes and departments and explaining how model performance will be maintained over time. If essential readiness conditions are missing, address them first or choose a more manageable use case.
3. Co-design the workflow with employees
Involve affected employees from the outset, including people who handle unusual cases and understand where the existing process breaks down. Their knowledge can reveal risks that are invisible in a high-level process map, and early participation helps make responsibilities and training needs concrete.
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Before a pilot, agree on the division of work: what the AI system may do, what a person must decide, how staff can challenge or correct an output, and where errors should be reported. Make clear whether AI output is a suggestion, a draft, or an action that can affect a customer or employee directly. Train people for the decisions they are expected to make, not merely for where to click.
4. Choose a bounded use case and set test conditions
Start with a manageable task and suitable available data. Define the test’s scope, who may use it, and what it is not authorized to do. Establish success and stop conditions before the pilot begins so that enthusiasm for a new tool does not substitute for evidence.
- Set a baseline using the current process, where practical, so you can judge whether the pilot changes the outcome.
- Specify quality and service measures, as well as time or effort measures relevant to the task.
- Define how errors, uncertain outputs, exceptions, and complaints will be handled.
- Set appropriate checks for fairness, privacy, security, and accessibility.
- State in advance who can pause the test and what evidence would trigger a pause or redesign.
NIST’s voluntary AI Risk Management Framework (AI RMF) describes testing and evaluation as a way to gather evidence about whether a system meets individual or organizational goals while minimizing negative impacts. Its Playbook is intended to be tailored, not applied as a one-size-fits-all checklist. NIST’s AI RMF 1.0 resources were described as being updated in the official material reviewed for this article; check NIST’s current guidance when setting a governance process.
5. Compare candidate use cases or systems consistently
If you have several options, assess each against the same questions. The following comparison axes synthesize OECD adoption guidance with NIST and OECD risk frameworks; they are a practical decision aid, not a scoring formula issued by one source.
| Comparison axis | Questions to answer |
|---|---|
| Problem fit | Does this address a clearly defined employee or customer problem, or is the problem being invented to fit the tool? |
| Expected benefit | Which employee and customer outcomes should improve, and what evidence would demonstrate that? |
| Data and integration | Are the data and existing systems suitable? What access, integration, and maintenance work is needed? |
| Risk to people and service | What could go wrong for privacy, fairness, security, accessibility, or service quality, and who might bear the impact? |
| Oversight and recovery | Can a person review consequential outputs, correct errors, and restore the prior process if needed? |
| Workforce impact | How will tasks, skills, autonomy, and workload change? Have affected employees helped assess those changes? |
| Operating effort | Who will monitor performance, handle incidents, train users, and manage updates after launch? |
A use case with a compelling potential benefit but no reliable way to evaluate it, assign responsibility, or recover from errors is not ready to scale.
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6. Evaluate employee and customer results separately
At the end of the pilot, examine the outcomes defined for each group rather than relying on adoption rates, time saved, or user enthusiasm alone. Check whether a gain for one group came at another group’s expense. For instance, shorter handling time is not a complete success measure if resolution quality falls or complex requests become harder to complete.
For employees, look at work quality, task burden, autonomy, new training requirements, and how the system shifts work among roles. For customers, evaluate the service outcomes chosen for that use case and look for errors or uneven results across affected people. Document what the system did well, where human intervention was needed, and what remains uncertain. Use that evidence to decide whether to proceed, revise the workflow, run another bounded test, or stop.
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7. Set governance and review rules before scaling
Make ownership and escalation explicit before a pilot becomes routine. NIST organizes its voluntary AI RMF around four functions: Govern, Map, Measure, and Manage. In practical terms, establish how accountability works, document the system’s context and potential impacts, evaluate its performance and risks, and decide how to respond when risks or conditions change.
- Name the accountable business owner and the people responsible for technical operation, oversight, and incident response.
- Provide a cross-functional route for decisions that involve issues such as privacy, security, workforce impact, or customer harm.
- Define human review, escalation, incident handling, and conditions for pausing or withdrawing the system.
- Tell staff what the policy requires, what their duties are, and how to raise concerns or report problems.
- Set a review schedule and specify what will be re-evaluated, including performance, risks, workflow effects, and system changes.
OECD due-diligence guidance complements this operational structure by emphasizing communicated policies, staff training, worker and representative involvement, and preparation for incidents and system changes. Record the decision to proceed, revise, or stop, along with the evidence and accountable decision-makers. Revisit the decision when the system, workflow, data, or observed impacts change.
8. Plan workforce capability and change support
Training and employee voice are part of implementation, not an optional add-on after deployment. Match learning to the roles affected: some staff may need to check outputs, others may need to manage exceptions, and managers may need to interpret performance and escalation reports. Give employees a practical way to flag errors and suggest workflow improvements, and make responsibilities clear when AI output is wrong or incomplete.
AI’s effects on work are not uniform. The 2025 OECD/ILO compendium reports International Labour Organization estimates that, in G7 countries, 6.5% of jobs—25 million—are in a highly exposed category, while 28% of employment—109 million jobs—may be transformed as AI is incorporated into tasks. These are G7 estimates, not forecasts for every country, organization, or individual job.
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The same compendium reports OECD survey findings that around 80% of workers using AI reported improved performance, while 8% reported negative effects. Those survey results describe workers’ reported experiences; they do not guarantee that a particular deployment will benefit its users. Use them as a reason to monitor both positive and adverse effects, not as a substitute for evaluating your own implementation.
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