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Build an AI adoption plan around a specific work or service problem—not a favored product. Map who will be affected, check whether your organization is ready, involve employees in designing the workflow, and test one bounded use case. Measure employee and customer outcomes separately, then scale only when owners, safeguards, and a review process are in place. A tool’s deployment is not evidence that it has benefited either group.
1. Define the problem and the people affected
Describe the workflow or customer problem in concrete terms. For example, replace “use AI to improve support” with a specific issue such as “reduce the time agents spend locating policy information without reducing the accuracy of answers.” Identify who performs the work, who receives the service, and who else might be affected by a changed decision or process.
Set employee and customer outcomes separately. That makes it harder for a productivity gain to conceal worse service, extra rework, or a transfer of effort onto employees. OECD adoption guidance emphasizes defining the business problem before choosing a solution; NIST’s AI Risk Management Framework (AI RMF) likewise calls for mapping the system’s context and potential impacts.
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A promising use case can still fail if the underlying process, data, or support is not ready. Assess the current workflow and the organization’s ability to operate and evaluate a system over time. OECD adoption research describes maturity assessment and starting proofs of concept with comparatively straightforward problems and available data.
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- Process: Is the workflow sufficiently stable to define what the AI should and should not do? Are exceptions and handoffs understood?
- Data: Is suitable data available, accessible to the people and systems that need it, and fit for the intended task?
- Technology: Can the candidate system fit the existing workflow, and is there an owner for integration and ongoing operation?
- People: Do affected staff have the skills, time, and support to use the system and report problems?
- Evaluation: Can the organization measure the intended outcomes, detect errors or uneven impacts, and maintain performance checks after launch?
Deployment planning should consider effects across processes and departments, as well as how performance will be maintained over time. If the organization cannot monitor the system or respond when it fails, that is a readiness gap to resolve—not a reason to assume the system will remain reliable on its own.
3. Co-design the workflow with employees
Bring affected employees into planning from the outset, including people who handle exceptions and edge cases. Their practical knowledge can expose failure modes that are invisible in a high-level process map. OECD, BCG, and INSEAD’s 2025 report says: “The implementation plan should be co-developed with the firm’s staff from the outset to secure co-operation and draw on employees’ collective knowledge.”
Work with staff to decide what the system will do, what remains a human decision, how workers can question or override an output, and where they can report errors. Clarify whether AI changes task allocation or responsibilities, and identify training needs before the workflow is introduced. Employee involvement should continue as the system changes, rather than ending when the initial plan is approved.
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If there is more than one candidate, compare them against the problem and operating conditions rather than choosing by novelty or a single headline performance claim. The criteria below are practical decision prompts synthesized from OECD adoption guidance and NIST and OECD risk frameworks; they are not a scoring formula prescribed by one source.
| Criterion | Question to answer |
|---|---|
| Problem fit | Does this option address the defined employee or customer problem? |
| Expected benefit | What change should it produce, and how will that change be measured? |
| Data and integration | Are suitable data and workable connections to existing processes available? |
| Impact and risk | What could go wrong for people, privacy, fairness, security, or service quality? |
| Human oversight and recovery | Who can review or correct outputs, and can the workflow recover from an error? |
| Workforce effects | How will tasks, skills, autonomy, and workload change? |
| Operating effort | What ongoing effort will monitoring, support, and updates require? |
5. Test a bounded use case with clear stop conditions
Start with a manageable use case and suitable available data. Define the test’s scope, who may use the system, and what it is not authorized to do. Before the test begins, write down what would count as success and what would trigger a pause, revision, or stop. Include quality, time or effort, error handling, and any use-case-relevant fairness, privacy, security, and accessibility checks.
NIST describes testing and evaluation as evidence about whether a system meets individual or organizational goals while minimizing negative impacts. Its AI RMF Playbook is voluntary and intended to be tailored to the organization’s context; it is not a substitute for deciding which outcomes matter in the particular workflow.
6. Measure employee and customer outcomes separately
Choose measures that correspond to the problem defined at the outset. The appropriate customer measure depends on the service; there is no single universal metric. Record a baseline before the test where possible, then assess results during the test and after meaningful workflow changes. Pair speed or volume measures with quality and impact checks so that faster processing does not mask more errors or extra work elsewhere.
| Who is affected | Possible measures to select for the use case |
|---|---|
| Employees | Task burden and time spent; work quality and rework; ability to exercise judgment; training needs; changes in how work is distributed. |
| Customers | Accuracy; successful completion; waiting time; accessibility; complaint resolution; errors or uneven outcomes for affected groups. |
Choose and explain the measures rather than treating a convenient metric as proof of benefit. For example, a lower average handling time would not establish improved customer service if answer accuracy or complaint resolution worsened.
7. Assign governance, oversight, and incident responsibilities
Before expanding beyond the test, assign an accountable owner and establish how cross-functional decisions will be made. NIST organizes its voluntary AI RMF around four functions: Govern, Map, Measure, and Manage. In practical terms, the organization needs accountability, context and risk assessment, testing and evaluation, and a way to respond to risks as they arise.
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- Name the person or role accountable for the system and its outcomes.
- Set human review and escalation routes appropriate to the consequences of an error.
- Document how staff and customers can report a problem and who handles incidents.
- Decide who can approve material changes to the system or workflow.
- Set a review schedule and record whether the decision is to proceed, revise, or stop.
OECD due-diligence guidance adds practical expectations such as communicating policies and staff duties, using cross-functional groups, involving workers and representatives, and preparing for incidents and system changes. Periodic evaluation should check both whether risk-management practices are being carried out and whether expected outcomes are actually being achieved.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Plan training and workforce support as part of adoption
Training should match the roles and decisions affected: employees need to understand how to use the system, its limits, when to rely on human judgment, and how to flag a problem. Make changed responsibilities explicit and provide a channel for staff to raise concerns and suggest improvements. OECD adoption research identifies on-the-job training as one way to address skills bottlenecks; responsible-AI guidance also recommends staff policies, training, and regular stakeholder participation.
Workforce effects vary, so use evidence about a particular deployment rather than assuming either universal benefit or universal harm. The 2025 OECD/ILO compendium reports ILO estimates that 6.5% of jobs in G7 countries—25 million jobs—are highly exposed to generative AI, while a further 28% of G7 employment—109 million jobs—may be transformed as AI is incorporated into tasks. These are estimates for G7 countries, not forecasts for every country, employer, or job.
The same compendium reports OECD survey findings that around 80% of workers using AI report improved performance and 8% report negative effects. These survey responses describe workers’ reported experiences; they do not predict the outcome of a new implementation or establish that every worker benefits.
When to scale, revise, or stop
Use the test evidence and assigned governance process to make an explicit decision. Scale only when the intended employee and customer outcomes are supported by the results, risks have workable controls, and the organization can sustain oversight, training, and performance review. If results are mixed, revise the workflow or safeguards and test again; if important harms cannot be controlled or the benefits do not justify the operating effort, stop the deployment.
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