Plan AI adoption as an ongoing organizational change—not a software purchase. Before a tool enters a workflow, define its purpose and limits, identify the expertise and people the change could affect, and assign someone accountable for oversight. Then pilot with staff, record what the system does and what the organization learns, train users and reviewers, and prepare a way to pause or retire the system without interrupting essential work.
Why AI adoption can put institutional knowledge at risk
Institutional knowledge is more than documents and databases. It includes records, practical know-how, local history, awareness of exceptions, and the judgment employees use when a case does not fit the usual process. If an AI tool changes who performs a task or how decisions are made, that knowledge can become harder to find, validate, or pass on—even if the tool performs its assigned task adequately.
Preservation therefore has two sides: keep reliable records about the system and its decisions, and retain the human expertise needed to interpret, check, and handle work the system cannot manage. Guidance from the Australian National AI Centre emphasizes accountability, documentation, oversight, and decommissioning; the American Library Association’s recommendations offer a sector-specific example of preserving professional expertise while using AI to assist some tasks.
1. Define the purpose and boundaries before choosing a tool
For each proposed AI use, write down the organizational purpose, intended users, expected outcomes, data sources, and tasks the system must not perform. Record assumptions and known limitations. Compare the AI option with a non-AI approach; a tool is not automatically the best answer simply because it is available.
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Assess the use in context. Drafting marketing copy and assessing job applications can involve different consequences for people, even if the same general-purpose technology is involved. The Australian National AI Centre and Microsoft both advise evaluating AI according to its specific use rather than treating a tool as having one fixed level of risk.
For each candidate use, consider:
- How clear and valuable is the purpose?
- Are the data appropriate, sufficiently reliable, and suitable to use?
- Who could be affected, and what happens if the output is wrong?
- Can the output be validated by someone with the right expertise?
- Is there enough human oversight capacity to review, correct, or override it?
- Can the organization reverse the change, and what happens to continuity if the system fails?
- Would a non-AI process meet the need more effectively?
2. Map the workflow, expertise, and people affected
Map the existing workflow before changing it. Ask the people who do the work where they rely on tacit knowledge, professional judgment, local history, customer or community context, and exceptions that do not appear in formal procedures. Identify which steps are routine and which require interpretation or escalation.
Consult affected workers early, not just after a pilot is designed. Include the people whose work, data, or services may be affected in identifying expected benefits, risks, and safeguards. The ALA explicitly recommends worker consultation and labor-impact assessment for libraries; UK government guidance also highlights human and organizational factors when scaling AI. These are useful principles to adapt, not a claim that every sector has the same workforce or legal requirements.
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Make explicit which work remains human-led. The ALA calls for libraries to preserve core expertise—including cataloging, subject knowledge, access services, instructional design, reference, readers’ advisory, and community support—even when AI can help with parts of those tasks. Other organizations should identify their own equivalent areas of expertise rather than assuming library roles map directly to theirs.
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3. Assign accountability and keep an AI system record
Name a senior accountable owner as well as operational owners for development, testing, day-to-day operation, oversight, handling concerns, and continual improvement. The Australian National AI Centre recommends documenting these responsibilities so people know who can answer questions and act when something goes wrong.
Maintain an AI register or equivalent system record. The Australian guidance describes recording:
- Purpose, capabilities, and limitations.
- Accountable people and operational responsibilities.
- Datasets and their provenance.
- Acceptance criteria and test results.
- Risk assessments and decisions about controls.
- Audit requirements and review dates.
Also preserve material decisions and lessons from pilots. A record should help a new employee, reviewer, or successor vendor understand why the system was adopted, how it was evaluated, what it is not meant to do, and what changes have been made. Treat the record as operational documentation, not merely a one-time approval form.
4. Run a bounded pilot that tests the workflow, not just the output
Choose a limited use case with a clear scope. Before the pilot begins, define what success looks like and what results or incidents would trigger a pause or stop. Assess risks, identify affected stakeholders, and set up ways for users to give feedback, report incidents, seek review, and escalate concerns.
Evaluate more than whether generated output looks plausible. Test whether staff can understand, verify, correct, or override it; whether exceptions reach a qualified person; and whether the new workflow preserves the expertise needed to perform the work. Involve affected staff in identifying possible harms and judging whether safeguards work in practice.
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Keep pilot decisions, test results, feedback, and changes in the system record. That makes lessons reusable when staff change or another team considers a similar use, and it helps prevent a pilot’s assumptions from silently becoming permanent operating rules.
5. Train people for their roles and share what teams learn
Assess training needs across the people who will use or review the system and those responsible for management, procurement, privacy, and technical support. Training should match each role and the risk of the task: a person who reviews consequential outputs needs a different level of preparation from someone using a low-impact drafting aid. Plan support and refresh it when tools, duties, or workflows change.
Share reusable policies, templates, evaluation results, and lessons between teams. Canada’s federal AI strategy identifies a central hub as a way to share implementation knowledge, code, tools, and departmental lessons. That public-sector example does not mean every organization needs a centralized AI office. A hub is one possible model when teams need common support; local teams still need a way to bring forward knowledge specific to their work.
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6. Monitor changes and plan for intervention or retirement
Review the system when its tools, data, workflow, or operating context changes. Monitor incidents, feedback, and unintended effects, and update controls or training when evidence shows deficiencies. Set out who can intervene, pause, or retire the system and how those decisions will be communicated.
Plan continuity before a system becomes critical. Decide what records must be preserved, how data will be handled at retirement, how affected people will be informed, and what alternative process will keep essential work running. The Australian National AI Centre specifically recommends planning intervention and decommissioning, preserving required records, communicating retirement, and maintaining alternative pathways for critical functions.
Choosing how adoption is organized
Organizations can coordinate AI centrally, let teams lead locally, or combine the two. A central approach can support consistent standards and shared expertise, while team-led work stays closer to local workflows and expertise. Consider the trade-offs before choosing:
| Consideration | Central coordination | Team-led adoption |
|---|---|---|
| Consistency of standards | Can make common policies and controls easier to apply across teams. | May require extra coordination to keep practices aligned. |
| Proximity to local expertise | May be farther from the details of each team’s work. | Can keep decisions close to staff who know the workflow. |
| Support and speed | Can pool specialist help, but approvals may become bottlenecks. | Can move quickly on local needs, but teams may lack specialist capacity. |
| Sharing lessons | Can provide a natural place to collect and distribute reusable learning. | Needs deliberate channels so lessons do not remain isolated. |
| Accountability | Can clarify shared ownership, provided responsibilities are explicit. | Can clarify local ownership, provided organization-wide oversight is retained. |
Microsoft describes an AI Center of Excellence as one possible source of shared expertise and consistent adoption, while warning that it can create approval delays or knowledge bottlenecks. The choice is a governance decision, not a prerequisite for responsible adoption.
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