Source: [AI training follow-up: resolve the questions a workshop leaves open — Skills for Business]() [← All articles](https://skillsforbusiness.ai/blog/) HR & people · Guide # AI training follow-up: resolve the questions a workshop leaves open An unanswered question after AI training may require practice, an access fix or a policy decision. Route it correctly before generating an FAQ answer. [Skills for Business](https://skillsforbusiness.ai/blog/editorial/)2 November 20267 min read On this page 1. [Diagnose the obstacle before commissioning more training](https://skillsforbusiness.ai/blog/ai-training-unanswered-questions-follow-up/#diagnose-the-obstacle-before-commissioning-more-training) 2. [Preserve what the person actually needs to decide](https://skillsforbusiness.ai/blog/ai-training-unanswered-questions-follow-up/#preserve-what-the-person-actually-needs-to-decide) 3. [Make an answer candidate visibly different from approved guidance](https://skillsforbusiness.ai/blog/ai-training-unanswered-questions-follow-up/#make-an-answer-candidate-visibly-different-from-approved-guidance) 4. [Make it worthwhile to raise uncertainty](https://skillsforbusiness.ai/blog/ai-training-unanswered-questions-follow-up/#make-it-worthwhile-to-raise-uncertainty) 5. [Report what changed and what remains open](https://skillsforbusiness.ai/blog/ai-training-unanswered-questions-follow-up/#report-what-changed-and-what-remains-open) 6. [Check resolution without rewarding a quiet room](https://skillsforbusiness.ai/blog/ai-training-unanswered-questions-follow-up/#check-resolution-without-rewarding-a-quiet-room) 7. [Sources](https://skillsforbusiness.ai/blog/ai-training-unanswered-questions-follow-up/#sources) What to take away - Diagnose the obstacle before prescribing another training session. - Keep generated answer suggestions separate from approved guidance. - Close a question with evidence of resolution, or keep its specific uncertainty visible. After an AI workshop, give each unresolved employee question a route to resolution before asking an assistant to turn the discussion into an FAQ. Some questions need practice. Others need working access, clearer reference material or a decision from someone authorized to set policy. A well-written answer cannot substitute for those changes. For learning teams and AI adoption leads, the decision is how to handle the questions left open when the session ends. Our proposal is a small follow-up record that connects each question to its underlying obstacle, responsible owner and evidence needed to close it. AI can help organize that record within an approved environment; its suggested answer should remain a suggestion until checked. This is an editorial workflow, not a tested training intervention. The workshop and outcomes below are fictional. We have not run an employee study or independently evaluated an assistant for this article. ## Diagnose the obstacle before commissioning more training The CDC’s [February 2025 needs-analysis guidance](https://www.cdc.gov/training-development/php/about/assess-training-needs-conducting-needs-analysis.html) recommends identifying the source of a performance gap before choosing a solution. It distinguishes knowledge and skill needs from possible changes in policy, technology, work environment and operating procedures. This is training-development guidance, not evidence that a particular AI adoption program works. Instructional designer Cathy Moore makes a related distinction in her [practitioner explanation of a performance-analysis flowchart](https://blog.cathy-moore.com/is-training-really-the-answer-ask-the-flowchart/). Her worked illustration considers reference information and workflow improvements alongside a smaller practice activity. It is an instructional design approach and illustrative argument, rather than an experiment measuring AI training outcomes. Applied to an AI workshop, these sources motivate a diagnostic question: if the employee already knew exactly what to do, could they do it with the resources and permissions available? Treat the answer as something to investigate. Do not infer a skill deficit simply because someone asked for help. ## Preserve what the person actually needs to decide Imagine a fictional service team learning to use an approved assistant to draft customer replies. At the end of the session, employees leave these questions: | Unresolved question | Initial hypothesis to check | Useful next step | | --- | --- | --- | | “How do I notice a promise the assistant invented?” | Judgment needs practice | Work through a reply containing an unsupported commitment | | “Why can I not see the approved assistant?” | Access or configuration problem | Ask the support owner to check the employee’s intended setup | | “Can we include an attachment containing customer account details?” | Scope of permitted use is unclear | Obtain an applicable decision from the responsible policy owner | | “Which refund document should I use?” | Reference information is hard to locate or ambiguous | Confirm the authoritative source and make its location usable | These classifications are provisional. The access problem might turn out to be navigation confusion, which a short demonstration could solve. The apparently missing policy may already exist but use unfamiliar terminology. Allow an owner to change the classification and explain why. Preserve the task context that changes the answer. “Can we use attachments?” is too broad if permission depends on the application, data type and intended audience. Record those distinctions without copying actual customer files into a general learning tracker. Use a fabricated sample when a demonstration will suffice. Do not merge superficially similar questions until someone checks whether they have the same resolution. Two employees asking about document access may belong to different groups with different entitlements. One shared heading can organize the issue while retaining the separate cases underneath it. ## Make an answer candidate visibly different from approved guidance For this proposed process, a question record needs the original question, relevant task context, current status, owner, next update date and closure evidence. Add an answer field only with a clear status such as suggested, being checked or approved for the stated context. Keep links to the supporting material beside it. An assistant could propose tags, identify possible duplicates or draft a response from supplied sources. Give the reviewer the original question alongside that draft. The review should catch a changed scope, a missing condition or a claim that the supplied material cannot support. For the attachment question, a plausible generic paragraph about removing personal details is not an organizational permission decision. If the responsible owner has not established what is allowed, record the pending decision and the interim instruction supplied by that owner. Do not let the assistant invent either one. For the access question, creating an IT ticket records a handoff. It does not establish that the colleague can now use the intended assistant. For the practice question, sending a slide deck records delivery of material. It does not show that the person can identify an unsupported promise. Choose closure evidence that fits the obstacle. Where approved guidance changes, use the existing source-maintenance process. Our [HR policy-change guide](https://skillsforbusiness.ai/blog/hr-ai-assistant-policy-change-workflow/) addresses keeping an assistant’s answer path aligned with authoritative documents. Here, the earlier decision is whether the unanswered question requires a policy decision, a technical fix or learning support in the first place. ## Make it worthwhile to raise uncertainty Amy Edmondson’s [June 1999 study](https://web.mit.edu/curhan/www/docs/Articles/15341_Readings/Organizational_Learning_and_Change/Edmondson_1999_Psychological_safety.pdf) found an association between team psychological safety and learning behavior across 51 teams in one manufacturing company. Its methods combined interviews, observation and surveys. Learning behavior included asking for help and discussing errors. The paper explicitly notes that its cross-sectional survey design prevented demonstrating causality and that one company’s teams limit generalization. It predates workplace generative AI. It does not prove that a question tracker creates psychological safety or improves AI adoption. Our practical inference is narrower: design follow-up so asking a question is not treated as evidence of personal incompetence. Explain who sees identifiable questions and how they will be used. Separate shared learning issues from private support cases, and use established channels for sensitive employment matters. Do not promise anonymity if the process retains identities or recognizable details. A facilitator can acknowledge an unresolved issue and state the next update point without pretending to know the answer. Avoid a leaderboard of employees’ “knowledge gaps.” The proposed record exists to resolve obstacles to work, not to score the people who made those obstacles visible. ## Report what changed and what remains open Microsoft’s [Copilot feedback guide for Champions](https://adoption.microsoft.com/en-us/copilot/feedback-guide/) recommends tagging feedback, using several collection channels and returning a brief account of what was heard and done. It also includes escalation and explaining constraints when a change is not yet possible. This is vendor adoption guidance, not independent evidence of the effectiveness of this article’s workflow. For the fictional workshop, a follow-up could report that access was restored and confirmed with the affected colleague, the refund source was clarified, and a practice case is ready. It should separately say that the attachment question still awaits the designated owner’s decision. A polished recap should not turn that last item into an answered FAQ entry. Set the update date according to the consequence and the owner’s capacity. It is a commitment to communicate progress, not a promise that every policy or product issue will be fixed by then. If nothing has changed, say what remains blocked and who is responsible for the next decision. Keep the original question linked to the resolution. If the issue recurs, check whether the previous response was applicable, findable and usable before adding more teaching material. A repeated question may reveal that an answer was technically correct but unavailable at the point of work. ## Check resolution without rewarding a quiet room For an initial trial, review a small set of questions across the different routes. Can an authorized reviewer find the reason for each closure? Can affected employees use the answer or restored capability in the intended task? Are any items marked resolved merely because somebody sent a message? Record reopened issues and overdue updates alongside closed items. Interpret counts cautiously: fewer questions could mean better understanding, less participation or a less accessible way to ask. An increased number after opening a new channel is not, by itself, evidence that training became worse. For genuine learning needs, use a related task to check the capability the employee must retain. Our [guide to learning after AI assistance](https://skillsforbusiness.ai/blog/ai-workplace-training-measure-learning-after-assistance/) covers that assessment decision. Keep it separate from whether a follow-up message was delivered. Begin with the questions from one workshop. Route them, assign the appropriate owners and report the evidence behind each resolution. Leave a clearly described uncertainty open when the organization has not yet resolved it; that is more useful than a confident answer nobody is authorized to stand behind. ## Sources & further reading Sources checked for this article. Research findings and vendor claims are distinguished in the text. 1. [Assess Training Needs: Conducting Needs Analysis](https://www.cdc.gov/training-development/php/about/assess-training-needs-conducting-needs-analysis.html)US Centers for Disease Control and Prevention · Accessed 2 November 2026 2. [Is training really the answer? Ask the flowchart.](https://blog.cathy-moore.com/is-training-really-the-answer-ask-the-flowchart/)Cathy Moore; practitioner blog · Accessed 2 November 2026 3. [Psychological Safety and Learning Behavior in Work Teams](https://web.mit.edu/curhan/www/docs/Articles/15341_Readings/Organizational_Learning_and_Change/Edmondson_1999_Psychological_safety.pdf)Amy Edmondson; Administrative Science Quarterly, hosted by MIT · Accessed 2 November 2026 4. [Microsoft Copilot feedback guide for Champions](https://adoption.microsoft.com/en-us/copilot/feedback-guide/)Microsoft Adoption · Accessed 2 November 2026 Produced with AI-assisted research and writing. Examples and recommendations are our editorial synthesis unless attributed. This article does not claim independent product testing or human expert review. [Read our editorial policy.](https://skillsforbusiness.ai/blog/editorial/) ## Keep reading [AI pilots: decide where recovered capacity goes](https://skillsforbusiness.ai/blog/ai-pilot-workload-recovered-capacity/)[AI-rewritten surveys: decide whether the question still measures the same thing](https://skillsforbusiness.ai/blog/ai-survey-rewrites-question-comparability/)