Checklist · 4 minute read
AI User Training Checklist: Teaching People What It Does
Training on AI tools usually covers features and skips the limits, which is backwards. Users need to know what the system does badly, when to escalate, how to report a problem, and that their corrections improve it. Without that, they either over-trust it or abandon it.
Training on AI tools usually covers features and skips the limits, which is the wrong order. This checklist covers what users actually need, drawn from FISTA Solutions' AI enablement delivery work.
What do users actually need?
Six things, in this order.
| Topic | Why it comes here |
|---|---|
| What it does badly | Prevents over-trust |
| What it does well | Drives appropriate use |
| When to escalate | Concrete criteria |
| How to report a problem | Feeds quality improvement |
| What happens to their role | The unspoken question |
| How to check the output | The skill they need daily |
Capabilities and limits
Limits first, with examples.
- Cases the system handles well, with examples
- Cases it handles badly, with examples
- Cases it should never be used for, stated explicitly
- Known failure modes described in plain language
- Confidence signals explained if the system provides them
- What the system can and cannot see explained
- Examples drawn from their actual work, not generic ones
Checking output
The skill they will use every day.
- How to verify a factual claim against the source
- How to open and check citations
- What typically goes wrong and where to look for it
- Practice on real examples including wrong ones
- Time expectations for review set realistically
- What to do when the output is partly right
- Guidance on when reviewing costs more than doing
Escalation
Concrete criteria, not judgement.
- Case types that must always go to a person listed
- Value or risk thresholds stated numerically
- How to escalate, with the actual mechanism shown
- Expected response time on escalation
- Who to contact for different problem types
- Permission to escalate stated explicitly by a manager
- Assurance that escalating is not penalised
Feedback
Close the loop or the reports stop.
- How to report a wrong or poor output
- What happens to a report explained
- Corrections explained as becoming test cases
- Examples of past reports that produced changes
- Reporting made low-friction, ideally in the interface
- Someone named as the recipient
- Follow-up to reporters when their issue is addressed
Role and expectations
Address the question everyone has.
- Effect on their role stated honestly
- Changes to targets or metrics explained
- New responsibilities described, including exception handling
- Training for the new work provided
- Management position on headcount stated clearly
- Questions taken openly rather than deflected
- Concerns recorded and responded to
Delivery and refresh
Training that happens once decays with the system.
- Training delivered before access, not after
- Hands-on practice included, not slides only
- Reference material available afterwards
- Refresher when scope or behaviour changes materially
- New joiners covered by a standing process
- Effectiveness checked by observing actual use
- Training updated from what users report
What are the most common failures?
Features before limits. Escalation guidance that says use your judgement. Feedback with no visible outcome. Avoiding the job security conversation. And training delivered once at launch.
Who should own this?
The business function using the system owns the training; the build team supplies the content on capabilities and limits. Training owned by engineering tends to explain the system rather than the work.
How often should it run?
Before access, on material change, and for every new joiner. A short refresher after the first month catches the questions that only arise from real use.
What evidence should it produce?
Training completion records, observed usage after training, escalation rates against expectation, and feedback volume. Falling feedback volume usually means the loop is not closing.
What if users stop using it?
That is information, not a training failure to be solved with more training. Ask them why, and the answer is usually that reviewing costs more than doing, or that it was wrong in a way that cost them.
Both are product findings. Adoption problems are frequently quality problems wearing a change management costume. See AI adoption strategy.
What should you do first?
Ask three users what the system does badly. If they cannot answer, the training covered features and skipped the part that matters.
How FISTA Solutions helps
FISTA Solutions builds and operates production AI systems through AI agents, AI enablement, and forward deployed engineering: training that leads with limits and concrete escalation criteria, and feedback loops closed so users see their reports produce changes, decisions documented with their reasoning, and handover that leaves your team able to maintain what was delivered. The record is 150+ projects for 50+ companies across 12+ countries.
To adapt this checklist to your environment, message FISTA on WhatsApp, or read AI adoption strategy.
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Clear answers
Questions raised by this field note.
Straightforward guidance for evaluating scope, fit, and the next step.
01Why teach limits first?
Because over-trust is the more damaging failure. A user who knows where the system is weak uses it well; one who only knows the features accepts a wrong output confidently.
02What makes escalation guidance work?
Concrete criteria rather than judgement. A list of case types that should always go to a person is actionable; an instruction to escalate when unsure is not.
03Why does the feedback loop matter?
Because users who see their reports produce changes keep reporting. Users whose reports disappear stop, and the team loses its best source of production quality information.
04Should job security be addressed?
Yes, directly. Everyone is thinking about it, and avoiding the subject produces quiet resistance that is harder to address than an honest conversation would have been.
05How often should training be refreshed?
When the system's scope or behaviour changes materially, and for new joiners. A model change that alters behaviour should reach users as guidance, not as a surprise.
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