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These are different modes of work. A surprising suggestion can be useful during brainstorming and inappropriate in a customer explanation that must be accurate.
Separate creative AI at work from factual verification
Tell the tool which mode the task requires. If you are exploring, invite alternatives. If you are preparing factual material, define the evidence and limits.
Microsoft's article on the art and science of working with AI examines this balance. The practical framework below separates possibility generation from the evidence needed to use a result.
In a hypothetical training team, AI can suggest several workshop activities from fictional scenarios. Once the team selects one, it must check the activity's learning purpose, accessibility, equipment and suitability for the actual audience.
Creative value does not excuse a misleading factual claim. Equally, the fact that an untested idea is uncertain does not make it useless as a starting point.
Label the output you are asking for
Use explicit categories: ideas, hypotheses, source facts and decisions. This makes review more focused.
| Output type | Useful treatment |
|---|---|
| Idea | Explore whether it solves the problem |
| Hypothesis | Identify what evidence would test it |
| Factual statement | Check against a reliable source |
| Recommendation | Examine assumptions and consequences |
| External action | Obtain the required authority and review |
A proposal can contain several categories. Ask the tool to separate them rather than presenting everything in the same confident tone.
For the hypothetical workshop, “use a short role-play” is an idea. “Role-play will improve performance by a particular percentage” is a factual claim requiring evidence. Do not let the second appear simply because the first sounds plausible.
Give exploration useful constraints
State the audience, available time, resources and boundaries. Constraints can produce options that are more relevant than an unrestricted list.
Try:
Suggest different ways to practise explaining a process change to a team. Use fictional scenarios, no special equipment and a short session. For each option, state the skill practised, a limitation and what a facilitator should observe. Do not make effectiveness claims.
Review for variety and relevance. Discard ideas that merely rename the same activity or assume resources you do not have.
At this stage, avoid asking the tool to crown a winner. First determine what the team needs and what can be tested.
Change the review standard before implementation
Once you select a possible approach, stop expanding the idea list and examine the chosen option carefully. Look for missing information, unintended effects and practical obstacles.
Ask the people affected to review it. A facilitator may notice that the exercise requires more preparation than expected. A participant may identify a scenario that feels unrealistic or exposes private workplace issues.
Test a small version with appropriate permission. Record what happened rather than treating a persuasive proposal as proof.
NIST's AI Risk Management Framework provides risk-management context. Your own checks should be proportionate to the task and its consequences.
Avoid the trap of polished uncertainty
AI-generated prose can make an assumption look settled. Keep unresolved questions visible when you turn an idea into a proposal.
In the hypothetical workshop, you may know the activity fits the available time but not whether remote participants can use it comfortably. Put that uncertainty in the plan and assign someone to investigate it.
Do not ask the tool to manufacture supporting testimonials, research or experience. If evidence is absent, say what you know and what remains to be tested.
Keep an idea log separate from a decision record
An idea log can contain speculative options, unusual combinations and questions worth exploring. A decision record should contain the selected action, evidence, limitations and responsible owner. Keeping them distinct prevents an interesting possibility from acquiring authority through repetition.
In the hypothetical training team, the idea log might contain several role-play formats. The decision record would explain which format was chosen, what the team checked, what remains uncertain and who approved the pilot.
If a suggestion has not been tested, label it accordingly. Do not remove the qualification when moving it into a presentation or email. The same statement can become misleading simply because its context disappears.
After a pilot, update the decision record with what actually happened. Note a failed assumption as clearly as a useful observation. This gives the next person a more reliable starting point than a polished proposal preserved as though it were a completed case study.
The separation also makes experimentation less intimidating. Colleagues can contribute tentative ideas without implying a commitment, while decision owners have a clear place to document the evidence needed for action.
Make judgement a deliberate step
Before using an output, ask which parts are creative suggestions and which parts require verification. Review the second group against actual evidence.
For your next task, create two separate stages: generate options, then check one selected option. That small boundary can make experimentation easier to manage without confusing possibility with proof.
Explore PiOfAI, the AI workflow guide and leading through change for the transition from a useful idea to an accountable process.
AI-assisted researched guidance from PeakBelt. Hypothetical examples are labelled in the text. See our editorial approach.