Suppose a planning brief begins by asking how to add AI. The conversation has already chosen a class of solution before it has established the problem. An assistant can help generate options for a product, a career, or an organizational change. The reasons for choosing among those options still need a reference outside the model’s favorite answer.
A preprint on AI-related recommendations examines how training and question wording affect whether models suggest AI or machine-learning options. The researchers report differences between pretrained and released instruction-tuned systems, and changes associated with training data and framing. The measured frequency is not a direct score of whether the advice is appropriate. Some problems genuinely call for an AI component.
Put the decision criteria on the table before asking for the recommendation. What should improve for the person doing the work, and what evidence would show it? Those criteria give the team a way to examine a familiar answer instead of letting familiarity fill the gaps in an underspecified problem.
State the problem without naming the implementation
A planning brief should describe the user, the work, the current constraint, and the outcome that would count as an improvement. If the brief begins by assuming the desired technology, the subsequent conversation is already narrowed before the model offers any advice.
Ask reviewers to identify which requirements are essential and which are preferences. A proposal can then explain how it satisfies the requirements and where uncertainty remains. This makes it easier to recognize a sophisticated solution to a problem the organization did not actually need to solve.
Keep the problem statement even if the eventual choice includes AI. You can use it to divide the work: what belongs to the model, what the surrounding application must provide, and what still needs another method. That division turns a broad recommendation into something the people building and operating the service can examine.
Compare recommendations with the same rubric
Evaluate options against criteria chosen for the task, such as usefulness to the intended user, evidence quality, operating responsibility, cost assumptions, and the ability to verify an outcome. Keep those criteria stable while comparing proposals.
Do not give an option credit merely because its description includes an advanced model, an agent architecture, or a fashionable interface. Ask which requirement that component satisfies and what evidence supports the connection. The same scrutiny should apply to a more conventional option that is familiar but poorly matched to the work.
A model-generated score is not an independent reference unless the scoring process has been evaluated. Have accountable people review a sample of recommendations and their reasons. Their disagreements can expose missing requirements or ambiguous criteria that a numerical ranking would otherwise conceal.
Test the effect of framing
For a bounded assessment, present the same underlying problem with several reasonable wordings. Keep the factual constraints constant and observe which recommendations change. The research makes wording a concrete variable to examine rather than treating a single polished prompt as a neutral instrument.
Inspect the reasons given for the changes. A different answer may be justified if the wording clarifies a real preference. It is more concerning when the preferred solution changes while the requirements and evidence remain the same. A review should distinguish those cases rather than declaring all variation to be a failure.
Preserve the tested prompts and outputs with the evaluation record. That allows a later model or prompt change to be assessed against the same questions. Without the record, a team may mistake a change in framing for an improvement in strategic judgment.
Ask what would change the recommendation
Ask the proposal to show the conditions holding it up. What new evidence would change the recommendation? What must the pilot establish? Which failure would make the option unsuitable? Answering those questions gives the people approving the work something they can test later. The argument remains useful even when the confidence of its original presentation has faded.
Keep operational requirements in the discussion. A proposed AI capability needs an owner, a source of appropriate inputs, an evaluation method, and a support path. The recommendation should account for those responsibilities rather than treating them as details somebody else will resolve after approval.
This is our practical interpretation of the research, not a claim that the study measures enterprise project success. Recommendation frequency and business value are different quantities. The local evaluation needs to connect an option to the outcome the organization cares about.
Require the proposal to state what evidence would favor a simpler alternative. That might be an existing search feature, a deterministic rule, or a process change. Recording that condition before a pilot helps distinguish learning from a demonstration designed to validate the first recommendation.
Use the assistant to broaden judgment
A small starting point is to revisit one recent proposal. Remove the implementation name from the problem statement, ask for several plausible approaches, and evaluate them with the original requirements in view. Compare the result with the initial recommendation and investigate any material difference.
Keep an independent account of what makes the proposal worthwhile. The assistant can help expose options and assumptions, including good reasons to choose AI, while the team keeps responsibility for the judgment. Return to that account when the preferred answer changes. If you can still explain what would improve the work and how you would know, the conversation has given you something useful to decide with.