The 10 questions that separate a credible AI research brief from a vague ask. How to specify deliverables, avoid common brief failures, and evaluate the quality of what you receive.
Most AI research briefs fail before the research begins. A leader asks "can we benchmark these three models against our use case?" A team spends two weeks running evaluations. The results come back: a spreadsheet with accuracy numbers, no context, no recommendation, and no actionable conclusion.
This is not a team failure. It is a brief failure. The leader did not specify what "benchmark" means, what the evaluation task is, what a good result would look like, or what decision the research needs to support. The team answered the question they were asked : and the question was too vague to produce something useful.
A research brief does not just specify what to study. It specifies what decision the study must inform, what quality of evidence is needed to make that decision, and what the deliverable must contain for the reader to act.
Name the specific decision: "Proceed to pilot with Model A" or "Allocate budget for fine-tuning" or "Reject vendor proposal." Research that does not point at a decision tends to produce findings that cannot be acted on.
Not "how well does it do document review" but "how accurately does it identify clauses requiring legal review in commercial lease agreements, with a false negative rate below 2%." See Module 4 for the input/output specification framework.
Sample size, confidence level, effect size. Written before the evaluation begins : not negotiated after seeing results.
Require the team to identify them before running the study. Prompt variation, sample selection, evaluator bias. Name each one and specify how it will be controlled.
Automated metrics, human raters, or LLM-as-judge. If human raters: what is the rubric, what is the inter-rater reliability target (Cohen's kappa above 0.6 is a reasonable floor), and are evaluations blinded?
The comparison set should be motivated by the actual decision, not by what is easy to test. If you are choosing between vendor A and vendor B, both should be evaluated under identical conditions.
Production data from the last 4-8 weeks is preferable to synthetic or historical data. Specify source, sampling method, and any preprocessing. Include the data preparation steps in the deliverable.
Not "a report." Specify: executive summary (one page, decision-ready), methodology section, raw results with confidence intervals, and a recommendation section that states the supported decision. See the deliverable spec below.
Methodology review before running the study catches design flaws that cannot be corrected after the fact. Designate a reviewer who is not on the team running the evaluation.
Define this upfront: "If no model exceeds 80% with the required sample, we will run a second stage with a different task decomposition" or "we will delay the decision by 30 days and run the evaluation on a larger sample." Do not leave this undefined : it determines whether an inconclusive result is informative or a wasted two weeks.
| Section | Required content | Common failure |
|---|---|---|
| Executive summary | Decision supported, evidence quality, recommendation, confidence level | Describes the methodology without stating a conclusion |
| Methodology | Task specification, sample description, confounder controls, evaluation approach | Lists benchmarks used without explaining why |
| Results | Primary metric with confidence interval, secondary metrics, error analysis on failures | Raw accuracy numbers with no statistical context |
| Limitations | What the evaluation does not cover, what could change the conclusion | Missing entirely : implies false certainty |
| Recommendation | Explicit go/no-go on the decision, conditions under which the recommendation holds | "Results are promising" without a clear recommendation |
When a research deliverable lands, scan for these patterns before accepting it as input to a decision.
Accuracy without confidence interval. A number like "87.3% accuracy" with no confidence interval does not tell you whether the result would hold on a different sample. Require error bars or sample size disclosure for every headline number.
Recommendation missing from executive summary. A summary that describes what was done but does not state what to do next is not decision-ready. The team either does not know the answer or is hedging. Ask which it is before accepting the deliverable.
Limitations section absent. An evaluation without stated limitations is overclaiming. Every real evaluation has scope boundaries. If the team did not write them, they did not think about them, which means they may have built a study that misses important failure modes.
A high-quality research deliverable makes you less confident in the answer, not more : because it shows you exactly where the evidence is weak and what would change the conclusion. Overconfident deliverables are a sign of incomplete analysis, not strong results.
You can now commission AI research that produces decision-ready deliverables. The final module covers communicating uncertainty: how to translate research findings into board-ready language and executive decisions.
The 10-question brief framework, deliverable quality standards, and three red flags to scan for when a report arrives.
From Research Findings to Enterprise Decisions: how to communicate uncertainty to a board, and when evidence is strong enough to act.