An AI company often has data no publisher, analyst or competitor can reproduce: usage patterns, evaluation results, implementation failures, support questions or market observations. Publishing a bold percentage without method creates attention but weak evidence. Useful original research makes the question, sample, method and limitations inspectable.
The direct answer
Build research around one decision-relevant question and publish the population, sample, period, definitions, method, results, limitations and update policy. Give every material number a stable explanation that a buyer, journalist or answer system can inspect.
1. Choose a question the company can answer
Use proprietary access, not a generic survey that could be produced by anyone. Examples include where AI deployments stall, how evaluation criteria differ by use case, which integration errors recur or how buyer requirements change by market.
The question should help a buyer make a decision and connect naturally to the company's expertise without predetermining a flattering result.
2. Write the method before collecting results
- Define the population and inclusion rules.
- Specify sample size and period.
- Define every category and metric.
- Record missing data and exclusions.
- Separate observation from interpretation.
- Set privacy, consent and confidentiality controls.
- Choose the analysis before seeing the preferred conclusion.
3. Create a claim ledger
| Published claim | Evidence record |
|---|
| Percentage or rate | Numerator, denominator and exclusions |
| Change over time | Comparable periods and methodology |
| Comparison | Same test conditions and sample basis |
| Customer behavior | Privacy-safe definition and segment |
| Expert conclusion | Named author and limits of inference |
4. Publish an extractable canonical page
State the main finding early, then provide methodology, findings, tables, definitions and limitations in visible HTML. Use a descriptive title, clear headings, stable URL, publication/update date, named organizational source and relevant images.
Google's Article guidance recommends clear author and date information and representative high-resolution images. Markup should match the visible article.
5. Distribute the evidence, not a slogan
Prepare a concise media brief, expert commentary, social extracts, sales summary and partner note, all linked to the canonical research. Give external readers enough context to quote accurately.
Correct misquotation rather than amplifying it. Record where the research is cited and which buyer questions it triggers.
6. Measure research value
Track relevant links and citations, qualified referral visits, branded query movement, sales usage, invitations for expert comment, product conversations and corrections. Do not value a research asset only by page views.
Update the study only when the method remains comparable. If it changes, explain the break rather than presenting a false trend.
What Crynet can help decide
Crynet's crypto market research and audience intelligence work frames the question and method. Crypto content marketing produces the canonical asset, while executive thought leadership turns the findings into responsible expert communication.
Send us the data you can access, the buyer question, privacy constraints and the claims leadership hopes to test. We can determine whether the evidence supports a credible study—and what not to claim.
Sources and methodology
Original research can improve usefulness and citation eligibility, but publication does not guarantee coverage, links, AI citations or commercial outcomes. Privacy, legal and methodological review may be required.
