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When I worked for Erick Goss, then VP of Marketing for Magazines.com, he said, “Always make new mistakes.” He meant managers don’t mind coaching you, but they do mind repeating themselves. AI is similar.
AI systems weren’t designed to showcase generic information repeated across the web. They were designed to bring facts and context together to present the latest, most useful information to users. Publishing your own data and drawing on your own analysis and information provides the unique, first-hand material tools like ChatGPT, Claude, Gemini, and Grok look for when answering questions. When you make relevant and reliable information accessible, you give AI useful material to draw on.
This creates a valuable opportunity: if your company has accumulated proprietary and useful industry insights, you should publish them.
Why Is Original Research Important for AI Search?
Research-driven content rooted in measurements, observations, results, or expert analysis provides the evidence that AI looks for to answer a specific question.
Google’s guidance on helpful content is an excellent resource to consult. It encourages reliable, people-first content, including original reporting, research, and analysis. Similarly, in its 2024 announcement introducing ChatGPT search, OpenAI explains that the tool searches the web to provide timely answers with links to relevant sources, connecting users with original, high-quality content they can then explore further.
Neither guarantees citations for publishing something new, but aligning with these criteria can help you develop content that contributes useful evidence for AI systems while giving readers the context they need when seeking new information.
Your business has this evidence already. The challenge is in identifying it, testing and clarifying what it supports, and then making it accessible to AI and search systems. Here is a list of sources for research-driven content types you can publish.
What Unique Data and Insights Can You Publish?
Here are ten types of original content you can publish to improve your chances of getting cited in AI search engines.
1. Proprietary Benchmark or Index Reports
Aggregate your platform, transaction, or usage data into recurring benchmarks for pricing, salaries, performance, or conversion rates. For example, a software provider could report typical onboarding times across customer segments. Define the sample and reporting period so readers understand whose experience the benchmark represents.
2. Original Research Studies and Whitepapers
Commission or run rigorous studies on questions your industry hasn’t adequately answered. Publish the methodology alongside the findings, including participant recruitment, sample size, question wording, and limitations. A whitepaper earns credibility through its evidence; the format alone doesn’t make it authoritative.
3. First-Party Product and Telemetry Data Analysis
Mine anonymized usage, sensor, or log data to reveal trends only you can see. Equipment records might reveal repeated failures or show which maintenance intervals are associated with fewer breakdowns. Always protect customer information and explain what the data measures.
4. Expert Practitioner Knowledge and Frameworks
Turn your senior and specialized staff’s experience into guides others can follow. Ask about their process: how they make decisions and when their approach changes. Include examples and explain when the advice applies. Give this process a clear name so readers can recognize, reference, and use it.
5. Longitudinal Trend Tracking
Publish the same measurements over time to build a useful time-series in your field. Tracking quarterly costs, lead times, or customer behavior can reveal shifts a single snapshot misses. Keep definitions consistent and disclose methodology changes so readers can judge whether comparisons remain valid.
6. Structured Comparison and Teardown Content
Create objective, data-backed comparisons of tools, methods, or approaches that answer “Which is better, and why?” Explain your testing conditions, evaluation criteria, and commercial relationships. Show where each option performs well and where it falls short. Let the evidence establish which option suits which users. Tables are a good way to illustrate these insights clearly.
7. Customer Outcome Case Studies With Hard Numbers
Document results with specific metrics, before-and-after data, and conditions. Explain the starting point, intervention, timeframe, and other changes that could have influenced results. Secure permission to publish. Clarify that one customer’s outcome doesn’t guarantee similar outcomes for every customer.
8. Community and Forum Aggregation
Review customer forums, support tickets, and community discussions over a defined period, then group questions by topic. Use recurring questions to build a how-to guide, and publish the themes that reveal customer needs. Explain which sources you reviewed, remove identifying details, and clarify which customer segment, industry niche, or community the findings represent.
9. Predictive Models and Forecasts
Turn your data into forward-looking projections that others can reference and revisit. Explain the trends, data, and assumptions behind your forecast, along with its date and range of possible outcomes. Update the forecast as new information becomes available so readers can track emerging trends and use your insights to guide their planning.
10. Open Datasets and Data Tools
Release cleaned, well-documented datasets or interactive calculators that help people find answers to their own questions. Include definitions, sources, update dates, and usage terms to deliver a valuable resource people can understand and turn to confidently.
How Do You Make Original Insights Trustworthy and Findable?
Start with a customer question, then choose the evidence and format that answers it best. Clearly present the finding, its scope, and limitations. Identify the authors’ expertise, explain the method, and distinguish results from interpretation.
Publish your main findings directly in a relevant blog post, research page, landing page, or service page, using clear text and tables where helpful.
You can also offer downloadable data for readers who want more detail. Google’s AI search guidance says that, to appear as a supporting link in AI Overviews or AI mode, a page must be indexed and eligible, so we recommend having your SEO team confirm your site is fully crawlable by standard search indexers like Googlebot and OAI-SearchBot, rather than inadvertently blocking them in your robots.txt file.
Pro tip: Originality does not replace SEO fundamentals; it demands them. Generic and superficial content is dead in the AI era, but original research is bulletproof. Research-driven content provides unique data that large language models (LLMs) can cite and quote directly.
At GoEpps, we bring your firsthand insights into a content strategy built around customer questions, helping you inform and engage audiences and boost visibility in the AI search era.
Book a free consultation to discuss your goals, and let’s identify existing opportunities to put your industry expertise to work.
