Research remains one of the most effective tools in public relations and thought leadership. Revelatory insights from well-crafted surveys continue to cut through crowded inboxes and provide journalists with evidence they can trust. Data gives media campaigns a foundation rooted in fact. In a media environment defined by shrinking newsrooms and constant information flow, credible findings from research are often the difference between a pitch that gets noticed and one that is ignored.
What is changing today is how that research comes together. Artificial intelligence is beginning to reshape the process, giving communicators new ways to analyze more information and data faster. These capabilities can help research keep pace with a faster news cycle, but they do not alter the fundamental requirement: if findings are not credible and transparent, journalists will not use them.
That influence now extends across the entire research process, from anticipating stories to designing surveys, analyzing data and presenting results. At each stage there is room to apply AI, but credibility remains the standard that determines whether research earns coverage. Here are a few best practices for professionals to consider in a new era of research and technology.
1. Stay Ahead of the Story
For journalists, timing can be just as important as the findings themselves, and this is an area where AI is beginning to make a clear impact. Even the most rigorous dataset may be overlooked if it arrives after a story has peaked. AI offers a way to spot signals of an emerging issue earlier, giving communicators a head start.
By scanning coverage, public records and online conversations across social media platforms and forums, AI tools can highlight where attention is building before a topic dominates mainstream headlines. That early signal gives PR teams the opportunity to prepare credible supporting research for journalists as they begin reporting, instead of trying to compete in a media landscape that is already saturated.
AI may help communicators move earlier, but speed on its own does not secure coverage. Journalists are inundated with pitches, many backed by weak or unverifiable data. What earns attention is research that is both timely and credible.
2. Design Questions That Withstand Scrutiny
If AI can help identify when to act, the next step is deciding what to ask. How questions are framed greatly impacts the credibility of research.
AI tools can produce lists of survey items in seconds, but speed does not equal sound design. Questions that look acceptable on the surface can easily be too broad to be useful. Asking “Do you trust artificial intelligence?” is simple, but meaningless without context. Trust in the workplace? In media? In government? The wording matters, because the answers are only as credible as the question.
There is also the risk of bias. AI systems trained on existing data may suggest questions that lean toward expected outcomes instead of uncovering new insights. In earned media, leading or biased questions undermine the very credibility that research is meant to provide.
This is why human oversight is essential. Experienced researchers know how to frame questions that avoid bias, align with campaign goals and generate insights reporters and editors can rely on. AI can draft quickly, but it cannot ensure that research will withstand scrutiny once it reaches the newsroom.
3. Separate Insight from Error
Collecting and analyzing data has always been the most time-intensive part of research. Here, AI delivers clear efficiencies. It can flag questionable responses, sort large volumes of open-ended feedback and produce summaries that speed up the work.
Those gains matter when news cycles move quickly. But they also create a new risk: results that look reliable on a dashboard but fall apart once examined closely. AI often misses nuance, misreads sarcasm or reflects bias from the data it was trained on. For example, a negative comment delivered with sarcasm can easily be misclassified as “positive sentiment” by automated systems. That kind of mistake may not be obvious in a data summary but becomes a problem when a journalist asks for detail.
This is why validation is critical. Human oversight is needed to confirm that results are accurate, relevant and defensible. AI can accelerate analysis, but it cannot replace accountability.
4. Deliver Research Journalists Can Trust
The real test comes when research is shared with the press. Journalists want to know where the numbers came from, who was surveyed and how the findings were verified. If those answers are unclear, the story will not run.
AI can support this stage by creating summaries, visuals or data briefs that make findings easier to digest. Those tools save time, but presentation does not replace rigor. Editors will not risk publishing results that cannot be explained and defended.
Many reporters and editors are also experimenting with AI themselves. At The New York Times, for example, the technology is used to support human journalists in their data analysis, headline drafting, translations and audio production. The Columbia Journalism Review has also documented how journalists in other newsrooms are testing AI for tasks like drafting, categorization and editorial support. This hands-on use makes journalists even more critical of the research they receive. They understand the technology’s strengths and shortcomings, and they expect communicators to be transparent about when and how it was used.
Credibility remains the deciding factor. Research earns coverage when it is transparent and relevant, not when it is automated.
The Takeaway: Credibility Remains Key
Artificial intelligence is reshaping how research is conducted in PR. It can speed up timelines, broaden the range of signals that can be analyzed and give communicators new ways to bring insights into stories while they are still unfolding. These shifts matter, but they do not change the core principle: research earns coverage only when it is credible.
Journalists still expect clarity on methods, transparency in sourcing and confidence that the data is accurate. No tool can deliver that on its own. That responsibility belongs to the communicators who design and oversee the work.
Disclosure may soon become part of that standard. Industry groups such as the American Association for Public Opinion Research (AAPOR) have already set expectations for polling transparency. And just as researchers today are expected to explain methodology and sample size, PR research may need its own guidelines for how AI is used.
The teams that succeed will treat AI as an assistant, not a substitute. They will use it to move faster without cutting corners, delivering research that journalists can trust, because it meets the standard that has always mattered in earned media: credibility.
