‘Businesses are using AI more.’
That’s the kind of line that ends up in a press release and goes nowhere. There’s nothing wrong with it, but it just feels…empty. There’s no mention of a sector, no scale, nobody’s assumptions are challenged, no new learnings, and sadly no reason for a journalist to pick it up.
Now compare it with this example, ‘94% of organisations use AI, but only 14% trust it to act without a human checking it.’
There’s a reason to keep reading. Part of that are the numbers themselves, 94% is close to universal, and 14% sits at the other end of the scale. But the real work is done by the gap between them. The first stat pulls you up, almost everyone is on board with this technology. The second one drops you straight back down, a minority to trust it will work alone. It’s a small rollercoaster in two sentences, and that swing is what makes the finding stick an hour later, not the size of either number on its own.
When writing headlines, focus on sectors as well, especially if you’re talking about a niche industry
A generic stat about AI adoption in a naturally tech-forward industry (software, telecoms, IT services, cybersecurity, etc) barely registers, because it confirms what everyone already assumes. But the same stat in an industry (say education or healthcare) with a cautious adoption strategy creates instant tension. The finding is specific, and the context works against expectation. People pay attention when research contradicts what they think they know about an industry, not just when it hands them a bigger number.
The same principle shows up again when comparing two ways of describing the same kind of finding.
For example, ‘A significant proportion of organisations report inefficiencies’ tells a reader almost nothing. No number, no detail on what the inefficiency actually is. But, ‘62% of finance directors have never checked whether their software licences are still in use’ does both jobs at once. It’s numerically precise, and it names the exact behaviour behind the number.
Most people think specificity is only about the stat. But it’s just as much about being precise on the situation the stat describes. A number without a situation is still an abstraction, however accurate it is.
That’s really the whole argument. Handed a percentage with nothing else attached, no situation, no example, no sense of who it affects, most readers won’t believe it and won’t find it interesting. Data has to connect to something a reader recognises from their experience, and it has to answer plainly why it matters outside the spreadsheet it came from.
Once you’ve got a finding worth publishing, you need to decide on how much of it to use straight away.
Putting everything into one release has a case for it. A detailed release covering the full scope of what you found can position you as an expert on the subject.
The reader gets full context in one go. But there’s a cost…
Important findings can get buried under everything else you’re saying, and you only get one moment of attention before the news cycle moves past you. Holding some of the research back and releasing it in stages solves both problems – Short, sharp takeaways land harder than a long, detailed release, and they’re easier for a reader to absorb.
Spacing your findings out over multiple releases lets you explore the same research from different angles over time, building repeated engagement rather than one spike of attention that fades. It also lets you move quickly on the strongest finding first, instead of waiting until everything is packaged together.
But the risk with phasing the report, is in your timing.
If you’re holding data back for a later release, you need to keep watching what’s happening in the market while you wait, what competitors are saying, how the conversation around your topic is shifting, rather than treating the release date as final. That’s exactly where a good PR agency can help you.
There’s an exception worth naming here too.
If you’re a genuine thought leader on a subject, and the research itself is distinctive enough, there’s nothing wrong with talking about the same piece of work for an extended stretch of time. Staying visible matters less than staying relevant to what people are paying attention to right now. In a market that moves as fast as most B2B tech sectors do, a brand that isn’t consistently showing decisive thinking starts to look like it’s stopped developing.
Finally, a stat needs a reason to surprise someone, a situation to sit inside, and a sense of timing that responds to the market rather than ignoring it. Get those three things right when writing a press-release, and the research will help you shine.
If this piqued your interest, we’re certain you’ll find our ‘Quickfire Insights’ recording helpful. In under 19-minutes you’ll learn how to extract the best headlines to enrich your press-releases and thought leadership content.
About the author: Who is Lauren Kerr?
She is our summer-intern (2026), and a student at the Royal Holloway, University of London. She is pursuing a BA in Geography.
Lauren has been helping us analyse trends and checking data, learning about research, and applying her knowledge to build research-driven stories.
In her spare time she loves playing football and watching films.
This opinion-piece is a reflection of her learnings and understanding after working on a couple of projects and reading the ‘From Data to Story’ guide.
