AI is increasingly good at helping researchers analyse data. That part is no longer controversial. What’s less talked about is what happens after the analysis – when insights need to be shaped into a story that people can actually understand and act on.
Recently, I found myself in an unfamiliar position. I’d done thorough research, validated the findings, and used AI appropriately to synthesise a large volume of data. And yet, when I presented the work, it didn’t land in the way I expected.
The issue wasn’t the quality of the insights. It was the story I told with them, and how subtly that story had been shaped by the tools I used along the way.
This is a reflection on using AI in research storytelling: where it helped, where it quietly constrained my thinking, and what I’ll do differently next time.
The context: lots of data, sensible intentions
I’m currently working on a programme integrating a new off-the-shelf online data management system. As part of this work, I conducted research with two different internal stakeholder teams, as well as external users of the existing process/system.
The aim was to understand the “as-is” experience in full detail: the challenges, how they showed up across teams, and how they played out across the end-to-end journey.
The interviews returned a lot of data. Rich, nuanced, and detailed. The kind of dataset that’s incredibly valuable and slightly intimidating.
Where AI genuinely helped
This is where AI did exactly what it promised.
I used it to help:
- Synthesise large volumes of qualitative data
- Identify recurring themes and patterns
- Surface challenges across the end-to-end journey
It gave me speed, confidence, and reassurance that key insights weren’t being missed. I mapped the findings out across the “as-is” journey on a MIRO board and structured a report that presented challenges by each stage of the process.
At the outset, this felt entirely reasonable. Logical, even. If the team could clearly see where the pain was occurring, they could start to address it.
The problem I realised too late
As I moved into report writing – and later, presenting the findings – I could feel something wasn’t quite right.
The work was thorough.
The insights were accurate.
The facts were checked.
And yet, the findings felt repetitive. The narrative felt flat. Instead of a clear articulation of the big issues, the audience was being taken through a long list of challenges without a strong sense of what really mattered or how it all connected.
This was unusual for me. I’ve always considered storytelling a strength when presenting research. Normally, I move from synthesis on a MIRO or Mural board into a deck with relative ease, shaping insights into a narrative that helps teams think and act differently.
This time, that flow wasn’t there.
The subtle trap of AI-assisted structure
After the session, I spent time reflecting on what I’d done differently.
The key difference wasn’t the project or the complexity of the work; it was my starting point.
This time, I began with two detailed word reports that AI had helped me generate, outlining challenges by process stage. That’s not how I usually work. In the past, I’ve tended to move straight from visual synthesis into storytelling, shaping the narrative myself as I go.
Instead, I found myself reacting to a structure that already existed.
The structure made sense. It was coherent and comprehensive. But it wasn’t necessarily the story that needed to be told.
This is where AI can quietly lead you down a path you didn’t consciously choose:
- It produces a logical, complete structure
- That structure feels “right,” so it goes largely unquestioned
- You start optimising within it, rather than stepping back and reframing
Fact-checking didn’t solve this, because the problem wasn’t accuracy – it was meaning.
Why checking the facts isn’t enough
Everything in the report was correct.
That didn’t make it effective.
Good research storytelling isn’t just about describing what’s happening at each step of a journey. It’s about:
- What really matters
- What connects issues together
- What explains why things are breaking down
- What decision-makers actually need to understand
AI is excellent at surfacing what.
It’s far less capable of deciding so what.
That still requires human judgement, context, and a point of view.
Going back (and reframing the story)
I went back to the findings and reworked them into a shorter report with a very different structure. Instead of following the process end to end, it focused on a clear narrative about the core issues shaping the experience overall.
The result was:
- Shorter
- Clearer
- Easier to follow
- More actionable
In the process, I created two detailed Word reports, a needlessly long deck, and finally the report I should have produced at the outset.
That’s not time I’ll get back, but it is a lesson I’ll take forward.
What I’ve taken away
A few reflections I’ll be carrying with me:
- AI is extremely helpful in making sense of complexity
Especially when working with large volumes of qualitative data and needing confidence that key themes haven’t been missed. - The biggest risk isn’t over-reliance, it’s unexamined influence
I didn’t outsource my thinking to AI, but I did allow an AI-generated structure to become the default frame for the story. That influence was subtle, logical, and easy to accept, which is exactly why it’s worth paying attention to. - Accuracy alone doesn’t create insight
Everything in the report was correct. That didn’t make it coherent, compelling, or easy to act on. - Storytelling requires conscious human framing
I was using my judgement throughout, but I wasn’t always aware of how my framing had been shaped upstream. The lesson wasn’t to “use my brain more,” but to pause earlier and ask whether this was truly the story I wanted to tell.
AI didn’t weaken this work, but it did make it easier to follow a path I wouldn’t have consciously chosen.
What I’ll do differently next time
- Use AI heavily for synthesis, but pause before locking in any AI-generated structure
- Sense-check the narrative before writing detailed reports or decks
- Ask: “If I had to explain this in three slides, what’s the story?”
- Separate process mapping from insight storytelling more deliberately
- Treat AI outputs as prompts, not starting points
AI can help you find the insights, but it’s still up to you to decide which story is worth telling.
About the Author
Lisa Mills
Lead User Researcher
