Amplifying Data Stories with AI: Unleashing the Power of Generative Tools
A small surprise arrived today: my mom’s Ancestry.com results. She hadn’t mentioned she was getting them, so I was naturally curious about what I might discover about her and our family. As I clicked through the charts and images, I saw some expected and unexpected things. I was surprised by the fact that napping is an inherited trait. Now, I know why I can’t and don’t take naps—there’s a gene responsible for it. I guess napping isn’t likely in my future. And that means my daughter’s ability to recharge with a cat nap is something she didn’t inherit from either of us. This discovery sparked my curiosity about other inherited traits. It also made me wonder if Ancestry.com uses generative artificial intelligence (AI) tools to enhance content. This experience shows the power of a data story—how a simple insight can tell us something new and meaningful.
Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we’ll augment our intelligence.
— Ginni Rometty
Marrying Human and AI talents
Storytelling is a skill in market research that elevates our value as a researcher. Much like how my family’s traits were revealed through AI and data, the use of generative AI tools offers the potential to amplify the impact of our data stories and improve our storytelling.
This is the year Gartner predicted data stories will be the most widespread way of consuming analytics. As researchers, we need to pivot our storytelling to take advantage of generative AI tools and augment our natural capabilities. Generative AI tools are most useful when leaning on aspects where they are more efficient than a manual approach (Dykes, 2024).
There are two types of tools available for data storytelling with generative AI. AI-supported tools occur in the authoring process and don’t generate content without prompting. Think of these tools as personal assistants who help spark ideas and bring a story to life (Restackio, 2025). For example, Data Analyst by ChatGPT can analyze a data set, give insights, create graphs, and help with narrative, including recommended action (Franklin, 2024). Tableau and Power BI visual analytics tools allow you to ask questions about the data through a chat feature that will generate supporting narratives, charts, or dashboards. For example, a Tableau user can type “show me sales by region over time,” and AI will create a trend chart for you (Kumar, 2024). This video shows how the Copilot AI feature can be used in Power BI (Guy in a Cube, 2024).
Other generative AI tools autonomously create data stories, namely AI-generator tools. These tools are beneficial in data preparation by automating data cleaning and modeling (Restackio, 2025). AI-generator tools like DataRobot can transform raw data into clear, concise narratives with key insights, trends, and anomalies.
AI tools like CanvsAI and Listen Labs, which rely on text analysis, are especially valuable for qualitative analysis, enhancing our ability to analyze customer feedback. Knaflic (2023), the author of Storytelling with Data, highlights the power of these tools: “Analyzing thousands of free-text comments…in practice was a tedious and time-consuming process, taking countless hours to produce anything meaningful. The Insight7 tool automatically categorizes and analyzes text, drawing out themes and insights in seconds. What struck me wasn’t just the speed, but the quality of the analysis. Tools like this can augment our work, help us uncover insights we might have missed, and ultimately free us to focus on what we do best — crafting stories and building understanding.”
It’s important to learn how to use generative AI tools to augment our natural human capabilities throughout the data storytelling process. The heart of a great story is narrative, narrative, narrative. Ultimately, we are better storytellers. We know how to build a solid narrative by connecting the dots in the data. It helps to organize the story both across and within slides, using horizontal and vertical logic (Madson, 2023). As researchers, we are inherently more curious. We are better problem solvers. As people, we are better curators of engaging content. We have empathy and emotional intelligence for reacting to business considerations and stakeholder needs. Rather than only telling what happened in incomplete story fragments, our human strengths allow us to explain why something happened in a complete data story. (Dykes, 2024).
Dykes (2024) uses this analogy, “narrative reporting offers disjointed menu items, whereas data storytelling provides a complete combo meal.”
A Word of Caution
Before embracing AI tools, researchers should take these precautions. First, unless you use a secure, enterprise-level solution that adheres to data privacy regulations, it’s best to use generic datasets to get the information needed from AI tools (Madson, 2023). Second, AI is not foolproof and has potential data quality issues —always validate results to ensure accuracy. For example, AI hallucinations can occur, where false information is presented as fact (Harris, 2023).
No one ever made a decision because of a number. They need a story.
– Daniel Kahneman
Conclusion
Our role as researchers is to create powerful data stories by setting a clear vision, understanding the audience, leveraging relevant evidence and tools, asking questions, interpreting content within the right business context, and honing the narrative to persuade action. Together with generative AI technology, we can enhance our storytelling by developing content more quickly and accurately – ultimately driving better business decisions.
References
- Dykes, B. (2024, February 27). The future of data storytelling is augmented, not automated. Forbes.
- Franklin, Joe. (2024, January 16). Data Storytelling with ChatGPT | Share Your Insights Effectively [Video]. YouTube.
- Guy in A Cube. (2024, March 28). Copilot for Power BI: Your ultimate copilot guide [Video]. YouTube.
- Knaflic, Cole Nussbaumer. (2023, November 13). How AI is transforming qualitative data analysis. Storytelling with Data.
- Kumar, Rajesh. (2024, August 6). AI Data Storytelling: 6 Ways to Enhance Your Insights. DataCamp.
- Madson, Andrew. (2023, September 29). The Secrets of Data Analysis with ChatGPT – A Guide to Generative AI in Storytelling [Video]. YouTube.
- Restackio. (2025 February 6). AI storytelling frameworks and examples. Restackio.
Kendra Jones
Kendra brings over 25 years of experience in market research, working with top brands across the CPG, telecom, and technology sectors. She is currently the...
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