Stephanie is interested in how research has become more widely accessible across organisations, and what that means for who owns insights today. A few years ago, the concern was that too many people doing research could lead to poor-quality insights. Now, the focus has shifted.

Today, the goal is to enable people outside traditional research roles to run studies and access insights more easily. But that shift comes with a challenge: making sure tools are designed with the right guardrails so users are guided toward meaningful and reliable outcomes.

She also points to a key tradeoff in modern research tools. More features can seem like a good thing, but without clear structure, they can actually make tools harder to use and lead to weaker decisions. For Stephanie, the key question is how to make research easier for more people to use, without the quality or reliability dropping.

Stephanie believes there is a growing definitional problem around AI-enabled sampling, particularly when it comes to synthetic data, and that this is creating confusion in how synthetic data is understood and used.

She believes there is a real difference between basic, out-of-the-box LLM-generated responses and more deliberately designed, research-grade synthetic sampling systems that are built to reflect real-world populations and behaviours more accurately.

In her view, this is beyond technical distinction. It directly affects the quality and reliability of insights being produced. Because of that, she believes the industry needs to be more precise about what kind of synthetic data is being used, rather than treating it as a single category.

She also believes there’s an important education gap. Many teams are starting to use “AI sampling” without fully understanding its limits or how it’s designed. This can lead to overconfidence in results that may not actually be reliable or predictive.

Stephanie challenges a common belief in research: that good data alone is enough. 

She points out that many researchers focus on being “right” instead of being influential. From her experience, even strong insights can be ignored if they’re not clearly tied to business decisions. 

She admits this was uncomfortable to realize, even for herself. The real job of a researcher isn’t just to present facts, but to shape decisions, something many researchers often ignore. That means simplifying the message, telling a clear story, and speaking in a way that leaders understand. Without that, research is nothing more than statistics.

Through her work on the intention-behavior gap, Stephanie proves that a lot of survey data isn’t as accurate as people think. 

Consumers often say what they intend to do, not what they actually do. This leads to inflated or misleading insights. She argues the problem isn’t just bias but how questions are structured. 

By separating intention from actual behavior, researchers can get much closer to the truth. With a PhD in social psychology and a background in implicit cognition, Stephanie brings a behavioural perspective to how people respond to surveys, especially how unconscious biases shape answers.

Her broader point is simple: better insights don’t come from more data, but from smarter questions. If you don’t design research carefully, you risk building strategies on flawed assumptions.

Stephanie is deeply focused on what the future of research roles looks like, especially in the context of AI-assisted workflows. She believes the shift happening is much bigger than learning new tools, and more about understanding where AI adds value and where human judgment is still essential.

Many traditional research tasks are becoming automated, which raises an important question: which parts of the process can be handled by AI, and which still require interpretation, context, and critical thinking? She is especially focused on the risk of teams over-relying on AI outputs without questioning what they actually mean.

At the same time, she’s optimistic. She sees a future where researchers act less like report generators and more like strategic partners, working closely with businesses to interpret signals, challenge assumptions, and co-create insights.