Authors: Sunny Shah, Head of US Insights, Alexandra Hunt, Research Manager, Beyond Blue
Data is EVERYWHERE
Clients are sitting on more data than ever, from an expanding range of sources. This is reshaping how they prioritize insights needs, allocate budgets, and determine when primary research is truly required. They’re increasingly expected to synthesize existing data, interrogate it from new angles to address broader business questions, and identify the critical gaps that remain unanswered.
AI tools & technology continue to evolve
Swanky capabilities decks highlighting new, cool AI-featured tools don’t have the same power and hoopla as they did a year ago. Now that the industry has had a chance to learn more about the technology that is available and assuage concerns about our roles being eliminated, they are really looking for how AI tools can support their decision-making process through a variety of lenses: speed, efficiency, cost, and more.
We need to rethink traditional methods by not just adding an “optional tool” into our research design but reimagining how we create tailored research workstreams for that specific client/business need. For example, we need a new framework that is built with the specific tool(s) we want to use, to highlight not only their ability to enhance the insights, but how the traditional methodology is altered to create a new research design. Positioning it as a customized workflow, as opposed to optional add-ons, will go a long way in acceptance and value.
Five key takeaways:
1. Market research is shifting from insight generation to decision enablement
One of the clearest themes was the growing expectation that market research directly supports and enables business decisions, rather than simply delivering findings. Stakeholders are increasingly focused on what to do with insights, not just what the insights are.
Speakers emphasized:
- Framing research around specific decisions rather than broad learning objectives
- Clearly articulating implications, trade-offs, and recommended actions
- Delivering insights in a way that is accessible and compelling for senior leadership
What this means for us:
We can increase the impact of our work by consistently anchoring research to decision points, clearly stating implications, and focusing on “so what” and “now what” alongside the findings themselves. We already do this well (thinking strategically), so keep doing what we are doing!
2. AI is becoming a practical accelerator, not a replacement
AI and automation were discussed extensively, but with a notably pragmatic tone compared to last year. Rather than positioning AI as a replacement for researchers, most conversations focused on how it can increase speed and efficiency while allowing teams to spend more time on higher-value work.
Common applications discussed included:
- Faster synthesis of qualitative data
- Early theme identification and hypothesis generation
- Automating repetitive or time-intensive tasks
At the same time, there was strong agreement that human judgment remains critical, particularly in healthcare. Interpretation, context, nuance, and ethical considerations cannot be fully automated.
What this means for us:
AI can be leveraged as a tool to enhance workflows, but insight quality, interpretation, and storytelling must remain human-led. Clear governance and validation are essential.
3. The market research skill set is expanding
Several sessions highlighted how expectations for market researchers are evolving. While technical and methodological expertise remains foundational, success increasingly depends on communication, influence, and business acumen.
Skills emphasized included:
- Executive-level storytelling and synthesis
- Comfort challenging assumptions and reframing questions
- Strong partnership with cross-functional teams
Researchers are increasingly expected to act as strategic advisors, not just insight providers.
What this means for us:
How insights are framed and communicated can be just as important as the data itself. Clear narratives and confident recommendations help ensure insights land and drive action.
4. Integrating primary research with secondary and real-world data
Another recurring theme was the growing expectation that insights teams integrate multiple data sources rather than treating primary research in isolation. Many presenters highlighted the value of triangulating primary findings with secondary research, real-world evidence, social, and digital or behavioral data.
Benefits discussed included:
- Greater confidence in conclusions
- Better context for interpreting attitudes and stated behaviors
- More efficient use of primary research by focusing on true gaps
What this means for us:
Thinking holistically about data, what we already know, and what we truly need to learn can improve both speed and impact. Integrated insights are increasingly becoming the norm rather than the exception.
5. AI, patient literacy, and the changing control of medical information
One of the most thought-provoking discussions we attended was a round-table focused on AI and patient literacy. The central insight was that AI-powered tools have dramatically expanded patient access to medical information, fundamentally changing the traditional patient-HCP dynamic. We even heard this from patients with PV during our recent CL days for 4379 (they were asking ChatGPT about polycythemia vera)!
Patients are now:
- Researching symptoms, diagnoses, and treatment options independently
- Using AI tools to summarize, translate, and interpret medical information
- Arriving at appointments with pre-formed views, expectations, and questions
As a result, doctors are no longer the sole gatekeepers of medical knowledge, and the traditional notion of tightly controlling the medical narrative is no longer realistic.
Implications of this shift:
While increased access can be empowering, it also introduces risk:
- AI tools may surface mixed-quality or incomplete information
- Patients may feel more informed but also more anxious or confused
- Health literacy gaps may widen if information lacks context or clarity
The consensus at the round table was that the industry should not attempt to regain control over what patients learn. Instead, the opportunity lies in supporting understanding, clarity, and trust.
How the industry can respond
Rather than focusing on message control, clients and vendors emphasized:
- Supporting informed decision-making rather than directing conclusions
- Investing in patient-friendly, clear, and balanced education
- Anticipating patient questions and misconceptions shaped by AI-driven information
- Equipping HCPs to engage productively with increasingly informed patients
What this means for us:
Market research can play a key role in this new environment by helping us to understand:
- What patients are learning from AI tools and when they’re using it
- Where confusion or misinterpretation commonly occurs
- How their interactions and learnings from AI tools are shaping their experiences with HCPs
In this context, success is less about controlling the narrative and more about earning trust through transparency, relevance, and clarity, helping patients and providers navigate information together.
Final reflection
The conference reinforced that pharma market research is at an inflection point. New tools and data sources are expanding what is possible, while expectations for speed, relevance, and impact continue to rise. The most effective teams will be those that combine methodological rigor with strategic clarity, leveraging technology thoughtfully while maintaining a strong human focus on interpretation, communication, and trust.
If you’d like to discuss these developments and reflections, our team is always up for a conversation.