If you were an executive a few years back, you knew that true insights came with a delay and a cost. For simple things that can be expressed in numbers (e.g., customer satisfaction scores, daily channel performance or financial data) you could look at the right dashboard or ask for a quick analysis, but if you wanted to dig deeper and understand why things were happening, any qualitative research would take days or months to commence. And the result would be static, necessiating further analysis or even new primary research for any follow-up questions.
The high costs associated with qualitative insights meant that the supporting teams would be kept small. The fact that the raw data was hard to process in an automated way (beyond simple keyword matches or sentiment analyses) meant that the research team would always be busy and overworked. As a result, many companies were essentially flying blind instead of having access to true insights.
In 2026 this is still the reality in many if not most companies - but with increasing AI capabilities we're seeing change is possible. The democratisation of qualitative data is one of the trends for 2026, and holds great promise.
What democratisation of qualitative data enables
Imagine a world where a product manager doesn't need to wait weeks for a research report to understand why users are churning. Instead, they can access a shared workspace and within minutes use actual user quotes to create bespoke themes around reasons for churn. They are seeing real themes emerge from the data (not just hard coded simplistic reasons). If they want to dig deeper, they can take another angle to the data, for example determining what expressions of emotion can be found in the quotes and how that drives retention. Or look at what use cases are associated with churn if they suspect specific customer needs are not served well enough. This isn't science fiction in 2026—it's what data democratisation is enabling for qualitative insights.
Direct access to insights, not just summaries
The traditional model was simple: research specialists collected data, spent weeks analysing it, then delivered a polished presentation with sanitised findings. Everyone else got summaries of summaries, losing the richness and nuance in translation. When employees can access reliable data directly, they no longer need to wait for IT or analysts to generate reports, which reduces bottlenecks and enables real-time insights that support agile responses to market shifts or operational issues.
With AI-powered platforms, non-specialists can now:
- Search across interview transcripts to find specific themes or quotes relevant to their current work
- Generate custom analyses tailored to their specific questions rather than consuming generic reports
- Drill down from themes to source quotes to verify insights and understand context
- Explore unexpected patterns that weren't part of the original research brief
This mirrors what happened with quantitative data over the past decade. Self-service analytics dashboards didn't replace data analysts—they multiplied the value of data by making it accessible when and where decisions were being made. The same transformation is now coming to qualitative research, for example into analysing interview transcripts.
Second-order benefits: A more data-driven, insight-rich culture
The real transformation isn't just faster and cheaper access to data - it's the change in mindsets and culture related to qualitative data and insights. When qualitative data becomes accessible, organisations start to change how they think and work:
1. From backwards-looking metrics to forward-looking understanding
Most organisations are drowning in quantitative data (page views, conversion rates, NPS scores) but starving for actual understanding. Numbers tell you what happened; qualitative data tells you why it's happening and what might happen next. Research shows that 79% of organisations state that looking ahead, data will be more important to their organisation's decision-making, and this increasingly includes qualitative insights alongside metrics.
When a marketing team can search customer interviews for emerging needs before competitors spot them, or when product teams can identify friction points in real-time rather than through quarterly research cycles, organisations become genuinely insight-led rather than just data-informed.
2. From researcher dependency to research literacy
Democratisation doesn't mean everyone becomes a qualitative researcher—it means everyone develops basic literacy in interpreting qualitative evidence. When product managers regularly interact with customer voices rather than filtered findings, they develop better intuition about what makes credible evidence versus anecdotal noise. This literacy compounds: teams start asking better research questions, designing smarter studies, and integrating insights more effectively into decisions.
3. Cross-functional alignment through shared understanding
One of the most powerful but overlooked benefits is how shared access to qualitative data breaks down silos and encourages collaboration across departments. When marketing, product, customer success, and sales all have access to the same customer interview repository, they stop arguing about whose interpretation of "customer needs" is correct. Instead of each function commissioning separate research (or worse, making decisions based on different anecdotal evidence), they develop shared understanding grounded in the same rich qualitative data.
As one research leader put it: "The fundamental shift that people have to make is that you're no longer a data collector. You're a data connector." Democratisation makes insights a shared language rather than specialist knowledge.
What it concretely takes to democratise qualitative insights
Democratisation sounds appealing, but it's not automatic. Done poorly, it creates chaos—inconsistent interpretations, contradictory conclusions, and a flood of superficial "insights" that undermine trust in qualitative research. Done right, it requires three concrete foundations:
Foundation #1: A collaborative platform for storing and accessing qualitative data
Unlike quantitative dashboards that can aggregate numbers from any source, qualitative democratisation requires a centralised workspace where interview transcripts, focus group notes, and observational data can be stored, organised, and collaboratively analysed. Successful qualitative collaboration platforms must support real-time collaboration, secure file storage (in the right geographic region e.g., Europe for European data), multiple sources of data, and maintain rigorous data protection standards. This is table stakes for democratisation—without it, you just have scattered data that nobody can actually use.
Foundation #2: High-quality analysis, not just AI-generated summaries
Here's where most organisations get it wrong: they assume democratisation means giving everyone access to ChatGPT or similar chatbots to "analyse" interview transcripts. As we've explored in depth in our article on whether AI is destroying our ability to think and perform qualitative analysis, there's a crucial difference between AI-powered analysis that follows rigorous qualitative methodology versus one-shot "analyse this" prompts that produce plausible-sounding but shallow summaries.
Can basic out-of-box ChatGPT, Gemini, Claude or other chatbots analyse qualitative data? The short answer is: not in any defensible way. Basic chatbots don't do actual thematic analysis - they perform sophisticated pattern matching that can miss nuance, hallucinate themes that don't exist, and produce different results every time you run them. This isn't democratisation; it's chaos disguised as insights.
For democratisation to work, the AI tools must:
- Follow established qualitative analysis frameworks like thematic analysis or grounded theory
- Provide full transparency from every insight back to source quotes so users can verify interpretations (what we call two-way transparency)
- Allow editing and refinement of categories and coding so specialists can correct errors and non-specialists can learn
- Maintain methodological rigour rather than taking shortcuts for convenience
This is the difference between giving people a powerful microscope versus giving them a kaleidoscope—both produce patterns, but only one produces reliable insights. Quality must remain the differentiator, especially in the era of AI.
Foundation #3: Training and literacy development
Even with great tools, democratisation fails without investment in capability building. Research shows that 46% of organisations taking steps to become more data-driven have invested in improving data literacy and skills, and qualitative literacy is equally critical.
Non-specialists need training on:
- How to judge the quality and credibility of qualitative findings (sample size considerations, saturation, researcher bias)
- When findings are actionable versus when more research is needed (the difference between a pattern and an outlier)
- How to search and interpret qualitative data without cherry-picking quotes that confirm existing beliefs
- Understanding the limitations of AI-assisted analysis and when specialist review is essential
This isn't about turning everyone into master expert qualitative researchers, it's about developing enough literacy to use insights responsibly. People need to appreciate great coffee and brew it with automated machines, not become artisan baristas.
The evolving role of research specialists: From gatekeepers to enablers
One of the biggest misconceptions about democratisation is that it makes research specialists obsolete. Nothing could be further from the truth. What changes is the nature of their work—and many researchers find the new role far more fulfilling and strategic than being perpetually buried in transcripts.
Curator of high-quality data
In a democratised environment, research specialists become the guardians of data quality and methodological rigour. They:
- Design and conduct primary research using proper sampling, interview protocols, and quality controls
- Curate data repositories by tagging, organising, and contextualising qualitative data so it's findable and usable
- Assess data quality and flag when findings from legacy studies are outdated or when sample sizes are too small
- Create and maintain internal research resource hubs with relevant articles, videos, and courses for team members
This curation work is invisible but essential—it's the difference between a well-organised library and a pile of books. Without it, democratisation descends into noise.
Builder of organisational research literacy
Researchers' roles are moving more toward that of a research coach than a research practitioner. Instead of being the sole performers of research, specialists spend more time:
- Training non-researchers on how to conduct simple studies and interpret findings responsibly
- Reviewing and quality-checking analyses done by non-specialists to catch methodological errors
- Providing consultation on research design, question formulation, and interpretation challenges
- Building self-serve templates and frameworks that make rigorous research accessible to more people
This shift from doer to accelerator can be immensely satisfying. Instead of being a bottleneck who receives endless research requests they can't fulfil, specialists multiply their impact by empowering others to answer their own questions well.
Provider of bespoke expertise for complex analyses
Democratisation works well for straightforward questions ("What themes emerge as challenges in our onboarding flow?"), but many research questions require specialist expertise. Research professionals remain essential for:
- High-stakes strategic research where methodological rigour is critical (M&A due diligence, major product pivots)
- Complex cross-cutting analyses that require synthesising multiple data sources and theoretical frameworks
- Novel research designs for questions that don't fit standard templates
- Training custom AI models or configuring analysis parameters for unique organisational needs
The democratisation of basic research frees specialists to focus on these higher-value, more intellectually engaging challenges rather than spending weeks on routine analysis.
Guardian of ethics, privacy, and compliance
Perhaps most importantly, research specialists are the keepers of ethical and legal guardrails. In a world where more people access sensitive qualitative data, specialists:
- Design and enforce data governance policies to protect participant privacy and comply with regulations (GDPR, HIPAA, etc.)
- Review research protocols to ensure informed consent, anonymisation, and ethical treatment of participants
- Monitor for bias in how data is being interpreted and flag problematic uses of research
- Maintain audit trails showing who accessed what data and how findings were generated
These responsibilities become more important, not less, when qualitative data is democratised.
Getting started: Your path to democratised qualitative insights
The transformation from specialist-only qualitative research to democratised, accessible insights doesn't happen overnight—but you can take concrete steps today.
Start with a pilot using Skimle
Rather than attempting organisation-wide transformation, begin with a focused pilot:
Identify a high-value use case where faster access to qualitative insights would genuinely improve decision-making (e.g., product team accessing recent user interviews, customer success team searching support call themes)
Try Skimle for free with a small team and a defined dataset. Skimle provides systematic AI-assisted analysis following proper thematic analysis methodology, with full two-way transparency from every insight back to source quotes, and collaborative features that let teams work together on the same datasets.
Run a comparison between traditional research workflows and self-serve analysis. While tempting to just measure speed or productivity, the real question is the quality of insights and if they help with better decisions.
Build literacy alongside tools. As your pilot team learns to use self-serve qualitative analysis, document what works, what's confusing, and what guardrails are needed. These learnings will inform broader rollout.
Scale with organisational plans and expert support
Once you've validated the approach with a pilot, scale thoughtfully:
Contact us for organisational plans which enable
- Centralised data repositories where your entire organisation can access and analyse qualitative data securely
- Team collaboration features with role-based permissions and shared datasets
- Training and onboarding support to build qualitative literacy across your organisation
- Custom workflows and integrations tailored to your research processes and compliance requirements
- Ongoing consultation from our research methodology experts as you build your democratised insights capability
Democratisation isn't about replacing specialists with self-service tools, it is about creating an ecosystem where high-quality qualitative insights flow freely to where decisions are made, while maintaining the rigour and ethics that make those insights trustworthy.
The organisations that get this right won't just make decisions faster and cheaper, they'll make fundamentally better ones, grounded in deep understanding of the humans they serve rather than superficial summaries of what people said.
What could this enable for your organisation?
About the Authors
Henri Schildt is a Professor of Strategy at Aalto University School of Business and co-founder of Skimle. He has published over a dozen peer-reviewed articles using qualitative methods, including work in Academy of Management Journal, Organization Science, and Strategic Management Journal. His research focuses on organizational strategy, innovation, and qualitative methodology. Google Scholar profile
Olli Salo is a former Partner at McKinsey & Company where he spent 18 years helping clients understand the markets and themselves, develop winning strategies and improve their operating models. He has done over 1000 client interviews and published over 10 articles on McKinsey.com and beyond. LinkedIn profile