Real Qual in an Age of 'Good Enough': How AI Is Reshaping Research Culture

The last few articles in The Campaign for Real Qual explored how qualitative research has evolved, from its foundational principles to the impact of research technology over the years.

Episode 4 was originally set to focus on AI’s impact on research design (we’ll return to that next week). But a more urgent theme emerged: AI isn’t just changing how research is done - it’s redefining what ‘good’ research means. The demand for immediacy is shifting business priorities, where speed and efficiency increasingly trump depth and nuance. AI-driven knowledge management, DIY research tools, and generative AI now deliver insights in minutes - leading many decision-makers to accept ‘good enough’ answers.

What does this mean for qualitative research, which thrives on depth, nuance, and human interpretation? In a world optimised for speed, qualitative research must prove where it adds irreplaceable value.

AI is Helping Clients Get More from Existing Data

With the ‘big data’ revolution of the last decade, new data streams emerged and presented insight teams with a big challenge – how to break down data silos and integrate data and insights in a way that was accessible to a variety of stakeholders.  Now, AI-powered platforms act as knowledge hubs, aggregating and structuring data from multiple sources.

This shift makes existing knowledge instantly accessible and re-purposable, aligning with businesses’ growing preference for fast over forensic insight. If an answer is available internally in hours, many stakeholders may see it as ‘good enough’ - valuing speed over depth.

The Expansion of ‘DIY Research’

According to ESOMAR, about one half of all research projects are conducted in-house, marking a significant departure from the traditional model where external agencies led most projects. This shift has been fuelled by the rapid growth of DIY research platforms and the increasing number of non-specialists conducting their own research. In the qualitative space, platforms like Yasna and BoltChatAI have made qualitative research more accessible than ever, allowing businesses to run large-scale qual at speed and scale.

Traditionally, qualitative research was led by trained specialists - professionals who spent years honing techniques in interviewing, observation, and interpretation. Today, AI-assisted platforms allow non-specialists to bypass these steps, producing research at scale without necessarily understanding the complexities of qualitative inquiry. The appeal of speed and accessibility is undeniable, but what is being sacrificed in the process?

The Impact of Generative AI

Perhaps the most profound cultural shift is the influence of Generative AI on research users. A new generation of marketers and decision-makers are growing up with AI as their default tool for insight generation. The expectation is not just that information will be readily available, but that AI-generated insights are inherently valid and actionable.

This shift was evident in a recent ‘AI for Marketing Professionals’ training course I attended, where participants were taught how to use ChatGPT to generate synthetic respondents and conduct AI-driven focus groups in minutes. The underlying message was clear: “You don’t need a research agency; AI can do this for you.” This shift is already positioning AI as a substitute, rather than a supplement to human-led inquiry.

At the same time, OpenAI, Google & Perplexity are revolutionizing desk research, allowing users to:

• Conduct comprehensive research in minutes rather than hours or days.

• Aggregate insights across sources with AI-generated citations.

• Customize searches across academic, social, and industry-specific databases.

If logically structured, well-presented AI-generated insights become accepted without question, what happens to critical thinking and intellectual scrutiny? 

Living in a Culture of Immediacy and ‘Good Enough’

The rise of AI-driven knowledge management, DIY tools, and GenAI is not just a technological shift – it’s altering the mindset of decision-makers. Expectations of what is necessary for decision-making are shifting – with a stronger focus on immediacy and cost, ‘good enough’ has become the prevailing mantra.

In Episode 2: The Essence of Good Qual, we explored what makes qualitative research valuable. We highlighted that great qualitative research is about understanding human complexity, capturing emotional nuance, and turning insights into compelling narratives that drive change. But as AI transforms the research landscape, qualitative practitioners must rethink their role and adapt to stay relevant. Here are three possible directions that qualitative practitioners can adapt:

1.     Meet Clients Where They Are - But on Your Terms

Speed and efficiency now drive business priorities. Rather than resisting this shift, qualitative researchers can expand their toolkit to offer fast, scalable approaches while embedding their expertise where it matters most.

• Hybrid approaches that combine qual-at-scale with traditional methods will ensure that speed does not come at the expense of depth and nuance.

• AI-driven workflows - from AI-assisted analysis to qual-at-scale - can be powerful tools, but expert oversight is needed in research design, analysis, and reporting to ensure rigor.

• Clients must be educated on the trade-offs of AI-powered research - when it’s ‘good enough’ and when human insight is essential.

2.     Reclaim the Role of Qualitative Researcher as an Interpreter of Human Meaning

AI-powered moderation platforms like Yasna and BoltChatAI can adapt to responses, but they currently lack true human interpretation beyond language. AI can process words, but it cannot understand context, create empathy, or read tone, emotion, or contradictions in the same way a skilled moderator can.

In Episode 2, we explored how great qualitative research is not just about gathering responses, but about probing deeper, reading between the lines, and understanding what isn’t being explicitly said. While AI can structure and categorize information efficiently, it cannot replace the ability of a skilled researcher to interpret meaning, pivot discussions in real time, and push beyond surface-level answers.

3.     Strategic Storytelling – Turning Insights into Influence

AI can summarize data, but it does not persuade. The power of qualitative research lies in how insights are framed, contextualized, and made compelling to drive business decisions.

Qualitative researchers must position themselves as strategic storytellers, ensuring that research findings do not just sit in reports but actively shape decision-making. The ability to connect the dots, synthesize complexity, and bring insights to life is what will continue to set human researchers apart.

The future of qualitative research isn’t about rejecting AI, nor racing to match its speed - it’s about demonstrating where human expertise is irreplaceable. AI can automate, aggregate, and accelerate - but it cannot interpret, empathise, or disrupt conventional thinking. That remains the role of an experienced qualitative researcher. As AI reshapes the research landscape, qualitative practitioners must reclaim their role as interpreters of meaning, ensuring that insights remain not just fast - but transformative.

As we continue The Campaign for Real Qual, the next few episodes will explore how AI is reshaping research design, moderation, analysis, and reporting - diving deeper into how qualitative practitioners can evolve their methodologies to remain indispensable.