Expertise First for Human Insights
We believe that an expertise-first approach is the best way to ensure human insight remains at the heart of qual market research even as AI reshapes the landscape, now and for the foreseeable future. Unlike the traditional qual research model that embraces a one-researcher-fits-all-studies approach, an expertise-first methodology focuses on delivering specialized and highly targeted insight from researchers who bring in-depth industry-specific experience and subject-matter expertise.
There are significant advantages to this approach. Researchers who bring specialized knowledge in the industry, subject, or niche they’re researching are able to ramp up quickly and dive right into even the most complex studies. They also know how to talk to sophisticated and high-level respondents with peer-to-peer confidence, as well as how to ask the right follow-up questions. An expertise-first research approach means that clients get nuanced, relevant, and actionable insights that secure stakeholder buy-in and lead to faster, smarter business decisions.
Great, so how does AI fit into this model?
AI: the Best Intern You’ll Ever Have
At Thinkpiece, we’ve been closely watching AI and its impact on market research for years. Our lead tech researcher even earned his bachelor’s in computer science with an emphasis AI many decades ago. As software engineers who became tech researchers, we bring a slightly different perspective than many of our colleagues when it comes AI, and how it should be used to liberate, rather than replace, human researchers.
Before the recent AI frenzy, our team had already been using AI as an assistant to help us with the more mundane tasks of research. Our technologist researchers even wrote an AI Reference Box Set — a practical, jargon-free guide on foundational AI concepts and safe usage. It’s designed to be as evergreen as possible in a field that changes by the hour, a resource you can turn to in years to come. Reach out to us and we’ll be happy to send you a copy, no strings attached.
But despite — or perhaps because of — our familiarity with AI, we’re taking a cautious and controlled approach to incorporating it in our own research model. That’s largely because, while full of exciting potential, we also know that AI can be highly disruptive — and not in a good way. So until AI stops hallucinating and making stuff up, we don’t think it’s ready to do the actual research.
We absolutely recognize that AI has a role in expertise-driven qual market research — but not as the primary generator of insight. Rather, we see AI as a super-efficient intern who takes over the tasks that suck up valuable time we humans would rather be spending on, you know, the research.
So, what can and should this AI intern do? For starters, AI can automate vast amounts of data collection, perform detailed analyses, and identify patterns the human eye and brain might miss — all within seconds. AI-powered platforms can shift through months of social media conversations and extract relevant customer feedback; detect trends across thousands of survey responses; and run predictive models that can help forecast customer behavior — all of which are invaluable to researchers and their clients. What’s more, AI platforms can process this data with greater accuracy, in a faction of the time and cost.
That’s probably the last thing researchers — especially quantitative researchers — want to hear. But humans are and will continue to be essential to quality market research. For all its workhorse capacity, AI is rife with inaccuracies and ripe for fraud. Quant research needs human intervention to identify and weed out AI-spam respondents and maintain data integrity. Humans are still needed to design nuanced survey questions and interpret outlier data that AI may misunderstand. And humans are still necessary to provide context for the data and ensure research models align with real-world variables, ethics, and compliance issues.
The role of human — especially one with specialized expertise — is even more critical when it comes to qualitative research.
Qualitative Research Will Always Be Human-Dependent
Here’s why. Unlike quantitative research, where AI can excel at number crunching and pattern recognition, qualitative research goes beyond the cold, hard data. Qual research delves into human emotions, motivations, and experiences to tell the story behind the data — and that’s something AI can’t do. Yes, AI can handle routine research tasks: transcribing interviews, tagging key words, collecting, culling, and analyzing data. But AI can’t think like you do. It can’t replicate the nuances of face-to-face conversations or the intuition of a skilled moderator. And it doesn’t have your specialized knowledge and expertise.
AI can’t interpret facial expressions, body language, or tone of voice. You, human researcher, can. AI can’t adapt its line of questioning on-the-fly based on nonverbal cues, group dynamics, off-hand comments, or unexpected shifts in the conversation. With your human intuition and specialized understanding of the topic, you know how to direct and guide the discussion.
AI doesn’t bring lived experience or contextual knowledge to the conversation. You, human researcher, have years of experience to call on as you dig deeper into a respondent’s thought process or motivations to provide additional shading. AI can’t build trust, rapport, or empathetic connections with respondents, or reassure uneasy participants when discussing sensitive or emotionally charged subjects. You can, and know how to sense when a respondent is upset, frustrated, or flustered and recalibrate accordingly. AI is constrained by pre-programmed logic, training data, algorithms, and machined-learned patterns. You aren’t limited by those constraints, and can use your creativity and spontaneity to reveal the nuanced, game-changing insights businesses need.
Qual research isn’t just about asking questions that any AI could ask. It involves actively listening to participants to understand the hidden meaning behind their words. It requires strategically probing to uncover buried insights. It relies on interpreting body language and tone to detect subtle emotional cues that reveal what data alone can’t. These skills don’t get much attention, but they’re absolutely vital to revealing truly game-changing insight. And they’re skills that AI doesn’t have. But you do.