Expert Interviews: GTRI’s 2026 Tech Insights

There’s an overwhelming amount of misinformation out there about how to effectively conduct expert interviews offering practical advice, especially within the fast-paced realm of technology. Many assume it’s just about asking questions, but that couldn’t be further from the truth. This guide will dismantle common misconceptions and arm you with the strategies you need to extract genuinely valuable insights.

Myth 1: You just need a list of questions

Many believe that preparing for an expert interview simply means drafting a long list of questions. I can tell you from years of experience running product development teams for startups in Midtown Atlanta, this approach is a recipe for disaster. It turns the conversation into a robotic interrogation rather than a dynamic exchange. The biggest pitfall? You end up with superficial answers or, worse, information you could have found with a quick search on Google Scholar.

Instead, your preparation should focus on understanding the expert’s specific domain and their unique contributions to it. For instance, when I was interviewing Dr. Anya Sharma, a lead researcher at Georgia Tech Research Institute (GTRI), about her work on explainable AI for medical diagnostics, I didn’t just ask, “How does explainable AI work?” That’s a textbook question. My preparation involved reading her recent papers published in the Journal of Machine Learning Research and reviewing her keynotes from the AI in Healthcare Summit. This allowed me to formulate targeted questions like, “Given the challenges of data bias you discussed in your 2024 paper, ‘Mitigating Bias in Clinical AI,’ how have you specifically engineered your XAI models to maintain transparency without compromising diagnostic accuracy in diverse patient populations?” This approach immediately signals that you’ve done your homework and respects their time, encouraging them to share deeper, less generalized insights. The goal isn’t just to ask questions, but to ask the right questions that unlock novel perspectives.

Myth 2: More experts mean more insights

It’s tempting to think that the more experts you speak with, the richer your data will be. This is a common misconception, particularly in the tech space where “data volume” often equates to “better insights.” However, when it comes to qualitative research like expert interviews, saturation, not volume, is the true metric of success. I recall a project at my previous firm, a software consultancy headquartered near the King & Spalding building in downtown Atlanta, where we were exploring the future of serverless computing. My junior researchers initially scheduled interviews with over twenty different cloud architects and DevOps specialists. After the first five or six interviews, we started noticing a significant overlap in the core challenges and opportunities being discussed. The subsequent interviews, while still informative, rarely introduced truly new themes or groundbreaking insights.

As outlined by researchers at Nielsen Norman Group, qualitative research often reaches a point of diminishing returns after a relatively small number of participants. My advice? Aim for depth over breadth. Start with a core group of 3-5 highly relevant experts. Focus on extracting rich, detailed narratives from them. Once you begin to hear the same themes and perspectives repeated, you’ve likely reached saturation for that particular topic. Adding more interviews at that point often just adds noise and transcription time without yielding genuinely new intelligence. The real value comes from deeply understanding the nuances of a few diverse perspectives, not from superficial chats with many.

Myth 3: The interviewer should lead the conversation

Many new interviewers believe they must tightly control the flow of the conversation, adhering rigidly to their prepared script. This is a fundamental misunderstanding of how to get the best out of an expert. While structure is important, a heavy-handed approach stifles serendipitous discoveries. Experts often possess a wealth of tacit knowledge—insights they might not even realize are valuable until prompted by an unexpected turn in the conversation.

My most successful interviews have been those where I allowed the expert to occasionally steer the ship, within reason. I once interviewed a cybersecurity expert, Dr. Evelyn Reed, who specialized in zero-trust architectures for industrial control systems. My initial plan was to focus on implementation challenges. However, during our discussion, she organically pivoted to a fascinating tangent about the psychological warfare tactics employed by state-sponsored actors targeting critical infrastructure. Instead of redirecting her back to my script, I leaned in. I asked, “That’s incredibly compelling, Dr. Reed. Could you elaborate on how understanding those psychological elements informs your architectural design choices? Are there specific vulnerabilities that emerge from human factors you’ve observed?” This shift led to an entirely new line of inquiry that proved far more illuminating than anything on my original agenda. It’s about active listening and being agile enough to follow an unexpected thread that promises richer insights. As research from the International Journal of Information Systems and Social Change suggests, allowing for emergent themes can significantly enhance the depth of qualitative data.

Myth 4: Transcription and analysis are afterthoughts

A common, and frankly negligent, belief is that the real work ends when the interview does. “Just record it and listen later,” they say. This couldn’t be further from the truth. The actual gold is mined during the transcription and, more importantly, the rigorous analysis phase. Without a systematic approach here, even the most insightful interview becomes just another audio file gathering digital dust. I’ve seen countless teams rush through this, leading to superficial conclusions or, worse, misinterpretations.

Effective analysis starts with high-quality transcription. I’m a firm believer in using specialized transcription services or AI tools like Otter.ai (with human review for accuracy, especially for technical jargon). Once transcribed, the real work begins. We employ a rigorous thematic analysis approach, often using qualitative data analysis software like NVivo. This involves iteratively coding segments of the transcript, identifying recurring themes, emergent patterns, and specific examples. For instance, in a project evaluating user experience for a new FinTech application, we coded for “frustration points,” “delight moments,” and “feature requests.” One clear pattern emerged: users consistently struggled with the multi-factor authentication setup, even though individually, they described different aspects of the problem. This wasn’t just about noting that they struggled, but understanding why—the sequence of steps, the wording of prompts, the timing of code delivery. This level of granular analysis is impossible if you’re just casually re-listening to recordings. It’s a meticulous, time-consuming process, but it’s where the actionable insights truly crystallize. To avoid similar pitfalls, consider how organizations can miss solutions due to data failure.

Myth 5: Experts expect you to know everything

This is a subtle but pervasive myth that can paralyze new interviewers: the fear of appearing uninformed. Some believe they need to sound as knowledgeable as the expert to earn their respect or to avoid “wasting” their time. This leads to interviewers making assumptions, nodding along to complex explanations they don’t fully grasp, or shying away from asking clarifying questions. This is a fatal mistake.

The truth is, experts expect you to ask probing questions, even seemingly basic ones, if they lead to deeper understanding. They are often passionate about their field and appreciate the opportunity to explain complex concepts to an attentive, curious listener. My philosophy is this: if I don’t understand something, it’s my responsibility to ask for clarification, not to pretend. I once interviewed a data scientist from a global logistics firm about their predictive analytics models for supply chain optimization. He started explaining a proprietary algorithm that incorporated “probabilistic graphical models with Bayesian inference.” Instead of just writing it down, I stopped him. “Forgive my lack of familiarity with that specific model,” I said, “but could you break down, in simpler terms, how the Bayesian aspect specifically informs the probability outcomes here, perhaps with a concrete example related to forecasting container delays?” He not only obliged but seemed genuinely pleased to elaborate, providing a much clearer explanation that I could then accurately integrate into my research. Asking for clarification isn’t a sign of weakness; it’s a sign of intellectual honesty and a commitment to accurate data collection. It builds trust, frankly, because it shows you’re truly engaged in understanding. This approach is key to dispelling digital myths and getting to the core truths.

Mastering expert interviews in technology isn’t about following a rigid script or collecting vast amounts of data; it’s about thoughtful preparation, active listening, and rigorous analysis to unearth genuinely novel and actionable insights.