Interview AI Leaders: 2026 Insights from Tech Square

The future of AI is not just about algorithms; it’s about the brilliant minds shaping them, and understanding their perspectives requires direct engagement. This article provides a step-by-step walkthrough on how to successfully conduct and publish high-quality interviews with leading AI researchers and entrepreneurs, ensuring your content stands out in a crowded digital space. So, how can we consistently capture profound insights that truly resonate with a technology-focused audience?

1. Identify and Vet Leading AI Voices

Finding the right people to interview is half the battle. You don’t just want anyone with “AI” in their LinkedIn profile; you need individuals who are genuinely pushing the boundaries of the field. My process always starts with a deep dive into recent academic publications, patent filings, and industry news. I’m looking for names consistently appearing in groundbreaking research at institutions like Georgia Tech’s AI Center or startups making significant waves in places like Atlanta’s Technology Square.

Pro Tip: Go Beyond the Obvious

Don’t just chase the biggest names. Often, the most insightful interviews come from emerging leaders or specialized researchers. Look for individuals who have recently published a seminal paper, secured significant funding for an innovative project, or launched a product that addresses a novel problem. For instance, I once spent weeks tracking down Dr. Anya Sharma, a principal AI scientist at Verily Life Sciences, whose work on federated learning in healthcare wasn’t making mainstream headlines but was absolutely critical to the future of medical AI. Her insights were gold.

Common Mistake: Over-reliance on PR Pitches

Relying solely on PR agencies for interview suggestions often leads to generic conversations. PR firms are excellent for access, but their primary goal is usually exposure, not necessarily profound technical insight. Always do your own vetting.

For my initial scouting, I use tools like Semantic Scholar to identify authors with high citation counts in specific AI sub-fields (e.g., reinforcement learning, natural language processing for low-resource languages). I also monitor industry events like the NeurIPS conference proceedings or the AAAI annual symposium for keynote speakers and award recipients. Cross-referencing these academic leaders with founders or CTOs of successful AI startups (Series B funding and above, typically) gives me a strong pool. For entrepreneurs, I check Crunchbase for recent funding rounds and significant product launches.

2. Craft a Compelling Interview Outreach and Pre-Questionnaire

Once you’ve identified your targets, a tailored outreach is essential. These individuals are busy; a generic email will be ignored. Your initial contact should be concise, professional, and immediately highlight why their specific expertise is valuable to your audience.

My outreach email template typically follows this structure:

Subject: Interview Request: [Their Name] – Insights on [Specific AI Topic] for [Your Publication Name]

Dear [Dr./Mr./Ms. Last Name],

My name is [Your Name], and I’m a technology journalist/editor at [Your Publication Name], focusing on the future of AI. I’ve been deeply impressed by your recent work on [mention specific paper, project, or company achievement – e.g., “your groundbreaking research into causal inference in large language models,” or “the innovative deployment of your AI-powered diagnostic tool at Piedmont Hospital”].

Our audience of [describe your audience – e.g., “AI developers, tech executives, and investors”] would greatly benefit from your unique perspective on [1-2 key themes – e.g., “the scalability challenges of multimodal AI” or “ethical frameworks for autonomous decision-making”].

We propose a [duration, e.g., “30-minute virtual interview”] to discuss [reiterate themes]. To ensure our conversation is as productive as possible, we would provide a brief pre-interview questionnaire.

Would you be open to a brief introductory call next week to discuss this further?

Thank you,
[Your Name]
[Your Title]
[Your Website/LinkedIn]

Pro Tip: The Power of the Pre-Questionnaire

A detailed pre-questionnaire is non-negotiable. It serves multiple purposes: it helps the interviewee prepare, signals your professionalism, and allows you to refine your live interview questions. My questionnaires typically contain 10-15 open-ended questions, structured to elicit both technical detail and broader strategic insights.

Example questions I often include:

• “Given the advancements in [specific AI subfield, e.g., generative adversarial networks], what do you see as the most significant technical hurdle to achieving truly robust, real-world applications in the next 3-5 years?”
• “From an ethical standpoint, what specific guardrails or regulatory considerations do you believe are paramount for the responsible deployment of AI systems like yours in [specific industry, e.g., financial services]?”
• “Looking at the current market, where do you anticipate the greatest opportunities for AI entrepreneurship to emerge in the next 12-18 months, particularly concerning [mention a specific challenge, e.g., ‘supply chain optimization’]?”

I send this questionnaire about a week before the scheduled interview, giving ample time for thoughtful responses.

Common Mistake: Vague Interview Requests

“I want to talk about AI” is not an interview request; it’s a plea for general conversation. Be specific about the topics and why their input is uniquely valuable.

3. Conduct the Interview with Precision

For remote interviews, I exclusively use Zoom for its reliability in recording both audio and video, alongside Otter.ai for real-time transcription. Before each interview, I ensure my microphone (a Rode NT-USB Mini) is properly configured and a quiet environment is secured. I always start by confirming the interviewee is comfortable with the recording and transcription process.

My approach is to listen far more than I speak. I use the pre-questionnaire responses as a launchpad, but I’m always ready to pivot based on an unexpected insight. I’ve learned that the most profound insights often come from follow-up questions to something the interviewee says off-the-cuff. My screen setup during an interview includes:

1. Zoom window (primary focus)
2. Otter.ai live transcription (secondary focus, for quick keyword spotting)
3. My prepared questions and pre-questionnaire responses (reference)

Pro Tip: Active Listening and Follow-Up

Don’t just run through your list of questions. Truly listen to the answers. If an interviewee mentions “the emergent properties of large models,” don’t just nod. Ask, “Could you elaborate on what you mean by ’emergent properties’ in this context? What’s an example you’ve observed?” This deepens the conversation significantly. I had a client last year, a brilliant researcher from Emory University’s Department of Biomedical Informatics, who kept using the term “explainable AI” without fully defining it for a general audience. By asking for specific, relatable examples, we transformed a technical term into an accessible concept for our readers.

Common Mistake: Interrupting or Dominating the Conversation

Your role is to facilitate, not to pontificate. Avoid interjecting with your own anecdotes or opinions unless it’s to briefly clarify or encourage further elaboration.

4. Transcribe, Analyze, and Structure the Content

Immediately after the interview, I download the audio recording and the Otter.ai transcript. While Otter.ai is excellent, especially with its custom vocabulary feature (I pre-load it with specific AI terms like “transformer architecture” or “causal inference”), it’s never 100% accurate. I manually review and correct the transcript, focusing on technical terms and proper nouns. This typically takes about 1-2 hours for a 30-minute interview.

Once the transcript is clean, the analytical work begins. I read through the entire conversation, highlighting key themes, powerful quotes, and actionable insights. I’m looking for the “story” within the interview – what’s the central message the interviewee wants to convey?

For article structuring, I generally follow a thematic approach, not a chronological one. I group related insights, even if they were discussed at different points in the interview. My standard structure includes:

• An introduction setting the stage and introducing the interviewee.
• Sections dedicated to specific topics (e.g., “The Technical Hurdles of X,” “Ethical Implications,” “Market Opportunities”).
• A “Future Outlook” section.
• A concise conclusion.

Concrete Case Study: The “Autonomous Logistics” Deep Dive

Last year, we interviewed Dr. Lena Chen, CEO of AutoLogix AI, a startup based out of the Atlanta Tech Village, focusing on AI for autonomous supply chain management. The 45-minute interview, conducted via Zoom, generated 8,000 words of raw transcript.
Our goal was to produce a 1,500-word article for a logistics technology publication.

1. Transcription & Correction: We used Otter.ai, then spent 1.5 hours correcting the transcript, particularly for industry-specific jargon like “digital twin synchronization” and “last-mile optimization algorithms.”
2. Theme Identification: We identified three core themes: 1) The technical challenge of integrating disparate legacy systems with AI, 2) The economic impact of predictive maintenance on fleet management, and 3) The ethical considerations of AI-driven job displacement in logistics.
3. Drafting: I drafted the article over two days, pulling direct quotes and paraphrasing insights under these themes. I made sure to weave in Dr. Chen’s personal journey founding AutoLogix AI for narrative depth.
4. Expert Review: We sent the draft to a logistics consultant (a subject matter expert) for technical accuracy review, which caught a subtle misinterpretation of a data point regarding container throughput efficiency.
5. Outcome: The article, published in Q3 2025, generated over 15,000 unique page views in its first month and was shared widely within the logistics tech community, leading to two follow-up interview requests for Dr. Chen.

5. Draft and Refine for Clarity and Impact

Writing the actual article requires balancing direct quotes with your own narrative explanation. Always contextualize quotes. Don’t just drop them in; explain why that quote is important and what it signifies. I aim for a conversational yet authoritative tone.

When drafting, I prioritize:

• Clarity: Is the technical information understandable to an intelligent layperson, or at least to someone familiar with the broader tech sphere?
• Impact: Does each section contribute to the overall message? Am I highlighting the most compelling insights?
• Flow: Does the article move logically from one point to the next?

I also make sure to use strong, active verbs and vary my sentence structure. A common pitfall is repetitive sentence beginnings – try to mix it up. Sometimes, I’ll write a short, punchy paragraph (e.g., “That’s the real challenge, isn’t it?”) right after a longer, more detailed explanation to provide a moment of reflection for the reader.

Pro Tip: The Editorial Aside

Here’s what nobody tells you: your own informed opinion, judiciously placed, can significantly enhance the article’s authority. For example, after discussing a researcher’s concerns about AI bias, I might add: “My experience working with several startups in the fintech space confirms this; the sheer volume of historical data often entrenches biases that are incredibly difficult to untangle without rigorous, proactive auditing.” This isn’t about injecting personal bias, but rather demonstrating expertise and adding a layer of professional validation.

Common Mistake: Over-quoting or Under-quoting

Too many direct quotes make an article feel disjointed. Too few means you lose the interviewee’s unique voice. Find the right balance where quotes enhance your narrative without overwhelming it. I typically aim for 3-5 impactful direct quotes per 1,000 words.

6. Review, Fact-Check, and Publish

Before publication, a rigorous review process is critical.

1. Self-Review: I read the article aloud. This helps catch awkward phrasing, grammatical errors, and ensures a natural reading rhythm. I also check for internal consistency and logical flow.
2. Technical Review: If the topic is highly specialized, I send the draft to a subject matter expert (often a peer or a contact in academia) for a quick technical accuracy check. This is invaluable for catching subtle inaccuracies or misinterpretations of complex AI concepts.
3. Interviewee Review: I always offer the interviewee a chance to review the article for factual accuracy and to ensure their quotes are represented fairly and in context. I make it clear that this is not an opportunity for them to rewrite the article or add promotional material, but solely to verify accuracy. This builds trust and ensures the information is sound. In one instance, a leading researcher from Georgia Tech’s Institute for Robotics and Intelligent Machines pointed out that I had conflated two distinct sub-categories of reinforcement learning, a crucial correction that prevented a significant factual error.
4. SEO Optimization: While writing, I naturally weave in my primary keywords (“AI researchers,” “AI entrepreneurs,” “future of AI”). During the final review, I ensure these are present in headings, the introduction, and naturally throughout the text, without keyword stuffing. I also ensure all internal and external links are correctly formatted and functional.

Finally, publish the article on your chosen platform. Consider accompanying the article with the full audio recording as a podcast episode or a video version of the interview to cater to different content consumption preferences. Distributing across platforms like LinkedIn and relevant industry newsletters maximizes reach.

The journey from initial contact to published article is iterative, demanding meticulous attention to detail and a commitment to journalistic integrity. By following these steps, you not only capture the insights of leading AI minds but also present them in a way that truly informs and engages your audience. For more on the strategic aspects of AI adoption, consider exploring AI Adoption: Strategic Wins for 2026. Understanding these strategies can further enhance your interviews by providing a broader context for the technical discussions. Furthermore, gaining insights into demystifying AI in 2026 can help you frame your questions to address common misconceptions.