AI-Driven Insights For Market Research

Stop pasting interview transcripts into ChatGPT and asking for a summary. You’re not getting insights—you’re getting blabla. Here’s how to actually extract signal from qualitative data with AI. A lot of product teams are experimenting with AI for user research. But most are doing it wrong. They dump all their interviews into ChatGPT and ask: “Summarize these for me.” And what do they get back? Walls of text. Generic fluff. A lot of words that say… nothing. This is the classic trap of horizontal analysis: → “Read all 60 survey responses and give me 3 takeaways.” → Sounds smart. Looks clean. → But it washes out the nuance. Here’s a better way: Go vertical. Use AI for vertical analysis, not horizontal. What does that mean? Instead of compressing across all your data… Zoom into each individual response—deeper than you usually could afford to. One by one. Yes, really. Here’s a tactical playbook: Take each interview transcript or survey response, and feed it into AI with a structured template. Example: “Analyze this response using the following dimensions: • Sentiment (1–5) • Pain level (1–5) • Excitement about solution (1–5) • Provide 3 direct quotes that justify each score.” Now repeat for each data point. You’ll end up with a stack of structured insights you can actually compare. And best of all—those quotes let you go straight back to the raw user voice when needed. AI becomes your assistant, not your editor. The real value of AI in discovery isn’t in writing summaries. It’s in enabling depth at scale. With this vertical approach, you get: ✅ Faster analysis ✅ Clearer signals ✅ Richer context ✅ Traceable quotes back to the user You’re not guessing. You’re pattern matching across structured, consistent reads. ⸻ Are you still using AI for summaries? Try this vertical method on your next batch of interviews—and tell me how it goes. 👇 Drop your favorite prompt so we can learn from each othr.

✨ 5 Lessons from Teaching Strategy with AI ✨ Over the past months, I’ve had the privilege of working with senior executives to explore how AI is transforming strategy-making. These workshops were eye-opening—not just for the participants but for me as well. AI isn’t just about automation or faster analysis; it’s reshaping how we think, collaborate, and innovate. Here are five lessons I’ve taken away: 🤖 AI accelerates insight—but it needs scrutiny. It’s astonishing how quickly AI can process trends or scenarios. But speed comes with risks. Critical thinking is the necessary counterweight to ensure we’re not rushing past nuance. 🤝 It creates a shared foundation for debate. AI outputs level the playing field, allowing teams to start from the same point and focus on solving complex problems collaboratively. 🔄 Iteration is the real game-changer. AI thrives when treated as a sparring partner, refining ideas through back-and-forth exploration rather than delivering a single “answer.” 🌍 Innovation gets a boost. By connecting unexpected dots—like regulatory changes and consumer behaviors—AI encourages us to push boundaries while keeping strategies grounded in reality. ⚡ Competition is shifting. With everyone gaining access to similar tools, differentiation depends not on the AI itself but on how creatively and responsibly we use it to stay ahead. I elaborate on these lessons—and share more insights from the workshops—in my latest article. Read it here: https://lnkd.in/e7nD5_6s How do these lessons resonate with your own experience? I’d love to hear how you’re leveraging AI in your work. #Leadership #Strategy #AI #Innovation #StrategicThinking

Your AI initiative has a budget, a team, and a roadmap. What it likely doesn’t have is a clear definition of success. That’s not a technology problem. It’s a measurement problem. Most organizations are scaling AI investment faster than they’re scaling accountability and the board is starting to notice. AI ROI isn’t a single number. It’s a system of signals. Here are 9 metrics leading organizations use to prove AI impact. Most companies track a few. Very few track all of them. 1. Process Cycle Time Reduction → Measure before/after time for key workflows AI has touched → Translate time saved into capacity, output, or cost impact Insight: If time savings don’t show up in financial or capacity models, they don’t count. 2. Pilot-to-Production Conversion Rate → Track the % of AI pilots that reach deployment → Review conversion rates at the executive level quarterly Insight: Most organizations aren’t failing at AI — they’re failing at operationalizing it. 3. Revenue Attribution → Track AI-influenced deals, campaigns, and pipeline performance → Compare AI-enabled teams vs. non-enabled equivalents Insight: Revenue-linked use cases get funded. Everything else gets questioned. 4. Cost Avoidance vs. Cost Reduction → Separate eliminated costs from avoided future costs → Document both clearly in business cases and reporting Insight: Boards reward hard savings. Operators understand avoided cost. You need both. 5. Employee Adoption Rate — by Role → Track active usage by function, not license allocation → Identify high-value teams with low adoption Insight: Adoption shows whether AI is embedded in workflows or ignored. 6. Decision Velocity → Measure time from data to decision for key workflows → Track across areas like pricing, resourcing, and risk Insight: Faster decisions don’t just improve efficiency, they shift competitive advantage. 7. Error and Rework Rate → Baseline error rates before AI deployment → Measure reductions in corrections, escalations, and rework Insight: Quality gains compound even when they’re underreported. 8. Customer Experience Metrics → Track NPS, CSAT, and resolution time for AI-assisted interactions → Compare AI vs. human-only experiences Insight: Customers experience AI through friction, not functionality. 9. Strategic Optionality → Track new capabilities, data assets, and use cases unlocked → Document how AI expands future strategic choices Insight: The biggest AI value rarely shows up in quarterly reporting. Here’s what separates organizations seeing real returns: They define success before the investment, not after the board asks. The question isn’t whether your AI initiative is working. It’s whether you can prove it. Get the playbook that expands on the 9 metrics organizations used to prove AI impact: https://lnkd.in/gUgz8ub8 Save this for future reference.

After 1.5 years of using AI in my user research, here’s what I’ve learned: 1. AI doesn’t replace researchers—it amplifies them. AI handles the repetitive parts of research. This frees you up to focus on: - Asking why instead of what next - Deeply synthesizing insights that change designs and strategy Your expertise combined with AI efficiency delivers deeper impact. 2. The “blank page problem” wastes hours—and it’s avoidable. Staring at a blank document kills productivity and momentum. AI tools give you a starting point instantly through being a thought partner. Some of my favorite prompts: - What pushback will I get on this? - What critical points/questions am I missing? - What risks am I taking with this and how do I mitigate them? Instead of struggling to begin, you can focus on refining, analyzing, and delivering results. 3. Efficiency isn’t about cutting corners—it’s about focus. Think of AI as a research assistant: - It helps you frame the questions - It structures your approach - It organizes your thoughts You have more time for strategic thinking, aligning stakeholders, and delivering clear impact. 4. Busywork is creativity’s biggest enemy. Every minute spent rewriting frameworks or brainstorming prompts is a minute you’re not: - Talking to users - Synthesizing insights - Influencing decisions AI can help eliminate busywork so you can focus on uncovering what users really need—and how to act on it. 5. The future of research is AI-powered—but human-driven. AI can structure the work, but it’s your judgment, expertise, and experience that turn data into decisions. - AI drafts the a response - You add the nuance - Together, you deliver better, faster insights AI enhances the process—you’re still the expert in the room. That’s why I’m building the AI Prompt Library for User Researchers. Over 50 expertly crafted, ready-to-use prompts, reflection questions, and follow-ups. Support for researchers who want to work smarter, not harder. Early bird access is coming In January 2025, with at least 50 UXR prompts I use in my research work weekly -- these are prompts I have experimented on, developed, and actually use. If you’re ready to streamline your research and focus on what matters, drop a ‘I’m in’ in the comments and I’ll send you the link to the waitlist! 💬

Tara Behrend and I have just published these fully open-access guidelines, plus accompanying code for Qualtrics, to use LLMs/AI to create custom content for surveys and experiments, quantitative or qualitative! The code for Qualtrics is as close to plug-and-play as we could make it, only requiring one copy-paste followed by changing a few settings at the top of the code block. It enables researchers to easily: 1) Create unique AI-generated content per participant (Case 2) 2) Engage participants in an LLM-based conversation with a researcher-designed system prompt (Case 4) 3) Experimentally assign participants to different LLM configurations (Case 5) My hope is that this tool increases access to LLMs for social scientists of all backgrounds. All you need is a Qualtrics account (provided for free by many universities) and a OpenAI API key. Research studies with a few hundred participants will generally cost less than $5 in API credits from OpenAI. Beyond the software itself, we developed a framework for the general use of LLMs to create content for research participants to experience/react to: Case 1) LLM as Research Assistant Case 2) LLM as Adaptive Content Provider Case 3) LLM as External Resource Case 4) LLM as Conversation Partner Case 5) LLM as Research Confederate Across cases, we provided detailed instructions on how to effectively engineer an LLM for research, including an iterative design thinking framework for prompt engineering and foundation model specification, as well as recommendations for a comprehensive audit before launch. We also present a nine-dimensional model of prompt design alongside recommendations for how to create effective prompts for research! I hope you find it useful, and I'm happy to help troubleshoot as you explore it! https://lnkd.in/gwtfH-HG

𝗔𝗹𝗶𝗴𝗻𝗶𝗻𝗴 𝗔𝗜 𝘄𝗶𝘁𝗵 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗳𝗼𝗿 𝗠𝗮𝘅𝗶𝗺𝘂𝗺 𝗜𝗺𝗽𝗮𝗰𝘁 AI should be more than just a technological upgrade, it must drive measurable business outcomes. By mapping AI use cases directly to strategic goals like fraud detection, hyper-personalized customer service, and operational efficiency, organizations ensure that AI investments deliver real value. A dedicated AI governance committee is essential to oversee project alignment, prioritize resources, and mitigate risks. Additionally, setting clear KPIs (such as efficiency gains, cost reductions, and customer satisfaction improvements) allows businesses to track success and refine strategies. When AI is purposefully integrated with business objectives, it transforms decision-making and customer engagement. How is your organization ensuring AI delivers tangible results? Let's exchange ideas and strategies to ensure it with Digital Transformation Strategist. #digitaltransformation #businessstrategy #ai #aigovernance #customerexperience

𝐌𝐨𝐬𝐭 𝐀𝐈 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐬𝐭𝐫𝐮𝐠𝐠𝐥𝐞 𝐧𝐨𝐭 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐢𝐬 𝐢𝐦𝐦𝐚𝐭𝐮𝐫𝐞, but because they begin with tools and trends instead of business intent. Leaders don’t need more AI demos or vendor pitches. They need a practical way to decide where AI fits, what it should change, and how value will be measured over time. 𝐓𝐡𝐢𝐬 𝐯𝐢𝐬𝐮𝐚𝐥 𝐬𝐞𝐫𝐯𝐞𝐬 𝐚𝐬 𝐚𝐧 𝐀𝐈 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐜𝐡𝐞𝐚𝐭 𝐬𝐡𝐞𝐞𝐭 𝐟𝐨𝐫 𝐥𝐞𝐚𝐝𝐞𝐫𝐬, 𝐠𝐫𝐨𝐮𝐧𝐝𝐞𝐝 𝐢𝐧 𝐥𝐞𝐬𝐬𝐨𝐧𝐬 𝐟𝐫𝐨𝐦 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧: • Start with business outcomes like revenue, cost reduction, speed, or quality — not tools • Separate hype from value by prioritizing use cases with clear, measurable upside • Understand that adoption always comes before ROI • Focus on high-leverage, repetitive, and decision-heavy workflows where AI compounds value • Think in systems rather than standalone tools • Redesign workflows instead of layering AI on top of broken processes • Keep humans in the loop to preserve trust, accountability, and decision quality • Measure value beyond cost savings — including time saved, quality improved, and better decisions • Pilot small, learn fast, and scale what proves its impact • Avoid tool sprawl that increases cost, confusion, and governance risk When done right, AI isn’t a side project or experiment. It becomes a core operating capability embedded into how work actually gets done. Strategy first. Execution next. ♻️ Repost this to help your network get started ➕ Follow Prem N. for more

My article on conversational analysis with AI is now published in Qualitative Inquiry: Friese, S. (2026). From Coding to Conversation: A New Methodological Framework for AI-Assisted Qualitative Analysis. Qualitative Inquiry, 0(0). (link in the first comment) What I still see in many projects is this: AI is used to “optimize” old workflows. Faster coding. Semi-automated coding. Better coding tools. But the bigger question is rarely asked: Why are we still organizing analysis around coding at all? Coding became central because, for decades, we had no better way to analyse a larger number of interviews, response to open-ended questions and the like. It was a technical necessity, not a methodological ideal. Today, that constraint is gone. Instead of asking: “How can AI help us facilitate current workflows?” We should ask: “What is the analytical goal—and how can today’s technology support it?” Start with the outcome: understanding, explanation, grounded interpretation, decision-relevant insight. Then look at what current AI systems can do: retrieval, comparison, pattern surfacing, dialogue. Then design workflows around that. The paper proposes conversational analysis as one answer: moving from segmenting data to engaging with the corpus through structured, evidence-linked dialogue. Not replacing judgment. Not automating interpretation. But changing how we get there. If you are working with qualitative data and AI, I’d be curious how you see this shift. #QualitativeResearch #AIinResearch #HybridIntelligence #Methodology #QInsights

There’s a growing myth that you have to choose between deep qualitative insight and the speed AI offers. Well, that is false! AI is changing qualitative UX research not by replacing the researcher, but by freeing us to focus on interpretation and strategy. When applied responsibly, AI can automate the mechanical steps like transcription, summarization, and first-pass coding. That allows us to spend more time on what matters: asking better questions, interpreting context, and communicating meaning. What does responsible use of AI in qualitative research look like? Here is what I have learned through recent works: ▪️ Use AI for structure, not for answers. Tools can cluster themes and summarize feedback, but they cannot determine what is important in your context. That still requires your judgment. ▪️ Human-in-the-loop is not optional. Any AI-assisted coding or theme generation must be reviewed, verified, and often corrected. Without this, AI outputs can reinforce bias or miss subtle but crucial insights. ▪️ Prompt design is method design. If you are using large language models, the way you frame instructions, including step-by-step reasoning, constraints, and examples, directly impacts the validity of the output. Think of prompting as a new research skill. ▪️ Ethics are not a footnote. Consent, data sovereignty, and bias audits are core to trustworthy qualitative AI workflows. This is especially important when working with sensitive user feedback. ▪️ Choose tools based on your data’s density and scale. Interview-heavy projects benefit from systems that support rich annotation and causal mapping. High-volume survey feedback may require scalable tagging and sentiment tracking. Use the right method for the right problem. My rule of thumb: let AI do what humans are slow at. Let humans do what AI is shallow at. And always leave space for interpretation, nuance, and contradiction, because that is where the user lives. For a more detailed manual on how to use AI in your qualitative UX research, read the full guide here: https://lnkd.in/eUHSmZWk

𝐇𝐚𝐫𝐧𝐞𝐬𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐀𝐈 & 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐭𝐨 𝐃𝐫𝐢𝐯𝐞 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐌𝐚𝐤𝐢𝐧𝐠 In today’s rapidly evolving business environment, leveraging AI and data analytics has become critical to drive strategic decision-making. But true value comes not just from implementing these technologies but from how effectively they are integrated into business processes and culture. Here’s a deeper dive into maximizing their impact: 𝟏. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐟𝐨𝐫 𝐅𝐮𝐭𝐮𝐫𝐞-𝐑𝐞𝐚𝐝𝐲 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: AI-powered predictive models go beyond historical analysis to forecast future trends, risks, and opportunities. Companies leveraging predictive analytics can anticipate shifts in market demands, customer behavior, and emerging industry patterns. For example, by analyzing millions of data points, AI algorithms can predict product demand, reducing inventory costs and minimizing waste. 𝟐. 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 & 𝐇𝐲𝐩𝐞𝐫-𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: AI-driven analytics enable organizations to segment their customer base with pinpoint accuracy and deliver hyper-personalized experiences. Consumer goods companies, for instance, have used AI to create tailored marketing campaigns and product offerings, resulting in a 20-30% increase in customer retention rates. This capability turns data into a competitive advantage by fostering deep customer loyalty. 𝟑. 𝐃𝐚𝐭𝐚-𝐁𝐚𝐜𝐤𝐞𝐝 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐄𝐱𝐜𝐞𝐥𝐥𝐞𝐧𝐜𝐞: Operational inefficiencies often drain resources and hinder growth. AI systems analyze complex datasets to uncover inefficiencies in supply chains, manufacturing processes, and service delivery. For example, machine learning models can identify patterns of equipment failure before they occur, enabling predictive maintenance that reduces downtime by up to 50%. This optimization ultimately leads to increased productivity and lower costs. 𝟒. 𝐀 𝐃𝐚𝐭𝐚-𝐂𝐞𝐧𝐭𝐫𝐢𝐜 𝐂𝐮𝐥𝐭𝐮𝐫𝐞 Data-driven decision-making extends beyond technology; it demands a cultural shift. Companies must foster a mindset where data insights are valued and applied at every organizational level. This requires training teams, promoting data literacy, and breaking down silos. When data informs every decision, from boardroom strategy to daily operations, organizations are equipped to innovate faster and adapt to change. To drive meaningful outcomes with AI and analytics, leaders must focus not just on adoption but on embedding these tools into the organization's DNA. The real power lies in cultivating an environment where data-driven insights guide every move. 💡 How is your organization embedding AI and data-driven practices into its strategy? #DataDrivenLeadership #AIandAnalytics #StrategicPartnerships #DigitalInnovation #BusinessTransformation #TechLeadership #OperationalExcellence #ConsumerGoodsInnovation