Insights from AI in Product Management

🤖 The Future of Product Management is AI 🤖 (And no, it's not replacing me!) As a VP of Product, I often need to quickly synthesize customer feedback to guide strategic product decisions. This requires analyzing large datasets - surveys, support tickets, usage metrics - to surface key insights. 📊 RECENTLY, I TESTED some new functionalities of a new #AI assistant (Claude by Anthropic) that demonstrated how advanced #NLP (natural language processing) can automate repetitive analysis tasks. I provided the assistant with a raw customer survey dataset (stripped of PII 👀) and asked questions like: 1. Summarize the key themes in the open-ended responses 2. Calculate the average rating for each survey question 3. Analyze trends over time by grouping responses by time period 4. Identify outliers on the low and high ends of the rating scale Within seconds, the AI generated summarized findings, crunched numbers, and highlighted meaningful patterns in the data. It handled follow-up questions seamlessly, adjusting analyses based on my feedback. For example, when I asked the assistant to recalculate averages excluding outliers, it updated the results accordingly. The assistant extracted the essence of a complex analysis that would have taken me a bit of time (even as someone who is very comfortable in Excel). BUT IT DIDN'T REPLACE ME. Rather, it freed me to focus on interpreting results, uncovering deeper meaning, and connecting insights to strategy. The AI unearthed the signals so I could determine next steps. It helped me get to the AHA! moment much faster. 💡 This technology (which is all around us now) foreshadows the future of product management. AI will handle the grunt work - data cleaning, aggregation, basic reporting - while we focus on leadership, strategy, and vision. It's our job to ask the right questions. AI will help us find the answers faster. AI is not a threat but a powerful assistant, enhancing our uniquely human strengths. By combining emerging technology with human insight, product teams can innovate faster and smarter. The future is bright for product managers who embrace AI as a partner, not a replacement. The most strategic products of tomorrow will be created by humans supported by machines. #product  #productmanager  #productmanagement  #technology #b2b #strategy #userexperience  #customerexperience #data #dataanalysis #ai #aiassistant #aha Thanks Lisa for the nudge get some feedback from our latest beta and thanks Juan for running the beta!

Just wrapped up the season finale of Unsolicited Feedback with my co-hosts Joff Redfern, Ravi Mehta, and Brian Balfour. I thought it would be a good idea to add any new thoughts we have to some of the big topics that arose this year. We started by discussing some hardware product trends and the unstructured data revolution, but then, in this post, I want to focus on our third topic - the future of product management. 🌟 Unsolicited Feedback on Lenny Rachinsky's Article 🌟 Our friend, Lenny Rachitsky, recently penned an article on how AI will impact product management. He feels AI will have more impact on the highly strategic aspects of Product Management, as opposed to the low-level communication and execution tasks needed to build a product. We respectfully disagreed in a few areas. 🤝 Human Creativity Remains Irreplaceable 🤝 While AI excels at recognizing patterns, the counterintuitive insights that drive breakthrough innovations come from human intuition. The balance between AI efficiency and human creativity is crucial for effective decision-making. Ravi provides an excellent example here from Booking.com and Airbnb. Booking.com A/B tested its way into a nearly perfect hotel and travel booking experience, something AI could have significantly contributed to. Airbnb had a fundamental hypothesis that in the sharing economy, people would be open to sharing their homes and renting space in others' homes. This is probably something AI could not have determined. 🤖 Design Takes on a More Strategic Role 🤖 As tools like Figma become central to the product lifecycle, designers are taking on more strategic roles, integrating customer insights directly into the design process. The question here is whether it’s a designer that moves into more of a product role, or a product manager / engineer that becomes more skillful at design. 🛠️ Full-Stack Roles Will Become More Prevalent 🛠️ AI is poised to collapse the talent stack, leading to more generalists who can leverage AI to handle specialized tasks. This shift may result in fewer specialists but more integrated teams capable of handling a broader range of tasks. 🎯 The Future is Prototype-Driven 🎯 The future of product management might involve rapidly building and modifying prototypes, which allows for swifter validation and refinement of ideas. Guiding V0s of products into releasable versions, much like turning a script into a finished film. 👥 The DNA of Future Product Leaders are Builders 👥 While product management is essential, the traditional role of product managers might diffuse across teams. Product management tasks could be shared among designers, engineers, and marketers, each bringing their unique strengths to the table. Check out the full episode for our detailed discussion. Do you think AI is coming for the strategists? Let's continue the conversation below!

📕 The new product cycle for AI “Data → Model → Product” TO “Product → Data → Model” In the previous waves of AI, many companies began their product life cycle by: (1) analyze companies' existing datasets to identify feasible approaches and project business ROI (2) data engineering and model training to validate business needs, with human-in-the-loop (3) launch the product and improve the model iteratively based on the data collected Now with the advent of Large Language Model (LLM) applications, a new inverted pattern is emerging: (1) launch a lean product using the off-the-shelf best-performing foundational model to validate core business hypotheses and establish baseline performance (2) collect new data through the launched product to accumulate proprietary datasets. Given the current nondeterministic nature of LLM, it is key to have many quick and small iterations, measurements, and evaluations (3) fine-tune & retrain model, optimize product flows with smaller and more cost-efficient models, and enhance product ROI using techniques like RAG — all helping facilitate continuous product evolutions This shift is still unfolding, with fascinating downstream impact. It is accelerating the cycles of innovation and lowering the cost of mitigating market risk early on, particularly in conjunction with the democratization of software creation. High-velocity and learning-driven organizations will have the upper hand. I see great potential in this approach and am eager to see how startups and product teams will adapt their strategies, customer validation processes, and methods of establishing a competitive moat. #startup #ai #b2b #enterprise #founder #product