Ways to Incorporate Emerging Technologies in Product Development

This seems to be on everyone’s mind: how to operationalize your product team around AI. Peter Yang and I recently chatted about this topic and here’s what I shared about how we are doing this at Duolingo. For improving our product: -Using AI to solve problems that weren’t solvable before. One of the problems we had been trying to solve for years was conversation practice. With our Max feature, Video Call, learners can now practice conversations with our character Lily. The conversations are also personalized to each learner’s proficiency level. -Prototyping with AI to speed up the product process. For example, for our Duolingo Chess, PMs vibe-coded with LLMs to quickly build a prototype. This decreased rounds of iteration, allowing our Engineers to start building the final product much sooner. -Integrating AI into our tooling to scale. This allowed us to go from 100 language courses in 12 years to nearly 150 new ones in the last 12 months. For increasing AI adoption: -Building with AI Slack channels. Created an AI Slack channel for people to show and tell and share prototypes and tips. -“AI Show and Tell” at All-Hands meetings. Added a five‑minute live demo slot in every all hands meeting for people to share updates on AI work. -FriAIdays. Protected a two‑hour block every Friday for hands-on experimentation and demos. -Function-specific AI working groups. Assembled a cross-functional group (Eng, PM, Design, etc.) to test new tools and share best practices with the rest of the org. -Company-wide AI hackathon. Scheduled a 3-day hackathon focused on using generative AI. Here are some of our favorite AI tools and how we are using them: -ChatGPT as a general assistant -Cursor or Replit for vibe coding or prototyping  -Granola or Fathom for taking meeting notes -Glean for internal company search #productmanagement #duolingo

Building AI-powered products vs. traditional software — what’s the difference? Here are the key points I shared at the Harvard Business School Tech Conference about what makes AI product development unique: 🔹 Start with the problem, not the technology ⟹ AI is often treated as a technology in search of a problem. Always start with the problem and try solving it without AI first. The key question isn't "How can we use AI?" but "What meaningful problem can AI uniquely solve, and does it make financial sense?" 🔹 Use the right type of AI ⟹ Don't focus on generative AI because it's trendy. Traditional machine learning or predictive analytics might solve your problem better and more efficiently. 🔹 Focus on data ⟹ We often think of AI as magic, but the real magic is in the data. AI effectiveness depends on the availability and quality of data, so ensure your data infrastructure is robust—including privacy and security. 🔹 Manage expectations ⟹ There’s a lot of hype around AI, and not all of it matches reality—be clear about what AI can and cannot do. If you're using probabilistic AI, be upfront about uncertainty and variability in outcomes. 🔹 Iterate continuously ⟹ AI systems aren't set-it-and-forget-it. They require far more iteration than traditional software as data and user behavior evolve. 🔹 Prioritize evaluation ⟹ Set up continuous evaluation frameworks from the start to monitor performance, safety, and alignment with business goals. Incorporate human judgment (Human-in-the-Loop) to ensure reliability, build trust, and prevent system drift. 🔹 Develop ethically ⟹ Address potential biases proactively and engage diverse teams throughout the development process to create inclusive and responsible AI solutions. 🚀Do these points resonate with your experience? What would you add or challenge? I’d love to hear your thoughts in the comments. #AI, #ProductManagement, #Innovation, #DigitalTransformation, #ProductLeadership, #ArtificialIntelligence, #SoftwareDevelopment, #MachineLearning, #ProductDevelopment, #BusinessStrategy, #HBSTechConference, Tech Club at Harvard Business School

AI Product Management AI Product Management is evolving rapidly. The growth of generative AI and AI-based developer tools has created numerous opportunities to build AI applications. This is making it possible to build new kinds of things, which in turn is driving shifts in best practices in product management — the discipline of defining what to build to serve users — because what is possible to build has shifted. In this post, I’ll share some best practices I have noticed. Use concrete examples to specify AI products. Starting with a concrete idea helps teams gain speed. If a product manager (PM) proposes to build “a chatbot to answer banking inquiries that relate to user accounts,” this is a vague specification that leaves much to the imagination. For instance, should the chatbot answer questions only about account balances or also about interest rates, processes for initiating a wire transfer, and so on? But if the PM writes out a number (say, between 10 and 50) of concrete examples of conversations they’d like a chatbot to execute, the scope of their proposal becomes much clearer. Just as a machine learning algorithm needs training examples to learn from, an AI product development team needs concrete examples of what we want an AI system to do. In other words, the data is your PRD (product requirements document)! In a similar vein, if someone requests “a vision system to detect pedestrians outside our store,” it’s hard for a developer to understand the boundary conditions. Is the system expected to work at night? What is the range of permissible camera angles? Is it expected to detect pedestrians who appear in the image even though they’re 100m away? But if the PM collects a handful of pictures and annotates them with the desired output, the meaning of “detect pedestrians” becomes concrete. An engineer can assess if the specification is technically feasible and if so, build toward it. Initially, the data might be obtained via a one-off, scrappy process, such as the PM walking around taking pictures and annotating them. Eventually, the data mix will shift to real-word data collected by a system running in production. Using examples (such as inputs and desired outputs) to specify a product has been helpful for many years, but the explosion of possible AI applications is creating a need for more product managers to learn this practice. Assess technical feasibility of LLM-based applications by prompting. When a PM scopes out a potential AI application, whether the application can actually be built — that is, its technical feasibility — is a key criterion in deciding what to do next. For many ideas for LLM-based applications, it’s increasingly possible for a PM, who might not be a software engineer, to try prompting — or write just small amounts of code — to get an initial sense of feasibility. [Reached length limit. Full text: https://lnkd.in/gYY-hvHh ]

Product development in 2024 - the old way: • Design low-fi wireframes to align on structure • Create pixel-perfect Figma mockups • Socialize designs with stakeholders • Wait weeks for engineering capacity to build • Build core functionality first • Push "nice-to-have" animations to v2 • Ship v1 without thoughtful interactions • Iterate based on limited feedback • Repeat the cycle for 3-6 months Product development in 2025: • Quickly prototype in code with AI tools like Bolt • Generate functional prototypes in hours, not days • Deploy to real URLs for immediate testing • Add analytics to track actual usage patterns • Test with users while still in development • Designers directly create interaction details • Engineers implement interaction details by copying working code • Ship v1 with thoughtful animations and transitions • Iterate rapidly based on both qualitative and quantitative data • Implement improvements within days Last week, we hosted William Newton from Amplitude to share how this shift is fundamentally changing their product development approach. "I made those interaction details myself. I made those components myself, and I sent them to my engineer and he copied and pasted them in." Features that would have been pushed to "future versions" are now included in initial releases. Loading animations, transition states, and micro-interactions that improve user confidence—all shipped in v1. This approach doesn't eliminate the need for thoughtful design and engineering. Instead, it changes the order of operations: - Traditional process: Perfect the design → Build the code → Ship → Learn - Emerging process: Prototype in code → Learn while building → Ship with polish → Continue learning The limiting factor is shifting from technical implementation to your taste and judgment about what makes a great experience. When designers and PMs can participate directly in the creation process using the actual medium (code), they make different—often better—decisions about what truly matters.