Trends in AI Product Management

I’ve been a PM in the Generative AI field for quite some time now, and here are some of the trends I’m noticing—trends that are shaping how we build and scale AI products. Product management in Generative AI isn’t just about building features—it’s about shaping intelligence itself. Traditional PM frameworks don’t fully apply when your product learns, evolves, and creates on its own. So, how do we build and scale GenAI products? Here are some key insights from the trenches: 1️⃣ Problem-Solution Fit > Model Capabilities Many AI products fail because they start with “What can this model do?” instead of “What real problem can AI solve?” ✅ Example: Canva’s Magic Write focuses on speeding up content creation, not just showcasing LLM capabilities. 📌 Takeaway: AI should seamlessly fit into a user’s workflow rather than forcing users to adapt to AI. 2️⃣ The Interface is the Product In GenAI, design is crucial. If the AI’s responses feel unpredictable, slow, or overwhelming, users won’t trust it. ✅ Example: ChatGPT’s regenerate response button gives users control, reducing frustration when AI misfires. 📌 Takeaway: Transparency, control, and iteration in UI/UX build trust in AI-powered products. 3️⃣ Data is Your Moat In traditional SaaS, features create the moat. In GenAI, high-quality proprietary data is the differentiator. ✅ Example: OpenAI partnered with Reddit for richer conversational data—this improves response quality & context awareness. 📌 Takeaway: The best AI products don’t just use bigger models—they use better, domain-specific data. Data is the new GOLD. 4️⃣ Expect (and Design for) Hallucinations AI will make mistakes. The question is: how do you handle them? ✅ Example: Microsoft Copilot cites sources in responses, making it easier for users to verify information. 📌 Takeaway: Users don’t need AI to be perfect, but they need ways to fact-check & correct it. 5️⃣ AI Adoption is Behavioral, Not Just Technical AI adoption isn’t just about performance—it’s about human psychology. Users need to trust, understand, and feel comfortable using AI. ✅ Example: Adobe Firefly emphasizes “commercially safe AI”—addressing creators’ concerns about copyright and ownership. 📌 Takeaway: The best AI products solve not just technical, but emotional friction points. 🔮 What’s Next for AI Product Management? As multimodal AI (text, image, video, code) and personalization improve, I believe PMs will need to: ✅ Build AI experiences that feel human & intuitive ✅ Design feedback loops for continuous learning ✅ Prioritize safety & transparency for long-term trust 💡 What’s the biggest challenge you see in AI product management today? Let’s discuss! 👇 #GenerativeAI #ArtificialIntelligence #ProductManagement #AIPM #TechTrends #AIProductManagement #MachineLearning #Innovation #FutureOfWork #AITrends #UXDesign #DataDriven #ProductStrategy #DeepLearning

AI is no longer just an experimentation tool. It’s reshaping the entire optimization landscape. With this shift comes many untapped opportunities. Working with Andrius Jonaitis ⚙️, we've put together a growing list of 40+ AI-driven experimentation tools ( https://lnkd.in/gHm2CbDi) Combing through this list, here are the emerging market trends and opportunities you should know: 1️⃣ SELF-LEARNING, AUTO-OPTIMIZING EXPERIMENTS 💡 Opportunity: AI is creating self-adjusting experiments that optimize in real-time. 🛠️ Tools: Amplitude, Evolv Technology, and Dynamic Yield by Mastercard are pioneering always-on experimentation, where AI adjusts experiences dynamically based on live behavior. 🔮 How to leverage it: Focus on learning and developing tools that shift from static A/B testing to AI-powered, dynamically updating experiments. 2️⃣ AI-GENERATED VARIANTS 💡 Opportunity: AI can help you develop hypotheses and testing strategies. 🛠️ Tools: Ditto and ChatGPT (through custom GPTs) can help you generate robust testing strategies. 🔮 How to leverage it: Use custom GPTs to generate test ideas at scale. Automate hypothesis development, ideation, and test planning. 3️⃣ SMARTER EXPERIMENTATION WITH LESS TRAFFIC 💡 Opportunity: AI-driven traffic-efficient testing that gets results without massive sample sizes. 🛠️ Tools: Intelligems, CustomFit AI, and CRO Benchmark are pioneering AI-driven uplift modeling, finding winners faster -- with less traffic waste. 🔮 How to leverage it: Don't get stuck in a mentality that testing is only for enterprise organizations with tons of traffic. Try tools that let you test more and faster through real-time adaptive insights. 4️⃣ AI-POWERED PERSONALIZATION 💡 Opportunity: AI is creating a whole new set of experiences where every visitor will see the best-performing variant for them. 🛠️ Tools: Lift AI, Bind AI, and Coveo are some of the leaders using real-time behavioral signals to personalize experiences dynamically. 🔮 How to leverage it: Experiment with tools that match users with high-converting content. These tools are likely to develop and get even more powerful moving forward. 5️⃣ AI EXPERIMENTATION AGENTS 💡 Opportunity: AI-driven autonomous agents that can run, monitor, and optimize experiments without human intervention. 🛠️ Tools: Conversion AgentAI and BotDojo are early signals of AI taking over manual experimentation execution. Julius AI and Jurnii LTD AI are moving toward full AI-driven decision-making. 🔮 How to leverage it: Be open-minded about your role in the experimentation process. It's changing! Start experimenting with tools that enable AI-powered execution. 💸 In the future, the biggest winners won’t be the experimenters running the most tests, they’ll be the ones versed enough to let AI do the testing for them. How do you see AI changing your role as en experimenter? Share below: ⬇️

Folks interested in AI / AI PM, I recommend watching this recent session by the awesome Aishwarya Naresh Reganti talking about Gen AI Trends. ANR is a "Top Voice" that I follow regularly, leverage her awesome GitHub repository, consume her Instagram shorts like candy and looking forward to her upcoming Maven Course on AI Engineering. https://lnkd.in/g4DiZXBU Aishwarya highlights the growing importance of prompt engineering, particularly goal engineering, where AI agents break down complex tasks into smaller steps and self-prompt to achieve higher-order goals. This trend reduces the need for users to have extensive prompt engineering skills. In the model layer, she discusses the rise of small language models (SLMs) that achieve impressive performance with less computational power, often through knowledge distillation from larger models. Multimodal foundation models are also gaining traction, with research focusing on integrating text, images, videos, and audio seamlessly. Aishwarya emphasizes Retrieval Augmented Generation (RAG) as a successful application of LLMs in the enterprise. She notes ongoing research to improve RAG's efficiency and accuracy, including better retrieval methods and noise handling. AI agents are discussed in detail, with a focus on their potential and current limitations in real-world deployments. Finally, Aishwarya provides advice for staying updated on AI research, recommending focusing on reliable sources like Hugging Face and prioritizing papers relevant to one's specific interests. She also touches upon the evolving concept of "trust scores" for AI models and the importance of actionable evaluation metrics. Key Takeaways: Goal Engineering: AI agents are learning to break down complex tasks into smaller steps, reducing the need for users to have extensive prompt engineering skills. Small Language Models (SLMs): SLMs are achieving impressive performance with less computational power, often by learning from larger models. Multimodal Foundation Models: These models are integrating text, images, videos, and audio seamlessly. Retrieval Augmented Generation (RAG): RAG is a key application of LLMs in the enterprise, with ongoing research to improve its efficiency and accuracy. AI Agents: AI agents have great potential but face limitations in real-world deployments due to challenges like novelty and evolution. Staying Updated: Focus on reliable sources like Hugging Face and prioritize papers relevant to your interests. 🤔 Trust Scores: The concept of "trust scores" for AI models is evolving, emphasizing the importance of actionable evaluation metrics. 📏 Context Length: Models can now handle much larger amounts of input text, enabling more complex tasks. 💰 Cost: The cost of using AI models is decreasing, making fine-tuning more accessible. 📚 Modularity: The trend is moving towards using multiple smaller AI models working together instead of one large model.