Essential Skills for Tech Product Management

AI didn’t just change how we build -  it changed what we need to build. What once required months of work and a team of engineers now takes hours and a few prompts. The cost of building has collapsed. But here’s the real question:  Does anyone actually need what you're building? As AI democratizes speed and scale, the real differentiator isn't velocity -  it's clarity. Knowing what to build, when, and why. These are the skills product managers and product leaders need to double down on: 1.  Financial & Market Fluency Understand the levers your customers care about. What are they solving for 𝘳𝘪𝘨𝘩𝘵 𝘯𝘰𝘸? How are macro shifts reshaping the problem space?     2. Discovery Mastery Dig deeper than feature requests. See the unspoken needs. Ask better questions and connect the dots others miss.     3. Hypothesis-Driven Mindset AI tools make testing faster and cheaper — use them. Explore bold bets without overcommitting. Ship learning, not just features.     4.  Strategic Prioritization Just because you 𝘤𝘢𝘯 build something doesn’t mean you should. Tie product bets to long-term outcomes. For platform PMs, that includes balancing internal vs. ecosystem value.     5. Relationship Building Talk to customers. Align with stakeholders. Influence across functions. Empathy and trust are still your sharpest tools.     6. Storytelling Your ability to shape a vision, influence decisions, and rally teams depends on how well you tell the story — especially in a world flooded with noise. In a recent mentoring session, someone asked me: “With AI evolving so fast, how do I stay relevant as a PM?” This is how. Use AI to accelerate execution -  but build your edge in the skills AI can’t replace. 👇 Which of these are you investing in this year? What else belongs on this list? #ProductLeadership #ProductStrategy #ProductManagement #AI #PlatformProducts #

AI is Changing Product Management is Forever 🚀 The era of the "traditional PM" is over. AI is transforming how we build products. The Traditional PM (2023) 📝 Writing PRDs Creating user stories Managing backlogs Coordinating with design & engineering Defining requirements The AI-Enabled PM (2024+) 💡 Generating working prototypes Creating high-fidelity designs Writing & testing code Building AI workflows Managing data pipelines Developing conversational AI This isn't "PM vs Engineering" or "PM vs Design"—it's about skill flattening. What's Skill Flattening? 🔄 The ability for PMs to perform tasks that traditionally required specialized roles: Design & Prototyping ↳ Generate UI/UX with Midjourney/DALL-E ↳ Build functional prototypes with v0/Replit ↳ Create interactive flows with Framer Development & Testing ↳ Write code with Cursor/Copilot ↳ Test functionality directly ↳ Debug with AI assistance AI Integration ↳ Design conversational flows (Voiceflow) ↳ Manage data pipelines (SmartSuite) ↳ Create AI training datasets The Impact? ⚡ Faster iteration cycles More informed technical discussions Better cross-functional collaboration Rapid prototype-to-production Data-driven decision making This isn't about replacing anyone—it's about empowering PMs to: ✓ Move faster ✓ Validate ideas quickly ✓ Make better technical decisions ✓ Create higher quality specifications What's Next? 🎯 PMs need to assess their AI readiness across: Technical capabilities Data literacy AI/ML understanding Tool proficiency Implementation expertise Don't wait to adapt. The future of Product Management is here. 🔥 We're building an AI Readiness Assessment platform specifically for product teams. Want early access? Comment "Ready" below. #ProductManagement #AILiteracy #AI #Innovation #FutureOfWork #ProductStrategy

The role of product management, especially for AI-based products, is changing a lot. Interestingly, a significant number of products are becoming "AI-based" products. You'll often see requests for a stronger technical background alongside traditional PM skills. It's not enough to just know the market and users anymore; product managers now need to understand things like algorithms, data pipelines, and machine learning. This isn't a small change; it's a real shift in what's required. It’s not about knowledge of a toll but the technology. I'm seeing this trend firsthand. Look at product manager job descriptions, and "understanding or working knowledge of AI" is becoming standard. We're also seeing more data scientists and AI engineers moving into product management. This isn't just a career switch; it's a sign that technical knowledge is crucial for building good AI products. For people without this background, it's a big challenge, requiring a lot of learning and a willingness to try new things. Being able to explain complex technical ideas in a way that users understand is now a must-have skill. The key to AI product management is balancing big ideas with what's actually possible. Without understanding AI's strengths and limitations, product managers can easily get swayed by marketing hype or struggle to create realistic roadmaps. It's the difference between a dream and a practical vision. Equally important is building strong communication with engineering teams, not just for technical alignment but for building trust. Don't believe the idea that you don't need technical skills in PM. This trend is only going to get stronger. It's better to adapt and learn than to struggle later. #ExperienceFromTheField #WrittenByHuman

Writing software, especially prototypes, is becoming cheaper. This will lead to increased demand for people who can decide what to build. AI Product Management has a bright future! Software is often written by teams that comprise Product Managers (PMs), who decide what to build (such as what features to implement for what users) and Software Developers, who write the code to build the product. Economics shows that when two goods are complements — such as cars (with internal-combustion engines) and gasoline — falling prices in one leads to higher demand for the other. For example, as cars became cheaper, more people bought them, which led to increased demand for gas. Something similar will happen in software. Given a clear specification for what to build, AI is making the building itself much faster and cheaper. This will significantly increase demand for people who can come up with clear specs for valuable things to build. This is why I’m excited about the future of Product Management, the discipline of developing and managing software products. I’m especially excited about the future of AI Product Management, the discipline of developing and managing AI software products. Many companies have an Engineer:PM ratio of, say, 6:1. (The ratio varies widely by company and industry, and anywhere from 4:1 to 10:1 is typical.) As coding becomes more efficient, teams will need more product management work (as well as design work) as a fraction of the total workforce. Perhaps engineers will step in to do some of this work, but if it remains the purview of specialized Product Managers, then the demand for these roles will grow. This change in the composition of software development teams is not yet moving forward at full speed. One major force slowing this shift, particularly in AI Product Management, is that Software Engineers, being technical, are understanding and embracing AI much faster than Product Managers. Even today, most companies have difficulty finding people who know how to develop products and also understand AI, and I expect this shortage to grow. Further, AI Product Management requires a different set of skills than traditional software Product Management. It requires: - Technical proficiency in AI. PMs need to understand what products might be technically feasible to build. They also need to understand the lifecycle of AI projects, such as data collection, building, then monitoring, and maintenance of AI models. - Iterative development. Because AI development is much more iterative than traditional software and requires more course corrections along the way, PMs need be able to manage such a process. - Data proficiency. AI products often learn from data, and they can be designed to generate richer forms of data than traditional software. - ... [Reached length limit; full text: https://lnkd.in/geQBWz6s ]