Essential Skills for Next-Gen Product Managers

Artificial intelligence (AI) isn’t just adding new features to our products. It is rewriting the entire rulebook for what it means to be a product manager (PM).

With AI evolving from a “nice-to-have” feature into a core infrastructure layer, the role of a product manager revolves no longer around managing backlogs or coordinating features. In this fast-moving era of intelligent systems, generative models, and agentic frameworks, PMs must evolve into strategic, tech-savvy leaders who understand how intelligent systems work, frame business use cases for AI solutions, and at the same time deliver business value at scale and speed. Today’s most impactful PMs are adaptive, AI-literate, and strategic operators fluent in AI systems, grounded in user empathy, and capable of orchestrating business, data, engineering, design, and compliance considerations to thrive in this new landscape.

Must-Have Skills for AI Product Managers

To lead in this AI-powered world, PMs need a mix of technical, strategic, and human-centered skills.

1. Prompt Engineering is the New Literacy
   Generative AI tools are becoming core to product work—from crafting smart inputs for AI systems, summarizing customer feedback, prototyping ideas, writing user stories, and generating specs. Being fluent in prompt engineering is no longer just a “nice skill,” it’s the new literacy. It’s a multiplier that improves speed, clarity, and creativity in product development.
   How to Start: Using tools like ChatGPT, Grok, Claude, or Copilot, try automating a small task, like generating user survey questions, and refine your prompts until you get the desired output.
2. From Features to Systems Thinking
   AI-native products aren’t isolated features. They’re complex ecosystems that connect users, data pipelines, user interfaces, and algorithms in real-time. PMs need to see the big picture and must think in flows, capabilities, and user journeys that stretch across domains, interfaces, and tech stacks. This systems-level thinking allows PMs to anticipate ripple effects across business, data, engineering, and user experience dimensions, helping them deliver impactful experiences.
   How to Start: Draw your product’s flow like data, features, and users on a whiteboard (or a tool like Miro) and identify opportunities to use and implement AI.
3. Mastering Low-Code and No-Code Prototyping
   With generative AI low-code and no-code platforms (e.g., Retool, Bubble, LangChain), PMs can prototype concepts, generate POCs (proof of concepts), UIs, in hours instead of weeks. These tools enable rapid prototyping and iteration, largely shrinking development cycles. Proficiency in these tools empowers PMs to test, iterate, and validate ideas rapidly; reducing dependency on engineering cycles and accelerates learning.
   How to Start: Explore tools like Figma for UI or Airtable for workflows. Start with something small and get quick feedback.
4. AI-Native Technical Fluency
   PMs don’t need to code, but they must understand APIs, data infrastructure, and AI architecture. The convergence of analysis, design, and implementation demands that PMs speak the language of AI systems, know how models are trained and deployed, and evaluate agentic frameworks where multiple LLMs collaborate autonomously.
   How to Start: Sharpen understanding of AI basics by pursuing a crash course. “AI for Everyone” by Andrew Ng is a good start to learn terms like “training data” and “inference” in plain English.
5. Building Empathy and Trust in AI Products
   With AI evolving so fast, it is hard for users to build trust using AI features. It has become imminent for PMs to understand users’ perspectives and concerns, and to design with empathy, thoughtful cognitive friction, and ethical considerations. As AI becomes more integrated into user interactions, it’s not just about functionality, but about creating human-centered AI experiences, intuitive interfaces, and building confidence for users to embrace AI solutions confidently.
6. Embedding Risk and Compliance in the Product Lifecycle
   As AI usage increases, complexity arises and so do risks. PMs must adhere to evolving regulations while building products. This means integrating risk management, legal compliance, and safety governance into every stage of the product lifecycle, right from ideation to deployment to avoid costly rework and reputational damage.
   How to Start: Get a good understanding of frameworks like GDPR, EU AI Act, NIST AI RMF, etc.
7. Strategic Adaptability Over Specialization
   The most successful PMs don’t over-specialize; they stay versatile. They learn quickly, and can juggle domains like business, data, design, AI, and engineering with ease. They understand trade-offs and identify leverage points where AI can deliver maximum impact. In AI-driven context, this adaptability becomes a competitive advantage.
   How to Start: Upgrade your skills and domain knowledge collaborating with engineers, designers, and data scientists regularly. Understand their perspectives to get a clear picture of the product from their viewpoint.
8. Building Continuous Feedback Loops
   AI systems and platforms get trained and thrive on high-quality feedback. PMs must have regular data collection mechanisms in their product core workflows and design user experiences based on the feedback. This routine enables continuous learning and performance improvement of the AI systems.
9. Collaborating Across AI-Centric Teams
   AI product development involves a broader range of stakeholders, from data scientists, ML engineers, compliance teams, UX researchers, and business analysts. PMs must align these roles around clear objectives ensuring that everyone work towards common product goals and outcomes.

Beyond these, PMs should grasp AI basics like algorithms and model training, leverage data science, gather feedback effectively, and apply UX best practices to create personalized user experiences.

Becoming an AI Product Manager: How to Start

Ready to step into the role of an AI Product Manager? There is no one-size-fits-all path, but here are ways to join the AI Product managers bandwagon:

• Join an AI-Driven/Native Company: Dive into a company steeped in AI for hands-on learning and mentorship of domain experts.
• Champion AI implementation in your current Job: Identify opportunities to use and implement AI, spot value addition through AI usage, and build skills to become the team’s AI go-to resource as a start.
• Launch an AI Startup: Ideate and launch an idea for a problem that only AI can solve. Build a team of experts and collaborate with them to tackle problems with AI.
• Learn from Experts: Enroll in a course or learn from a mentor, do a certification in AI product management to master the essentials like generative AI and data-driven decisions.

PMs Who Can Ship and Steer Will Win

AI will empower product managers. As businesses become more automated, the need for product managers who can bring in their creativity, strategy, and insights, fusing AI’s power with a sharp focus on users and business goals will be the need of the hour. Companies will look for product managers who can both ship with speed and steer with strategy.

The age of the AI-native product manager has arrived. Are you ready to evolve? Embrace these skills, adapt to the challenges, and lead the charge in AI product management.

Vivek Sunkara is a Technology Product Manager at Citi, transforming Risks & Controls data into actionable insights that drive strategic growth. A BCS Member, IEEE Senior Member, IETE Fellow, and ACM professional member, he is an ‘AI-first’ product leader focused on building products and emotionally resonant user experiences.