What does it mean to build products in a world where AI is reshaping every layer of the experience? In this episode of our virtual speaker series, Microsoft Senior Product Lead for AI Agents Sukhmani Lamba unpacks practical AI strategies for building smarter
This conversation offers a clear, real-world look at how AI is transforming product expectations and execution. It highlights the skills AI product leaders should elevate—and the ones that matter less as automation matures. It also explores the tools and routines that help PMs stay sharp as the landscape evolves, along with tactics for navigating ambiguity and influencing cross-functional teams.
The session closes with a forward-looking view of emerging AI product trends and what today’s leaders should begin building for the next wave.
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Show Notes:
- The product manager (PM) role is shifting to focus more on strategic thinking, clarity, relationship-building, and user advocacy, with operational tasks increasingly handled by AI.
- AI has transformed how products are built and evolved the PM playbook to prioritize value-driving work.
- AI can now automate tedious tasks like number crunching, research, writing PRDs, and routine follow-ups.
- Products exist on a spectrum from classic recommender systems to generative AI and agentic, workflow-orchestrating AI.
- Distinguishing between AI infrastructure (protocols, models, integration) and AI applications (consumer/business-facing tools) is essential.
- Industry protocols (like Model Context Protocol and agent-to-agent standards) are crucial for enabling interoperability and agent collaboration.
- The biggest opportunities lie not in foundational models but in vertical applications—contextual “wrappers” built on AI infrastructure.
- As engineering efficiency rises due to AI, demand for visionary product managers increases, shifting the PM-to-engineer ratio.
- Modern PMs need AI literacy, prompt engineering, evaluation skills, rapid prototyping, and cross-functional influence.
- Product cycles are now continuous, probabilistic, and data-driven—traditional linear cycles are obsolete.
- User insight, understanding domain specifics, pain points, and needs is more important than ever for effective AI-powered product development.
- PMs should build “AI-first” habits: always questioning “why,” “when,” “who,” “what,” and “where” when integrating AI into products.
- Daily AI-use habits, like brainstorming, rapid drafting, simulating feedback, and stress-testing, are essential for PMs.
- Automating routine work (notes, summaries, reminders, follow-ups) with AI can save significant time and boost productivity.
- Staying current by subscribing to newsletters and actively testing new AI tools is important for ongoing PM growth.
- No-code prototyping (“vibe coding” tools like Lovable) lets PMs quickly validate product ideas, democratizing development.
- Natural language interfaces are overtaking traditional rigid UIs, making products more accessible and customizable.
- Specialized vertical AI agents, combining domain knowledge with AI, are outperforming generalist agents in fields like healthcare and law.
- Tool integrations and standardized protocols are making ecosystem “plays” larger and more impactful.
- Focused “wrappers” built on general models—like Perplexity, Gamma, Lovable, Harvey—succeed through targeted user flows, domain expertise, and strong distribution.