January has a very particular energy.

The holidays are behind us. The inbox is slowly filling up again. Calendars are waking up. And there's always this short window, just a few quiet days, where it feels like everything could still go in a different direction.

I've been thinking a lot during this pause.

Over the last couple of years, AI and large language models have gone from experiments to expectations. What used to feel optional is now part of daily work, whether someone asked for it or not. And the biggest shift I've personally noticed isn't technical.

It's psychological.

People aren't asking "What can AI do?" anymore.

They're asking "What should we actually build?", "What do we trust?", and "What's worth shipping versus waiting?"

That question shows up everywhere, especially in product teams.

Because as exciting as LLMs are, shipping the wrong AI feature is worse than shipping none at all.

And that's exactly why today's conversation matters.

This episode is not about hype.

It's about judgment, timing, and responsibility in product leadership.

Chapters:

00:00 Introduction to Angshuman Rudra
01:06 The Impact of Large Language Models on Product Management
03:14 Balancing Innovation and User Needs
04:37 Navigating Generative AI in Product Development
06:46 Driving Adoption of New Features
09:34 Challenges and Lessons in Generative AI Products
11:15 Evolving Roles of Product Leaders with AI
12:39 The Future of Multi-Agent Systems
14:36 Translating User Requirements into Product Features
17:31 Finding the Next Big Feature
19:56 Adopting AI in Development Cycles
21:24 Tips for Job Seekers in Tech
23:10 Market Shifts in Marketing Technology
25:01 Exciting Use Cases in Marketing Technology
26:52 Concluding Thoughts and Future Outlook

Episode # 178

Angshuman Rudra is a senior product executive at TapClicks, where he leads a portfolio of data, analytics, and AI products for a market-leading martech platform.

• Website: Angshuman Rudra

What Listeners Will Learn:

• How to evaluate real user demand for AI features (not hype)
• When AI adds value and when it creates unnecessary complexity
• How product leaders should think about LLMs as tools, not magic
• Why many AI features fail after launch
• How to balance innovation with resource constraints
• What "AI adoption" actually looks like inside real companies
• Why multi-agent systems are promising but not ready to be fully autonomous
• How PMs can use AI for research, specs, and design without losing judgment
• What skills will matter most for product leaders over the next 3–5 years

• Angshuman Rudra