The new skill: Knowledge as a Product

We built a powerful AI feature, and yet adoption was zero.

The fix wasn’t a better model, but a smarter integration: combining guided UX (embedded in real user flows) with a structured, product-grade knowledge base that the AI could actually rely on.

This session shows how aligning experience and knowledge turns AI from a cool feature into something users consistently come back to.

The new skill: Creating tactical Validation for Strategic Decisions - using smoke test

In this talk, I'll share how we extended Amazon's Working Backwards framework beyond PR/FAQ by using smoke tests to validate not just solutions, but the assumptions behind them. Through a real case from Fiverr, we'll explore how a simple in-product experiment helped resolve a strategic deadlock between growth and monetization, turning a perceived trade-off into a scalable win-win.

You’ll learn how to design smoke tests that go beyond clicks, identify meaningful signals of user intent and quality, and use them to sharpen your value proposition before committing resources.

If you're moving fast but want to make smarter product decisions, this session will give you a practical way to reduce risk and gain clarity early.

We could have evolved the way we work. We chose a revolution instead.

 What happens when PMs, designers, and engineers all become product builders, overnight? At Camunda, we chose revolution over evolution and changed the way we work while still building a new product with an already committed release date. This is the story of what happened next, what we learned along the way, and what “everyone is a builder” actually looks like in practice.

The new skill: Building a Personal AI Copilot

While working on a vibe-coding project, I found my discovery interviews getting stuck surprisingly fast, really after just a few conversations, I wasn’t really learning anything new. The issue wasn’t volume, but my ability to process what I’d already heard, identify what was still unclear, and adapt in real time. So, naturally, I did what everyone on LinkedIn says and built an AI agent. In this talk, I’ll show how it helps me prepare for interviews, debrief patterns across conversations, and even get live guidance mid-interview and why the agentic layer matters: not just summarizing, but actively identifying gaps, suggesting next questions, and turning interviews into a continuous learning system. You’ll leave with a practical approach to building your own AI-powered discovery workflow.

While AI enthusiasts were publishing blog posts, demos, and browser extensions showing how easy it is to summarize hotel reviews with LLMs, we were busy tackling the much harder challenge of building a feature that actually helps travelers. Building an AI-powered review summarizer is no longer the hard part. Building one that works consistently across nearly 4 million properties and hundreds of millions of reviews, while measurably helping travelers make better booking decisions, is. Join me to learn about our two-year journey of experimenting with prompts, models, data sources, evaluation methods, and UX at Booking.com. We’ll explore why offline AI metrics are only the beginning, why A/B tests often tell a very different story, and the surprising lessons we learned when real traveler behavior challenged our assumptions. 4. Senior Product Manager

Ask three AI tools "what's our churn?" and you get three different numbers. Nobody lied - each silently chose a different population, definition, time basis, and pipe. MCP gave every PM access to the data lake; it didn't give them understanding, and a confident wrong answer costs more than no answer.

This is about the layer nobody put on the roadmap. Adaya will show the four hidden choices behind any metric, why a metric is a product, and the agent architecture we run at ReasonLabs - plus three tests to run on your own org tomorrow morning.

The new skill: Distribution in the AI Era

After three years of fighting internal battles to open up the funnel, the arrival of AI proved the old model is fundamentally broken.

An inside look at how a 20-year-old sales-led giant is forced to abandon seats and landing pages to survive a new reality and what that means for product.

Marketing has never kept up with product. Inside the company, people didn't know what shipped yesterday, and outside, nobody did. With AI, R&D now ships faster than marketing can even notice, and that old problem got a thousand times worse. I'm not a classic marketer. After 25 years as an entrepreneur, I joined Base44 in January 2026 to help marketing keep up with an agentic R&D. In the old world, a single landing page took months: design, UX, copy, frontend, and a developer. So I built MarketingOS, an agentic operating system that tells marketing what R&D shipped, decides what's worth building, and lets marketers

  write on-brand pages with a coding agent and open pull requests straight into the product. The hardest change wasn't the technology, it was how the team works. I'll close by discussing what this means:

Does every marketing team need someone like me, or does every marketer need these skills?