Most product managers eventually face the same challenge: integrating products, teams, and systems that were never designed to work together.

It happens in partnerships.

It happens in platform integrations.

And in the most extreme version of the problem, it happens during an acquisition.

Nine months ago, we found ourselves on opposite sides of one.

Both of us were newly joined product leaders, pulled into a live M&A process during due diligence, before it was even clear whether the deal would close.

Two companies, two products, two languages, and no shared roadmap, ownership, or even a single source of truth.

Instead of waiting for organizational clarity, we focused on the one thing that could anchor everything: the problem this “product marriage” was meant to solve.

Before the acquisition was signed, we started joint story-mapping sessions and began designing the future product together. That shared view allowed us to navigate uncertainty, align teams across companies, and move forward even while the deal itself was still in motion.

Two months after the acquisition closed, the first customer was already live on the integrated platform.

In this talk, we’ll share what worked, what almost broke, and how product managers can act as cultural translators and integration leaders when different worlds need to become one product.

What happens when the person holding the product vision finally has the power to push it directly to production?
Over the last year, as a non-technical VP Product, I bypassed the traditional engineering queue and shipped over 100 features directly to production.

The magic isn't (just) the AI tools; it's the framework we built: Product-Led Development. By deleting the PM-to-dev translation layer, we collapsed the idea-to-production flow into a matter of days.

In this talk, I’ll share the exact blueprint to rewire your organization for this flow: starting with a small task force, defining non-negotiable guardrails, and earning R&D's trust.
I’ll show how this model accelerates business velocity, transforms how you handle customer requests, and ultimately frees your engineering team to focus entirely on core architecture.

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.

Product packaging is often treated as pricing or go-to-market decisions. But for customers, packaging is the product; it shapes how they understand what you offer, how they evaluate it, and whether they choose to engaged or walk away.
When packaging reflects internal structure instead of customer value, it creates hidden friction across the entire sales process
In this talk, we’ll examine how packaging shapes customer decisions, where it creates friction across the funnel, and how small decisions can impact conversion and adoption, along with how product managers can help shape these decisions based on a deep understanding of customers and the market.

Imagine managing a complex product while navigating through a thick fog with your eyes closed. Due to strict security constraints, your product is installed on-premises with no external access: you are barred from seeing the customers' content domain, you cannot use their data for quality assurance, and you can't even track user actions to understand how they interact with the product on a daily basis.

As a Product Manager at AudioCodes, I faced this exact scenario while working with the Defense and Government sectors. I discovered that standard methodologies, such as analyzing usage metrics or conducting direct user interviews, were simply inaccessible or inapplicable. In this talk, I will share how we cracked the product discovery process in a "closed lab" environment and delivered significant value without ever seeing a single word of the customer's data.

Key Takeaways:

• The Shadow Persona Method: How to characterize needs through organizational "intermediaries," leveraging AI tools to synthesize partial information into a comprehensive user profile.
• Building a Synthetic Golden Data Set: Principles for creating an internal benchmark that accurately simulates customer challenges without relying on sensitive or classified data.
• The Proxy Validation Model: Using indirect signals (installation reports, support tickets, and pre-sales feedback) as quantitative success metrics to validate your roadmap.

Over the past 18 months, we built and scaled an AI-powered code review agent at Baz, now used daily by thousands of developers.

Along the way, we made many architectural, product, and UX decisions that felt reasonable at the time. Some worked, but several turned into costly mistakes that impacted adoption, user trust, and revenue.

In this talk, I share the lessons from building AI agents in production, where there are still very few established best practices. While most talks focus on success stories, this one focuses on what breaks when AI features meet real users.

if you could validate your riskiest strategic assumption before building anything?

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.

In a data-driven world, we often silence our inner sensor: professional intuition. In this talk, I’ll share how I navigated leading the product under significant doubt, and how I intentionally stopped in a fast-moving environment to challenge "proven" assumptions. This approach led me to choose a path that ultimately drove a fundamental shift in our results.

When you build products around GenAI, you quickly realize the old rules don’t apply - traditional QA won’t cover you. The product isn’t binary; it’s probabilistic. A tiny tweak in a prompt chain can ripple through the entire experience.

When your product’s core value depends on an LLM’s ability to reason, "hoping it works" isn’t a strategy.

In this talk, I’ll share how we moved from vibe-based decisions to building real confidence in our product. I’ll walk through the messy, iterative process we went through, and how evaluations became a core part of how we build, test, and improve AI-driven experiences.

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.