All are welcome! Whether you are trying to break into the product world (product, ux, ui, data, dev, marketing, etc.) or are a seasoned veteran, we can all learn from one another!

We often call data the lifeblood of our organizations; but rarely do we design user experiences with that principle in mind. This talk explores the critical intersection of user experience and data quality. Drawing from years of experience across industries, I'll highlight common UX anti-patterns that quietly sabotage data integrity, erode trust in reporting, and stall progress in AI and machine learning initiatives. More importantly, I'll share practical strategies for spotting these patterns early, redesigning interactions to generate high-quality, usable data, and embedding user empathy into the foundation of your data programs. Whether you're a designer, engineer, or data leader, you'll leave with a sharper eye for experience-driven data risks—and tools to turn them into opportunities.

AI product requirements usually describe what the system should produce, but they often leave out the decisions the system is allowed to make. That gap creates confused ownership, weak human oversight, and surprises after launch. In this practical session, product managers will learn how to map an AI-enabled workflow from business goal to individual decisions, impacts, controls, and evidence. We will work through a realistic product example and identify where AI should recommend, act, escalate, or stop. Drawing on experience leading AI products in a Fortune 50 company and reviewing risk as a former federal bank examiner, Frank Sellhausen will show how product teams can make decision authority explicit before launch. Attendees will leave with a one-page tool they can use during discovery, requirements, design reviews, and launch readiness—and a clearer way to work with engineering, legal, risk, and business leaders.

The hiring process today feels less human than ever. The first eyes on your resume are often not human. Communication throughout the process is automated. AI is even in the interview. After 14+ years in product marketing, I found myself back in the job market and decided to fight fire with fire. I now use Claude to find jobs, determine my fit, help me apply, prepare for interviews, track progress, and stay on top of all the moving pieces. In this session, I'll walk through what I built, the privacy tradeoffs I navigated, and where I intentionally kept AI out of the driver's seat. You'll leave with practical guidance for how to apply AI in your own job search without losing the human thread that gets you hired.

Linguistic debt is structural infrastructure debt. When teams drift in meaning—not just terminology—it cascades into broken alignment, duplicated work, and scope creep. This session unpacks why shared semantics matter as much as shared tools, and how to catch linguistic drift before it hardens into org DNA: • How drift shows up in your artifact trails (PRs, issues, planning docs) • Why the "human API"—how people interact with process and each other—breaks down under linguistic debt • Why it compounds across roadmaps and org changes • One high-leverage intervention PMs can run today For teams feeling the friction of "we're saying the same words but meaning different things

The 3 Questions Every Product Manager Should Ask using the IOR Framework: 1. INWARD: Are we solving the right problem? Before jumping to solutions or roadmaps, understand the customer, the problem, your assumptions, and what really matters. 2. OUTWARD: How will we turn insight into value? Turn what we learn into clear priorities, useful products, and experiences customers value. 3. RESULTS: What does success actually look like? Go beyond shipping features. Measure impact, learning, and real customer and business results. It's not your ROI. It's your IOR. Your story is still being written. Write it intentionally. Write it strategically. Build it like a product, with you as the product.

Your product works, can your customer use it? Can they install it? Can they maintain it? Can they troubleshoot it? Product managers coordinate engineers, technical writers, content designers, customer support, and the feedback customers generate. Then reality intervenes. Engineering releases version 3.2. Someone wrote the installation instructions 18 months ago. Support discovers customers consistently get stuck at step 7. Engineering fixes it in 3.3, but the old YouTube video remains online. Google finds an obsolete PDF, and suddenly the customer has six sources of truth. How do we make that manageable? Here's one approach. https://tinyurl.com/27wrt7cp

Most Product Managers are already using AI to write faster, summarize faster, and produce more polished artifacts. But speed is not the same as better product judgment. In fact, the riskiest AI output is often the one that sounds convincing while hiding weak context, missing evidence, or untested assumptions. In this practical session, we'll look at how PMs can use AI more effectively without outsourcing the thinking that makes product work valuable. You'll learn five prompting moves that help structure better AI collaboration. This session is designed for PMs, product owners, technical PMs, and cross-functional product partners who want to use AI not just to move faster, but to make better decisions.

The layoff-survivor talk. How to re-scope honestly instead of pretending nothing changed: ruthless cut criteria, what "maintenance mode" really means, how to communicate reduced scope upward without career damage. This is where your agentic toolkit becomes the punchline — AI-assisted PRD and code-gen workflows as the force multiplier when headcount disappears. That connection (adversity → agentic adoption) is a genuinely fresh thesis.