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.

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.

Build AI-Powered Products: Masterclass with a Meta PM Leader Step into the next era of product management. In this hands-on workshop, a Meta PM Leader reveals how top tech teams leverage AI across research, analytics, strategy, and rapid prototyping. Move past the hype to master the frameworks, tools, and workflows that will immediately elevate your daily output. What You'll Gain: Real-World AI Skills:** Automate research synthesis, run data analysis, sharpen product strategy, and build interactive prototypes—no coding required. Start building a portfolio: Construct a portfolio of live AI product artifacts during the session to showcase your skills to leadership or hiring managers. Monday-Ready Impact: Walk away with plug-and-play skills, and templates you can apply to your current role instantly. Transform from a traditional manager into an AI-powered Product Builder, future-proof your career, and multiply your impact.

I spent 10 years as a product manager at Apple, Zello and other companies, big and small. Now I recruit PMs for Silicon Valley startups. I read what founders actually ask for in a role, and I see which candidates pass the first screen and which get cut. In this session I'll show you how PM hiring works from my side in 2026. What founders look for has changed. And many strong candidates get rejected for reasons that have nothing to do with how good they are. I'll show you what actually helps you get hired now, and the common mistakes that get good people rejected early. Come if you're looking for a job, thinking about a new role, or hiring PMs yourself. You'll leave knowing what gets a PM hired in 2026.

Every few years, most of us land at a fork. Whether to go to college, and where. How to make those years actually count. Which first job to take. Whether to change direction ten years in. The information is usually there. What is missing is a calm way to weigh it against what actually matters to you, instead of deciding at 3am on a gut that keeps moving. I built MapMyDecision for exactly that, and I will run it live. You answer a few maps, and it pulls them into one plan: where your priorities agree, where they pull against each other, and how much would have to change to flip your answer. It does not tell you what to do. It shows you your own decision, clearly enough to act. You leave with a method you can run yourself, and a sharper read on the fork you face.

Product work assumes we can carry complex context, switch gears repeatedly, answer questions on the spot, and resume work without losing the thread. But real life doesn't always cooperate. Pregnancy, new parenthood, caregiving, perimenopause, high stress, illness, poor sleep, and major life changes can all create periods when attention, working memory, recall, or cognitive stamina feel less predictable. So here's the product question: How would we structure work differently if we designed for real human variability? I'll share practical ways to reduce context-switching costs, offload working memory, recover faster after interruptions, and prepare for high-stakes work. Then we'll turn to the room to identify where product work creates unnecessary cognitive load—and how we might design it differently.

Two strangers have to trust the same thing: an environment neither of them controls. One hands over a workload to run on hardware they'll never see. The other hands over idle hardware to run code they didn't write. Neither one is being unreasonable. This session walks through the discovery and architecture problem behind that setup. Idle compute isn't a business until you solve incentive design - and real cash payouts introduce fraud opportunities that most product discovery never accounts for. The two fears are mirrored in tampering and data exposure, lost visibility and runaway cost, integrity and performance. The trust method marketplaces lean on - reputation and reviews - assumes a window to catch bad behavior. Here, the transaction completes before that window ever opens. If you're building anything two-sided where each party hands control of a resource to a stranger, this is a problem worth learning from.

Most professionals don't realize they're sitting on a goldmine of expertise they could turn into income today. In this session, you'll learn a fast, repeatable system for transforming your everyday knowledge into digital products such as, courses, templates, workbooks, toolkits, and more. Designed for product professionals, founders, and career-transitioners, this talk shows how to identify hidden expertise, extract it into structured value, and convert it into sellable digital assets you can publish on Shopify, Etsy, or Gumroad. You'll see how a typical Fortune 500 employee with a master's degree can turn their daily work into a digital product portfolio and promote it through social media and LinkedIn-bio funnels. You'll also get a chart of the best AI tools for different product types and the best distribution channels for each. You'll walk away with a system you can use today to build your first digital product in one session.

AI isn't replacing the PM role. The role is being re-scoped, though, and PMs who understand that new line will still be crucial going forward. Those who don't risk being left behind. I've trained thousands of product managers at companies ranging from startups to the Fortune 50. The organizations getting real value aren't doing anything exotic. They've automated the repetitive, low value work inside the product development lifecycle and retained human judgment where it still belongs. Most companies have not worked this out yet, which is exactly the opening. You will leave this session with four patterns that separate the best companies from everyone else, a way to find that line in your existing workflows, and four questions to ask about your own team. Learn how and where to automate so you can future-proof your career.

You see headlines about enormous AI-talent offers and people changing companies. They expose a question every business faces: when information is incomplete and the stakes are high, how much should you invest in a person or capability? At Amazon, I supported compensation for 30,000+ employees. I began with a simple AI hypothesis: automate the busywork - data gathering, validation, calculations, routing, and documentation - so compensation teams could focus on business decisions. Fifty-plus customer interviews changed my view. Pain is not the same as urgency. Companies may complain about broken workflows yet tolerate workarounds, defer investment, or rely on their HRIS. The real opportunity isn't better automation. It's learning to distinguish three things: a painful workflow, a real buying problem, and a high-stakes decision that needs AI-supported human judgment. This interactive session shares a practical test for distinguishing a painful workflow, a real buying problem, and a high-stakes decision that needs AI-supported human judgment. Using compensation as the case study, we will examine the remaining decision boundary: where AI should automate, where it should advise, and where accountable humans must decide.

An urgent issue lands in Slack. Someone needs to gather context, coordinate calendars, organize a response, and keep everyone informed. What would it take for an AI Chief of Staff to help carry that work through? I'll demonstrate the agent system I built and walk through how it turns a request into a coordinated workflow: gathering information, dividing work among specialized agents, asking for approval, and recording what happened. We'll look at both the useful parts and the rough edges: where automation saves effort, where people remain essential, and what happens when a step fails. You'll leave with a practical way to identify a workflow worth automating on your own team—and clearer expectations for what an AI agent should actually deliver. No coding or prior agent experience required.

What if your product is working exactly as designed—but still not solving the customer's problem well enough? Product teams are skilled at generating ideas, building features, and launching products. But those accomplishments can create a dangerous illusion of success. A product can be well designed, widely used, and still leave the original problem insufficiently changed. That is part of the Last Mile of Innovation: the gap between delivering a solution and actually creating the outcome that justified building it in the first place. In this session, we'll challenge common assumptions about product success and examine how teams can lose sight of the original problem as attention shifts toward adding more features, handling ownership across functions, and driving more sales. Product managers and product marketers will leave with a practical framework for asking the question that matters most. Is the customer's problem fixed?

Building an AI feature is only half the product decision. Product managers also need to understand the business case behind it. AI changes the traditional SaaS model. Every interaction can introduce model, infrastructure, data, and human-oversight costs. Growing adoption can increase customer value while simultaneously reducing gross margin. This forces companies to reconsider what they charge for, what they include in existing packages, and when to introduce usage limits, credits, premium tiers, or outcome-based pricing. In this session, we will explore why companies are racing to build an AI growth and valuation story, how AI changes product unit economics, and how different pricing and packaging choices affect adoption, revenue, and margin. Attendees will leave knowing what belongs in an AI product business case and which commercial questions to answer before taking an AI investment proposal to their leadership team.

Anyone can be creative sometime. I teach people how to be creative anytime. Most companies say they want innovation, then chase it through brainstorm meetings and hackathons — exactly the settings neuroscience says are least likely to produce a real breakthrough. Real insight shows up in diffuse-mode thinking: the shower, the drive, the walk — never at the whiteboard or behind the keyboard. Last spring I showed you how to reverse-engineer "Shower Thoughts" with the A.H.A.A. Algorithm. This time, you build a personalized "A.H.A.A Toolkit": a tailor made repeatable process to get into that headspace on command, and a way to make sure what shows up there actually survives past the moment it hits.

Every product talk tells you it is okay to fail, and then goes quiet on the part that actually matters: what you do the morning after a bad call. This session skips the permission slip and goes straight to the recovery playbook. We start by separating two things product teams constantly confuse, a bad decision and a bad outcome, because you can decide well and still lose, and you can luck into a win off a terrible call. Then we do it live with some famous examples. Bring a decision you are unsure about, and you will leave with a way to think about it.

Everyone uses the word strategy—from a seven-year-old opening with a Sicilian Defense in chess to an executive presenting a deck in the C-suite. Yet, in many corporate environments, "strategy" has devolved into a vague synonym for "important things we want to do" or "ambitious metrics we hope to hit." In this session, we strip away the corporate buzzwords to uncover what strategy actually is: a coherent set of hard choices about where to play, how to win, and what not to do. We'll bridge the gap between rigid analytical planning and lateral creative thinking, proving that the most defensible product strategies are not created in a vacuum of cold data—they are born where structured positioning meets creative problem-solving. You will leave with 5 actionable best practices to craft a product strategy that is disciplined, innovative, and easily understood across all levels of the organization.

We've all heard that we should build our network before we need it. But what if you need help now? What if you're starting from zero? And is asking someone for something really what makes networking transactional? In this interactive session, we'll explore how connections become professional relationships, both online and IRL, and how to build and maintain a network without keeping score. We'll unpack the weight behind different asks—from information and introductions to referrals and advocacy—and the trust and credibility both people may be putting on the line. Whether you're building your network for the long game, need it today, or find yourself on the receiving end of the ask, we'll explore practical ways to connect, ask for help, set boundaries, support others, and build professional relationships rooted in trust rather than transactions.

Many organizations have shifted their language from projects to products while retaining the structures, planning assumptions, governance, and measures that were designed around project delivery. What happens when persistent products are managed inside systems built for temporary projects? This session examines how project assumptions can shape product strategy, planning, dependencies, decision-making, funding, and the flow of work. We will look beyond product practices themselves and examine the organizational conditions surrounding them. Participants will leave with questions they can use to identify where product problems may actually be symptoms of the system in which product work takes place.

You will learn how to quantify your wins and create an AI Boardroom skill that will make you think like a CEO.

Your employer is about to hand you an AI agent for your job. The one for your life — your goals, your decisions, the reasons behind them — is about to land on a commercial platform whose business model is you. This session is the alternative: rent the model, own the memory. Models get replaced every few months; your intent graph shouldn't be the casualty. I'll show a memory layer that lives in your cloud project under your keys, structured as a map you can change and watch the changes propagate — and a live demo of what one real user gets out of it. Then the part that makes it sustainable: a co-op, where shared skills, a disaster-recovery network, and model-replacement testing are leverage for every member instead of a burden on one. You'll leave knowing what to demand from any platform that wants to host your personal agents.

New to product, or just want to fall back in love with the fundamentals? In 20 minutes I'll walk you through the big picture, and the core toolkit every PM needs — problem framing, prioritization, roadmaps, and metrics — then open it up for your questions. - Why is product work so important and so fun? - How does it fit into business and software work? - When do product people collaborate? Titles and teamwork in small and large orgs - What tools and methods might I need to use at work? - Where can I learn more? After 10 years as a PM I still love product management because it's interesting and challenging... and it all makes more sense when you know what you're trying to do before you get lost in asking AI to help you do it.

Information does not, in and of itself, change behavior. A wearable, app, dashboard, notification, or AI recommendation is only valuable if it helps someone take the next realistic action in their life. In Let's Get Phygital, we'll explore how connected physical and digital experiences can use readiness to change to support behavior change where people work, live, and play. Through a pet adoption case study designed around readiness to adopt and to address a real-world pet-return problem, we'll imagine what is possible when digital guidance and physical products connect people to programs, communities, and experts. You'll leave with a practical framework for designing products around where customers actually are, not where the workflow assumes they should be.