LaunchPod | Product Management Podcast

Ask ten product leaders what AI means for UX and Product teams, and you'll get ten answers. Our guest today is working it out in real time, with his own team right now. If Engineering is 10x faster and AI can build, then what’s left for Product and UX teams? Myk Konrad is Chief Product Officer at Euna Solutions. After spending 25 years in product at companies like Microsoft, Avaya, and Brightcove, he now leads a team building software for government agencies, where both stakes and scrutiny are exceptionally high. That gives him a front-row seat as responsibilities across the product delivery cycle change seemingly day-to-day, leading to one of the biggest open questions in product: What happens to UX now that AI can design? In this episode, Myk shares: Which parts of UX he sees moving to PMs, developers, and agents, and which — like payments and compliance — still need a human in the loop How he's evolving his team with clear goals, hands-on training with AI tools, and a simple mantra, "If you can, then do." And why token spend is a bad measure, but a good signal, of AI adoption, and why the real speed limit is how fast your customers can absorb change Links LinkedIn: https://www.linkedin.com/in/mykolakonrad/ Euna Solutions: https://eunasolutions.com/ Chapters 00:00 Introduction 00:42 Myk's path from electrical engineer to product leader 01:43 Will AI replace UX? What's shifting to PMs and devs 04:57 What stays human: Research, high-stakes flows, and accessibility 07:44 Fewer UX roles with more output, and moving from a triad to a pod of two 12:04 Getting your team through change: Tools, training, and new timelines 14:56 Token spend as an adoption signal and finding team bottlenecks 17:28 Inside Euna's AI-enhanced software factory 19:48 The road to full autonomy, from product agent to dev agent 22:42 Why customers' tolerance for change limits how fast you can ship 24:05 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Mykola Konrad.

When AI does the heavy lifting inside your product, it changes the historic economics of software: you pay for that work every time a user touches it. For a funded team, that’s a line item. For a bootstrapped project, it can destroy the business. Arjun Singh Jamwal is a Senior PM at Verizon, where he’s worked on Verizon Cloud and now Verizon Home. On the side, he’s bootstrapping Goodmuncher, an app that helps people eat fewer ultra-processed foods, so every token comes out of his own pocket. His solution is to give the model a layup, as he puts it. Instead of having AI analyze every ingredient from scratch each time a user scans a label, Goodmuncher scores ingredients and products ahead of time and stores embeddings in a vector database. When a user looks something up, the model finds the answer that was already processed instead of analyzing from scratch each time. Same result for the user, a fraction of the inference cost for the company. In this episode, Arjun gives a live demo of that approach with a pipeline that turns a single command into a 10-slide social carousel in about 30 seconds. Along the way, he shares: How better upfront prep lets smaller, cheaper options perform like frontier models and makes swapping models easier down the line Why he hard-codes the rules he can’t break instead of trusting the model to remember them His step-by-step framework for evaluating agents, from golden datasets to LLM-as-judge and observability tracing Links LinkedIn: https://www.linkedin.com/in/arjunjamwal/ Goodmuncher: https://goodmuncher.com/ Chapters 00:00 Introduction 02:16 Treating tokens as unpredictable OpEx vs. a One-time CapEx 06:21 Giving the model a layup by pre-processing data upfront 10:58 Arjun's demo: Turning one command into a 10-slide social carousel 15:44 Inside the pipeline architecture and its agents 19:09 How breaking up workflows cuts costs 30x and lowers user pricing 23:15 Arjun's framework for evaluating AI agents 27:56 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Arjun Singh Jamwal.

Ask a CEO how their AI roll-out is going, and 81% will tell you that they're highly confident. Talk to the people doing the work, and that number drops to 56%. Our guest today sits right in the middle of that gap. Annan Patel is VP of Product at Tricentis, a leader in enterprise software testing, where he builds AI products for companies where a bad release can cost tens of millions of dollars. He's spent over 15 years in product across ad tech, Microsoft, and WP Engine. Tricentis recently surveyed more than 2,500 people across the tech industry. The headline: 83% of companies say they trust AI agents to make release decisions, while 60% admit they're shipping untested code. The unsurprising top reason why? Pressure from leadership to move faster. Annan's take: AI gets sold as a tool you plug in. In practice, it's a new operating system for how work gets done. In this episode, he shares: Why "plug and play" AI only gets you about 60% of the way there, and why the rest is context your team has never written down Why their first AI products stalled after a strong start, and how they rebuilt them around one key concept: the AI is never the one held accountable And why tokens spent and lines of code shipped tell you nothing about whether your AI initiatives are actually adding value Links LinkedIn: https://www.linkedin.com/in/annanpatel/ Tricentis: https://www.tricentis.com/ Chapters 00:00 Introduction 01:53 Why executives and teams see AI’s capabilities differently 03:39 Adopting AI as a new operating system, not a plug-and-play tool 07:40 From coding assistants to unattended agent workflows 10:38 AI theater on LinkedIn vs. What advanced teams actually achieve 12:27 Enterprise risk and treating AI as a long-term investment 14:55 Survey findings on agent trust, untested code, and the wrong metrics 17:13 How product teams use AI beyond vibe coding 19:05 Why early AI products stalled and how to build tools people trust 22:44 Thinking like a CEO: Customer empathy and matching velocity to your market 29:20 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Annan Patel.

If you built your product team from scratch today, what would it look like? How does it change in an AI world? Usually these answers are hypothetical. Our guest today is working hers out right now, three weeks into a new leadership role at a new company, in a new vertical. Maya Keekattil had just started as Chief Product Officer at Cin7 when we recorded this, after nearly five years leading product for Smarsh's small and mid-market business. Cin7 is PE-backed, with a new leadership team and a board that wants results fast. Most leaders tell their first-90-days story years later, after the mistakes have been edited out. Maya is telling hers from the middle of it. In this episode, Maya shares: Her 30-60-90 day playbook that actually started before day 1 at Cin7. To date, she’s done over 50 one-on-one meetings across the company and asks these 3 questions at every one. How she's organizing her product team around customer outcomes instead of product lines, and why she'd rather develop the team she inherited than hire around it And what to tell a board when its mandate and your customers point in different directions. Her focus is always based on 2 things: why do customers buy from you, and why do they leave? Links LinkedIn: https://www.linkedin.com/in/mayakeekattil/ Cin7: https://www.cin7.com/ Chapters 00:00 Introduction 00:38 Maya’s path from software engineer to chief product officer 01:42 Why the first 30 days are for listening and learning 04:20 Three questions to ask in every cross-functional one-on-one 06:08 Finding quick operational wins without breaking what works 09:12 When to shift from operational fixes to long-term strategy 13:52 Defining success by why customers buy and why they leave 19:28 How AI is reshaping product team structure around outcomes 24:20 Developing the team you inherited vs. hiring for talent gaps 26:21 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Maya Keekattil.

AI has made building faster and cheaper than ever. That leaves product leaders with an uncomfortable question: if engineering isn't the bottleneck anymore, what is? And do we still need product managers? Our guest today thinks much of the PM job over the past decade was spent more on tickets and stand-ups than on understanding customers and markets. She calls it getting "Scrumful, not Agile," and AI is finally a chance to hand that work off and get back to the real job. Naomi Lariviere is Chief Product Officer at ADP, where she runs a roughly $4 billion mid-market portfolio for a company that pays one in six Americans. Her take: AI makes building cheaper, but it doesn't make choosing what to build any easier. In this episode, Naomi shares: What happened when ADP's dev team tried to deliver without a product team, and why they came back How ADP studied what its PMs actually do all day to find the busywork AI can take off their plates, and what they want PMs doing with that time instead Why she's skeptical of the push to turn every PM into a "product builder" who ships code, and why storytelling, empathy, and judgment matter more than ever And how ADP keeps innovating quickly when a single mistake could mean thousands of people don't get paid Links LinkedIn: https://www.linkedin.com/in/naomilariviere/ ADP: https://www.adp.com/ Chapters 00:00 Intro 00:51 Naomi's path from business analyst to ADP product leader 02:40 Is AI the end of product management? (Spoiler alert: No) 04:28 AI makes building cheaper, but it doesn't make choosing easier 06:47 How product teams became "Scrumful, not Agile" 11:18 Why ADP has to innovate carefully at its scale 13:52 Process with a purpose vs. Process for its own sake 15:50 Should product managers become product builders? 18:42 ADP's jobs-to-be-done study of the PM role 20:30 Fluid roles and why less technical PMs can deliver more 24:10 Storytelling, empathy, and the human skills AI can't replace 26:46 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Naomi Lariviere.

Every product team is shipping AI. But the dirty secret is that, for many companies, very few users are adopting. The feature launches, the people who try it love it, and adoption stalls anyway. Our guest today thinks that's because teams obsess over building AI and barely think about adoption. Charanya Kannan is VP and GM of Navan Anywhere, which puts Navan's AI travel and expense tools inside Slack, Teams, and Gemini, where people already work, instead of asking them to open another app. She joined Navan when the company was still called TripActions and doing under $50 million in revenue... now it's well on its way to a billion. Her take: if customers have to work harder to use your AI, it isn't actually better. In this episode, Charanya shares: How Navan Anywhere was built distribution-first, bringing booking and expenses into the tools people already use every day Why Navan's margins went up in the AI era, while the rest of SaaS braces for compression And why she believes PMs who mostly manage process and Jira tickets will fade away, while those who can actually drive user benefit and business outcomes will matter more than ever Links LinkedIn: https://www.linkedin.com/in/meetcharanya/ Navan: https://navan.com/ Chapters 00:00 Introduction 04:17 Why AI features don't always get adopted 06:30 Building Navan Anywhere distribution-first 10:13 When to use AI vs. deterministic systems 12:01 Conversational cognitive load and why good UI still matters 15:57 Evals, distilled models, and the AI reliability pyramid 20:14 Product owners vs. product managers in the AI era 26:10 How Navan is growing margins while SaaS faces AI compression 31:40 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Charanya Kannan.

Most of the AI products getting hype right now are built for people like us, the tech industry. But your customers don’t care how the AI works — they just want their problem solved in a way that’s easy for them. Brian McMullin, SVP and Head of Product at Network Solutions, has spent 15 years building for small businesses at companies including HubSpot, ezCater, Wayfair, and SamCart. In that time, he's yet to meet a business owner who wakes up thinking, "I can't wait to use AI today." For many of them, a prompt box might as well be a terminal window. So his team doesn't ship the box. They ship the finished work artifact, and all the user has to do is claim it. In this episode, Brian shares: Why doing the work for small businesses first, instead of handing them an empty prompt box, is what gets them to try AI His warning about baking inference into the core of your product before token pricing settles — and the two models he's testing to find out what small businesses will actually tolerate And what SamCart taught him about churn: customers left because basic things were broken, not because features were missing. Links LinkedIn: https://www.linkedin.com/in/brianemcmullin/ Network Solutions: https://www.networksolutions.com/ Chapters 00:00 Introduction 00:48 Brian's product journey from developer to SMB product leader 02:42 How small businesses went from distrusting AI to have AI FOMO 06:10 SMBs don't wake up excited to use AI, they want to close the deal 08:42 Stop showing the agent, ship the finished work 11:48 Inference costs break the old SaaS margin math 15:00 Testing free credits and tiered usage with small businesses 16:47 What usage-based pricing does to ARR and predictability 22:28 Churn is a pile of micro-annoyances, not one event 25:06 What SamCart's churn data revealed about broken basics 28:55 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Brian McMullin.

Every day, more of your traffic isn't human. AI agents show up, grab what they need, and leave — and YOU pay to serve every visitor, whether they see your brand or not. Today’s guest has seen this from both sides. Brandon Harris is VP of Product at Pantheon, the WebOps platform behind a huge slice of the WordPress and Drupal world. But before that, he was sending the bots — at Wiser, where his team scraped millions of pages a day to power retail pricing intelligence. His take: agents aren't killing the web. They're just the next way information travels — and the companies that adapt are about to pull way ahead of the ones that don't. In this episode, Brandon shares: How AI is turning the whole web into one giant CMS — and quietly rewriting the job of anyone who publishes content Plus, why visits are the new impressions, why trying to keep agents out is the modern-day version of blocking Google from indexing your site — and what to do instead Links LinkedIn: https://www.linkedin.com/in/cxoplus/ Pantheon: https://pantheon.io/ Chapters 00:00 Introduction 01:02 Brandon's product journey: From film school to Wiser and Pantheon 02:24 The dead web debate: Agents are just the next mode of transport 05:51 Why traffic-based pricing can break when the majority of your visitors are agents 08:15 Site visits are becoming the new impressions 10:46 Grading traffic and the identity arms race it will start 14:11 When the whole web becomes your CMS 16:40 Putting your brand's fingerprints in content that AI will pull without accessing your site 21:23 How people decide to trust a site 26:17 Watching sessions to catch user drift before customers quietly leave 31:09 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Brandon Harris.

“Product sense” is the thing that nobody can define, but everyone says will keep product managers employed in the age of AI. Our guest today has a definition, with receipts. But he takes it somewhere that’s less comfortable than you might assume: if all you do is move numbers, you’re obsoleting yourself because AI will eventually do it better than you can. Kevin Sung is VP of Product at Life360, the family safety app with roughly 100 million monthly active users. Before that, he spent years at Dropbox, Smule, Zynga, and Google, a path that took him from a growth-hacking playbook to a very different conclusion about where product management’s value actually lives. In this episode, Kevin discusses: What the PM job becomes once engineering isn’t a bottleneck, and why the line between lazy product management and good product craft is the strength of your opinion How he got Dropbox leadership to fund a year of unglamorous quality work that no financial model could justify, and how it produced $24 million in ARR from churn reduction And what this all means now at Life360 — where the question in a product review isn’t “how’s this move the number”, it’s “does this create a moment of worry for a parent?” Links LinkedIn: https://www.linkedin.com/in/kevinsung/ Life360: https://www.life360.com/ Chapters 00:00 Introduction 04:06 Defining "product sense" 07:45 How the PM role is evolving 10:20 Kevin's growth lessons from Smule 14:22 The $24M retention bet 18:29 Resentment builds until users snap 22:38 Selling a bet you can't model 24:36 Enshittification and paper cuts 27:51 Life360, where trust is the product 32:23 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Kevin Sung.

At dating app The League, Kunal Thadani's team found that when daily matches took longer to load, people stopped skimming profiles and started actually reading. The wait wasn't a cost — it was the product saying these matches are worth your time. Now he's Senior Director of Product at Houzz, a platform for homeowners to plan renovations, and his take is that the goal was never no friction. It's to create the right kind of friction. In this episode, Kunal drops his: His three-part framework for productive friction, and The two ways friction turns toxic, including the one that punishes your most loyal customers Plus, everyone is building AI workflows for themselves right now. Almost nobody is building them for a team, and Kunal has the clearest answer I've heard in a long time for what that looks like. Links LinkedIn: https://www.linkedin.com/in/kunal-thadani-72a13722/ Insider Growth Group: https://www.insidergrowthhq.com/ Houzz: https://www.houzz.com/ 0:00 Introduction 0:28 From The League to Houzz: Finding the DIY Decorating Gap 2:03 Not All Friction Is Bad 4:39 Trust Signals, Progress Arcs, Commitment Moments 6:41 The Signup Test That 10X'd Email Conversions 10:25 Two Ways Friction Fails: Fake Protection and Endless Re-Verification 15:16 Why Personal AI Setups Break Down at Team Scale 16:47 Inside the Team OS: One Repo and a Shared Product Context Layer 20:38 Claude Code in the Terminal: Mining Gong Calls and Killing Handoff Tax 24:41 Feedback Loops: Why a Team OS Improves Faster Than a Personal One 25:58 Conclusion Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Kunal Thadani.