LogRocket

Most AI products getting hype right now were built for people like us: the tech industry. But your customers don't care how the AI works — they just want their problems solved. Brian McMullin, SVP and Head of Product at Network Solutions, has spent 15 years building for small businesses at HubSpot, ezCater, Wayfair, and SamCart. In that time, he's yet to meet a business owner who couldn't wait to use AI. For most of them, an empty prompt box might as well be a terminal window. On today’s episode of LaunchPod, Brian breaks down what it actually takes to build AI products for the other 95% — the people who don’t care how the AI works but just want the outcome. TL;DR: 1️⃣ Why the answer is never a big empty text box, and why Brian’s team prioritizes shipping the finished artifact instead of selling AI as the feature 2️⃣ How proactive AI falls flat the moment it feels generic, and why the context you build on a specific customer is going to be your biggest differentiator at a time when everyone has access to the same models 3️⃣ Brian’s warning about baking inference into the core of your product before token pricing settles, and the two monetization models he's testing to find out what SMBs will actually tolerate 4️⃣ What SamCart taught Brian about churn: customers left because basic things were broken, not because features were missing If you're building AI into a product for customers who don't necessarily care about AI, this episode is a playbook for selling the outcome instead of the technology. Check out the full episode with Brian below 👇 📖 Read on Substack: https://lnkd.in/gFcQaEnH 📺 Watch on YouTube: https://lnkd.in/gFCYJSbi 🎧 Listen on Apple Podcasts: https://lnkd.in/g_v8BQkw 🎧 Listen on Spotify: https://lnkd.in/gkj8YWwq

AI agents are turning the web into one giant CMS. Every day, less of your traffic is human, but you're still paying to serve bots that never see your brand. Brandon Harris, VP of Product at Pantheon, has seen this shift from both sides. Before helping power a large part of the WordPress and Drupal world, he was the one sending the bots, leading a team at Wiser that scraped millions of pages a day for retail pricing intelligence. On the latest episode of LaunchPod, Brandon explains why AI agents aren't killing the web, and why the companies that adapt now are about to pull way ahead. TL;DR: 1️⃣ Why agents are just the new "mode of transportation" for information, and why human traffic remains as valuable as ever 2️⃣ Why site visits are the new impressions, and why you should start grading your traffic now, tracking which agents drive real conversions and which are pure cost, so you know what's worth paying to serve 3️⃣ How AI is turning the entire web into one giant CMS, and how to leave your brand's "fingerprints" on content that gets pulled when agents don’t visit your site 4️⃣ How to catch user drift before it quietly erodes trust in your product Check out the full episode with Brandon below 👇 📖 Read on Substack: https://lnkd.in/gwqUycVE 📺 Watch on YouTube: https://lnkd.in/gtVK3S-S 🎧 Listen on Apple Podcasts: https://lnkd.in/gBu43rCG 🎧 Listen on Spotify: https://lnkd.in/gUmE47XQ

Millions of recordings. One smart summary 💡 AI just changed my debugging routine 🎯 On one of our test apps, we capture full sessions by using LogRocket. Every API call. Every network error. Every user click. That's great for debugging chaos. But it also means millions of recordings pile up every month. Nobody has time to watch all of that. So today I connected Claude to our LogRocket data by using Claude Connectors. And used prompt engineering to automate the process. Now it delivers, once a week: 📌 Bottleneck reports 📌 Graph-based visualizations 📌 Issues sorted by severity 📌 Suggested solutions for each It's scheduled and running directly in production. My team just gets the report. Clean, simple, useful. Small setup. Big time saver. This is a small change with a big impact on how we handle QA. This is what AI-assisted QA should look like. #SoftwareQA #AITools #TestAutomation #EngineeringLife #AIinTesting

AI can build almost anything you ask it to, but it has no idea whether it should. That gap is what product sense is for. Kevin Sung, VP of Product Management at Life360, joined LaunchPod today to define the term every PM uses and very few can actually define, and to explain why AI is making it the most valuable skill in product. TL;DR 1️⃣ What product sense actually is: knowing a customer's pain well enough to have a real opinion about it, and being able to turn that opinion into business value 2️⃣ The Dropbox retention bet that was wrong and worked anyway, raising $24M in ARR thanks to churn reduction 3️⃣ Kevin's "moments of resentment" theory: your most loyal users aren't always happy — sometimes they’re stuck; and the small frustrations that pile up until a price increase or an outage finally gives them a reason to churn 4️⃣ How to get leadership to fund “unglamorous” quality work when the driver model says it can't possibly move the number If you've ever been told to justify a good idea with a financial model you didn't totally believe in, or you're trying to work out which part of the PM job AI genuinely can't do, this episode is for you. Kevin's secret to developing product sense is straightforward — but it's the thing a lot of teams have quietly stopped doing. Check out the full episode with Kevin below 👇 📖 Read on Substack: https://lnkd.in/gacyUen5 📺 Watch on YouTube: https://lnkd.in/gf-HS2gj 🎧 Listen on Apple Podcasts: https://lnkd.in/gPwirfAr 🎧 Listen on Spotify: https://lnkd.in/gWAz8BwE

Not all friction in your product is bad. Sometimes the step you're about to delete is the reason your users trust you. That's Kunal Thadani's take. He's a Senior Director of Product at Houzz, where he works on a genuinely hard consumer problem: helping people who have no idea where to start make expensive decisions about their homes. Before Houzz, he was Head of Product at The League, the dating app Match Group acquired. He also writes Insider Growth, a newsletter about how companies like OpenAI, Asana, and Uber actually grew. Kunal visited LaunchPod today to talk about which steps in a flow are worth keeping, and why the personal AI setups everyone is lauding don't hold up in a team setting. TL;DR 1️⃣ Kunal’s three buckets for friction that's doing real work, including steps that signal trust, delays you can turn into progress, and constraints that get people to commit 2️⃣ The two ways friction goes bad, including one most companies never notice 3️⃣ What Kunal’s team built instead of individual AI setups: A team OS with a shared context layer, one repo, and a bunch of non-engineers working directly in the terminal Check out the full episode with Kunal below 👇 📖 Read on Substack: https://lnkd.in/gGtkUwyR 📺 Watch on YouTube: https://lnkd.in/giN88vGF 🎧 Listen on Apple Podcasts: https://lnkd.in/gjpYyUiu 🎧 Listen on Spotify: https://lnkd.in/gQZeVQZV

It might seem like every product org needs to redesign itself around AI or risk falling behind. Miro’s CPTO disagrees. AI didn’t reinvent their processes, it changed what people bring to them. Jeff Chow, Chief Product and Technology Officer at Miro, runs product and engineering at a company whose entire job is helping cross-functional teams work together, which makes it an especially good place to watch what AI is actually doing to how teams build. Today, Jeff visited LaunchPod to explain what actually breaks when your team can build faster than it can decide. TL;DR 1️⃣ Why Miro kept its three milestones (kickoff, solutions review, pre-release review) and changed the artifact instead — the PRD and 50 Figma art boards are now a working prototype on real data that teams can use right away 2️⃣ The trap of the "yes-man” agent: Why a week of building alongside a model that agrees with everything you say feels like alignment but isn't, and how to build disagreement and productive critique back in 3️⃣ How "process barnacles" accumulate as you scale 4️⃣ Why saying "no" is about to become the hardest job in product If building has gotten cheaper on your team but decision-making hasn't, this episode is for you. Check out the full episode with Jeff below 👇 📖 Read on Substack: https://lnkd.in/gfnAYMFV 📺 Watch on YouTube: https://lnkd.in/gn-hzxqB 🎧 Listen on Apple Podcasts: https://lnkd.in/g-Ymc-nZ 🎧 Listen on Spotify: https://lnkd.in/g7w8wfxb

How are product managers using the LogRocket MCP? Here’s a great example. Elliot is a senior PM in the software industry. He uses the MCP to automatically surface bugs, monitor feature adoption, and identify user issues before they become support tickets. Elliot built Otto, an internal product intelligence tool that brings together signals from LogRocket and other data sources across the business (internal databases, data warehouse, CRM, etc). Every Monday, Otto queries the LogRocket MCP to analyze user behavior and surface new developments, such as: → Anomalous user behavior and potential bugs, automatically creating Jira tickets with relevant LogRocket sessions attached → Feature adoption and conversion trends, helping the team pinpoint where users are dropping off or struggling Check out the full walkthrough on our blog: https://lnkd.in/gec_wk_T

Reading every AI article on your feed won't make you fluent in AI — it'll just make you anxious. And give you FOMO. That's the wall Parul Goel hit about a year ago. Most recently Senior Director of Product Management at Indeed, she led the orders and billing platforms behind a multi-billion-dollar business. She’d spend months consuming everything about AI, but feeling no better about it. So, she stopped reading and started building. The result is a PM OS — an open-source system she and three collaborators built using Claude Code to automate four core PM functions: - Exec updates - Cross-functional communication - Customer interview synthesis - PRD drafts Today, Parul visits LaunchPod to demo the PM OS live! TL;DR 1️⃣ A live walkthrough of the PM OS and the four-layer architecture behind it: perception, execution, critique, and continuity 2️⃣ Why the "context library" (writing down your stakeholders' priorities, pet peeves, and how your company actually talks) is the layer most people skip but the one that’s most important 3️⃣ How Parul built engineer, designer, and exec sub-agents that push back on every draft before she ever sees it, catching the exact questions she'd get in an in-person review 4️⃣ Why engineering has gotten easier thanks to AI, but the judgment hasn’t The anxiety most product leaders feel isn't a knowledge gap – it's an experience gap. If you've been debating AI strategy abstractly, this episode will help you close that gap and teach you to build something instead. Check out the full episode with Parul below 👇 📖 Read on Substack: https://lnkd.in/gZn2349C 📺 Watch on YouTube: https://lnkd.in/gkCr6QvS 🎧 Listen on Spotify: https://lnkd.in/gv4X9MQK 🎧 Listen on Apple Podcasts: https://lnkd.in/grEmaaFY

Everyone romanticizes the 0 → 1 founder in a garage. But oftentimes, the more impressive skill is building something new inside a company that already has everything to lose. Gaurav Jaiswal has done this. Twice. During his time at SAP, he co-founded SAP Digital, where he took an entirely new B2B2C business unit from $0 to $180M and across 130+ countries. Then, as VP of Digital Product at Pearson, he took the 150-year-old publisher from ideation to launching a subscription electronic textbook product in about 6 months. Today, he visited LaunchPod to break down what intrapreneurship actually takes inside a big, public, heavily scrutinized company. TL;DR 1️⃣ How Gaurav got SAP board members to approve a brand-new business unit, and why bottom-up and lateral buy-in can matter as much as top-down approval 2️⃣ The four-P framework (product, platform, policy, people) he used to rewire an enterprise company for self-serve digital commerce 3️⃣ How Pearson went from ideation to market launch in 6 months 4️⃣ The real trade of building inside a public company: Inheriting the brand and the customer base, but inheriting the scrutiny and stakeholder pressure, too If you've ever wanted to get a business unit off the ground within an already-established company, this episode is a playbook for moving startup-fast without the freedoms a startup gets. Check out the full episode with Gaurav below 👇 📖 Read on Substack: https://lnkd.in/gsYbQq2m 📺 Watch on YouTube: https://lnkd.in/g6bJMZBQ 🎧 Listen on Apple Podcasts: https://lnkd.in/gPMK73xt 🎧 Listen on Spotify: https://lnkd.in/gMrmrCEA

The LogRocket MCP is now in Claude's connector directory! Claude can use LogRocket along with your other connected tools to: ➡️ answer product questions in plain language ➡️ find and watch the exact sessions behind an issue ➡️ surface the root causes of issues ➡️ catch regressions after deploy, and more It's all powered by Galileo AI. Search "LogRocket", hit Connect, authenticate with OAuth, and Claude can see how your users actually experience your product. No custom connector setup required, and no need to paste server URLs. Check out LogRocket in the Claude connector directory: https://lnkd.in/dA8v8Vwr