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Product in Practice

Atlassian

Product leaders show you exactly how they work - the frameworks, tools, and workflows they use to build great products. Every episode includes live demos and practical techniques you can use immediately, covering everything from prototyping and research to strategy and team collaboration. Walk away with tactics that make you better at the craft, not theory you'll forget tomorrow.
Hosted by Axel Sooriah.

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  • August 26 · 43 min

    Rachel Wolan: Webflow's CPO on Running 10 Agents at Once in Claude Code

    Rachel Wolan runs product, design, and data at Webflow, and she is trying to delete every status meeting on her calendar by the end of the year. In this episode she shows Axel Sooriah exactly how: the builder days that took her org from roughly 15% to 95% building with AI, the one-line field that replaced a recurring roadmap meeting, and a live walkthrough of the system she built in Claude Code on top of Jira Product Discovery to triage feedback, generate mini-specs, and tell her what changed without anyone writing an update. This is a hands-on episode. The second half is screen share. What you'll learn: Why AI fluency does not come from training on how models work, and what builder days do instead How Rachel took her design team from roughly 15% tool usage to sustained adoption after a single builder day The rule that changed how her team preps: no meeting with her without a prototype Why she believes PMs should own evals, and what happens to the ones who don't live at the model layer The difference between developing something and engineering it, and why a designer and PM should be able to ship to 20 users alone How she replaced a recurring status meeting with a five-minute field update in Jira Product Discovery Her live build: auto-triaging sales and partner feedback, pulling the source Gong call, and generating a mini-spec with three candidate solutions The agent she trained on her own product review transcripts so her team gets her questions when she isn't in the room Why she runs 5 to 10 Claude Code agents at once, and how she thinks about agent permissions as the real safety question Chapters: 00:00: Intro 01:07: Welcome to Product in Practice 02:49: From "I use AI" to a team that ships AI products 06:59: The biggest workflow shift: fewer meetings 09:43: How design, product, and data are blurring 11:52: Building in orders of magnitude 13:00: Why scale surfaces corner cases, and what adaptive UX changes 14:28: How the PM role changes: own the evals 17:30: A model swap that moved conversion overnight 18:20: Golden evals and knowing when to tear up the roadmap 19:20: Why reading about AI won't get you there 21:06: The personal apps she built to learn faster 24:40: Hands-on: Jira Product Discovery as the system of record 29:10: Rebuilding triage across sales, partners, and Gong 32:29: Autonomous prototypes and the mini-spec 33:32: Pulling PRDs, tech specs, and launch readiness into one view 37:07: Where this stops, and the real cost of coordination 39:47: How it's built: Claude Code and 5 to 10 agents at once 41:06: Where to draw the line on agent permissions 42:54: One piece of advice: always be building Connect with Rachel: LinkedIn: https://www.linkedin.com/in/rachelwolan/ X: https://x.com/rachelwolan Website: https://www.rachelwolan.com/ Connect with Axel: Website: https://www.axelsooriah.com/ About Rachel Wolan: Rachel Wolan is Chief Product Officer at Webflow, where she leads product, design, and data.She has more than twenty years building product, engineering, design, and data teams. Before Webflow she was VP and GM of Dropbox's core business, serving over 700 million users worldwide, and led Dropbox's AI strategy including the acquisition of Command E. Earlier roles include LiveRamp, Euclid, and Talkdesk. She describes herself as a builder first and an executive second. About Product in Practice: Product in Practice is Atlassian's product podcast. Product leaders walk through how they actually work: the frameworks, tools, and decisions behind the craft, with a live demo in every episode. Hosted by Axel Sooriah. Subscribe so you don't miss the next one. #productmanagement #PM #atlassian

  • June 2 · 43 min

    Crystal Widjaja: Why PMs Must Get Comfortable with the CLI

    Crystal Widjaja, former Chief Product Officer at Kumu and a Reforge instructor, has built data and growth systems at huge scale. Most product managers still use AI for quick summaries and one-shot artifacts. Crystal has spent her "semi-retirement" building something more ambitious: a system of AI agents that handles everything from her daily prep to a "friendship CRM." In this episode, she walks through the messy reality of her stack, built with Obsidian, Python, and Claude Code, and shows how PMs can get past the trap of polished GUI tools and start building infrastructure that scales their impact. What we discuss • Moving from surface-level prompting to mature agent infrastructure and "context engineering" • Automating low-leverage work like meeting notes and status updates, so you can spend time on strategy and customers • The AI advantage in Southeast Asia, where a culture of delegation has prepared leaders for working with agents • Three things that won't change: vision setting, creative voice, and deep customer empathy • The "Ralph Loop": chaining complex AI tasks together for autonomous execution Key takeaways PMs need to get comfortable with the CLI. Crystal argues that sticking to simplified AI interfaces stops you from understanding the system underneath. To improve your workflow and use AI fully, you have to be willing to open the terminal and work with the technical scaffolding yourself. Context is the new prompt engineering. The first wave of AI was about learning how to prompt. The next wave is about context engineering. Output quality depends on what you feed the system, from meeting transcripts to project files, and on building pipelines that sync that context automatically. Start with the smallest unit of work. Instead of aiming for a big-bang AI solution, automate one specific task you hate, like syncing meeting notes to your computer. Once that works, iterate and chain those units into a fuller agent system. AI should make you more human, not just more productive. Crystal uses her AI chief of staff to run a "friendship CRM," tracking things like a friend's offhand mention of a marathon or a gift idea. Offloading the menial memory work frees up energy to be a more present, thoughtful friend. Adopt a "semi-unemployed" mindset. The best way to stay relevant is to leave enough room in your schedule to play with new tools. Automating low-leverage tasks buys you the time to stay ahead and learn how agent orchestration is changing. Chapters • 00:00 - Introduction to Crystal Widjaja • 02:08 - AI adoption and practice in Southeast Asia • 04:24 - Demystifying AI through practical workflows • 07:41 - AI maturity: engineering vs. product management • 11:11 - Practical steps to build AI fluency • 14:34 - Critical skills for the AI era • 18:48 - From prompt engineering to context engineering • 22:13 - Timeless product management fundamentals • 23:59 - Demo: building an AI "chief of staff" • 27:59 - Automating work with "Ralph Loops" • 36:38 - Impact on personal productivity and relationships • 39:46 - Hype, reality, and the future of AI in 2029 • 41:51 - Final habits for effective AI use About Atlassian Behind every great human achievement, there is a team. From medicine and space travel to disaster response and pizza deliveries, we help teams all over the planet advance humanity through the power of software. Our mission is to help unleash the potential of every team.

  • April 1 · 55 min

    Elena Verna: Why PMs who can't build will get left behind

    Elena Verna is Head of Growth at Lovable, one of the fastest-growing AI-native companies. She's previously held growth and product leadership roles at Dropbox, Miro, Amplitude, and SurveyMonkey, where she built some of the most influential frameworks in product-led growth. We discuss The shift from "product manager" to "product builder" and why interviews now require working prototypes Automating Growth 101 work so you can focus on creative strategy The collapse of the talent stack: how roles in product, design, and engineering are blurring Three things that won't change: customer interaction, vision setting, and understanding distribution The "superpower framework" for career-accelerating decisions Key takeaways PMs who can't build are becoming obsolete. The shift isn't theoretical or five years away. Top tech companies are already changing their interview processes to require functional prototypes instead of case studies. If you can't translate your product thinking into something tangible, you're competing with one hand behind your back. Automate Growth 101, then invest the freed-up time in creative strategy. The repetitive mechanics of growth marketing (A/B tests, funnel analysis, campaign setup) are increasingly automatable with AI. Elena argues that the PMs who thrive will be the ones redirecting that time toward the harder, more creative work that machines can't do yet. The talent stack is collapsing, and that's exciting. Boundaries between product, design, and engineering are blurring as AI gives individuals more autonomy across disciplines. Elena sees this as liberating rather than threatening. The PMs who embrace building, not just specifying, can move faster and validate ideas without waiting for a team to assemble. Three foundations won't change, no matter how good AI gets. Customer interaction, vision setting, and understanding distribution remain irreplaceably human. AI can accelerate execution, but it can't replace the judgment that comes from sitting with customers, setting a compelling direction, or knowing how your product actually reaches people. Your superpower lives at the intersection of what you're good at and what you love. Elena's career accelerated when she stopped trying to be well-rounded and started leaning into product-led growth as her defining skill. Find that intersection, then make career moves that compound on it rather than scattering your energy across unrelated opportunities. Chapters 00:00: Introduction and highlights 01:13: Welcome and the current state of tech 02:10: Why Elena joined Lovable and automating Growth 101 05:36: The shift from product managers to product builders 08:39: Advising growth-stage companies on AI adoption 11:56: Fostering AI fluency and bottom-up adoption 14:54: Raising AI fluency at scale beyond checkbox exercises 17:38: The collapse of the talent stack: product, design, and engineering blur 20:42: Critical skills for the AI era PM 24:16: Three things that won't change: customers, vision, and distribution 28:20: Demo: Building a nonprofit offering spec and prototype with ChatGPT and Lovable 47:01: Lightning round: podcasts and Substacks 49:22: Career-accelerating decisions and the superpower framework 52:00: Desert island favourites and closing #productmanagement #PM #atlassian About Atlassian: Behind every great human achievement, there is a team. From medicine and space travel to disaster response and pizza deliveries, we help teams all over the planet advance humanity through the power of software. Our mission is to help unleash the potential of every team.

  • January 20 · 1 hr 4 min

    Cemre Güngor: How to ship AI features users actually use | Product in Practice

    Cemre Güngor has helped shape how billions interact with technology, from Facebook and Instagram's global sharing experiences to Figma's AI-powered workflows, and now reimagining the browser at The Browser Company with Dia.In this conversation, we break down: Product Craft in the AI Era Why starting with the end in mind matters more than ever when prototyping is democratized The difference between durable AI features and "demo candy" How to evaluate prototypes without clear problem definition Scaling Products Across Surfaces What changes (and what doesn't) when building for social platforms vs creative tools vs AI-native workflows Why product managers get too attached to their existing mental models The hidden costs of ignoring new interaction paradigms Real AI Workflows at The Browser Company Practical examples: automated communication audits, personality type predictions, and workflow optimization Why meeting transcripts are the most underutilized data source in remote work Jira Product Discovery is the home for all your product ideas, customer feedback, and bets, so you can prioritize what actually matters and share living roadmaps with your stakeholders. Turn scattered requests and spreadsheets into a single, shared source of truth that connects directly to delivery in Jira. Get it free at https://www.atlassian.com/software/jira/product-discoveryChapters01:31 Welcome back to Product in Practice03:21 Starting with the end in mind and focusing on problems05:36 Product management behavior that stopped working, moving from Instagram to Figma08:30 Shift from metric-driven to judgment-based product management09:18 Cemre's background with AI at Figma10:50 Learning by doing and shipping quickly14:39 How LLM-based features change product development16:59 The Browser Company's focus on design and intentionality19:10 The single most important test for an AI feature: internal usage22:20 How evolving AI models (reasoning models) impact product roadmaps23:03 The Bitter Lesson: When to wait for model improvements25:09 Why agentic workflows (like computer use models) are not a priority yet26:12 The necessity of shipping first to learn what good looks like for general-purpose AI assistants29:22 The browser's responsibility in an AI world: holding all your context32:47 The key value of an AI browser: bringing in your context34:33 Advice for PMs: Do the boring thing with AI37:06 Demo: Simulating your manager's perspective (the "Josh skill")42:18 Demo: Revising a document with real meeting feedback/transcript47:00 Demo: Daily priority recommendations based on all context (Memory, Calendar, Slack)51:09 How AI tools have transformed productivity and PM workflows53:54 Demo: Coaching and feedback on communication style from all channels54:41 The advantage of browser-level integrations like Dia'sReferencedArc Search (mobile app): https://arc.net/search Arc (browser): https://arc.net/ Gemini (model): https://gemini.google.com and https://ai.google.dev/gemini-api/docs... Cursor: https://cursor.com/ Claude Code: https://claude.com/product/claude-code GPT Codex: https://openai.com/codex/ Granola (note-taking tool mentioned): https://www.granola.ai/ Four Thousand Weeks: https://www.goodreads.com/book/show/54785515-four-thousand-weeksAbout Atlassian:Behind every great human achievement, there is a team. From medicine and space travel to disaster response and pizza deliveries, we help teams all over the planet advance humanity through the power of software. Our mission is to help unleash the potential of every team.

  • Dec 18, 2025 · 41 min

    Aakash Gupta: The skill every product manager must learn in 2026

    What if you could build product prototypes in 15 minutes instead of 3 weeks?In this episode, Aakash Gupta (creator of the Product Growth Newsletter and former VP of Product at Affirm, Epic Games, and ThredUp) reveals the AI prototyping workflow that's revolutionizing product management.Watch as he live-demos building a sleep tracker app using Bolt, V0, and Lovable, showing you exactly how to go from concept to a working prototype in minutes.What You'll Learn: The 6-step framework every PM needs to master AI product building How to use Bolt, V0, Lovable & Cursor for rapid prototyping Why AI prototyping is the most significant change product management has ever seen Real-time demo: Building a sleep score calculator from scratch The exact prompt engineering techniques that get better results How to iterate across multiple AI tools to find the best output Start with problem discovery (not jumping straight to solutions) Test across multiple tools (Bolt, V0, Lovable) Iterate with detailed feedback Move to Cursor for final refinements The Four-Step AI Prototyping Method:"What used to take weeks now takes literally minutes." This isn't hype; this is the new reality for product builders.About Aakash Gupta: Long-time product leader at ThredUp, Epic Games, Affirm, and Apollo. He now runs the Product Growth Newsletter (Substack), featuring practical playbooks and AI experiments for product builders.Where to find Aakash Gupta: LinkedIn: https://www.linkedin.com/in/aagupta/ Where to find Axel: Linktree: https://linktr.ee/axelsooriah LinkedIn: https://www.linkedin.com/in/axelsooriah/ About Atlassian: Behind every great human achievement, there is a team. From medicine and space travel to disaster response and pizza deliveries, we help teams all over the planet advance humanity through the power of software. Our mission is to help unleash the potential of every team. Connect with Atlassian: Subscribe: https://www.youtube.com/channel/UCL1yMVRMh3vxitPiVaXfkoA Follow Atlassian on LinkedIn: https://www.linkedin.com/company/atlassian Follow Atlassian on Instagram: https://www.instagram.com/atlassian/ Follow Atlassian on X: https://x.com/atlassian Follow Atlassian on Facebook: https://www.facebook.com/Atlassian/ If you're a PM, designer, or engineer building AI products, this is required viewing for 2025.

  • Oct 22, 2025 · 1 hr 6 min

    Kene Anoliefo: How to build context libraries and raise AI fluency

    Kene Anoliefo has led product and design at Google, Spotify, and Netflix. She founded Heard, an AI-powered user research platform, and is now building tools that help teams capture and structure institutional knowledge so both people and AI can produce high-quality work. We discuss The three Ps of AI adoption: people, process, and platformWhy most companies jump to platform tools whilst ignoring the people problem Moving from gatekeepers to architects in product organisations Context enablement: turning implicit knowledge into explicit AI inputs The coordination tax and how AI changes team collaboration Why AI is like a brilliant but clueless new team memberBuilding context libraries to 10x your AI output quality Key takeaways Start with people, not platform tools. Most companies rush to buy AI tools whilst ignoring the deeper questions: Who am I in this AI era? What makes me valuable when AI can do parts of my job? Anxiety about AI is rooted in identity, not technology. Address the people layer first before investing in new platforms. Transform from gatekeepers to architects. Don't own user research, product strategy, or design - architect the principles and standards so anyone can do great work in those domains. With AI giving everyone superpowers, your value shifts from creating scarcity to enabling abundance. Context is your competitive advantage, not just data. AI trained on generic internet data produces generic output. Context libraries - structured repositories of your product strategy, design principles, customer segments, and research standards - help AI understand how your company builds products. AI is brilliant but clueless without your context. Think of AI as a talented new hire who needs onboarding. It has capabilities but no understanding of your product, customers, or how your team works. Just like you'd explain these things to a new employee, you need to provide this context to AI tools through documentation optimised for machine reading. Don't wait for permission to engage with AI. The biggest barrier isn't technical knowledge - it's mindset. You don't need to have worked at ML-first companies or taken AI courses. The cure for AI anxiety isn't trying to learn everything; it's starting with work you already love and asking how AI can make it 10-20% better. Chapters00:00: Introduction to Kene Anoliefo01:00: Kene's journey from Spotify and Netflix to founding Heard04:00: Early interest in AI and building AI-powered research tools07:00: Overcoming AI anxiety and intimidation10:00: The three Ps framework: people, process, and platform14:00: Why feelings matter more than tools in AI adoption15:00: The coordination tax and new team dynamics18:00: Gatekeeping vs enabling in the AI era20:00: Context enablement and knowledge architecture23:00: AI as a brilliant but clueless team member26:00: Career advice: take more shots on goal28:00: Making context enablement practical and actionable30:00: Demo: Building context libraries for AI33:00: Creating user research context libraries36:00: Using AI to generate context documentation39:00: Before and after: brainstorming without context42:00: The multiplier effect of context investment45:00: Context engineering as the next frontier48:00: Design context libraries and component repos51:00: Prototyping with V0 using pre-built contexts56:00: Democratising product ideas across the organisation59:00: Lightning round: staying sharp in the AI era01:01:00: Desert island books and dishes01:04:00: Final advice and where to find Kene Where to find Kene Anoliefo:LinkedIn: https://www.linkedin.com/in/kene-anoliefo-68a8b714/Website: https://kene.ai Where to find Axel:Linktree: https://linktr.ee/axelsooriahLinkedIn: https://www.linkedin.com/in/axelsooriah/ #productmanagement #PM #atlassian About Atlassian:Behind every great human achievement, there is a team. We help teams advance humanity through the power of software.

  • Oct 22, 2025 · 54 min

    Ravi Mehta: Data-Driven Prototyping with AI

    Ravi Mehta is a senior product executive with experience leading product organisations at TripAdvisor, Facebook, and Tinder. He's the co-creator of Reforge's AI Strategy Program and currently works as a product strategy adviser and investor. We discuss:Why some companies are vulnerable to AI disruption whilst others have years to adaptThe three cohorts in every organisation: evangelists, interested learners, and scepticsHow AI changes team collaborationWhy AI generates the averageThe shift from craft to tasteData-driven prototypingWhich PM skills are changing and which remain essential Key takeaways AI generates the average - both its strength and limitation. Large language models produce "good enough" results quickly, but strong products are outliers. Use AI's speed to iterate towards something better. Product work is splitting into human-speed and AI-speed activities. Understanding customers, strategy, and adoption happens at human speed. Documentation, prototypes, and design happens at AI speed. Focus on customer discovery, product sense, and strategy. The emphasis is shifting from craft to taste. AI is making craft accessible. When anyone can generate designs or prototypes, taste becomes the main differentiator. Determining what to build becomes 90% of success, up from 50%. Data-driven prototyping unlocks more testing. Start with the data schema, generate mock data, then ask AI to create an interface. Three cohorts exist: evangelists, interested learners, and sceptics. Evangelists experiment daily. Interested learners need access and education. Sceptics need to understand that AI is transforming work. Where to find Ravi Mehta:Website: https://www.ravi-mehta.com/Substack: Ravi on ProductLinkedIn: https://www.linkedin.com/in/ravimehta/ Where to find Axel SooriahNewsletter: https://linktr.ee/axelsooriahLinkedIn: https://www.linkedin.com/in/axelsooriah/ What we cover(00:00) Introduction to Ravi Mehta(01:00) Why Reforge created the AI Strategy Program(03:00) The four parts of AI strategy: disruption risk, market, product, and execution(04:00) Insights from running Reforge's inaugural AI Strategy program(06:00) Three ways to raise AI fluency across product teams(09:00) The most powerful move PMs are making with AI(11:00) Why AI is also "average intelligence" and what that means(13:00) Skills moving at human speed vs AI speed(15:00) The shift from craft to taste in creative work(18:00) Which PM competencies are changing and which remain essential(21:00) Can AI actually think? The nuanced answer(23:00) How AI is transforming product discovery(26:00) Demo: Introduction to data-driven prototyping(28:00) Spec-driven vs design-driven prototyping(30:00) Building a trip planning feature the traditional way(35:00) Generating structured data with Claude and MCP servers(39:00) Creating prototypes from data schemas(42:00) Iterating on features with separated data layers(45:00) Switching data sets to test different scenarios(49:00) Recommended resources for product managers(50:00) Developing product judgement through mindful observation ReferencedReforge: https://www.reforge.com Magic Patterns: https://www.magicpatterns.com Claude MCP: https://modelcontextprotocol.io Smithery: https://smithery.ai v0: https://v0.app Lovable: https://lovable.dev Bolt: https://bolt.new GitLab: https://gitlab.com Miro: https://miro.com Descript: https://www.descript.com Stack Overflow: https://stackoverflow.com Chegg: https://www.chegg.com Airbnb: https://www.airbnb.com Ethan Mollick: https://www.oneusefulthing.org Peter Yang: https://creatoreconomy.so/ BooksThe Creative Act: https://www.goodreads.com/book/show/60965426-the-creative-actThe Namesake: https://www.goodreads.com/book/show/33917.The_Namesake #productmanagement #PM #atlassian About AtlassianBehind every great human achievement, there is a team. We help teams advance humanity through the power of software.

  • Oct 22, 2025 · 1 hr 3 min

    Laura Burkhauser: Building AI habits that ship

    Laura Burkhauser is CEO at Descript, where she's building AI-powered video editing tools that are changing how creators work. She's previously led product at Twitter, Rent the Runway, and The Tote. She's recognized for launching Underlord, Descript's AI co-editor, and expanding what's possible in creator productivity. Key takeaways: Don't lead with fear, lead with excitement. Rather than being driven by FOMO and the "burning platform" of AI disruption, discover your "beach moment" - something that genuinely excites you about AI's potential. For Laura, it was successfully shipping an undo button to production using Cursor and Claude. Once you experience that superpower feeling, investing in learning becomes natural. Start with simulate, then automate, then delegate. Avoid immediately purchasing AI tools or attempting to automate everything. Begin by simulating workflows manually to validate the concept (as Laura did with post-launch tasks). Then build or purchase automation once you've proven it works. Finally, delegate when the system is dependable enough to run with minimal oversight. Context is the new currency for career advancement. Junior employees previously earned their way into key meetings by creating slides or taking notes - tasks AI now handles. Today's juniors must identify new ways to provide value that leverage their AI-native abilities, gaining access to the context that will make them strategic leaders. AI won't replace creativity; it amplifies it. In creative workflows, AI isn't about delegation - it's about achieving things you never thought possible. Laura went from creating 30-second clips to producing 30-minute videos. The objective isn't to hand off creative work, but to give creators superpowers to execute their vision more quickly. Get your whole team to vibe code something. Laura required everyone on her product team to set up Cursor and prototype something they'd wanted to build but couldn't justify resources for. Once designers could handle their own VQA and PMs could ship features independently, the team was fully bought in. The experience of power drives adoption. Chapters: 00:00 Introduction to Laura Burkhauser 02:00 Laura's path from fashion tech to Descript 05:00 The AI acceptance scale: from hostile to rewired 08:30 Laura's "flipping moment" with Cursor and Claude 12:00 Building AI buy-in across your team 15:00 Translating AI adoption into customer value 18:00 The two-by-two framework: what to do less vs more 22:00 Maintaining quality whilst doubling engineering output 24:00 Responsible AI and voice synthesis ethics 26:00 Advice for PMs starting their careers in the AI era 29:00 Context as the new currency for career growth 31:00 Demo: Automating post-launch workflows 35:00 Using Cursor to generate progress reports 38:00 Creating release notes and change logs with AI 41:00 Building launch kits automatically 45:00 Creating launch videos with Underlord 52:00 AI in creative work vs other workflows 56:00 How AI is changing relationships between PM, PMM, and design 57:00 Quick-fire questions and final thoughts Referenced: Descript: https://www.descript.com Underlord: https://www.descript.com/underlord Cursor: https://www.cursor.com Claude: https://www.anthropic.com Whisper Flow: https://wisprflow.ai/ Runway: https://runwayml.com Recommended books: The Brothers Karamazov: https://www.goodreads.com/book/show/4934.The_Brothers_Karamazov Where to find Laura Burkhauser: LinkedIn: https://www.linkedin.com/in/burkhauser/ Descript:https://www.descript.com Where to find Axel Sooriah: Newsletter: https://www.axel.pm LinkedIn: https://www.linkedin.com/in/axelsooriah/ #productmanagement #PM #atlassian About AtlassianBehind every great human achievement, there is a team. From medicine and space travel to disaster response and pizza deliveries, we help teams all over the planet advance humanity through the power of software. Our mission is to help unleash the potential of every team.

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