Dave Killeen will tell you, on record, that his relationship with AI has crossed into unhealthy territory. He also says he has never felt more alive, that he is 50 and feels 30, and that he sometimes waits for his two small children to go to bed so he can start a coding session.

In between, he gives Axel Sooriah the most practical answer on this show yet to the question every product team is facing: your exec says "we need AI," and you have no idea what they actually want.

Dave is Field Chief Product Officer at Pendo, with 25 years in product across Mail Online, BBC iPlayer, Badoo, Bumble, and Productboard. He was made redundant, spent seven months unemployed going down AI rabbit holes, got dropped by a search firm for terrifying founders in interviews, and was eventually hired off the back of a YouTube rant about speech-to-text. He now builds Dex, an open-source personal AI operating system, on the side.

What you'll learn:

• Why the answer to top-down AI pressure is a KPI driver tree and a career ladder, not an AI strategy

• His argument for why the CPO seat turns over faster than any other in the C-suite, and what causes it

• Whether delegating thinking to AI erodes your judgment, and his case that it does the opposite

• The simplest possible starting point: one chat you never close, voice notes, and a Friday whinge

• How to go from that to running your own agent in about ten minutes

• What happens when your AI reads your color profile and starts telling you off on a Monday morning

• Why he open-sourced Dex, and the frontier-model pricing change that nearly killed it

• Why he is now shrinking a model onto the desktop and leaning into locally hosted open source

• The two mistakes he'd undo from 25 years: not aligning his exec, and over-protecting his team

Chapters:

00:00: Intro
00:57: Welcome to Product in Practice
02:09: What a Field Chief Product Officer actually does
03:22: From Mail Online to Bumble, then made redundant
05:42: The pressure product teams are under right now
07:41: Why your exec doesn't need AI, they need a driver tree
10:45: Does delegating thinking to AI erode judgment?
11:41: Is AI creative? Meet Dexter
14:38: Making intentional time to go down the rabbit hole
16:45: Start here: one chat, voice notes, and a Friday whinge
19:33: Why this whole thing feels emotional
20:53: Where the curiosity came from
22:43: Explain it to me like I'm five, and the rabbit hole problem
25:56: Crossing into unhealthy territory
28:01: What he would do differently
30:25: What Dex is, and why he open-sourced it
33:45: PMs have always been closeted builders
37:24: The pricing change that nearly killed Dex
38:53: Going local: open-source models on the desktop
40:22: One piece of advice: find the time
41:43: Building without analytics, and what changed
43:26: Where Pendo and Atlassian fit together
45:42: His biggest mistake in 25 years
48:34: Treasure chest: one book, one dish
52:08: Where to find Dave

Connect with Dave: LinkedIn: https://www.linkedin.com/in/davekilleen/

Connect with Axel: LinkedIn: https://www.linkedin.com/in/axelsooriah/

About Product in Practice:

Product in Practice is Atlassian's product podcast, where we dig into how AI is changing the craft of product management. Product leaders show you how they actually work: the tools, the frameworks, and the decisions behind the job. Hosted by Axel Sooriah.

Subscribe so you don't miss the next one.

#productmanagement #PM #atlassian

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:

LinkedIn: https://www.linkedin.com/in/axelsooriah/ Website: https://linktr.ee/axelsooriah

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

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.

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.

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.

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.

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.