Dr Bart Jaworski

You might have felt that Jira is the source of product management issues. Now it can become your product management central hub. Here's how: At Skype, we got feedback coming in with what felt like trucks. The best I could do was randomly look at it, hoping to see some patterns and inspiration. However, the problem was clear: there was too much feedback to make it actionable. Here's what most product teams know in their bones. The backlog isn't where product work starts. It's where it ends up. Every ticket in Jira traces back to inputs that live somewhere else: app store reviews, your feedback widget, research sessions, the help desk, stakeholder asks. The signal is scattered across a dozen places, and turning it into a roadmap usually means dragging data from tool to tool until it finally lands as a sprint. For years, product managers borrowed engineering's tools and made them work. We never really had a stack built for us. That's what's changed. Atlassian's Product Collection is a set of connected tools designed around how product teams actually work, distinct from Jira rather than bolted onto it: 1) Feedback captures and organizes customer signals from all those scattered sources, then uses AI to surface searchable, actionable insights. The mountain I drowned in at Skype? This is the tool I needed back then. 2) Jira Product Discovery turns insights into roadmaps, with built-in frameworks to prioritize ideas. It helps you align stakeholders around the right decision, so you're not rebuilding five versions of the same roadmap when goals shift. 3) Rovo works alongside you, surfacing insights, drafting PRDs, and connecting strategy to delivery, so engineering understands not just what to build, but why. 4) Finally, with the Pendo integration, teams can connect what customers are saying with what they are actually doing in the product. That's the full arc of product work, from raw signal to shipped decision, in one place. Honestly, the tool I wish I'd had back at Skype. See what Atlassian built for product teams: https://lnkd.in/dq74xqrH So here's my real question: where does your team's feedback actually live right now? Let me know in the comments. #productmanagement #AtlassianPartner #𝗔𝗱

You probably felt mentally checked out when management asked you to work on a project you know is going to fail. Here's what you do: Well, in short, you make the best of it. I presume that you did your best to say no, to show metrics that prove that there are better things to do, that you've been in a big fight over your backlog, and still you were "asked" to work on the ridiculous request anyway. Here's how to proceed in this uncomfortable situation: 1) Keep your cool Of course, it all depends on whether this is a one-time thing or you have no space to work on your own ideas and reach your goals as you see fit. Sometimes bad ideas are simply forced upon you. I know, I know, it goes against every product management principle I'm showing here on LinkedIn. However, since those things sometimes happen, the best you can do is not worry about it and just do your job. And by that I mean: 2) Get the most value out of a bad idea. There are two ways of looking at this: You either turn a bad idea good, or at least a promising one, or you put the least possible resources on it and create an MVP that will fail so quickly and so spectacularly that you can move on to your regularly scheduled roadmap. Basically, rather than despair, turn it into a challenge. 3) Don't show your discontent. You can't just express how you hate the project to your team and to everyone else. For one thing, it's unprofessional. And if your team sniffs out your bad attitude, they will definitely copy it. Instead, it's better to try to force a false optimistic narrative in order to, perhaps, hear an idea that will flip that bad proposal into a good one. Who knows? Perhaps this AI project that would consume millions of dollars in tokens can be optimized to be a neat feature that consumes very few tokens, and one of your teammates knows how to do it? 4) Just in case... keep the paper trail. In case you find yourself in the middle of a political power struggle where a bad project is being used to undermine someone, or even you, it's best to be able to prove that this bad project was not your idea and initiative in the first place. You don't want something you are not in control of to come back and bite you later. 5) Once it fails, record the learnings. So, once the poo hits the fan, all you can do is to make sure that no more poos are thrown in the fan as much as possible. Just document the failure, explain what happened, why it happened, and move on to something better, so you can better showcase your product management talents in your company. Hope this helps next time you are put into this situation. What was the worst update you had to develop that was forced on you? Let me know in the comments.

Are you confused about how to become an AI PM? Here's where to start, even without a coding or technical background: 1) Master the right skills. Technical: • LLMs, tokens, and context windows • RAG, embeddings, and retrieval quality • AI agents and tool calling • Evals and LLM-as-judge • Prompt architecture • Latency vs. quality tradeoffs • Inference costs • Hallucinations and guardrails • Fine-tuning vs. prompting vs. RAG • Human-in-the-loop workflows Soft: • Executive communication • Systems thinking • Strategic framing • Cross-functional influence • Risk communication • User empathy • Decision-making under uncertainty • Storytelling • Stakeholder alignment 2) Build proof-of-work projects. You learn by doing. Period. Build things that prove you are AI PM through and through. Examples: • meeting notes to action-items converter • churn-risk signal detector from support logs • automated competitor changelog tracker • RFP/security questionnaire drafting assistant    With that, build a portfolio, where you document the problem, the workflow, the prototype, the model choice, the eval plan, the cost tradeoffs, the failure modes, the UX decisions, etc. next up: 3) Fix your CV and outreach. Your resume should not say: “Used AI to become AI PM.” Dull. It should say: • Shipped an AI triage system that cut first-response time on support tickets by 45% • Built an LLM-as-judge eval pipeline to catch hallucinations before release • Automated weekly exec reporting, saving the team ~8 hours a week Your CV needs to scream, “I can create AI delivering real value.” And for outreach? Well, you already built your portfolio, didn't you? Send hiring managers: • a sharp AI product teardown • a prototype relevant to their company • a 1-page AI product strategy • a short Loom demo 4) Push further: Create your own AI opportunity Find a problem worth solving, build a prototype, and show how it provides value. Do it for your current company (that can promote you) or when applying for a new job (and build the prototype for that company's product). You can then say: “I think this could become an AI-native workflow for our team. I’d love to own the next version.” That is how internal transitions happen. >>> If you want to learn all of this without wasting time jumping between hundreds of YouTube videos, outdated resources, and advice from AI influencers who aren’t actually building, you’ll love Product Faculty’s #1 AI PM Certification. You’ll learn directly from Codex’s PM and other frontier AI operators who are building real AI products. Inside the certification, you get: • Live sessions • AI Build Labs • Build your capstone project with 1-1 support • Exclusive AI PM content library • Enterprise AI systems • 1:1 support (3,000+ students, 1,000+ reviews.) Enroll here for $500 off: https://lnkd.in/ds59DbeQ P.S.: Comment "AI PM" and I'll send you free resources.

Did a translation ever delay an important release for you? Well, you no longer need to wager quality vs timely release! Here's how: As a person who used to work on Skype and shipped the product for tens of languages, I personally know how difficult it can be sometimes to spend months on a piece of work and then have it delayed because the translations are missing. And very often there aren't too many strings. You can just pop in what you need to translate there and ship it off. But do you know what you're shipping? Is it good enough? Aren't you offending anyone by accident? I have a solution for you. If you ship in more than one language, QACAT from my patron, Alconost, is live on Product Hunt today, check it out: https://lnkd.in/dXJqBPQw For years, localization QA meant one thing: spreadsheets. Marked-up strings, a column of error notes, a final score nobody fully trusted. Slow, awkward, and the first thing to get cut when the ship date is breathing down your neck. Then AI made translation fast and cheap. The catch? It also made quality invisible. You get output in seconds, but no real read on whether it's actually right. So teams keep hitting the same bad choice every release: ship on time and hope the translations hold up, or hold the release for a proper review. QACAT, launching today on Product Hunt, takes that choice off the table. It's a hybrid translation QA platform from Alconost that lets you match the QA depth to the moment: • Rule-based checks when you need speed (fast, and NDA-safe). • AI analysis, or AI plus human review, when you want more confidence. • Full expert human evaluation when the stakes are high. A few things that stand out if you've ever lived this process: Review happens on real screenshots, not exported strings. Built-in OCR pulls the text for you, the translation auto-fills, and reviewers mark issues right on the UI. Every run produces a structured, scored report: severity breakdowns, error categories, language and engine splits, plus an AI summary that points straight at what needs fixing. And quality becomes trackable over time, so the same issues stop resurfacing release after release. 100+ languages, one environment. That's the whole point: speed and quality stop being a tradeoff. You dial QA up or down to fit what each release actually needs, instead of betting one against the other. What's the funniest bad translation you have ever seen? Let me know in the comments.

🍎 Apple is hiring an AI PM. Even Netflix is paying $900k/yr for the AI PM roles. Here are the good resources to become an AI PM: let's dive into the resources: 𝟏. 𝐁𝐚𝐬𝐢𝐜 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 Start with what an AI PM is: https://bit.ly/whatisaipm Next, for most PMs, it makes no sense to dive deep into statistics, Python, or loss functions. Instead, read about Transformers, and LLMs: https://bit.ly/3EZtCLs 𝟐. 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 Free resources: 🔗 GPT-4.1 Prompting Guide 🔗 Anthropic Prompt Engineering 🔗 Prompt Engineering by Google 🔗 System Prompt Analysis for Claude 4 🔗 Anthropic Prompt Generator 🔗 Anthropic Prompt Library 🔗 Prompt Engineering Course By Anthropic All links: https://bit.ly/pcprompts 𝟑. 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 Learn by doing. No coding: 🔗 OpenAI Platform (start here) 🔗 Hugging Face AutoTrain (best for other models) 🔗 LaMA-Factory (fine-tune open-source LLMs) All links: https://bit.ly/pcfinetune 𝟒. 𝐑𝐀𝐆 (𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧) RAG, by definition, requires a data source + LLM. But there are dozens of possible architectures. 🔗 A free, interactive RAG simulator: https://lnkd.in/dbERNB8u I also recommend a simple step-by-step exercise to build a RAG chatbot in practice. No coding: https://lnkd.in/dZ-e_C9G 𝟓. 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 & 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 My favorite tool, by far, is n8n. You can host a free version locally or in the cloud. Start with those guides: 🔗 MCP for PMs 🔗 Automate Anything with n8n 🔗 AI Agent Architectures My favorite free resources: 🔗 Google Agent Companion 🔗 Anthropic Building Effective Agents 🔗 IBM Agentic Process Automation All links: https://bit.ly/pcaiagents 𝟔. 𝐀𝐈 𝐏𝐫𝐨𝐭𝐨𝐭𝐲𝐩𝐢𝐧𝐠 & 𝐀𝐈 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 My default no-code tech stack: Lovable, Supabase, GitHub, Netlify, n8n, Stripe. Four practical tutorials: 🔗 AI Prototyping 🔗 How to Quickly Build SaaS Products With AI 🔗 How to Build a Full-Stack App with Lovable 🔗 No-Code B2C SaaS Template With Stripe Payments All links: https://lnkd.in/dt_q7RQC Thank you, Paweł Huryn for curating and writing these resources! Do you want to have a great strategy to land your next product job? Get a new PM job in 2026 with Aakash Gupta and my cohort "Land PM job". We're starting the 4th cohort very soon, after already getting many people hired from the previous ones. Check it out here: www.landpmjob.com #productmanagement #aipm ##ai