How to Use Technology in Product Management

𝗔𝗜 𝗠𝗮𝗸𝗲𝘀 𝗚𝗿𝗲𝗮𝘁 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀 𝗚𝗿𝗲𝗮𝘁𝗲𝗿 - But It Won’t Save Poor Thinking AI won’t make you a better product manager. It 𝗮𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝘀 the skills you already have—or don’t. A great PM doesn’t start with prompts. They start with 𝗰𝗹𝗮𝗿𝗶𝘁𝘆: a real problem, a business need, and the thinking to connect the dots. But here’s the good news: If you’re already strategic, AI can make you 𝗳𝗮𝘀𝘁𝗲𝗿, 𝘀𝗵𝗮𝗿𝗽𝗲𝗿, 𝗮𝗻𝗱 𝗺𝗼𝗿𝗲 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲. Here are 𝟱 𝘄𝗮𝘆𝘀 𝗴𝗿𝗲𝗮𝘁 𝗣𝗠𝘀 𝗮𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄—and how you can too: 1. 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗳𝗮𝘀𝘁𝗲𝗿 & 𝗱𝗲𝗲𝗽𝗲𝗿.    Great PMs understand their market →    Use AI to summarize earnings calls, analyze reviews, extract competitor positioning, or generate trend reports across industries in seconds.     2. 𝗕𝘂𝗶𝗹𝗱 𝘀𝘁𝗿𝗼𝗻𝗴𝗲𝗿 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗳𝗹𝘂𝗲𝗻𝗰𝘆.    Great PMs think like CFOs →    Use AI to break down unit economics, simulate pricing models, run revenue impact scenarios, or benchmark competitor pricing.     3. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗵𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗲𝘀 𝗺𝗼𝗿𝗲 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁𝗹𝘆.    Great PMs don’t guess - they test →    Use AI to quickly draft multiple positioning statements, survey questions, or user interview scripts.    Ask AI: “𝘞𝘩𝘢𝘵 𝘢𝘴𝘴𝘶𝘮𝘱𝘵𝘪𝘰𝘯𝘴 𝘢𝘳𝘦 𝘸𝘦 𝘮𝘢𝘬𝘪𝘯𝘨—𝘢𝘯𝘥 𝘩𝘰𝘸 𝘤𝘢𝘯 𝘸𝘦 𝘵𝘦𝘴𝘵 𝘵𝘩𝘦𝘮?”     4. 𝗥𝗲𝗰𝗼𝗴𝗻𝗶𝘇𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝘁𝗵𝗮𝘁 𝗱𝗿𝗶𝘃𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆.    Great PMs spot signals early →    Use AI to synthesize internal feedback, sales calls, support tickets, and roadmap themes to surface patterns others miss.     5. 𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 & 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 𝗮𝘁 𝗹𝗶𝗴𝗵𝘁𝗻𝗶𝗻𝗴 𝘀𝗽𝗲𝗲𝗱.    Great PMs move ideas forward →    Use AI to generate mockups, create product briefs, or prep storytelling decks that get stakeholder buy-in faster.     AI won’t teach you product thinking. But if you’re already building that muscle, it will take you from good → great → unstoppable. 👇 Which of these are you already using - and what would you add? #ProductManagement  #StrategicThinking

AI Product Management AI Product Management is evolving rapidly. The growth of generative AI and AI-based developer tools has created numerous opportunities to build AI applications. This is making it possible to build new kinds of things, which in turn is driving shifts in best practices in product management — the discipline of defining what to build to serve users — because what is possible to build has shifted. In this post, I’ll share some best practices I have noticed. Use concrete examples to specify AI products. Starting with a concrete idea helps teams gain speed. If a product manager (PM) proposes to build “a chatbot to answer banking inquiries that relate to user accounts,” this is a vague specification that leaves much to the imagination. For instance, should the chatbot answer questions only about account balances or also about interest rates, processes for initiating a wire transfer, and so on? But if the PM writes out a number (say, between 10 and 50) of concrete examples of conversations they’d like a chatbot to execute, the scope of their proposal becomes much clearer. Just as a machine learning algorithm needs training examples to learn from, an AI product development team needs concrete examples of what we want an AI system to do. In other words, the data is your PRD (product requirements document)! In a similar vein, if someone requests “a vision system to detect pedestrians outside our store,” it’s hard for a developer to understand the boundary conditions. Is the system expected to work at night? What is the range of permissible camera angles? Is it expected to detect pedestrians who appear in the image even though they’re 100m away? But if the PM collects a handful of pictures and annotates them with the desired output, the meaning of “detect pedestrians” becomes concrete. An engineer can assess if the specification is technically feasible and if so, build toward it. Initially, the data might be obtained via a one-off, scrappy process, such as the PM walking around taking pictures and annotating them. Eventually, the data mix will shift to real-word data collected by a system running in production. Using examples (such as inputs and desired outputs) to specify a product has been helpful for many years, but the explosion of possible AI applications is creating a need for more product managers to learn this practice. Assess technical feasibility of LLM-based applications by prompting. When a PM scopes out a potential AI application, whether the application can actually be built — that is, its technical feasibility — is a key criterion in deciding what to do next. For many ideas for LLM-based applications, it’s increasingly possible for a PM, who might not be a software engineer, to try prompting — or write just small amounts of code — to get an initial sense of feasibility. [Reached length limit. Full text: https://lnkd.in/gYY-hvHh ]

AI Product Management vs AI for Product Management: Hacks and resources for you. Regardless the path you're on, you need to evolve your PM Craft. 'Evolve' being the keyword here. 𝗙𝗼𝗿 𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 (This is for the PMs working directly with AI products) – think Research PMs, Recommendations PMs, Platform PMs, and so on. You really need to get good at handling AI's unique quirks: ✨ The Probabilistic nature of AI: It's not always 0 or 1, and you've got to navigate that uncertainty. ✨ The Deep dependency on good quality data: Garbage in, garbage out. You're constantly thinking about data quality. ✨ Developing deep AI awareness: This is key but it's not about you getting too deep into technical concepts you won't need. My secret hack is to make it a habit to read research blogs from big tech companies. Google AI, Meta AI, OpenAI and attending technical conferences. Here are some: -Google AI Blog: https://ai.google/ -DeepMind's blog https://lnkd.in/g3mi8Xxy -Meta AI Blog: https://ai.meta.com/blog/ -OpenAI Research Blog: https://lnkd.in/gR_kPSkt -Microsoft AI Blog: https://lnkd.in/gYkW63yz -Amazon Science Blog: https://lnkd.in/gMJzQrGG You'll literally see what's going to be the next big product in the next two years. The original Transformers paper came out in 2017 – a PM on top of their craft could have foreseen Generative AI tools coming years ago. 𝗙𝗼𝗿 𝗔𝗜 𝗳𝗼𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 ✨ This is about leveraging AI tools to have more impact as a PM, no matter what sector you're in. It's all about adjusting your work style and experimenting to see what actually works for you. My hack here is simple but effective: train your brain to try new things. I block my calendar for 2-hour "experimentation slots." During that time, I'm creating my own tutorials, trying out new AI tools on my actual work, and following the right people. You know most of the tools by now, here are some that you might want to check out: -NotebookLM: new features getting added very often -ChatPRD: https://www.chatprd.ai/ -Productboard AI: https://lnkd.in/gm2mfeDY -ProdPad CoPilot: https://lnkd.in/gWrZZd7W -Quantilope: https://lnkd.in/g3TUJ_-9 -Dovetail: https://dovetail.com/ -Notion AI: https://lnkd.in/gfUb8yKg -Mixpanel: https://mixpanel.com/ Regardless of your seniority, being hands-on and experimenting with these tools goes a long way.

I’ve been testing AI, shadowing PMs, and talking to hundreds of product managers about how they’re using AI in their workflows. Here’s what I’ve found. — 𝗧𝗛𝗘 𝗨𝗦𝗘 𝗖𝗔𝗦𝗘 𝗙𝗔𝗟𝗟𝗔𝗖𝗬 Most PMs think they know how to use AI. They can ask ChatGPT to draft emails, create summaries, or brainstorm features. But where they struggle is knowing how to integrate AI in a way that truly transforms their workflows. Last month, I shadowed a PM at a FAANG company working on a new feature spec. Their first AI prompt? Beautifully crafted but completely off the mark for their use case. The result? Wasted time, resources, and momentum. What matters isn’t just using AI. It’s using it the right way. — 𝗧𝗛𝗘 𝗡𝗘𝗘𝗗 𝗙𝗢𝗥 𝗔𝗜-𝗣𝗢𝗪𝗘𝗥𝗘𝗗 𝗣𝗠𝘀 Remember the classic PM nightmare? The clock’s ticking, it’s 4 PM, and your VP just asked for a detailed PRD — due first thing tomorrow. Well, it used to be a hurdle, but today it’s not, thanks to AI. That's why, AI is no longer optional for PMs. It’s the difference between: → Struggling with last-minute PRDs Or having an AI help you write one in 20 minutes → Spending hours on competitor research Or letting AI pull insights in 30 minutes → Losing hours prototyping manually Or iterating design ideas in real-time with AI tools The PMs who figure this out are going to 10x their impact. And those who don’t will fall behind. — 𝗪𝗛𝗔𝗧’𝗦 𝗜𝗡 𝗧𝗛𝗘 𝗡𝗘𝗪𝗦𝗟𝗘𝗧𝗧𝗘𝗥 𝗣𝗜𝗘𝗖𝗘 This is the exact focus of this week's deep dive: → The 3 Rules of Using AI Right → Top 5 AI Use Cases That Actually Save Time → The Mistakes Most PMs Make (and how to avoid them) Don’t miss it: https://lnkd.in/er5E5Buf

AI will eliminate the need for product managers. 🥴 Close, but not correct. Product management hinges on judgment, empathy, and strategy— areas AI can’t replace. Rather than replacing product managers, AI will remove the constraints that have always made the "do it all" product manager an impossible myth. You’re expected to be strategic, fast, and technical, but there’s never enough time. AI can finally level that playing field. Two fundamental constraints have always limited product managers: 1. The skill gap - developing expertise across design, business, and technology 2. Time constraints - not enough hours to execute well across all areas While AI won't magically close the skills gap (we'll still spend careers developing expertise), it dramatically changes the time equation. Competitive analysis that took 8 hours now takes 30 minutes. Here are 3 ways AI transforms product management: 1. Speed to insights: Research and analysis now happen at hyperspeed. Yesterday I synthesized notes and recordings from 4 hours of customer interviews in 30 minutes— previously a half-day’s work. 2. Prototype-to-production acceleration: Vibe coding lets us test ideas quickly, collecting user feedback faster and communicating more effectively with engineering. 3. Automated product analytics: Soon, AI will create dashboards and reporting on product outcomes without us having to put it all together manually. We’ve never had enough hours to live up to the ideal product management described in the books. While expectations for product remain astronomically high, AI gives us the ability to increase our output and maybe (finally) meet that bar. Read my full article diving into how AI will make product management faster and where product ops plays a role. Link down below.

👍 I am excited to hear that many of my clients are venturing into projects that include digital transformation, intelligent automation, machine learning and other artificial intelligence capabilities. I am passionate about these topics and how these capabilities deliver value to our end users.  If these topics are not already on your horizon, they will be soon, so here are some tips to help your intelligent automation and digital analysis: 1)  Analyze the customer journey. Remove touch-points, don't add them! Research and understand your customers' experiences with your organization! Map out their journey. Find out how they achieve their goals and understand their pain. With AI, look to remove touchpoints, the ones that don't add value of course, and make sure you are not adding touchpoints.  2)  Experiment and hypothesize.  These new technologies are complex, but can be quick to implement. To make sure you are on track with your ideas, build in "spikes" that serve as experiments to test the team's big assumptions and hypotheses. Learn from these spikes. Make sure the team is not trying to perfect every idea and feature before learning. 3)  Elicit user stories that are innovative! Is your backlog boring? Use creative facilitation techniques and collaborative games to liven up the backlog items and challenge the team to bring more innovation to backlog items. Your leadership team expects innovation. Don't be the team that blames the big backlog at the end of the year. Change your backlog!  4)  Be agile and split stories from a user point of view. Digital transformations and AI capabilities are definitely candidates for an agile approach. To make sure you are getting the most from agile, your team needs to know how to effectively split and slice user stories into small enough pieces that can be estimated and understood by the team, while keeping the user and value focus. #businessanalysis #agileBA #projectmanagement #productmanagement #businessanalyst #SoftwareDesign #batraining

Happy Friday everyone, this week in #learnwithmz, if you are a Product manager learning about AI this post is for you. PMs looking to get hands-on with AI side projects don’t have to be expert in AI, just a curiosity and willingness to experiment. Here’s a step-by-step guide to help you get hands-on with AI side projects. 💡 Start small: Automate Regular Tasks Identify tasks you do frequently that AI can streamline, examples: - Feedback theme collection - Feature request prioritization - Market research automation 📌 Example project: AI-Powered Market Research Assistant What is it? A tool that uses AI to gather and analyze market data, customer reviews, competitor strategies, and trending topics, delivering actionable insights for product or feature development. Why build it? - Get near real-time insights into customer needs and competitor strategies. - Accelerate decision-making for market opportunities. - Ensure your product strategy stays aligned with industry trends. Step 1 - Define Scope Inputs: - Customer reviews and feedback. - News articles or blog posts about competitors. - Social media trends and hashtags. Outputs: - Key themes in customer sentiment. - Competitor summaries. - A list of emerging trends or gaps in the market. Step 2 - Choose Tech Stack Web Scraping: BeautifulSoup or Scrapy to gather data from review sites and blogs. Sentiment Analysis: OpenAI, Hugging Face, or #Azure AI Language. Trend Analysis: Google Trends API or Twitter API. Visualization: Power BI or Streamlit. Step 3 - Build and Iterate Start simple, test test test, and refine based on feedback. I’m working on a prototype for this assistant, stay tuned for updates after the holidays. What kind of market data do you find most valuable? Let’s discuss in the comments! #ProductManagement #AI #Innovation #marketresearch P.S. Image is generated via DALL·E