Get the 30-60-90 Day Onboarding checklist for product marketers to help you start your new product marketing job with ease and confidence. Starting a new PMM job and already feeling overwhelmed? You're not alone, and you’re not failing. The first 90 days are tough for everyone. That’s why hundreds of PMMs swear by my free 30/60/90 onboarding checklist: the go-to playbook to help you turn overwhelm into clarity and confidence. By providing your email address, you'll also subscribe to my newsletter. You can unsubscribe at any time, and I respect your privacy. Angelea Ennamorato Product Marketing Lead "As I tackled my role as a startup's founding PMM, I knew I needed help. This list was a life saver as I started my new role and with it, I felt I had a real plan on what to do in my new role. that propelled me to a new phase of my career and job where I felt empowered to tackle opportunities and challenges."

Contact Yi Lin Pei, your career coach and advisor for the product marketing industry. If you're focused on career growth, seeking your dream job, or needing a strategic advisor, Yi Lin is here to assist. Offering a free consultation call, she tailors her programs to fit your specific needs. Fill out the form and get a response within 48 hours to schedule your career-transforming conversation. Begin your journey towards a rewarding career today! First Name Last Name Email LinkedIn How did you find me? Please describe your challenges: Which program are you interested in? Select As coaching and advising is an investment, it requires time, energy, and a financial commitment. My program costs range from $3,750 to $7,500 for 1-1 coaching, and start at $6,000 for advising/consulting. Payment plans are available. Are you on board? Select Yes or No? Submit Thank you for contacting me. I'll reply within 2 business days. What People Say “Having spent time in start-up and big-tech, Yi Lin shared amazing insights and frameworks. I absolutely would highly recommend her to anyone who is thinking through career opportunities specifically within Product Marketing” — Sadiya N., Senior Product Marketing Manager "I'm so grateful to have met Yi Lin. She provided insights on the right companies to target and held my hand through it all. With her help and support, I finally landed my dream role!" — Surbhi Gupta, Product Marketing Manager, Avalara CONTACT YI LIN PEI Interested in working with me 1-1? I am so looking forward to meeting you! I offer a free consultation call to learn more about you and provide more insights on how we can work best together. Please fill out the form below, and if there is a fit, I will respond within 48 hours to schedule a call. Got other inquiries, like collaboration or brand sponsorship opportunities? Email me directly at hello@courageous-careers.com

Learn about Yi Lin Pei, your dedicated career coach at Tech Growth Coach. From immigrant to a leader in product marketing, with solid credentials and first-hand experience, Yi Lin offers unique support, aiding you to overcome hurdles in your career path. Get acquainted with her values and understand why she's exceptionally suited to guide you towards your dream job. ABOUT YI LIN PEI My mission is to help you achieve your dream career goals with confidence and courage. My Career Story Hello! My name is Yi Lin Pei. On the surface, it may seem like I have it all figured out - I am the founder of a successful coaching business, a 3x Product Marketing Leader, and a mother to two wonderful girls. In short, I seem to be living the dream. But I didn’t start there. As an immigrant Asian woman who grew up in Zambia, the odds were against me from the beginning. I came to the U.S. alone when I was 16 to get an education at the University of Florida. I didn’t know anyone. My mother, who raised me all on her own, gave me her entire savings so I could pay for my tuition. So it was no-brainer when I chose to focus all my attention on getting the highest grades possible and securing a safe and respectable job out of college. I did exactly that and got an offer to work as a transportation engineer at a great consulting company (two college degrees later). For a long time, I thought that was my path for life. But even after having climbed the proverbial career ladder, I felt something was missing, and I was not happy in my role. A lightbulb moment went off in my head when I learned about marketing for technology companies. It was exactly what I had wanted to do and what I know was missing. So when I decided to quit my safe job and pivot into this new role that I barely knew (and had zero experience in), people thought I was crazy. Doing a 180-degree career pivot was no cakewalk. I was met with countless failures and rejections. Every time I was told I didn’t have the relevant experience or lacked certain skills. While I was initially devastated, I picked myself back up, changed my strategy, and developed a unique method that focused on my strengths while honoring my authenticity - and soon I landed my first role in tech marketing at Autodesk. Today, several promotions and roles later, I'm now a coach and advisor. I can truly say I feel a deep alignment between my work and my purpose. What my personal experience taught me is that no matter where you are in your career, and no matter your background, it’s possible to achieve your dreams and rise up. Since 2021, I have helped more than 200 clients thrive in their dream product marketing careers (and helped many companies with building strong product marketing foundations) . My clients work in a wide variety of industries like B2B, B2C SaaS and hardware, Edtech, Fintech, E-commerce, and more. I'm ready to help you on your journey. Why I am Uniquely Qualified to Help You 1. I deliver real results based on proven strategies. Having coached and interviewed hundreds of candidates from APMMs to VPs of product marketing, I know what it takes to land the exact job you are pursuing. As a result, you can achieve your career goals in the shortest time possible - with the most tailored support - based on a proven, repeatable, and structured process that has been tried and tested many times over. 2. I coach you on both hard and soft skills. Most coaches focus on either one or the other. I focus on the whole person since I believe it’s the most effective way to achieve long-lasting results for years to come. In every coaching program, I provide you with the hard skills you need to build your domain expertise and the soft skills you need to navigate your career, from effective communication to overcoming self-limiting beliefs. 3. I have the experience and credentials. As a career pivoter myself who went from newbie PMM to director in 3 years, I walk the walk and combine theory with practice in my coaching. I understand what it’s like to be an outsider, a minority, an immigrant, or a female in a world where you may feel like you don't belong. I will work relentlessly to cheer you on and become your biggest advocate. I am guided by my purpose to create better representation in tech and help everyone succeed. CREDENTIALS & EXPERIENCE Experience Founder and Coach of Courageous Careers PMM/GTM Advisor for Navattic (High Growth PLG Startup) Director of Product Marketing at Teachable (Edtech Startup, acquired) Director of Product Marketing at Brightflag (Series A Legaltech Startup) Sr Product Marketing Manager at View (NASDAQ: VIEW) Content Marketing Manager at Autodesk (NASDAQ: ADSK) Brand Management Associate at Nestlé (Global 500) Former startup founder of InnoWaste Social Impact startup Awards & Interests Top 25 B2B Marketing Voices on LinkedIn (Exit 5) - 2025 Top 100 Product Marketing Influencers (PMA) – 2024, 2023 Top 5 PMM Career Coaches (PMA) - 2024 Best Companies to Work for in Product Marketing – 2022 Award-winning Watercolor Artist and Painter Education M.B.A., U.C. Berkeley - Haas School of Business M.S. in Civil Engineering and City Planning, Georgia Tech B.S. in Civil Engineering, University of Florida (Summa Cum Laude) Ready to work together? Book a Free Consult My Values insight knowledge warmth authenticity directness

Sponsored by GetWhys AI can create the content. GetWhys helps you validate it. AI can draft a blog post, campaign brief, or set of sales battlecards before your coffee cools. But speed doesn’t tell you whether the content reflects what buyers actually care about. That’s the gap GetWhys fills. It validates your positioning and content against a proprietary database of real buyer interviews, with citations included. You can bring GetWhys an early idea and leave with content grounded in buyer intelligence. Through an MCP, GetWhys can also work inside the AI tools your team already uses. That means buyer validation can become part of your existing workflow, without adding another tab or disconnected tool. → Explore GetWhys’ buyer intelligence I've gone quiet for the last two months, and I want to tell you why. I’ve been busy building the AI PMM Academy, which I introduced in my last newsletter and is now officially open for applications 🚀. I talked about protecting your moat (the thinking, judgment, and relationship-building that actually make you valuable) and shared a practical framework for applying AI to product marketing workflows. That newsletter led more than 100 of you to join the waitlist and complete my survey. I also spoke directly with several PMMs who were interested in the program. Gotta do my own buyer research, right? :) Those conversations have directly shaped what I’ve built. Today, I’m going to share three things: How I think AI should be applied to product marketing A look inside the AI systems powering workflows built by three experienced PMM leaders, who also happen to be the Academy mentors More information about the first AI PMM Academy cohort *A quick note before we dive in: the insights I'm sharing today are drawn specifically from the first two sessions of our 11-week program. This newsletter will give you the mental model and a few real examples. Inside the program, we'll go much deeper into how each component works, how to build it, and how to apply AI to your own PMM workflows, from research to launches and more. Let’s get into it. Why downloading another AI Skill usually doesn’t solve the problem Most of the advice about AI and product marketing falls into one of two categories. Highly prescriptive: here’s how to set up Cowork, here’s a content writing Skill, and here are the steps to follow. Highly conceptual: here’s why AI matters, the mindset shift you need to make, and what the future of product marketing might look like. Both are useful. But there is still a large gap between understanding what’s possible and making it work reliably inside your own company. I saw this problem in my own experimentation, so I asked my LinkedIn community a simple question: Are the AI Skills you’ve downloaded actually working for you? The post generated more than 100 comments and replies. The consensus was remarkably consistent: Out-of-the-box Skills are useful for inspiration only. They can give you ideas, expose you to a better process, or provide a starting point. But they rarely work perfectly inside someone else’s company. For any Skill or workflow to work well, it needs to understand your: Product, market, buyers, and internal language Existing processes and how work moves between people and systems (internal logic) Standards for what a trustworthy, useful output looks like Reliability comes from testing and refinement - Longer instructions don’t automatically lead to better outputs. They can consume more context, create confusion, and make the workflow harder to maintain. The PMMs getting the best results weren’t creating one enormous instruction file. They were testing their systems on real work, identifying where the outputs broke, tightening the instructions, and adding clear verification steps and human checkpoints. Successful builders begin with their own workflow - The most effective process I saw was: Choose a repeatable task you already understand. Build the first version around your existing process. Test it through actual use. Compare it with relevant third-party examples. Incorporate only the ideas that meaningfully improve your system. You need to understand the work before you can reliably turn it into a workflow. This is especially important in product marketing, where so much of the work depends on judgment. AI can help you execute, analyze, and scale, but it still needs a strong PMM process underneath it. The mental model behind the AI PMM Academy The LinkedIn discussion and my waitlist survey reinforced two conclusions for me. First, PMMs are experimenting with Skills, Cowork, connectors, Projects, and other tools, but many still lack a clear understanding of how these pieces work together. Second, the most useful AI systems have to be built progressively around your company, expertise, and way of working. That is the thesis behind the AI PMM Academy. We start by grounding you in the principles of good PMM work and showing you how the core Cowork building blocks fit together. Then you’ll build your own context layer and apply it across the actual PMM workflow, from research and positioning to launches, enablement, and measurement. Let me give you a preview of that foundational layer. Foundations of great product marketing Great product marketers bring together three types of capability: Core product marketing expertise Visibility, influence, and emotional intelligence Business judgment and strategic thinking But the reality of the job rarely matches that expectation. A significant amount of our time is consumed by busywork: meetings, coordination, context switching, formatting, information retrieval, and other repetitive work. That is the gray box in the visual below. The initial goal of using AI is to reduce that gray box, and then enhance the quality of the work in the other boxes. You want to move faster through the repetitive parts of your job so you have more capacity for the work that requires judgment, influence, creativity, and strategic thinking. As your system becomes more reliable, you can also extend it beyond your individual work. Your team, sales organization, or broader company can begin benefiting from the expertise you’ve made more accessible and repeatable. Why Claude Cowork is best for creating AI product marketing workflows I’ve always said the thinking matters more than the tool. I chose Cowork because it’s the best environment for applying strong PMM principles to real work. (ChatGPT Work operates similarly, and many Skills are interchangeable, but most PMMs have access to Cowork at work, so that’s what we’ll use.) Cowork helps move AI beyond a single conversation and makes what I described above possible through agentic workflows. Instead of relying only on prompts and responses, Cowork can work through multi-step tasks, access relevant files and tools, and take actions within a defined process. There are three core pieces to understand. Connectors - Connectors allow Cowork to reach the tools, files, and information you already use. This helps it work with your actual business context instead of operating in a vacuum. Skills - Skills are reusable sets of instructions that tell Claude how to perform a particular task or process. You don't have to explain the entire process again every time. Once the Skill has been built and tested, Claude can reuse those instructions and produce more consistent results. As mentioned above, many people get Skills wrong. The best Skills should be created from your manual workflow based on your context. Context engineering - Context engineering is the practice of deciding what information Claude needs to do useful work. That might include your ICP, personas, positioning, product information, competitive landscape, voice principles, internal terminology, or examples of strong previous work. The challenge is giving Claude enough relevant context without overwhelming it with information it doesn't need. The three foundational elements of Claude Cowork Together, these pieces give Claude a more consistent foundation for doing the work. But the building blocks alone don't create a good system. You still need to decide: - What problem is worth solving - What context is actually relevant - How the work should move from one step to another - What Claude needs to verify - Where human judgment still matters This is where your PMM expertise and specific needs become essential. How three experienced PMM leaders built their foundations The right setup will look different depending on your company, role, product, operating model, and personal way of working. That is why I don't believe there is one universal AI setup for product marketers. To demonstrate this, I'm going to show you how the three AI PMM Academy mentors have built their foundations. They use many of the same components, including context, Skills, and connectors. But their final systems look distinct because they were designed to solve their specific problems. A quick note on scope: I’ll be sharing just enough of the architecture to help you understand the business problem it solves, the reasoning behind the design choices, and the measurable impact it has delivered. During the Academy, the mentors will walk you much deeper into how the various pieces of their setups work together. Mara Taylor: Building around productivity and alignment Mara is an experienced founding product marketing leader who built the product marketing function at Kenjo, a workforce management platform that helps SMBs manage scheduling, time tracking, payroll preparation, and other HR operations. Her setup began with a very human problem: attention. Like many PMMs, Mara was moving between strategic projects, stakeholder requests, Slack conversations, meetings, collateral, and constant follow-up. The information existed, but it was distributed across too many places. Every time she returned to a project, she had to reconstruct what had happened and where she had left off. The logic behind her setup Mara built two connected layers. The first is a personal productivity system that helps her capture and retrieve the context behind her work. The second is a company-wide marketing brain that makes Kenjo’s strategy, ICP, positioning, product information, and collateral more accessible and consistent across the organization. This dual setup allows her to both improve her personal productivity and set up PMM workflows that can scale to other parts of the company. Context At the company level, the “Marketing Brain” contains: ICP and market context Marketing strategy Positioning and messaging Product information Customer and sales insights Approved collateral and brand standards A screenshot of Kenjo's Marketing Brain Example Connectors Mara's setup has 20+ connectors. Here are a few: ClickUp: Acts as Mara’s operating system for projects, tasks, and project context. Slack: Provides the requests, conversations, and decisions behind the work. Confluence: Houses the company-wide Kenjo Brain, including the marketing strategy, ICP, positioning, and other core PMM context. HubSpot: Provides sales, pipeline, and win-loss data for strategic analysis. Product code and roadmap: Help Mara understand product capabilities and represent them accurately in messaging and collateral. Figma: Connects working files and final deliverables back to the original project context. Example Skills Mara's system has dozens of skills that are personal and shared. Here are a few. Getting Things Done: Creates and updates ClickUp tasks while preserving the relevant source links, decisions, and project context. End-of-Day Review: Summarizes completed work, upcoming priorities, and time spent on versus outside current projects. Copywriting: Applies Kenjo’s brand standards, banned language, and “anti-slop” patterns to new content. Kenjo Brain: Retrieves approved company, product, and marketing context from Confluence for use across new projects. What this unlocks Her productivity workflows allowed her to save a significant amount of time and reduced her context switching. This allowed her more time to make better strategic decisions for her organization. For instance, while reviewing Kenjo’s ICP, Mara connected Claude to HubSpot data and did some quick win-loss analysis that showed that the company’s planned focus on one specific sector was underperforming, while another sector was showing much higher win rates. She successfully brought the evidence to sales, marketing, and leadership to reconsider the company’s direction. What we’ll explore inside the Academy Mara will help us explore how to reduce context switching, build sustainable personal AI systems, and decide which parts of our work should remain intentionally human. We’ll also examine how these systems can extend beyond individual productivity into stronger team output, cross-functional alignment, AI adoption, and data-informed strategic decision-making. Natalie Marcotullio: Building around repeatable problems Natalie Marcotullio is the VP of Marketing at Navattic, a demo automation platform that helps SaaS companies create interactive and AI-guided product experiences for buyers. She has helped build the marketing organization from the company’s early stages. Her setup reflects the way she approaches AI: she experiments, identifies problems that keep recurring, and then decides which ones are worth turning into reusable systems. The logic behind her setup Navattic has several products, personas, and types of launches. Creating a new piece of content often requires pulling information from product documents, finding the right customer evidence, identifying the relevant persona, and applying the company’s brand guidelines. Natalie created a central Marketing Strategy Skill that brings this context together and provides a consistent foundation for the rest of the team. Screenshot of part of her Marketing Strategy Skill Context The most important contexts are all saved within the company's Notion site, and they include: Company and marketing strategy Product information and launch messaging The relationship between each product and target persona Brand voice and writing guidelines Approved customer quotes and stories More than 1,000 G2 reviews Much of this information stays in the Notion systems where the team already maintains it. The Skill retrieves the most current context rather than relying on one large, static document. Example Connectors Natalie's system connects to more than 30 different tools. Here are a few: HubSpot: Leads and MQL data. Salesforce: Opportunities, customer records, and event contacts. Linear: Product tickets, launch information, completed features, and internal demo requests. Slack: Lead activity, event notes, internal updates, and drafted team communications. Navattic: Demo performance, top-performing demos, and newly created demos. Sanity: Navattic’s CMS, including customer quotes, case studies, blog content, and article updates. Customer.io: Launch emails, in-app messages, and existing campaign automations. These connections allow the system to work with real product and customer information while reducing the risk of inventing quotes or using outdated messaging. Example Skills Natalie's system has 15 skills. A few examples of what Natalie has built: Marketing Strategy brain: Pulls live product messaging from Notion and customer proof from Sanity and G2, giving her other workflows a consistent foundation. Weekly Pipeline Analysis: Cross-references Ahrefs, Profound, HubSpot, and Salesforce to calculate weekly changes and flag significant swings. SEO and GEO Report: Reviews search and AI visibility, identifies underperforming content, and drafts recommended updates for Natalie’s approval. P2 Launch Creator: Duplicates an existing Customer.io campaign and drafts the email and in-app messaging for a new feature. Event Follow-Up: Finds event contacts across Vitally and Salesforce, then drafts customer emails or internal CSM notes for review. Customer Newsletter: Pulls recent product updates from Notion and Linear, lets Natalie select the stories, and turns them into a monthly or quarterly draft. What this unlocks Natalie’s system allows people across the marketing team to create stronger work without needing to begin with an empty chat or ask product marketing to locate every piece of context. It also gives Natalie more time for creative experimentation. That reclaimed time creates room for projects like Navattic’s digital customer scrapbook to be used for their upcoming major agentic product launch. Natalie used AI to plan the experience, structure the customer data, help build the website, and create a working prototype. Screenshot of one page of the Digital Customer Scrapbook created by Natalie for a Tier 1 Launch The value of the system shows up in both directions: routine execution becomes easier, and the team gains more capacity for work that is creative, differentiated, and difficult to standardize. What we’ll explore inside the Academy Natalie will help us explore how to identify recurring team-wide problems that are worth systematizing, build reusable Skills around them, and apply AI across launches, customer storytelling, content creation, and cross-functional execution. Her role as marketing leader gives a unique lens into where we can amplify results beyond PMM to the entire marketing team. We’ll also examine how to preserve experimentation and creativity as more of the repeatable work becomes easier. Natalie has built a really strong and authentic personal brand around the innovative product marketing and marketing work she has done. Mike Hetrick: Building for trust and quality at scale Mike brings more than 20 years of experience across engineering and product marketing. He previously led the global PMM team for Tableau’s $1B enterprise analytics platform at Salesforce. Today, he is building product marketing from the ground up as a founding product marketing leader at Meta Integration Technology, Inc., an enterprise metadata management provider that helps organizations connect, catalog, govern, and understand data, analytics, and AI. He also began his career as a data engineer, which becomes very obvious when you look at how he has approached his AI setup. 😉 The logic behind his setup Mike’s central problem was how to build an AI system that has the right guardrails in place. His company had decades of knowledge distributed across different files, and outdated information could easily find its way into new AI-generated work. He approached the solution like infrastructure: create a governed source of truth first, by building a really strong context layer, then designing workflows on top of it. Context Mike keeps what's true (facts), what's allowed (process/rules), and what it sounds like (voice) in different documents, because each changes on a different cadence and breaks in a different way if it drifts. Company facts and business strategy Marketing strategy and 12-month plan Positioning, messaging, and platform narrative Personas and audience-specific context Launch plans and narratives Voice, style, and editorial standards Mike stores this context in a version-controlled repository using Github, which allows him to update information at the source and track what has changed. He then uses Obsidian to provide him a visual map of how the different parts of the marketing brain (i.e., the context layer) relate to one another. Mike's Marketing Brain visualized in Obsidian Example Connectors Google Drive and Notion provide access to working documents. Exa supports deeper web research and voice-of-the-practitioner analysis. Firecrawl monitors and compares competitor websites. Example Skills An editorial process audits content against approved claims, banned language, required terminology, embargoed names, and brand standards. A Persona Council reviews content from the perspective of four different buyers. Additional Skills use the company context to produce and refine content consistently. What this unlocks With this foundation in place, Mike can scale his content without lowering the quality bar. He is building an entire content engine capable of producing one substantive blog every week through the end of the year, with every piece checked against the company’s approved messaging, editorial standards, and buyer personas. The same system also helps him monitor more than 200 companies and identify when competitors change their positioning, messaging, or website language. What we’ll explore inside the Academy Mike will help us examine how to build a trustworthy context layer and apply it across competitive intelligence, positioning, messaging, persona validation, and scalable content creation. Summary Mike, Natalie, and Mara use many of the same building blocks, but they have created three different systems. Their systems emerged from the work they were already doing. They were created from asking questions such as: - What problem in my work is worth solving? - What context does AI need to help me solve it? - What part of the process should become repeatable? - Where does a human checkpoint matter? - What should I deliberately keep manual? - How will I know whether the system is improving my work? That is the kind of AI fluency I want to help PMMs develop. It means knowing how to design the right system around the work you're actually trying to do, even as the tools continue to change. You don’t have to AI alone: Join the AI PMM Academy I hope these examples have piqued your curiosity and given you a glimpse into what we’re going to cover in the AI PMM Academy. I also hope they’ve inspired you to think about AI for product marketing the way I teach it: by starting with the core product marketing principles and problems to solve, and determining your AI strategy from there. I built the AI PMM Academy very deliberately to be small, cohort-based, and example-driven, because however advanced someone’s setup is, this IS still new, and we ARE still all learning, and the best way to do that is to learn from one another. I say it all the time, but it bears repeating: AI is the tool, not the outcome. Its purpose is to offload the busywork so you can focus on the best parts of being a product marketer. And that’s exactly what this program will do. In just a few months, you could be operating in a setup like Natalie’s, Mara’s, or Mike’s – and the AI PMM Academy will get you there. Yi Lin 💜

Introducing: The AI PMM Academy I'm launching the AI PMM Academy: a program for PMMs to hone their craft alongside peers and learn how to use AI in a way that amplifies, not replaces, their expertise. I'll share more at the end of the newsletter. ​ AI anxiety I've spent the last few weeks traveling to events and meeting PMMs, leaders, VCs, and founders. Everywhere I've gone, the conversation eventually turns to AI. There have been thoughtful discussions about how AI is changing our roles, GTM motions, and the way work gets done. But one thing has become increasingly clear: nobody has it all figured out. Even at the largest companies, people are navigating uncertainty with little guidance, evolving expectations, and very few established playbooks. Beneath all the excitement, there's a shared concern that we're somehow missing something important. Of course, that doesn't stop the AI hype machine from churning. This LinkedIn post I wrote resonated with many people, perhaps because it acknowledged the importance of AI without stoking fear. The message was simple: the skills that matter most haven't disappeared. Storytelling, judgment, relationship building, workflow design, and business acumen still matter. In many ways, they matter more than ever. That observation has only become stronger the more people I talk to. The PMMs getting the most value from AI aren't necessarily the ones chasing every new tool, building the most sophisticated workflows, or spending all day on social media. They're the ones with strong fundamentals who are using AI intentionally. I’ve written about AI before (here and here), but for this issue, rather than give you another list of tools or templates, I want to share the framework I've been using to think about using AI to become a better PMM. It starts with two principles: Protect your moat. Protect your human intelligence. It then walks through exactly how I think about applying AI in product marketing today. But first, let's start with what matters most. Principle #1: Protect your moat Before we talk about AI, we need to talk about something more important: why your role exists in the first place. Companies don't pay PMMs to generate content. They pay PMMs to create clarity and help products succeed in the market. That's your moat. And contrary to what some people fear, AI doesn't diminish that moat. If anything, it makes it more valuable. As AI becomes more capable, the quality of your human judgment matters even more. Here's what that moat looks like in practice: Telling the best stories - AI can't decide which customer insight matters most, which narrative will resonate, or how to connect a product to a larger business problem. That's why even AI companies are hiring storytelling leaders. I've seen the same thing in recruiting: the best PMMs now command up to $50K in salary increases due to their storytelling/positioning abilities. Setting the bar for quality - The flood of AI-generated content has made judgment more important, not less. Your value isn't producing more content. It's knowing what's worth publishing, what's incomplete, what's misleading, and what will actually resonate with customers. When there is more and more slop on the market, the role of the “quality checker” becomes more important. Being the strategist - You can ask Claude to build a launch plan in seconds. The problem is that it won't know your internal dynamics or how to be original in reaching your customers. That’s why we end up with many cookie-cutter, checklist-based launches that don’t move the needle. Strategy requires context and first principles thinking. Being the connector - This may be the most important moat of all. Companies are made of people, and people are messy. AI can't tell you which AE is the right partner for a pilot program, convince your CPO that a roadmap change is necessary, or navigate the complex stakeholder dynamics that make or break great work. Influence remains one of the most valuable skills in business because it is one of the hardest to automate. Principle #2: Protect Your Human Intelligence Understanding your moat is only half the equation. The next challenge is protecting the thing that powers it: your human intelligence. Something nearly everyone brought up with me is the AI brain rot. While the risk of AI replacing your job is real, the risk that your brain’s function will decline over time is significantly more dangerous, with lifelong consequences. This is because your skills in storytelling, judgment, strategic thinking, and influence aren't fixed traits. They are capabilities that strengthen through practice and weaken through neglect. If we outsource too much of our thinking to AI, we risk outsourcing the very skills that make us valuable. To avoid that: Use your brain first, AI second - Multiple studies (like this one) have shown that thinking critically first and then turning to AI for assistance helps preserve learning and decision-making skills. So, resist the urge to solve strategic or challenging problems with AI first. Instead, try your best to come up with your own approach, then use AI to critique and enhance it. Invest in craft development - Nobody went to school for product marketing, and there is no universally accepted benchmark for excellence. Most of us are learning on the job. That's exactly why you can't let AI do all the heavy lifting. Push yourself to understand the reasoning behind recommendations instead of blindly accepting them. Get human feedback - One of the dangers of AI is that it's endlessly supportive. It rarely tells you your positioning is weak, your strategy is flawed, or your messaging isn't landing. Humans do. Whether it's a peer, mentor, manager, coach, or someone you admire, seek out people who will challenge your thinking instead of simply validating it. Seek first-hand insight - AI is trained on existing information, but your best insights come from living in the analog world. Talk to customers. Listen to sales calls (and I mean, actually watch the calls instead of just using AI transcripts). Pay attention to stakeholder reactions. The more AI becomes part of your workflow, the more important it becomes to stay connected to the messy, human realities that generate original thinking. I learned this lesson when I tried to build an automated workflow for my LinkedIn content. I spent days feeding AI my past posts and more context, hoping it could generate new ideas for me. The results were terrible. It’s because my best content doesn't come from old content. It comes from coaching conversations, client work, travel, and observations I make in the real world. A Practical Framework for Applying AI Once you've protected your moat and your human intelligence, the next question becomes: Where should AI actually fit into your work? This is where you need a very clear plan to maximize the value out of it (while protecting your moat/brain). So let’s dive in. Step 1: Understand the Three Types of AI Workflows Over the past year, I've found it helpful to think about AI workflows in three categories. The category determines not only the workflow you build, but also the role AI should play. Level 1: Execution Workflows This is repetitive, reviewable, shippable work such as repurposing content, aggregating data, summarizing research, writing release notes, and coordinating information across teams. This is where the biggest time savings live, and it's where almost every PMM should start. The work is relatively low risk, the gains are immediate, and the time savings compound quickly. Level 2: Thinking Workflows This is where AI becomes a thought partner. You might use it to sharpen messaging, synthesize patterns, pressure-test positioning, refine a launch strategy, or explore different options. Unlike execution workflows, these tasks require conversation and iteration rather than automation. For many PMMs, this becomes a daily companion (like Chat) rather than a fully automated workflow. Level 3: Scaling Workflows This is where many people get AI strategy wrong. There's a belief that advantage comes from building increasingly complex agents or massive end-to-end workflows. In reality, autonomous agents are often quite poor at PMM work because PMM work is full of nuance, judgment, stakeholder management, and context. The strongest PMMs aren't necessarily using the most advanced tools. They're thinking clearly about work design. Take content creation as an example. Instead of building one giant workflow that handles everything, start with a process you already understand deeply and perform manually today. Break it into pieces. Understand where the friction points are. Then identify where AI can improve the work at each stage and connect those pieces intentionally. Step 2: Find the Right Use Case The next challenge is choosing the right use case. The most helpful way to determine your initial use case is by tracking how you are spending your time. Many of us spend time on things that don't add a ton of value (e.g., spending way too long rewriting similar Slack messages). Map out your week and estimate where your time goes. Most PMMs will find their work falls into a handful of broad buckets: Content creation Admin and coordination Research and analysis Customer and market understanding Strategy and planning Stakeholder management Then compare that to how you'd ideally spend your time. The biggest gap often reveals your best AI opportunity. For many PMMs, the highest-leverage opportunities tend to sit inside content creation and admin work because these areas contain a lot of repetitive, time-intensive tasks where the thinking has already happened. Once you've identified the category where you spend the most time, the next step is selecting a specific use case within it. For example, if "content creation" is consuming too much of your time, you need to identify the specific workflow within that category. That might be: Turning launch briefs into sales enablement assets Repurposing webinars into social content Drafting release notes Creating customer-facing emails Formatting competitive intelligence updates A good way to pick a good use case from your list (such as from the one above) is to check it against 3 key filters. If you can say “yes” to the three questions below, then it’s likely the best place to start: Does it recur frequently? Weekly, monthly, quarterly; the more it repeats, the more your workflow compounds over time. Is quality limited by time, not thinking? Many tasks look like they're limited by time, but when you dig deeper, the real bottleneck is judgment. For example, AI can summarize ten customer interviews in minutes. But deciding which insight matters most, how it should influence your positioning, or whether it reflects a broader market trend requires judgment. If the thinking has already happened, AI can create significant leverage. If your judgment is what drives quality, AI is more likely to play a supporting role. Can it be done without significant relationship context or political judgment? This filter can also be hard to discern, but it’s incredibly important because the nature of product marketing work is uniquely relationship-based. Take something like positioning and messaging: you can feed April Dunford’s framework into AI, but you’re going to fail if you can’t get stakeholders aligned around it. Your uniqueness doesn’t lie in your ability to spit out a document; it comes from bringing stakeholders together, resolving any conflict, and actually moving forward with something everyone agrees on. Example: One PMM’s Slack Intelligence Digest A client of mine is one of many PMMs at an 5,000-person company with 20+ products. Staying on top of eight relevant Slack channels, including global sales regions and industry news feeds, was consuming a huge amount of time every morning. Only a small percentage was relevant to her product area, while the rest was just a distraction. So she built a Cowork-scheduled digest that runs daily at 9 am, reads her specific channels, filters for what's relevant to her product area and key competitors, and returns three categories: what to read first, what to skim, and what to skip entirely. The best part is that if she's been offline for multiple days, it adjusts automatically to give her a consolidated summary instead of stacking up 20 individual outputs. She isn’t asking AI to make decisions; she’s having it triage so she can. And while this might not seem like a big deal because it didn’t take much effort to build, this is saving her a TON of time and helping her focus on what matters. It’s a great example of simple, recurring, high-leverage execution. Step 3: Choose the Simplest Tool That Works One thing I've noticed is that people often assume they need the most advanced tool to get the best results. They'll spend hours debating whether Claude is better than ChatGPT, whether Gemini is catching up, or whether they need to learn coding and agents to stay relevant. The reality is that these differences matter far less than most people think. Your success with AI will be driven much more by choosing the right use case and designing the right workflow than by picking the perfect tool. In fact, I've seen many PMMs get distracted by shiny tools when a much simpler solution would have solved the problem perfectly well. Using Claude as an example, most PMMs will spend the vast majority of their time in just three modes. Chat is where thinking happens. This is where you brainstorm, refine messaging, pressure-test ideas, summarize information, and work through problems. For many PMMs, this alone will account for a significant portion of their AI usage. Projects become useful when you need persistent context. This is where I would store things like messaging frameworks, positioning documents, competitor information, brand guidelines, and other context that you don't want to re-explain every time you start a new conversation. Cowork is where recurring execution lives. These are workflows that happen on a schedule and can run automatically without you manually triggering them each time. And honestly, that's where 95% of product marketing use cases live. And very little product marketing work requires code. More importantly, using a more advanced tool doesn't automatically produce a better outcome. A simple workflow that gets used every week is infinitely more valuable than an impressive workflow nobody trusts, understands, or maintains. Step 4. Iterate, don't perfect Now we come to the last point. The first version of anything I’ve ever built was not very good. And that’s okay. The only way to learn this stuff is by doing it. The best way is to start with something small, learn from it quickly, refine it, and keep going. Here's what that looks like in practice: Deploy fast, learn faster. Your first prompt, your first workflow, your first output is data to learn from, not a deliverable. Treat it that way. Incorporate human checkpoints. Know exactly where a person needs to review before anything goes anywhere external. If the output touches sales or another function, those people need to be in the loop from the beginning, not brought in after the fact to validate something they didn't help shape. Build in public. Post your workflows, demo at an all-hands, and create a shared prompt library. The PMMs becoming known as AI champions in their orgs are the ones building in public, sharing successes and failures. This will get you organizational buy-in, which makes every future workflow easier to launch. Ultimately, AI isn't a test you pass. It's a skill you develop. The more you experiment, the better your judgment becomes, and judgment is what creates the real advantage. How to approach each project: Use AI to become a better PMM: The AI PMM Academy If this newsletter resonated with you, you're exactly who I'm building the AI PMM Academy for. Every week, I talk to PMMs who know AI matters but feel overwhelmed by where to start. They're tired of the hype, tired of the fear-based messaging, and tired of generic AI advice that has nothing to do with product marketing. They don't need another list of tools. They need a practical way to combine strong PMM fundamentals with AI workflows that actually help them do better work. That's what the AI PMM Academy is designed to do. Together, we'll strengthen your core PMM skills like positioning, research, and product launch that works for today’s world, then layer AI on top in a way that actually makes sense for product marketing. We'll also focus on leadership, influence, and productivity so you can create more space for strategic work. You'll learn alongside other PMMs navigating the same challenges, see real workflows from practitioners successfully using AI on the job, and build systems you can apply immediately in your own role. This isn't a course full of talking-head videos and a certificate at the end. It's a hands-on learning program for PMMs who want to improve their craft, use AI thoughtfully, and grow alongside smart peers. If that sounds like what you've been looking for, I'd love for you to join the waitlist and get early access when the program launches. Yi Lin 💜 P.S. This newsletter was written human-first by me, edited by my amazing editor Christine Moore, and then proofread and polished with the help of AI.

Imagine you've just started a new product marketing role. Before you've even figured out where everything lives, your manager drops five things on you, all marked high priority: Revamp all the product pages Build a new sales pitch deck Help the CSM team improve their QBR deck Update the pricing page and revamp packaging Lead the repositioning exercise, which leadership wanted yesterday Where do you even begin? How do you tackle all of this without dropping the ball, or worse, accidentally burning a relationship you haven't even fully built yet? These are the exact situations so many PMMs and new leaders face every day. And the thing is, most of us underestimate how critical the first 90 days really are, until we're in it. That's why 2.5 years ago, ​I put together a framework​ I used to onboard successfully in my own career, one that helped me get promoted every single year in a new role. Since then, I've coached over 75 PMMs and leaders through this same process via my ​Grow onboarding program​, and I've learned a lot along the way. Given the world we're operating in now, I've given this proven plan a little makeover: a framework optimized for an AI-enabled environment. Before you groan inwardly (or outwardly) about "AI again", let me reassure you: this is a good thing. Because it's going to help you prove your worth more quickly. Like it or not, AI has changed the pace of product development. Product teams are now shipping in days what used to take weeks or months. As a result, PMMs are being asked to do more with less, get up to speed faster, and create more impact earlier in their tenure. The basic principles and activities are the same, but I have now added a layer that explicitly shows how you can use AI to accelerate what you’re learning and delivering, and made sure this plan works with the pace that hiring managers now demand. So let's dive in. The reframe that changes everything in your first 90 days as a PMM Before you even start your first day, here's an important mindset shift you need to make: Your first 90 days as a PMM are about reaching your break-even point, the moment when the value you're contributing to the business clearly exceeds the value you're consuming in time, training, and other people's attention (the line graph below). In the beginning, it's completely natural and expected for that balance to tip toward consuming (the yellow part of the graph). You're new. You need context. That's fine. But your goal is to close that gap thoughtfully and cross it, so you move to the value delivery part (blue part of the graph). When you do that, you can feel your manager relax, other team members start coming to you, and you stop feeling like you're playing catch-up, but that you actually belong there, because you do. The secret the best PMMs have figured out is this: you don't cross that threshold by doing more. You cross it by doing the right things, in the right order, with the right people. Often, that means doing less than you think you should, so that what you do deliver is genuinely excellent and genuinely aligned. To make that work, you need to be intentional about where you invest your time: what you learn, what you deliver, and who you spend time with. Here’s how that plays out across three phases. 30 Days: Establish trust and credibility Your main goal in the first 30 days is to make the right people feel heard, and to show, through a small number of well-chosen actions, that you understand what matters here. What you need to learn: The most common trap I see is PMMs who spend their first month consuming everything – support docs, demo recordings, Gong calls – and end up feeling overwhelmed and unanchored. One client described it perfectly: "I feel like I'm consuming so much, but I'm not actually absorbing anything." The fix is simple but counterintuitive: learn by doing. Find a small project to dive into immediately before you feel ready. For example, you could take on a project to do competitive comparison pages, which will then force you to understand your product, your market, and your differentiation all at once, and you walk away with something actually useful. What you need to deliver: Because you've been learning by doing, you're already partway toward your first wins. Pick 1–2 small but high-priority projects and execute them well. AI is your friend here: use it to synthesize call summaries, pull themes from release notes, and turn raw research into something usable faster than you ever could before. You’re not using it to replace your thinking, but to free up more of your time for the conversations that matter. By the end of 30 days, everything you've learned and delivered should feed into one key output: your gap analysis, which is a clear, honest picture of where PMM is strong, where it's thin, and where the biggest opportunities are. This is the foundation that makes everything in the next phase strategic rather than reactive, and this is usually the place I spend time with my client building. Who you should meet with: Your product managers, marketing leads, sales leadership, and your own manager. Go on a listening tour not to impress, but to understand. What does success look like from their seat? What are they worried about? Every one of these conversations is an investment that will pay off for months. 60 Days: Deliver larger wins, keep learning By now, you have some early wins behind you and a gap analysis in hand. In the next 30 days, you're moving from listening to doing. You've earned enough context to have a real point of view, and people are starting to see it. What you need to learn: Go deeper on customers. Shadow sales calls. Sit with CS. Your first 30 days gave you the foundations and now is when you learn how customers actually experience the product day-to-day: what makes them stay, what makes them leave, what keeps coming up in deals. This is also when you start mapping who really holds influence in your org, not just by title, but by internal trust. Every team has someone whose opinion others look up to. Find that person early and invest in that relationship. What you need to deliver: Turn your gap analysis into a prioritized project plan you can share with your manager and stakeholders. It should answer the question everyone is really asking: what are you working on, and why does it matter to me? If you’re in a founding PMM role where the function isn’t well understood, start by aligning on what PMM is meant to drive, and tie it directly to their goals, not yours. This is also a great phase to start building AI-powered workflows that support your bigger projects. Start with the problem to understand where time is being lost, and where AI could help. Show your work as you go, and invite people in before things are finished. Who you should meet with: Sales reps and CS for richer customer and prospect insights. Product design and engineering to understand the roadmap more deeply. And of course, any additional centers of influence you've identified. 90 Days: Ramp up for peak performance This is the phase where it all starts to come together. The focus shifts from building context and racking up early wins to showing that your work is creating real, measurable impact, and you cross the break-even threshold. What you need to learn: At this stage, your most valuable learning comes from feedback on your deliverables, your priorities, and how you're showing up as a partner to other teams. Pay close attention to what's landing and what isn't. This is the phase where your self-awareness compounds fastest. What you need to deliver: Show real progress on your most important project. It doesn't have to be finished as sharing work in progress and inviting input is often more valuable than waiting until something is perfect. Socialize what you've already shipped too: what changed, what the impact was, what you learned. This is especially important for solo PMMs who are still helping their organization understand what product marketing does and why it matters. On the AI front, this is a great time to formalize what you've been building and present a simple AI strategy to your manager and team, something you can fold into the broader team plan going forward. I'd suggest framing it as a crawl, walk, run approach: here's where we started, here's where we are, here's where we're headed. Who you should meet with: Key stakeholders to share your results and map out what's next. This is also the moment to schedule a 90-day check-in with your manager separate from your regular 1:1. Use a simple start, stop, continue framework: what should you keep doing, stop doing, and start doing? It removes the pressure of a formal review and gets you more immediately useful feedback than almost any other conversation you'll have. And start establishing the recurring rhythms that will define how you operate going forward, with your manager, and with the product, sales, and CS partners whose work intersects most with yours. These relationships are the infrastructure of everything you'll build from here. What's next? The framework above will take you a long way to go through your first 90 days with intention. But I also know from experience that knowing what to do and applying it to your specific situation, your company, your manager, and your team dynamics are two different things. That's where having someone in your corner makes a real difference, especially if you're navigating a transition like: Moving from a large company to a startup, or vice versa Stepping into a new industry without the domain shorthand yet Taking on a leadership role for the first time (e.g., director, head of roles) Coming in as a solo PMM with no playbook to inherit Here's what a couple of people said after working through this with me: "Yi Lin gave me a clear, actionable roadmap that helped me quickly build relationships and impress my leadership team from day one. Within six months, I received an award for my contributions, something I never expected so early on." — Terver Bendega, Sr. PMM, Tipalti "Working with Yi Lin has been like product marketing therapy; she's not just an expert, she's a true partner." — Emily Highstreet, Head of Marketing, RevenueRoll If any of this resonated, I'd love to hear from you. Reply to this email and tell me, what's the one thing you're most hoping to get right in a new role right now? I read everything personally and always write back. And if you'd like a guide beside you for the whole journey, learn more about the GROW program here. Before you leave, one more thing: Come meet me in person this month! If you have ever wondered what working with me is like (or if I am real 😄 ), I will be hosting two small, off-the-record PMM dinners in New York and San Francisco this month! No agenda, no pitch, just real conversation with a handful of mid-career PMMs navigating real transitions. Think of it as a supper club for the stuff we don't usually say out loud. New York (Manhattan): May 17th San Francisco: May 27th If you’d like to join any of the dinners above, just reply to this email, share a bit about yourself, and grab a spot. :) I would love to meet you! Until next month, Yi Lin 💜