Comment Engine

The way you’re presenting your product is probably costing you conversions. Saw Curtis Howlands' breakdown of how Grüns is approaching this: Single product. ~$500M valuation. And they’re not selling it the same way to everyone. The entry point changes depending on what the customer actually cares about. Someone coming in focused on gut health sees gut health proof, gut health framing, gut health benefit order. Someone coming in focused on weight loss lands on a completely different page. Same product. Different context. A lot of brands collapse all of that into one message: energy, gut health, immunity, focus. All of it's true. But none of it lands. When those reasons get separated, the product starts to feel like it was built for the person looking at it. That’s what drives conversion. And what brings acquisition costs down without touching channel or budget. Nothing new was built. The reasons people were already buying just stopped getting blended together. — 🔥 I've scaled multiple brands to 9 figures and built an 8-figure marketing agency with multiple exits. 🔔 Follow for growth marketing & brand scaling ♻️ Repost if this was useful 💾 Save to send to your CMO later ✅ Sign up to my weekly growth-hacks newsletter for easy-to-implement marketing tactics every Sunday: https://lnkd.in/eGMgpwUA

I believe that the most valuable skills for a Product Manager right now cannot be replaced by AI (for now...). Every new AI tool that launched in the past year carried the same announcement: "PM is dead. This one writes your PRDs. That one handles your roadmap. The next one triages your backlog faster than you ever could..." They're right. AI handles those things well. PRDs, tickets, epics, stories were never the core of being a Product Manager. What AI is doing now is stripping away that layer and leaving the real job exposed. Since the launch of agentic coding, the entry barrier to create and launch products has never been lower. Make a 'wish' (a prompt) and your favorite GenAI tool can create an app for you. This has also led to a stream of AI slop products that solve no problems and have little to no users and we are drowning in it. Even Apple is concerned and they recently (and quietly) blocked vibe coding tools like Replit from its App Store. According to Forbes, because experts cite concerns about code that could alter existing apps and data privacy risks. Building software is cheap. Knowing what to build isn't, and that's exactly why Product Managers should thrive in this era of AI slop products everywhere. These skills will make PMs thrive: A deep connection with why. Understanding why this problem matters to the user, why solving it now is the right call, and why this approach over others. Teams that skip this build perfectly functional products nobody needs. AI product sense. Knowing when AI belongs in a product and when it doesn't. Shipping AI because you can, produces demos that impress and features that frustrate when users actually sit down with them. Knowing what success looks like before you build. Defining success before anyone writes a line of code. AI generates options endlessly. Someone has to decide what good looks like before building starts, and what are you measuring to reach success. I believe every PM should be vibe-coding, using CLIs and automating everything about their work: research synthesis, prototyping before engineering gets involved, writing cleaner specs. In fact, automate your work away, but the thinking is still the job. In today's world where building products costs nothing, the PM who knows WHY they're building something is really going to thrive. --- 💎 I write about AI and Product Management in Product SideQuest ⚔️. Join the party! link in the comments below.

Product Management is the discipline of saying no. More often than yes. 1M downloads in 5 days. 200+ Mickey Mouse/ Marvel superheroes / Pixar characters / Star Wars characters. $1B on the table. OpenAI walked away. Sam Altman said no. Not because it failed. Because it worked just enough to become dangerous. Sora had all the signals PMs are trained to chase: growth, brand gravity, user pull. But no clear path to monetization. And worse, it was eating the only scarce resource that matters right now: compute. Every GPU generating videos = one not training the next model. That's the trade. So they killed the popular thing. Not the dying project. Not the zombie from Q1 2024. The one leadership loved. The one that made every roadmap slide look smart. The one with The Walt Disney Company attached to it. Gone. I've sat in that seat. You see the data. You know it's a distraction. But it's politically expensive to say it out loud. So you let it run. And it burns 2x the budget before dying anyway. What's actually happening here is a strategy shift. OpenAI is reallocating Sora's compute into robotics and world simulation. Doubling down on chips, capital, and data centers. They're building a platform. And they proved it by cutting something everyone else would protect. So here's the real question: You probably have a Sora. The high-visibility product. Strong metrics. Great story. Quietly draining resources from the thing that actually matters. Can you kill it? #OpenAI #Sora

A career high point followed by months of toil!🎢 It's been 4 months since the highlight of my working career and our incredible win at the Prolific North Tech Awards.🏆 Since then, it has arguably been the hardest few months of my career. Why? Growth📈 As a boot-strapped entrepreneur, scaling is a double-edged sword. In 18 months we'd quadrupled monthly revenue and our team, however the growing pains it brought with it, hit hard: ▪️ Processes - What worked once before, wasn't as reliable with scale. ▪️ Standardisation - We couldn't rely on heroics from individuals, we needed a consistent methodology. ▪️ Unwinding Decisions - What we thought was the right thing to do, months later had to be undone What did I learn? You cannot grow purely on momentum. If you don't consolidate and stabilise your foundations, growth can become crippling. 🧱 So, what did we do? ▪️Narrowed the focus - fewer target markets, fewer products, more expertise ▪️ Streamlined everything - consistency in our delivery has binned unnecessary operational cost ▪️ A new mantra - Our "Get it right first time" framework delivers projects to customer need, on time and to budget. We have stopped swaying from our proven process for the pursuit of our next deal. As we approach our Anumana's 5th birthday next week, I have never been prouder. 🎂 As a team, we have navigated a difficult period and built a framework "The Anumana Way" that actually gives our team the freedom to excel in spite of the structure. I've more excited than ever for the future, even if I'm a little more guarded about growth! #WhatATeam! #EntrepreneurLife #Growth

Your revenue is in Stripe. Your pipeline is in Salesforce. Your product data is in Postgres. And the only person who knew why “expansion_mrr” ≠ “expansion_mrr_final_final” left six months ago. Meanwhile, your team keeps shipping AI features. RevOps is still reconciling three versions of the same metric. Leadership is still asking why dashboards don’t line up. Data still gets pinged for “quick” questions. No one agrees on the numbers. Managing the data an organization actually runs on has quietly gotten harder, not easier. Structify just launched Data Maps. It's a system-level map of how your business actually works: • How revenue really flows (not how it’s labeled) • How product usage actually drives expansion • How your GTM motion behaves in practice No more "which number is right?" debates - go check it out: https://www.structify.ai/

We’ve gotten so acclimated to organizational overload, endless work in progress, and constant unplanned demands that it has become the norm. It is clearly too much for any person, but people adapt. Their defense mechanisms kick in, and over time that overloaded state just becomes what work feels like. When I look at how people are using AI, I see the same pattern. People are not really questioning whether this massive amount of cognitive overload is healthy or even appropriate for achieving their goals. They are using the tools to navigate and sustain the overload that already exists. Over time, people start to ground their professional identity in this. Being good at your job becomes being good at handling noise, juggling competing inputs, and staying afloat in the chaos. That becomes the work. One of the strange outcomes is that when you operate with calm, determined efficiency and real focus, it can feel uncomfortable. It feels like something should be happening. It feels like something is missing. What I am observing is that instead of looking at new technology as a catalyst for being more effective at a deeper level, we assume things are the way they are. Then we use technology to help us cope with a reality that we have largely created

Not only have I gone through the interviews myself, but I've also interviewed engineers at Google, AWS, and other top companies. That interview paradigm is already outdated. If you’re building an AI-first company, your hiring process should reflect that. That’s why our interview process looks different: 1) We start with system design. 2) Then we ask candidates to build, but with a catch... And we explicitly tell them: use whatever AI you want. Claude. Cursor. ChatGPT. Anything. Because the job is not to prove you can solve "critical thinking" Leetcode problems (yes, I hate them). The job is to solve the customer problem, move fast, and ship with judgment. One candidate told us last year that this was the first interview like this they had ever done. They also said: this is the future. I think they were right. AI does not make engineers less important. It makes real product engineers easier to spot. At Vibranium, we do not think of people as software engineers or developers. We think of them as product engineers and that distinction matters. A product engineer is not just someone who writes code. They have to understand the problem deeply. They have to work backward from user pain and business pain, not just technical pain. They need ownership, judgment, and taste. And as a CEO, part of my job is to keep painting that picture every day. Vision. Roadmap. Alignment. The best engineers in the AI era will not be the ones who avoid AI. They’ll be the ones who "harness" it (which is a whole another topic), while staying obsessively grounded in real user and business problems. By the way, that means best engineers are on our team 😀 Charles Kim Ash Hsien Jay Liang Justin Johns Russel Huang Rong Wayne Jinwon Park Van Hoang

Dashboards that talk back. Literally. 💬 We just launched a conversational AI layer for Power BI — ask your data a question, get an instant answer. We've made a short video showing exactly how it works. Watch it, try to poke holes in it, and tell us what you think — we mean it. 👉 Kindly find the link to the product page and video in the comments. Your feedback is the roadmap. 🙏 #PowerBI #ConversationalAI #DataAnalytics #Innovation

I notice some leaders being terribly unimaginative. OK. You say 20% on salaries with layoffs....yay....good for you....yet the business model, strategic plays, and trajectory of the biz remains unchanged.

This one skill built my entire $7M company. Two years later, that same skill almost killed it. For two years, I was StreetTalk. I filmed every interview, took every sales call, and ran every product to the park myself. 30,000 steps a day, nine hours straight, then uploading footage at the Apple Store at midnight because I didn't have WiFi. It worked. $50K months by myself at 21. I almost just kept going. But I started noticing something. If I got sick, revenue dropped. A day off meant nothing got filmed. Clients waited when I was shooting. I wasn't building a company. I was the company. The hustle that got me here was the same thing keeping me stuck. So I had to learn a completely different skill set. Not outworking people. Out-thinking them. I brought on partners who knew how to build what I didn't. Luke built the systems I never could. Jesse brought 17 years of scaling agencies from $2M to $250M. Then I started delegating everything that wasn't me at my best. I don't touch day-to-day production anymore. The business runs without me on the street. That was the hardest part. Letting go of the thing I was best at because it was no longer the most valuable use of my time. Now I spend my days on the problems that actually move the needle. New product lines, new cities, the stuff that gets us from $7M to $70M. Getting to $7M was about showing up every single day and outworking everyone in the room. Getting to $60M is about building something that works when I'm not in the room. If you're a founder doing everything yourself and it's working, pay attention. That's the most dangerous place to be. At some point you have to stop being the engine and start building one. We're looking for brands that want to work with the engine.

I've posted maybe five times (I have reposted aplenty) in the last two years on LinkedIn. Not because I didn't have anything to say. Because I was heads down working and convinced that talking about it was somehow less important than doing it (Caribbean work ethic, thanks, Dad!). That was the wrong call. In the last two years, I've been CPO and a fractional product leader across three continents simultaneously. I've helped a company in Italy transform from a services business into an AI product company with Fortune 500 distribution deals. I've been CPO of a platform in Brazil, trying to give first-generation entrepreneurs access to the capital that the system was designed to keep away from them. I've placed a senior fractional PM into a business in under 72 hours when a founder needed someone who could hit the ground running immediately. I've made good decisions. I've made expensive mistakes. I've learned things I didn't expect to learn. I'm going to start sharing what I know. Some of it will be useful. Some of it will be wrong. All of it will be honest — no ghostwritten takes, no content strategy. Just what I've actually learned. If you're a founder or CPO trying to figure out how to build a product function that actually works — I think we'll have a lot to talk about. www.produx.tech

My biggest takeaways from my conversation with Tim Soulo, CMO at Ahrefs: For context, Tim joined Ahrefs 10 years ago, and since then helped bootstrap (!!!) the company to well over $100m ARR - with only 160 employees, no sales team & while breaking every single marketing "best practice" And last week, I got to interview him. Takeaways: 1. Ahrefs runs marketing with no Google Analytics installed, no attribution setup, no A/B testing, no retargeting, no discounting, no free trial (!!!), no quarterly plans, and no formal reporting. Already just the fact that they’re doing $100m ARR should be a massive wakeup call for many CMOs. Tim’s answer for how they make decisions instead? Intuition and common sense. They decide what to do based on what talent they have, what formats they’re comfortable with, what makes sense, and what sounds exciting. I’ve done 100+ episodes with marketing leaders, and consistently, the companies that are doing the best marketing all seem to prioritize things that *seem fun to them*. 37signals, PostHog, Clay… Tim is another data point here 2. Tim didn’t know you were supposed to have a sales team, he just assumed people should find your website, sign up, pay (there’s no free trial), and that’s it. Only now, at quite a bit over $100m ARR, are they starting to build out an enterprise sales motion. There's a pattern where ignorance of the “best practices” can often lead to.... better practices 3. Instead of taking a podcast and chopping it into 15 LinkedIn posts, Tim starts with a LinkedIn post, tests the hook, reads the comments, then turns it into an article incorporating all the feedback, then combines multiple articles into a conference presentation, then discusses that presentation on a podcast. Small to big, not big to small. Not sure I agree here, Lenny Rachitsky, Hormozi, Garyvee all seem to be counter examples 4. Tim built a LinkedIn engagement tracker with Claude Code in one afternoon. It looks at his post engagements, enriches contacts through Apollo, pulls Ahrefs domain data, and shows him which companies are engaging with his content, sorted by ad spend and organic traffic. This is a CMO at a $100M+ company building his own social selling tool after lunch. We cover other vibe coded tools he built in the interview 5. Every piece of content you publish should be a sales page for your product. “Thought leadership” is overrated. If you publish an article, and within that article you mention a relevant feature of your product, and it gets 10k visits, if people don’t convert, that’s a product problem, not a marketing problem. As a marketer, you did your job: you got the attention of relevant people and showed them something relevant. It sidesteps the attribution/ROI debate I'd summarize the podcast as how to be a scrappy, AI-pilled CMO Listen to the full convo here: https://lnkd.in/gPCQ_WJh

I've sat in rooms where creator marketing was an afterthought. A line item. A "nice to have." The companies winning aren't in those rooms anymore. At YouTube, I was a creator partner manager — the bridge between what creators needed and what the platform built. At Meta, I was on product marketing, translating creator insights into roadmap priorities. Same goal from different seats: make creators core to the business, not an afterthought. Owned distribution is getting harder. Creators already have it. The difference between "experimenting with creators" and "building creator infrastructure" comes down to where creators sit in the org — and who owns them. This applies whether you're selling sneakers or software. The playbook scales. 𝗕𝗿𝗮𝗻𝗱: 𝗖𝗿𝗲𝗮𝘁𝗼𝗿𝘀 𝗯𝗲𝗰𝗼𝗺𝗲 𝘁𝗵𝗲 𝘃𝗼𝗶𝗰𝗲, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝗿𝘀 Starbucks embedded two creators into their core social strategy for 12-month partnerships. Not campaigns. Infrastructure. Ulta turned employees into creators when most companies keep their workforce silent on social. → The shift: creators shape messaging from day one, not just distribute it. 𝗚𝗿𝗼𝘄𝘁𝗵: 𝗖𝗿𝗲𝗮𝘁𝗼𝗿 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗳𝗲𝗲𝗱𝘀 𝘁𝗵𝗲 𝗳𝘂𝗻𝗻𝗲𝗹 Lowe's built an affiliate model rewarding creators for conversions over time. HubSpot signs creators to 3-placement minimums, then extends to 12 months based on performance. Why? Multiple touchpoints build familiarity. One post doesn't build trust. Consistency does. → The shift: creator content fuels paid media. Whitelisted ads. UGC in performance campaigns. Distribution you can measure. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁: 𝗖𝗿𝗲𝗮𝘁𝗼𝗿 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗶𝗻𝗳𝗼𝗿𝗺 𝘁𝗵𝗲 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 Steve Blank's customer development framework: build products by staying close to customers. Creators are the ultimate proxy. They're in constant conversation with your target audience — they know what resonates before your research team does. At YouTube, creator feedback directly shaped feature priorities. Creators weren't just users — they were the product's most important signal. → The shift: creators aren't just marketing. They're R&D. 𝗪𝗵𝗮𝘁 𝗺𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗺𝗶𝘀𝘀: They think creator partnerships are about distribution. They're actually about co-creation. To benefit fully, companies need to tie creator actions to business results — revenue, retention, acquisition. Something I've been wondering: should I also be talking to the CRO? If creators drive conversions, the revenue leader needs a seat at the table. Short-term: Write checks, get content. Long-term: Build infrastructure, own the category. This isn't a social media function. It's a creator-led media and growth performance engine. I don't have all the answers here — but I know the companies treating this like a side project are already behind. Who owns this at your company? #CreatorEconomy #ContentStrategy

Anthropic shipped a feature called /dream yesterday. Claude Code can now periodically stop, reflect on everything it's learned about your project, and synthesize it into higher-level insights. the community response was... medicinal. "OK well now we need /acid to handle all of it's hallucinations" - 681 upvotes then came /xanax for when it panics mid-refactor /therapy for when it gaslights you about a bug it introduced /rehab for people who can't stop typing --dangerously-skip-permissions and /shit to clean up AI-generated code anthropic's product roadmap is apparently a pharmacy now. 1,675 upvotes. 287 comments. the thread turned into the best comedy writing room on the internet for about four hours. meanwhile. the same day /dream shipped, half the subreddit was in full meltdown over usage limits. people on the $200 Max plan burning through their entire quota in two prompts. six separate complaint posts hit the front page simultaneously. "here's an incredible new feature" while your existing features are on fire. timing is everything. but buried under the jokes and the outrage was the actual story of the day. a deaf developer built a terminal flash notification plugin for Claude Code. pulses your terminal background when Claude finishes a turn or waits for input. because nobody else was building accessibility tooling for AI dev tools. a doctor built a website. a 73-year-old cardiac patient built a health app. someone built a 122,000-line trading simulator. I track 180+ posts across 5 subreddits every night and write up what actually matters. the daily digest auto-publishes at midnight. zero editing. fully automated pipeline. this is issue 8. full writeup in cb Shawn Tenam ⚡️....building with Ai that can now report on AI and sound better then your favorite creator updates 😉

I used to be proud calling myself a “growth hacker”. Yesterday my post went viral. I shared how we turned obsession into a playbook that grew 2 companies from $0 → $10M ARR. A lot of people asked about one thing: why i insisted on doing customer support myself. I used to think growth was about hacks. There are hacks. sure. some really work. but if you only see growth as hacks, you lose the game. 1️⃣ 🛞 Growth is not just output. It’s a loop. Founders try to “hire growth.” They bring in a content creator, a social media manager, etc. Looks legit, right? There’s a dangerous trap. Take influencer marketing. a typical flow: find 50 creators → send 45 outreaches → get 20 replies → close 15 deal → campaign runs → next cycle but look closer. one creator asks: “what’s your actual USP?” another says: “i tried your new video model. looks better, but consistency still breaks.” most teams move on. we don’t. we ask: - where did it break? - what prompt did you use? - what did you want instead? Suddenly, your marketing angle changes. Your product roadmap changes. Same signals, completely different outcome. That’s the loop. growth is not just output. Break the loop, you lose. 2️⃣ 🪐 Growth is a long-term game The highest ROI growth doesn’t show up immediately. - branding - SEO - product these compound quietly. Take branding. Branding isn’t just big moments. It’s so many small, even tiny, things. Your bug notifications, your weekly marketing emails, your... support ticket replies. Every touchpoint teaches users what to believe about you. You won’t see it in a dashboard tomorrow. but give it months, it stacks. Then one day, it looks like “sudden growth.” I don’t have a clean way to quantify this. a lot of it is intuition. But if you stay in the loop long enough, you start to feel it. None of this is hacky, none of this is fast. But this is how you actually win. 🎙️Curious how others think about this. especially where you disagree.

Everyone loves calling CSMs the quarterback. I see it in decks. I hear it in leadership talks. It shows up in hiring pitches. “The CSM is the quarterback of the customer journey.” And honestly… it is a brilliant analogy. The end zone is the customer’s outcome. The ball is progress toward value. The playbook is the success plan. Perfect. Except most companies do not actually set their CSMs up to be quarterbacks. Because a real quarterback is not doing everything themselves. They trust the wide receiver will run the route. They trust the running back will pick up the yards. They trust the line will protect them long enough to make the play. If that trust breaks, the quarterback scrambles. And scrambling looks heroic from the outside. But inside the game, it is chaos. It is inefficient. It is risky. It is unsustainable. Sound familiar? This is what happens when Support is under resourced. When Product feedback loops are slow. When TAM coverage is stretched. When RevOps is disconnected from post sale reality. The CSM starts “scrambling.” They chase tickets. They translate roadmap updates. They build decks for expansions. They negotiate commercials. They try to unblock onboarding. They firefight adoption. They still care about the end zone. But now they are also trying to play every position. And eventually, something gives. The play breaks down. The ball gets dropped. Or the quarterback gets injured. Usually all three. So if we are going to keep using the quarterback analogy, we should take it seriously. A quarterback is only as effective as the team around them. It is not just about giving CSMs more tools. It is about making sure every function touching the customer is equally equipped, equally trained, and equally aligned on the same outcome. Renewals are not won by Customer Success alone. They are the result of a system that works together under pressure. If your CSMs are constantly scrambling, it is not a performance problem. It is a team design problem.

Multi-location money services operators keep adding services to solve a revenue problem that existing services are already creating. The question nobody asks before the roadmap meeting: 𝗪𝗵𝗮𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗶𝗺𝗽𝗮𝗰𝘁 𝗼𝗳 𝗳𝗶𝘅𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝗲𝘅𝗶𝘀𝘁𝘀 𝘃𝗲𝗿𝘀𝘂𝘀 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗻𝗲𝘄? A bill pay service converting at 23% of eligible customers is not a mature product. It is a broken one. Getting it to 40% conversion through better UX and faster processing delivers more revenue than launching a new prepaid card program from scratch. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝘂𝘀𝘂𝗮𝗹𝗹𝘆 𝘀𝗵𝗼𝘄 → Best location converts 41% of eligible customers on bill pay  → Worst location converts 19%  → Same service, same fees, same market That gap is not a market problem. It is a technology and workflow problem. Recoverable revenue sitting inside your existing customer base. 𝗛𝗼𝘄 𝘄𝗲 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝘁𝗵𝗶𝘀 𝗮𝘁 Itexus Before scoping any new development we map existing service performance across the network. Conversion rates by location. Drop-off points in the transaction flow. Services customers abandon mid-transaction. Closing that gap is almost always cheaper than building something new. What is your lowest converting service right now and do you know why?

One of the trickiest parts of line managing for the first time for me, was letting go of my perfectionist tendencies and not expecting somebody new to do things "perfectly", or precisely how I would have done. The challenge for me, was to allow them to be good enough, make mistakes and not try to correct everything, so they can focus on what is important without feeling overwhelmed. A karate instructor once confided in me that he only ever points out one, or two things for a student to work on at a time. Any more than that, and they're so worried about trying to incorporate all this new technique, that they start making mistakes with the karate they were already doing well enough. I've seen that's as true in the workplace as it is in the dojo. You don't have to be a bully to delay (or crush) your team's development and therefore moral. And sometimes, you'll find that "not how I would have done it", is not only good enough for the job at hand... but actually better!

Strong lineup for our next Boston Generative AI Meetup on April 8. This one (#21) is focused on Coding and Engineering with AI — and the conversation is shifting. Not “Can AI write code?” But: What happens to the engineering org when it does? Over the past three years, this meetup has grown to nearly 6,000 members and become the largest continuously operating in-person AI community in the world. What matters most, though, is the room. Builders. Engineers. Founders. Operators. People actually working with these systems every day. For this session, we’ve pulled together a group that reflects that: • Alden Do Rosario, CEO and Founder, CustomGPT.ai — building production AI agents on proprietary data • Scott Weller, CTO and Cofounder, EnFi, Inc — founder/operator scaling agent-based systems and companies • Matt Flaherty, Director of Engineering Maven AGI — engineering leadership across AI, security, and product systems These are people building real systems — with real constraints — inside real companies. The format stays the same: moderated, audience-driven, and followed by strong networking. 📍 Microsoft AI NERD, Cambridge 📅 Wednesday, April 8 🕕 6:00 PM doors | ~7:00 PM panel 🍽 Food + drinks RSVP in the comments, and thanks to Squark AI and Microsoft for sponsoring. If you’re building with AI — this is one of the best rooms in Boston to be in. #BostonGenerativeAI #CodingWithAI #AIEngineering #BostonTech

After my last post, the real question I got was this: How do I actually gear myself up to have a difficult conversation? In my experience, these are the 5 things that help leaders most. 1. Keep it contained. The temptation is to make it a one-hour emotional summit because that feels more caring. Usually, the kinder move is to keep the initial conversation brief, clear, and contained. You can say: “I need five minutes with you to share some feedback. It may be challenging to hear, but I know you can handle it, and I want to give you the opportunity to hear it directly. I’m happy to make more time to hear your thoughts once you’ve had time to absorb it.” That communicates respect. It prepares the person, gives them dignity, and helps keep the conversation from becoming more emotionally loaded than it needs to be. 2. Step into your role. If you lead people, this is part of the job. Instead of thinking about how you, personally, would want to have the conversation, think about how someone in your role needs to handle it. Lead from the responsibility of the role, not from fear, guilt, or the desire to be liked. If it helps, borrow the posture of a leader you respect. Ask yourself: What does this moment require of me? 3. Find the common ground. Make the problem the problem. Not the person. Even when there is tension, there's still something shared: The mission, the product, the relationship, the customer... Name it. This is especially helpful when the conversation is with a peer or senior leader. 4. Get clear on what is yours. You are responsible for being clear, grounded, and effective. You are responsible for holding respectful boundaries. You are *not* responsible for controlling the other person’s feelings or predicting exactly how they will respond. When things go unsaid, the cost does not disappear. It gets paid somewhere else: in the team, in the relationship, and often in the people the work is meant to serve. 5. Do it scared. Sometimes, even with all the right tools, it is still hard. Notice what you are feeling. Notice where it shows up in your body. Breathe. Acknowledge it to yourself out loud. Then meet them. You do not need to feel perfectly ready. You need to be grounded enough to be clear and kind. And often, the conversation you are avoiding is one of the clearest ways you can show respect. #Leadership #Communication #PeopleLeadership

Today, I attended the annual AI State of the Union hosted by Northern Virginia Technology Council (NVTC). It was an insightful session that addressed a critical question for the tech industry: "Does AI Kill Software/SaaS?" The short answer: No, but it fundamentally changes the rules of the game. The slides presented by Bill Whyman highlighted several key shifts that resonate deeply with what we are seeing in the market for Independent Software Vendors (ISVs): - A Shift in Value Capture. Markets are questioning the "valuation premium" of traditional software. The future belongs to Agentic AI, where value is created not just by providing an interface, but by leveraging mission-critical data and executing complex workflows. - Architecture Matters. The dilemma is clear - is AI just a "copilot" to your app, or does your app become a tool called by an AI agent? This requires a deep architectural evolution, not just adding a chatbot on top. - The Risk of Disintermediation. A major risk for software today is when AI agents start orchestrating applications and APIs directly, bypassing the traditional user-to-software interaction. What does this mean for ISVs? To remain competitive and scale, companies must integrate AI into their product roadmaps now - ensuring it is done in a safe, stable, and "agentic" way. At Softengi, we have been providing software development services for ISVs for over 25 years, with a dedicated focus on AI integration and optimization for the last 10 years. We help product companies evolve their platforms at any stage of maturity, ensuring AI capabilities are deeply embedded into the overall product vision. If you are planning to scale your product this year or integrate AI in a safe and stable way, I would be happy to share some case studies on how we have successfully navigated these transitions for our customers. Feel free to reach out via DM and let’s schedule a brief meeting to discuss your roadmap! #AI #SaaS #ISV #NVTC #Softengi #DigitalTransformation #AgenticAI #SoftwareDevelopment

Early in my career, I sat in a boardroom where the CEO asked every department head to explain how they contributed to revenue. Sales went first. Operations went second. Finance talked about margins. Product talked about the roadmap. When it got to marketing, the CMO pulled up a slide about brand awareness and social media impressions. The CEO listened politely, said “that’s great,” and moved to the next topic. The meeting continued as if marketing hadn’t presented at all. I remember thinking two things. The first was that the CMO had done good work and deserved better than a polite nod. The second was that the polite nod was entirely predictable, because nothing in that presentation answered the only question the room cared about. (The question, in case you’re wondering, was not “how many people saw our logo.”) I’ve thought about that meeting a thousand times since. It shaped how I present to C-suites, build dashboards, and frame every recommendation I make. If you can’t connect what you’re doing to a number someone in that room is accountable for, you’re not presenting strategy. The CMOs who earn a seat at the table speak P&L. They show pipeline, conversion, margin impact, and customer acquisition cost. That’s the difference between being in the meeting and being part of the conversation.

The roadmap is the easy part of being a Product Manager. The hard part? Being the belief when everyone else has already moved on. Imperative - 1. Find the leader who protects the vision. 2. Earn the trust of the builder who’ll build it. Got all three? Scary things become possible. Jane Man Craig R

"The hardest part of fixing a data problem isn't technical. It's organizational." In 2022–2023, the logic changed. Growth at any cost became a problem. And that shift didn't just change our priorities. It changed what the entire company needed to build. We had a credit decision problem. To assess risk correctly in private payroll credit, we needed three inputs per employee: salary, available margin, and employment status. Without an API integration, that data came by spreadsheet. Sent by HR. On their schedule. With their accuracy. The consequences were direct: wrong credit limits, missed severance recovery, and credit extended to people who no longer worked there. The fix was clear. Build the integration. The problem: the product team had been deep into a different initiative for weeks. A roadmap built around growth. The old logic. We were now operating under a different one. Asking them to stop wasn't a data conversation. It was a leadership conversation. We translated financial exposure into something that couldn't be ignored. Not "our data is bad," but: here's what it costs us per account, every week. And here's how many accounts we have. That's leading by influence, not authority. Priorities shifted. The integration was built. When the business changes direction, the organization has to follow. Sometimes your job is to make the new reality impossible to ignore. The hardest problems in analytics don't live in SQL. Have you ever had to translate data into money to get things moving? #Analytics #DataDriven #Leadership #Influence #BusinessStrategy #DataLeadership

Darwin would have been a good startup investor. He famously wrote: "It is not the strongest of the species that survives, nor the most intelligent. It is the one most adaptable to change." He was talking about biology. But he might as well have been talking about startups. Because the companies that survive rarely win because they had the smartest original plan. They win because they adapt faster than the world around them changes. Every pitch deck tells a beautiful story. Clear problem. Obvious solution. Logical roadmap. Reality almost never cooperates. Customers behave differently from what was expected. Markets shift. Technology evolves faster than your product roadmap. The first plan almost never survives. And that's completely normal. This has always been true, but AI is making it more extreme. New models appear every few months. Capabilities improve rapidly. Costs drop. What felt like a breakthrough feature six months ago quickly becomes table stakes. The environment is evolving faster. Which means the companies that survive are the ones that evolve with it. In venture capital, you often see two companies with access to the same models, infrastructure, and tools. Yet one improves dramatically faster. The difference is almost never the technology. It's how quickly the team learns from reality. There's a subtle challenge here that trips up many founders: the line between persistence and stubbornness. Persistence means staying committed to the mission. Stubbornness means refusing to update your strategy when the evidence changes. Great founders hold the vision constant but let the path evolve. Those who struggle are often the ones who fall in love with the original plan rather than the problem they set out to solve. Darwin spent decades studying how species survive in complex environments. The key insight wasn't about strength or intelligence. It was about responsiveness to change. Startups operate in a similar ecosystem. Markets evolve. Competition appears unexpectedly. Technology shifts under your feet. Whenever I meet founders building in AI, I find myself coming back to one question. Not "Is this strategy perfect today?" But "How quickly will this team adapt when the world inevitably changes?" Because the world always changes. And in the age of AI, it's changing faster than ever. Darwin would have understood that immediately.

Is marketing becoming more like product management? With agents entering the workforce, some of our work is moving from creating one-and-done content to building tools that tailor information to specific audiences. That changes how we work. The deliverable isn’t the finish line – it’s the start line. That means we need to think of our output not as a set of assets, but as a product: with a roadmap, testing, feedback loops, and continuous iteration. Marketers, are you seeing the same shift?

Spring break felt like the right moment to reflect on a semester that's been more memorable than I expected. The equine leadership workshop through the CHARGE program was a standout. When direction was unclear, the horses made that very obvious, one wandered off, the other refused to move entirely. Funny in the moment, but a real lesson in how clarity and intention shape the people around you. Working through the obstacle courses with Bruna O'Sullivan, Amber May Barrington-Chappell, Koppany Jordan, and Divya Trivedi, we went from "so… what are we doing?" to actually communicating, aligning, and making real decisions together and that shift happened fast. The office visits rounded it out well. FanDuel opened up honest conversations about how data, strategy, and product decisions actually come together. Sage was a different kind of experience, their Atlanta headquarters is Georgia's first mass-timber office, and the space reflects it. Natural light, warm timber, greenery throughout. It felt calm and intentional. That same intentionality showed up in how they talked about their customers too, understanding what people actually need, and building differentiation around that. A few things I'm carrying forward: 1) Leadership is something you do, not something you hold — clarity of direction matters more than authority 2) Sustainability and great workplaces aren't a tradeoff — the best environments can reflect both 3) Features don't differentiate, understanding does — the teams that stand out are the ones who've done the work to know what their customers actually need 4) Teams are built, not assigned — alignment takes communication, and sometimes a stubborn horse to make that obvious #MBA #Leadership #Sustainability #CustomerValue #Reflection

What does it mean to focus on self-serve vs sales-led? It means you can maintain +250% CAGR over years and the team stays the same. But it means years of investing before it pays off. First, to build a product-led growth company, you need to find a big problem. Not create one and sell it. Building self-serve first is the growth engine that lets you grow without scaling a team. But it requires some courage — you have to say no to enterprise deals that can kill your focus. It requires years of building. Not a one-time shot. Years of iterations, fixing, and killing. But before you commit — look at your market. Are you selling to the mass market? Is the market growing on its own and you can grow with it? Can you measure the demand and the growth in the market? It's OK to have a sales-led company. There are a lot of great companies built on sales-led growth. But you have to answer honestly: are you actually selling enterprise-priced tickets, or are you selling mid-market tickets to companies that look like enterprise? Are your "enterprise" clients churning like SMBs? If your company and founders' DNA is sales and enterprise-led, do that. Don't force self-serve. You will not be able to keep investing in it. And you don't believe in what you're building. For us at Converted, we had been selling to enterprises and SMBs. We built a Frankenstein. Until we decided to build products the mass market can use. If enterprise wants to use it — they are welcome. But we don't build custom-made features for them. Grow or die. +270% YoY revenue growth Zero outbound sales 50% of users pay within 24 hours of signing up Users come back every 2 days on average Self-serve first isn't for everyone. But if it's in your DNA — build it.

When Everyone Is an AI Expert, No One Is Something strange is happening right now. The moment AI became accessible, everyone became an "expert." Your feed is full of it. → AI consultants → AI agencies → AI automation specialists But if you look closer, most of what's being sold is the same thing: A wrapper around ChatGPT. A handful of prompts. A polished demo. That is not expertise. That is packaging. Here is what actual AI implementation looks like: * It looks like mapping an enterprise platform that manages 1,000+ websites and designing the AI-assisted workflow layer that lets one admin do what used to take a full team. * It looks like pulling apart an investor pitch deck and rebuilding their GTM story around what buyers actually care about, not what the model can do. * It looks like stress-testing a voice-first social platform from the inside out, from prototype to investor narrative, and identifying exactly where the product logic breaks before a demo goes wrong in a board room. *It looks like running MCP connectors, building AI tooling into your own operations, and finding the real limits of what these tools can and cannot do in production. That is what separates implementation from performance. Real AI work is not about prompts. It is about systems. It is about understanding workflows, connecting tools, and designing processes that produce outcomes. Not just content. Results. If you are hiring or partnering with someone on AI, here is what actually matters: → They have built things, not just advised on them → They understand your business model before touching the technology → They can tell you what AI will not solve, not just what it will → They show working systems, not slide decks → They have skin in the outcome ***The gap right now is massive.*** A lot of people are talking about AI. Very few are actually implementing it at the system level. That gap is where the real work is. If you are trying to figure out how to actually use AI inside your business, not just experiment with it, send me a DM. I work with companies on: → AI-driven workflow automation that connects to real systems → Product and GTM strategy powered by AI at the roadmap level → Internal AI infrastructure that saves time and reduces cost → Turning complex, messy operations into scalable processes AI is powerful. But only if it actually does something useful.

What a treat it was to have lunch with Mehnaaz A. when she was in London. Not only is she one of the nicest humans on the planet and brilliant company, she has great suggestions on hot new places to eat. We ended up talking about one of the highest-impact things we did when we were working together: bringing a select few people from the Product and Marketing teams into one squad with the objective of building out a product aligned around a new and very specific audience. The team worked really hard to: 💥Frame the proposition 💥Build an agile roadmap to match and 💥Build automated GTM in from day one Result: new revenue + market share gain! Everyone talks about getting Sales and Marketing aligned as a big value unlock but in PE-backed SaaS, the bigger lever is Product and Marketing working closely together. When it’s disconnected: - You build what won’t sell - You market what doesn’t resonate When it’s aligned: - Product becomes commercial - GTM (and revenue!) hits faster - Growth compounds I firmly believe that it is one of the most underused value creation levers and I am delighted to be able to use it when I can! PS Anyone need a restaurant suggestion? Darjeeling Express, well known not only for spectacular food but also the all female team in the kitchen! --- 📌 Ready to turn your marketing into a growth engine? I'm Mary-Anne, a CMO and GTM consultant trusted by PE-backed and high growth businesses to accelerate performance with my GTM Diagnostic and growth frameworks. Let's unlock the next stage of your growth story

Day 2 at Shoptalk Spring—and a real highlight for me getting on the AI stage to interview Velia Carboni (CIO - SharkNinja), Ryan den Rooijen (MD AI and Monetization - Currys), and Rajat Shroff (VP and Head of Product and Design - DoorDash) on how AI is actually being applied in their daily operations. Here’s what stood out to me in what they said: Velia (SharkNinja) - AI is a powerful engine behind scaling innovation (SharkNinja has 25–30 new product launches/year) - Ability to extract and understand real-time consumer feedback is reshaping product design and the end to end customer experience - Supply chains are becoming AI led (demand sensing → rapid response) - Culture matters: democratizing AI across the enterprise is key. The key challenge: balancing freedom to experiment with governance at scale as we learn to 'fly the plane while we're building it' Ryan (Currys) - LLMs can't operate alone, they are like a brain without a body. Data, integrations, people & processes just as important to make the organization move - No such thing as a standalone AI strategy—it must be embedded into the overall business strategy - Trust and adoption matters. Even the best models fail if people don't use or trust them - Progress comes from solving real, everyday problems—not big-bang bets Rajat (DoorDash) - AI is a speed + scale multiplier for modern commerce compressing timelines (weeks → days) across core operations - Logistics is being redefined with real-time, AI-driven routing intelligence at a level of fidelity not previously possible (exactly where the Dasher should park, which door to enter the building, etc.) - DoorDash sets a high bar: no “AI for AI’s sake” - must drive customer + operational impact - Winning model = bottom-up experimentation (give broad tool access) combined with top-down ambition (leadership drives big goals) Big thanks to Velia, Ryan, and Rajat—and of course to Ben Miller and the Shoptalk team for the opportunity. My main takeaway: the leaders pulling ahead aren’t chasing AI—they’re operationalizing it. #ShoptalkSpring #AIinRetail #MakeItReal

Being a startup's first PM hire is not easy. You're the first product person. No process, no roadmap, no team. Just you and a founder who's been making every product decision since day one. A few founding PM challenges I've seen come up over and over: 1/ Can you shift from helper to owner? When you're the first PM, everything feels like your job. QA, support tickets, SQL queries, marketing copy. Some of that is unavoidable. But if you stay there too long, you train the founder to see you as execution support, not product leadership. ↳ pick 2-3 things that are uniquely PM work and start spending more time there ↳ build lightweight processes so the gaps you fill can be filled by someone else 2/ Can you build a system, not just execute inside chaos? Engineers were already shipping before you arrived. The founder was already making calls. So the question hanging over your role is simple: what changed because you joined? You ship. And ship. And ship. But if all you do is execute, you're a fast task-doer, not a product leader. The things that actually compound, like how decisions get made and how you learn from customers, are the things nobody is asking you to build. Ship fast in the first 90 days. But carve out time for infrastructure that compounds. 3/ Can you earn trust with the founder? The founder has been the PM since day one. Handing product decisions to someone they just hired is hard. At first, they still own most of the real product calls. That's normal. The way you flip that ratio isn't dramatic. Small wins. Demonstrated judgment. Knowing the customer better than anyone. Bring customer evidence, not opinions. 4/ Can you make your growth path explicit before someone else gets hired above you? This might be the biggest career risk of the founding PM role. The implicit deal is: do great work, and you'll grow into the leadership role. But that deal is rarely made explicit. And if you just keep your head down, there's a real chance someone more experienced gets hired above you. I've seen this inside Supra. The founding PM builds the product function from zero, does the unglamorous work nobody else wanted. Then the company raises and hires a "real" Head of Product from a bigger company. Have the conversation early. Not "promote me" but "what does growth look like for me here?" If the answer is vague, that's information too. If any of this feels familiar, we're spinning up a small founding PM group inside Supra. 7-8 founding PMs at seed and early-stage startups. Monthly 90-minute sessions. Real situations, not hypotheticals. Application here: https://lnkd.in/dq33ibHj Hard requirement: you must be a current founding PM or first PM hire at an early-stage startup.

AI isn’t one conversation. It’s four different expectations you’re expected to meet at once. Your tech team wants depth.  Your product team wants judgment.  Your CEO wants a commercial case.  Your board wants to know the risk. One reading list does not cover all four.  Most people are preparing for one room and hoping it travels. It does not. What changes when you actually understand all four rooms: • Stop the duplicate AI side projects. Build one team. • Replace the AI roadmap with clear bets and clear stop rules. • Every AI feature needs a P&L owner. Not a team. A person. • No before/after value metric. No green light. •  That is what the reading changes. Watching changes something else. • Spike first. Pilot second. Platform third. • Each pilot answers one question. One decision. Nothing else. • Give every PM the same template for every AI experiment. • If each launch doesn’t make the next one cheaper, it isn’t compounding. Most teams will walk into these rooms unprepared this quarter. Forward this to the person who has to answer all four. 👊 And follow Mita Patel for more content like this

For most of my life, I told myself I was a backstage director. The one who builds, not the one who presents. I wore it like a badge: "I let the work speak for itself" It took me years to realize that was not a philosophy. It was a hiding spot. #TheComfortableLie I'm creative. I have ideas that keep me up at night. I have proven what I'm capable of, and have the resources and people setting me up for success. But there is always this last mile. One more feature, one more tweak, and one more reason to convince myself and everyone else that it is "not ready yet." That's not perfectionism. That's fear wearing a product roadmap as a disguise. #TheCoFounderMirror One of the best things about my co-founder is that she sees right through it. Her question is never "what if it fails?" It is "what if it doesn't, and you never tried?" We're approaching something big right now. Something that matters for a company at our stage. And I had the usual urge to add just one more thing. She looked at me and said, "I don't care if you're ready. I don't care if the product's ready. We go out, we face it, we learn, and we come back and build again." She's been right about this more times than I'd like to admit. And she's not the only one. Past mentors and peers have told me the same thing. It just lands differently when it comes from someone building alongside you every day. #TheRealAudience Here's what I've learned. The people who believe in you will believe in your rough draft. The ones who don't trust you at your earliest won't trust you at your most polished either. And critics show up at every stage regardless. Both add growth. Supporters give you energy. Critics give you edges. But you have to be in front of both to get either. If you're a builder who keeps hiding behind "one more feature," I see you. I am you. The stage is not as scary as the story we tell ourselves about it. Ship it. Say the thing. Launch the page. Get on the call. Whatever your version of "front stage" is, the audience is already waiting. What's one thing you've been holding back because it doesn't feel "ready enough"? #ReveriextRhythm #FounderJourney #BuildInPublic #Startups #AI #Leadership #LearningInPublic #ShipIt #Entrepreneurship

Introducing Next Weeks Founders Coffee Speaker Klaudia McDonald Founder and COO of Bobo a platform that gives families and clinicians a shared, real-time view of health between visits. This talk explores how to build defensible, non-obvious GTM strategies tailored to your product, user psychology, and distribution specifically in complex spaces like healthcare and multi-stakeholder ecosystems. Drawing from experience scaling a pediatric digital health platform, Klaudia will cover how to: - Distinguish between buyers, users, and influencers -Build compounding GTM loops, not one-off campaigns -Turn partnerships into real distribution channels -Convert credibility into revenue -Shift from marketing spend to operational growth engines We’ll also challenge common advice like relying on paid ads or product-led growth and explain when those approaches fall short. This talk is for founders, operators, and investors, this session offers a practical framework to turn strong products into consistent, scalable growth. Register today! 📅 Tuesday March 31st : 8–9 AM 📍 Launch Downtown: 200–185 Carlton Street 🎟️ RSVP: https://lnkd.in/d3kstRDS ☕️Founders Coffee Page: https://lnkd.in/g-ezwZ-R 📲Bobo App: https://lnkd.in/eeZ3Bppf This program is brought to you by: Presenting Sponsor - Futurpreneur - Caroline (. Founded & Hosted by Launch Coworking Space Led by 360 coaching Group - 360Coaching Fuelled by Muse Cafe

Early in my career as a sales representative, my general manager was Mr. Yamaguchi. He shared maxims that sounded wise but were sometimes difficult to understand. One of his favorites was, “Remember, NIHITO.” The first time I heard it, I assumed it was a Japanese word. He laughed and said, “No, it’s American. I learned it when I moved here.” That surprised me. I thought I knew most of the American words. NIHITO, he explained, means: Nothing Important Happens In The Office. For salespeople, this was obviously true. Customers weren’t sitting in our cubicles waiting to be discovered. Deals weren’t often made in our conference rooms. If you stayed at your desk, you might feel busy, but you weren’t doing the sales work you were hired to do. Years later, I shared that story with my colleagues at Pragmatic, and we quickly coined the term “NIHITO visits.” And pretty soon, NIHITO was showing up in articles, slides, and webinars. However, in reality, product managers do spend valuable time in their offices—attending meetings, writing documents, reviewing roadmaps, and determining priorities. But you will not learn much about customers by sitting at your desk. So maybe for product managers, the acronym should be: LILITO: Little Is Learned In The Office. Customers don’t work in your office—and certainly not in your home office. They experience your product in the messy reality of their work, where shortcuts exist for a reason and “that’s just how we do it” often hides unnecessary friction. If you want better ideas, you need to leave the place where solutions are designed and go to the place where problems occur. It’s easy to believe that insights from sales, support, and other teams will suffice. Because no matter how smart the people in your office are, nothing important about your customers happens there. Learn how with Customer Conversations. https://lnkd.in/eyt5Wzj3

As I approach 5 years at Tellent, I’ve been reflecting on one thing I’ve invested a lot of time on: Integrations When I started, I thought of integrations as “features that connect systems.” After working on 220+ of them, I see them very differently. Before Tellent, at Booking.com, I worked on connecting complex Hospitality product types (OTA, RMS, PMS and CM). In HR Tech, I’ve navigated across 20+ different systems gravitating around ATS, Onboarding, and HRIS products. Through all this, one thing is clear: Integrations are never just about code. When you build a standard feature, you control the roadmap, the tech stack, and the timeline. When you build an integration, you are at the mercy of an ecosystem: - ​External APIs that evolve (or break) without your input. - ​Partner priorities that don't always align with your sprint. - ​Data quality you don’t fully control. - ​Breaking changes you didn’t introduce, but you still have to fix. Despite the lack of control, you still own the customer experience. Integrations work sits at the intersection of product, engineering, and partnerships. It requires: 1. ​Designing a seamless UX across two (often conflicting) systems. 2. ​Managing technical dependencies outside your own firewall. 3. ​Aligning commercial incentives across different companies. It’s not just product development. It’s ecosystem building. While it can be messy and unpredictable, it’s also one of the most challenging and rewarding spaces I’ve worked in, and I love it. Super cool ride so far and for everything these 220+ "connections" have taught me along the way. 🙌

The promotion came a year later than I expected. And I’m glad it did. ⸻ About a year ago, I didn’t make it. It was disappointing. Quietly so. But in hindsight, that phase did more for me than any early promotion could have. ⸻ Today, I’m stepping into the role of Associate Director – Business Finance. And the journey over the last 3 years has been… layered. Not just growth in title — but growth in how I think. ⸻ I moved from solving for outcomes → to understanding systems. From managing numbers → to shaping decisions. From execution → to influence. ⸻ Working across Ads, Loyalty, SuperCoins, and Grocery, a few things became clearer: • Business finance is not about reporting accuracy alone • It’s about decision clarity under ambiguity • It’s about connecting product, tech, and economics into one narrative • And most importantly, it’s about earning trust at the table ⸻ The biggest shift for me happened in the last 6 months. I stopped trying to “do more”. And started doubling down on what I’m uniquely strong at: → Systems thinking → First-principles approach → Deep dives into tech + product That’s where the real leverage came from. ⸻ Ironically, not getting promoted last year forced that shift. It pushed me to go deeper. To ask better questions. To build conviction, not just slides. ⸻ A small takeaway for anyone navigating similar phases: Delays are data. Sometimes they’re pointing you to a gap you wouldn’t have otherwise seen. And if you respond well, they become your unfair advantage. ⸻ Grateful for the mentors, leaders, and teams who trusted me with real problems. And for the debates, disagreements, and brainstorming sessions that shaped my thinking. ⸻ The next chapter feels even more exciting. Especially as finance, product, and AI start to intersect more deeply. A lot to learn. Again. ⸻ What’s one phase in your career that felt like a setback then—but became an inflection point later? ⸻ #Leadership #CareerGrowth #BusinessFinance #FPandA #Strategy #Pricing #Ecommerce #Learning #AI #Growth

Stop being a vendor. Start being a Partner. Most people think a Customer Success Manager (CSM) is just a glorified support agent. They couldn't be more wrong. ❌ The magic of a great CSM happens in Relationship & Account Management. It’s the art of bridging the gap between what a product does and what a customer needs. Here are the 3 pillars of a high-performing CSM: 1. Health Monitoring (The Early Warning System) You don’t wait for a cancellation email to know a customer is unhappy. You watch the data. Low login frequency? Zero support tickets (which often means they’ve given up)? That’s your signal to dive in before the "fire" starts. 2. Strategic Advising (The "Consultant" Mindset) A support agent answers: "How do I click this button?" A CSM answers: "How can this button grow your revenue by 20%?" Stop answering tickets and start suggesting better ways for your clients to run their businesses. 3. The Feedback Loop (The Product Bridge") A CSM is the eyes and ears of the Product Team. You aren't just listening to complaints; you are translating pain points into product roadmaps. You are the customer’s voice in the room where decisions are made. The result? Lower churn, higher expansion, and a customer who sees you as an indispensable part of their team. Are you firefighting or fireproofing today? 👨‍🚒🔥 #CustomerSuccess #SaaS #BeautyJonah #AccountManagement #CustomerExperience #CSM

After weeks of late-night coding, architectural debates, and way too much coffee, I’m incredibly proud to announce that Kaiten is finally live. As a CTO, I’ve seen the same "SaaS Scaling Paradox" break great teams over and over: 1. Sales sell custom plans in the CRM. 2. Product toggles features in a siloed dashboard. 3. Engineering deploys blindly, oblivious to the actual customer contracts. We built Kaiten to kill the "glue code," the flaky scripts, and the broken webhooks that try (and fail) to bridge these worlds. Kaiten is the missing link between your Business and your Infrastructure. We’ve unified three critical pillars into a single engine: - Orchestration & Deployments: Knowing exactly which version runs for which client, anywhere (AWS, GCP, or On-Prem). - Feature Management (OpenFeature compatible): Decoupling deployment from release to mitigate engineering risks. - Monetization & Entitlements: Forcing the code and the infrastructure to strictly obey business rules in real-time. No more "phantom resources" being consumed by expired licenses. No more manual synchronization. Just a clean, business-to-infrastructure state controller. We are officially opening our Private Beta. We’re looking for engineering teams and founders who are ready to stop "taping" their tools together and start scaling with a true Control Plane. Join the waitlist here: 👉 https://lnkd.in/eQU9TVXk Kaiten is just getting started. I can't wait to see what you’ll build with it. #BuildInPublic #SaaS #CloudNative #Kaiten #EngineeringManager

“Bad data” gets blamed a lot… but is it really the problem? At the CDO Inspired Summit, David Bruce from Financial Ombudsman Service led a thought-provoking fireside chat on “Bad Data Is Not the Problem: Why Transformation Fails and What Leaders Must Do Differently.” Challenging a narrative many organisations default to, David made a compelling case that data quality isn’t usually the root cause of stalled transformation — leadership, ownership, and decision-making are. The session explored how organisations often over-invest in tools while under-investing in accountability and clarity of purpose. And how, too often, data is treated as a by-product rather than a managed, strategic asset. The result? A breakdown in trust, slower delivery, and missed value. The key takeaway was clear: successful transformation isn’t just about fixing data — it’s about fixing how organisations think about ownership, responsibility, and decision-making. A refreshingly honest and insightful discussion. 👏 Thanks to David Bruce for a brilliant session. #CDOInspired #DataLeadership #DigitalTransformation #DataStrategy #Leadership #DataDriven #Networking #B2B #B2C #Data #CDO #London #Londonevents #Dataevents #Dataleaders #Inspiredbusinessmedia

The fastest way to kill an early-stage company is to make perfectly logical decisions based on hallucinated data. I see founders do this all the time. The logic is flawless. The inputs are made up. Here is how bad decision-making actually plays out in the $1M-5M transition zone: The Product Decision You survey your active users to make roadmap choices. 100% of them say your web app is perfect. You logically decide the data doesn't support building a mobile app. The Reality: You just made a strategic decision based on survivorship bias. You optimized for the people who said "Yes" and ignored the 50 deals you lost because your competitor had a mobile app. The Hiring Decision Revenue stalled at $2M ARR. You decide you are “not a sales founder” and logically hire a VP of Sales from FAANG to come in and scale the motion. The Reality: You don't have a motion to scale yet. You just hired an expensive executor to do builder work, because you tried to delegate a problem before you understood it. The Pipeline Decision You review the CRM for the board meeting. You have 40 deals sitting in "Proposal Sent." You logically decide to forecast a record quarter. The Reality: "Proposal Sent" is seller activity, not buyer evidence. Half of those deals are stuck in a 90-day legal battle over indemnification clauses, and the champion for the other half quit three weeks ago. A lot of bad strategic decisions happen because the system leaders rely on is feeding them fiction. If your CRM stages are based on sales optimism, your roadmap is based on survivor bias, and your hiring is driven by founder fatigue... you aren't actually making data-driven decisions. You are just guessing with spreadsheets.

Your CRM knows a call happened. But does it know if the buyer actually meant what they said? 🛑 Most modern CRMs are fantastic at storing data. They log the activity, update the stage, and generate the forecast. But they have a massive blind spot: they rely on subjective, rep-written notes to gauge deal momentum. We just published a new breakdown on CRM Call Intelligence and why the next evolution of SaaS platforms is moving from a "System of Record" to a "System of Understanding." Instead of just logging that a demo took place, what if your platform could automatically detect buyer hesitation, shifts in engagement, and sincerity...triggering alerts when a deal is actually at risk? 🚀 Embed this intelligence natively with our Founding Partner Pilot Program. Building multimodal AI from scratch takes years. We are partnering with forward-thinking CRM and SaaS product teams to integrate our behavioral APIs right now. ✅ Zero-cost integration for qualified partners. ✅ Shape the roadmap to extract the exact conversational signals your users need. ✅ Differentiate your product by turning raw dialogue into objective, actionable data. Stop competing on basic activity tracking. Start competing on conversational truth. Read the full guide and apply for the pilot below. 👇 #CRM #SaaS #ProductManagement #EmbeddedAI #SalesTech #Voicera

Hace unos días tuvimos la oportunidad de facilitar en Statkraft Perú un workshop de priorización de iniciativas junto a nuestro partner Ayesa. Fue un espacio clave donde reunimos al equipo de management y product owners para algo que muchas veces es difícil de lograr: alinear estrategia con ejecución de manera concreta. Más allá de definir un portafolio, el objetivo fue claro: convertir múltiples iniciativas en decisiones compartidas, basadas en valor para el negocio y en una visión común como organización. Durante la sesión trabajamos en: • Evaluar iniciativas desde distintas perspectivas del negocio • Generar consenso entre áreas • Priorizar con foco en impacto real (operacional, estratégico y de gobernanza) • Construir un primer roadmap alineado Lo más valioso no fue solo el resultado, sino el proceso: ver a todas las áreas involucrarse, cuestionar, aportar y finalmente decidir como un solo equipo. En lo personal, me llevo mucho aprendizaje de haber sido parte activa en la estructuración y facilitación de este espacio, conectando negocio, tecnología y estrategia. Ahora viene la parte más importante: llevar estas decisiones a la ejecución y convertirlas en resultados reales. Gracias a todos los que participaron Jessica Rozas Luis Miguel Tirado Juliet Agreda Luis Chigne Pedro Moisés Jacinto Calderón Eliana y a Ayesa Digital - Patricia Changanaqui y Daniella Angobaldo, AdvisoryEDGE Eliana Barrantes Núñez por el trabajo y experiencias en conjunto. #Statkraft #DigitalTransformation #DataDriven #StrategyExecution #Teamwork

Day 2 at Shoptalk The narrative shifted. Day 1 was about execution. Day 2 was about what happens when AI actually changes how people buy. A few things stood out: Resale is no longer niche. It’s growing significantly faster than traditional retail. Not as a side bet, but as a core growth lever. AI is quietly redefining discovery. Not through better ads, but through better data. As one theme repeated across sessions: “If AI can’t understand your product, it can’t recommend it.” What matters now isn’t just how your product looks. It’s how well it can be understood by machines. Because the new funnel looks different: People don’t search. They ask. And AI decides what shows up. Which means: Your product data is now your distribution strategy. The best example came from Shopify. Set your catalog once, structure it properly, and your products can show up everywhere AI operates. ChatGPT, Google, assistants all of it. Or as one speaker put it: “Add your product once, and let every AI surface it everywhere.” That’s a big shift. From optimizing for humans → to optimizing for agents. Hillary Super added an important layer to this: The brands that win won’t just be efficient. They’ll stay emotionally relevant. AI can optimize discovery. But brand still drives desire. At the same time, one reminder came through clearly: Physical retail isn’t going anywhere. In fact, younger consumers are leaning back into it. So the future isn’t AI vs stores. It’s AI enhancing everything around them. The takeaway: We’re entering a world where conversations drive decisions and data determines visibility. The brands that win won’t just tell better stories. They’ll be easier to understand. And harder to ignore.

🗣Academy Central Europe is gaining momentum Parts of our TÜV SÜD Central Europe #Academy team traveled to Munich headquarters this week to meet our German colleagues as well as the management to discuss our strategic foundation and collaboration – and I’m leaving with a real momentum for what’s next. My clear focus for this live meeting was to align us not just on topics, but on one common strategic direction and how we operate as a unified organization. Key areas we addressed were 👉 Customer communication and administration 👉 Strategic priorities and focus areas 👉 Shared revenue targets and financial alignment across the region 👉 Cross-country incentive models 👉 IT, e-commerce and multilingual capabilities 👉 Trainer management and multilingual product development 👉 Certification and marketing ⚠️A critical outcome: our firm commitment to a shared organizational structure across the region. This is more than a structural decision – it’s a strategic lever. It enables us to fully leverage our synergies, scale faster, and act as one high-performing unit. #ChangeManagement becomes real #ChanceManagement for me. What does this mean for our customers? 📍One consistent interface instead of fragmented structures 📍 Faster decisions and smoother processes 📍Consistent standards across all offerings 📍More relevant solutions, driven by real customer needs 💡In short: less complexity, more impact. We are very clear on where we want to go – and how to get there. Central Europe will play a much stronger, more visible role going forward, driven by a clear strategy and disciplined execution. This meeting was an important milestone. Now it’s about delivering from all of us - together! 🙏A big thank you to my Central Europe team and our colleagues in Germany who supported us from the beginning: strong strategy only creates value through execution with the right mindset – and that’s exactly what you deliver with excellence every day for our Central Europe region. Jule Mangold Ann-Kathrin Casar Adeline Dirig Steffen Kohler Ezgi Yücel Susanne Cichra Josef Antonio Kieler Andreas D. Nozomi Nakadate Carolina Gräf Annemarie Pejrimovsky Merima Pajalić Liana Behailu Patricia Kogler Andreas Berg Benjamin Buchmayer Esra Akbas-Adas Benjamin Kunze Tsvetan Donev Michael Rothmund Stefanie Nebel Susanne Scharf Vanessa Mangano Kevin-Florian Su Olivia Serttaş Eva Gerich Stephanie Juraske Brigitte Hanemann Ute Hannelore Bretfeld Dana Hack Jörg Schemat Sebastian Hofmann

Everyone thinks Alix Earle just launched Reale Actives this week… She didn’t. She cashed in on 2.5 years of trust — and built ~500K followers before selling a single product. Yes — the last ~10 days were everywhere: • “something is coming” • @wtfisalixdoing • Cryptic billboards • Puzzle pieces sent to influencers to complete them • The internet trying to figure it out in real time But that wasn’t the strategy. That was the amplifier. The real strategy? Years of documenting: Acne. Cystic breakouts. Sleeping in makeup. Trying everything (and not pretending to be perfect). So when Reale Actives finally dropped this week… It wasn’t a launch. It was a payoff. By the time products went live: • ~500K people were already following the brand • The audience was emotionally bought in • The demand was already there No heavy “convince them” moment needed. Most brands: Build product → launch → then try to create demand This flipped it: Build trust → build audience → then launch And this is where it gets really interesting from an ecommerce/CRO perspective 👀 Now you have: • Massive, high-intent traffic • Strong founder trust • A clear problem → solution narrative The opportunity is in scaling that into: • High-converting PDP storytelling • Acne journey → product mapping • UGC that mirrors her real lifestyle (not over-produced) • Retention loops that keep this audience engaged post-launch This is one of the best examples of earned attention → amplified → converted I’ve seen in a while. And it’s exactly the kind of brand where great CRO doesn’t replace the story… …it scales it.

Something I've learned from 25+ years managing the tension between technology transformation and product delivery: The friction is always there. It just changes shape depending on where you sit. 🛠️ As a software engineer: It’s the friction in the code; the brittle tests, the manual deploys that turn a 10-minute ship into a three-day ordeal, and the "temporary" workarounds that are now five years (or more!) old. 🧭 As a tech leader: It’s the friction in strategic partnerships and the widening gap between what the roadmap promises and what the team is actually shipping. It’s the constant struggle to integrate emerging tech with the weight of legacy infrastructure. It's the "innovation" that gets stalled by the very systems it was meant to modernize. 🚀 As a transformation leader: It’s the friction in the legacy process nobody wants to touch. It’s the meeting where everyone agrees, but nothing actually changes. As I’m deep-diving into AI and Machine Learning, I’ve stopped asking if AI will "fix" this. Instead, I’m wondering: How will AI make this friction impossible to ignore? We've focused so much on AI as a "creator" (e.g. writing code) that we’ve ignored its potential as a "truth-teller." It’s the difference between an AI that helps you build faster and an AI that tells you exactly why you’re standing still. I believe the true power of AI in transformation isn't just speed. It’s radical visibility. It’s the ability to identify and name the friction that we’ve become culturally conditioned to accept. But once the truth is on the table, the work is still ours. AI can point to the blockage, but it can’t lead people through it. Is your organization ready for that kind of radical transparency? Or are we just hoping AI will help us move faster through the same old friction? #DigitalTransformation #ExecutiveLeadership #GenerativeAI #TechnicalDebt #ProductDelivery #AgenticAI #MachineLearning

6am. Paddle in hand. Ocean ahead. 🛶 Before the Slack messages, before the meetings, before the spreadsheets — I'm out here doing Hawaiian outrigger canoe in my hometown. And this sport taught me something that clicks every single day at Deel: It doesn't matter how hard you paddle. If you're out of sync with your crew, you slow the whole canoe down. In outrigger, six people share one boat. One person going rogue — even with the best intentions, even paddling harder than everyone else — creates drag. The boat wobbles. You lose speed. You lose direction. The magic happens when everyone finds the same rhythm. At Deel, we talk a lot about Deel Speed — moving fast and building products that last and evolve. But speed without rhythm isn't speed. It's chaos. The teams I've seen move the fastest aren't the ones with the most individually talented people. They're the ones where everyone is rowing in the same direction, at the same time, with the same intention. That's when you don't just go fast. You go fast and build something that actually holds. Power matters. Rhythm wins. But rhythm at speed? That's the real goal. What's one lesson a sport or hobby taught you that changed how you work? #Deel #DeelSpeed #Leadership #Teamwork #HawaiianOutrigger #MorningRoutine

Hot take: handing your ads to an agency doesn’t guarantee better performance. I learned that the hard way. 2025 was a record year for us at Saltwater Boys Co., something I’m incredibly grateful for. Our DTC channel alone doubled from 2024 to 2025. It wasn’t always perfect… but we always figured it out. But if I’m being honest, I was not pleased with my experience handing over our ad accounts to an outside agency. Our cost to acquire a customer became exorbitant. ROAS was low. Ad spend was high. It just didn’t make sense. So on January 5, we went back to the drawing board and brought everything back in-house. No big team. No complicated structure. Just founder-led execution again. Since then: We’re scaling online like never before. CPA under $8 on some days. ROAS as high as 24. Conversion rate over 5%. (And yes… I’m proud of that.) Same brand. Same product. The difference? Closer to the customer. Faster decisions. Real-time intuition layered on top of data. Daily, hands-on account management. I don’t think agencies are “bad”, I really don’t. We have worked with a handful. But I do think there’s something powerful about founders staying laser-focused on what actually drives revenue. Because right now, the brands that are winning aren’t the ones outsourcing everything… They’re the ones closest to the customer. And I’ll always try to give credit where it’s due… What Ashley Alderson and Jeff Fenn are building with Boutique Hub Black is something special. And my coach, Dylan, is second to none. They continue to amaze me and I am super appreciative of all they have taught me about running ads and so much more! #ecommerce #digitalmarketing #founderled #growthstrategy #directtoconsumer

Some observations on AI: 👨🏼‍✈️ Directors are often not the source of innovation, although they are responsible for it. Their days are packed with meetings and their process of creating new heavily relies on tens of years of experience on a space, which for most is brand new. Therefore they use outdated methods for creating AI strategy and do not have systematic AI-native way of thinking about the problem space. 💬 While you can still catch up quickly, discussion on AI in orgs can become difficult because of the knowledge disparity and skill gap between persons holding similar positions but having vast difference in adoption of AI. Nobody wants to admit that they cannot switch the brain to a new mode. Ideating new things is tough by nature to begin with, even with great people onboard, but the skill gap creates a lot of friction on roadmaps and planning. 👶🏻Interestingly many companies have laid off a lot of juniors - forward-thinking talented junior can be a great superpower for building with AI without having the package of old workflows, combined with seniors and their industry experience. 🧠Maybe 2 or 3 designers out of 10 get it naturally and learn how to learn. The more tech-savvy and better knowledge on how computers work learn AI workflows easier. Some can break the syntax barrier and have unicornish qualities, while of course to reach that you need to have knowledge about software architecture. AI combined with remote work has done many roles already obsolete. In case you are design manager or used to be one who does not get AI, you should be worried. You are basicly a very expensive meeting organizer on a virtual water cooler without domain experience. 🤔The problem space however stays the same, which is choosing the right business strategy and right thing to build. Many teams waste time and get lost in prompting instead of talking to the customers (or bots of their customers) and solving real problems. 🧑🏻‍💻Dev team sprints – Mondays for planning and Fridays for reflecting…that is 3 days of building compared to agents working for you while you are away and then reviewing the work when back. 🤖 JIRA…do not build with JIRA, switch to Linear or something better than Atlassian products. 🧩The most natural application in design seems to be design systems, since they are systematic by nature. If you already have a mature design system, making the DS AI-friendly still is not probably the bottleneck in your work, but it can speed up things in the creation phase.

Will AI replace people in support? The honest answer is yes, and no. AI can already diagnose issues, draft responses, and route tickets faster than a human ever could. It’s hard not to be impressed by what it can do. But here’s something I’ve been reflecting on lately: It’s just as easy to forget what AI can’t do. Some companies are laying off thousands of support staff and automating everything they can. If you view support purely as an expense, that probably looks like progress. But it’s a short-sighted strategy that ignores how customers actually behave. Most don’t care if a solution is “AI-enabled.” They just want the right answer, as quickly as possible. Sometimes that’s self-serve. Sometimes it’s documentation. And sometimes it’s a human who can investigate, troubleshoot, and think critically. The best support models orchestrate all of the above to create the fastest path to resolution. That’s why our most knowledgeable engineers stay close to customers on the front lines. Their expertise is earned through experience. It’s also how our next generation of experts learn: on the job, alongside seasoned professionals. The best support teams are built by training and investing in people. The results speak for themselves: Three out of four product lines had 100% customer satisfaction on support surveys last year. Because what customers want hasn’t changed: continuity, results, and speed. Most importantly, they want to feel understood. You deliver that best when AI and humans work together.

We launched in 2023 with one SKU. One product. No variants. Decision fatigue is one of the fastest ways to lose someone who is already interested. Choices create friction, especially early on. One product also meant we could learn without carrying significant inventory risk or raising capital. Instead of chasing new products or complex logistics, we obsessed over quality and customer experience. We improved everything around the core product before adding anything new. We figured out what our brand stood for and built a community. Now we're scaling. But the discipline of staying focused, staying simple, for those first few years is what made it possible. Simplicity isn't a weakness. It's a strategy.

This is a special conversation. I sat down with my friend and former colleague Daiva Staneikaite Naldal to recall the work that we did together, scaling LEGO® IDEAS from an innovation pilot into a full-fledged product line. Most companies say they listen to their customers. Very few build the system that converts listening into a growing program. Daiva and I were part of the team that built LEGO IDEAS, the platform where fans submit product designs, rally community support, and see their creations turned into real LEGO sets sold worldwide. It grew into a global business and major product line, with products you've no doubt seen or even own. The hard part wasn't the technology. It was integrating a disruptive innovation engine into an 80-year-old organization without killing the trust of the creators who powered it. Three things stuck with me from this conversation: First, your most dedicated users read the market before you do. LEGO fans spotted Minecraft as a cultural phenomenon before anyone inside the company. Second, open innovation isn't a marketing campaign you turn on and off. It's an ongoing commitment to relationships with top users that compounds over time. Third, commercial proof creates organizational pull. Match the core business's numbers and resistance turns into demand. Whether you're scaling a startup or running innovation inside a large org, the principles are the same. Define your strategic intent. Build the circle of trust with your lead users, and close the loop and reward them for captured value. And for good housekeeping, please note that views expressed by the host and guest are their own and do not represent those of any current or former employer. LEGO® is a trademark of the LEGO Group of companies which does not sponsor, authorize or endorse this podcast. Links to the full episode in the comments below. ⬇️

I’ve been watching a lot of companies talk about AI transformation in customer service over the past 12–18 months. Most of the conversation is still centered around tools. Which platform. Which model. Which use case to pilot first. And almost every time, that’s where things start to go wrong. AI in customer service is not a technology problem. It’s an operating model problem. What I see repeatedly is organizations running pilots that actually work. The numbers look good, containment improves, handle time drops... There’s a sense that momentum is building. But when they try to scale, performance becomes inconsistent. Adoption slows down. Vendors don’t fully engage. CX starts to drift. And leadership ends up asking the wrong question: “Is the AI good enough?” That’s usually not the issue! The issue is that the organization was never structured to absorb AI at scale. - There’s no clear ownership between product, operations, and vendor management. - Commercial models still reward headcount instead of outcomes. - QA frameworks were never redesigned for AI-assisted interactions. - And governance is either too fragmented or too centralized in the wrong place. So the pilot works. The system doesn’t. If there’s one thing I’ve learned over the years, it’s this: You can’t scale new capability on top of an old operating model and expect stability. At some point, you have to redesign how decisions are made, how performance is measured, and how partners are aligned. That’s where real transformation happens. We put some of these thoughts together in a recent piece on how AI should actually be structured inside customer service environments, especially when you’re dealing with multiple vendors and large-scale operations. If you’re already running pilots and starting to think about scale, this is usually the point where the real work begins! https://lnkd.in/edRmBsvB

Something I often run into when I'm doing messaging work: I'll ask a founder to explain what their company does and it comes out sharp. It makes sense, and it's clear why it matters. But then I talk to other people on the team and hear something noticeably different (sometimes 3 completely different versions 😬). What's interesting is why this persists for so long in otherwise smart companies. Founders often see a quality problem: "Nobody can get this right without me." Marketing sees a missing-inputs problem: "We're doing real strategic work, we just don't have the actual strategy fundamentals to work from." Both are frustrated, and each side experiences the gap so differently that they're trying to solve two different things. Yet neither realizes they're describing the same gap. Meanwhile, companies with objectively worse products are outpositioning you because their story can travel without their founder having to personally tell it. And your buyer is absorbing those inconsistencies across every touchpoint, making it harder for them to champion you internally when it counts. That's pipeline you'll never know you lost, and it's a fixable problem that starts way before a deal even takes shape. 👀 I dug into this in the latest issue of The Content Brief. Link in comments. 👉 Sound like something you're struggling with? I have a few slots open for new clients in Q2. Booking link in comments, too.

Your best engineer just resigned. Cost to replace: 6 months and $180K. Exit interview reason? "I'm tired of making product decisions leadership won't make." You've heard this before. Different people, same pattern. 𝗪𝗵𝗲𝗻 𝘆𝗼𝘂 𝗮𝘃𝗼𝗶𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽, 𝘆𝗼𝘂 𝗱𝗼𝗻'𝘁 𝗲𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗲 𝘁𝗵𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 - 𝘆𝗼𝘂 𝗷𝘂𝘀𝘁 𝗱𝗲𝗹𝗲𝗴𝗮𝘁𝗲 𝗶𝘁 𝘁𝗼 𝗽𝗲𝗼𝗽𝗹𝗲 𝘄𝗵𝗼 𝘀𝗵𝗼𝘂𝗹𝗱𝗻'𝘁 𝗯𝗲 𝗺𝗮𝗸𝗶𝗻𝗴 𝗶𝘁. And it's costing you more than engineering talent. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁'𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴: • Clients call your dev team directly because there's no product authority to push back • Your roadmap is whatever the loudest client asked for last week • Development costs are 40% higher because your codebase is unmaintainable chaos You're bleeding talent, burning budget on technical debt, and losing deals to competitors who ship faster. You're avoiding decisions because you're afraid of choosing wrong. But every day you don't decide, your engineers make 50 micro-decisions that pull your product in conflicting directions. I've watched this pattern destroy product companies across three continents while scaling global delivery teams. The organizations that survive assign clear product ownership within 30 days. 𝗛𝗼𝘄 𝘁𝗼 𝗳𝗶𝘅 𝘁𝗵𝗶𝘀: 𝟭. 𝗔𝗰𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝘁𝗵𝗲 𝗰𝗼𝘀𝘁 Calculate what chaos costs: recruitment, tech debt, missed deals. Make it visible. 𝟮. 𝗔𝘀𝘀𝗶𝗴𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 Hire a PO, promote internally, or block CEO time. Ambiguity is more expensive than the wrong person. 𝟯. 𝗖𝗿𝗲𝗮𝘁𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗯𝗼𝘂𝗻𝗱𝗮𝗿𝗶𝗲𝘀 Define what needs your approval vs. what product/tech owns. Frameworks beat avoidance. 𝟰. 𝗜𝗻𝘀𝘁𝗮𝗹𝗹 𝗮 𝗰𝗹𝗶𝗲𝗻𝘁 𝗳𝗶𝗹𝘁𝗲𝗿 One person validates requests against strategy before engineering. This protects your roadmap. 𝟱. 𝗪𝗲𝗲𝗸𝗹𝘆 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 One hour. You + PO + tech lead. Review, align, document. This alone cuts chaos by 60%. 𝗧𝗵𝗲 𝘁𝗿𝘂𝘁𝗵: You don't need to become a product expert. You need to own the outcomes. Start here: • Make one product decision per week. Build confidence through repetition. • Stop asking "what should we do?" Start saying "here's what I'm thinking - what am I missing?" Your board will forgive you for making the wrong product call. They won't forgive you for making none. Your team needs you to decide and be wrong sometimes. They can't function when you disappear. I work with executives and technical leaders ready to stop avoiding product decisions and start owning them - even when it's uncomfortable. If you're experiencing this (or reporting to someone who is), write me. I guide executives move from avoidance to confident ownership in 90 days. --- Follow me (Dan Tudorache) for leadership insights that help executives own their authority. Share this with a leader who's losing talent to decision avoidance.

Most of the time, I join projects when things are already working. >> There’s traffic. >> There’s revenue. >> There’s some kind of product-market fit. But growth is stuck. • No clear KPIs. • No idea which channel is doing what. • No system for testing or scaling. In the last few years, I’ve stepped into projects like that and helped them break through: – One DTC brand went from $0 to $579K in 4 months – A mobile app hit $400K/month ad spend in 6 months – A marketplace scaled to $1M/month in 6 months Not because we did anything crazy. But because we simplified. → Focused on the right KPIs → Built the systems to hit them → Took full ownership and executed fast Sometimes that means fixing tracking. Sometimes it means changing creative strategy. Sometimes it’s just…saying no to things that don’t matter. Because growth isn’t one big unlock. It’s a bunch of small ones - stacked the right way.

My daughter is a visionary. She sees the product. She sees the brand. She wants it all in there. More stickers. More squishies. More everything. I had to be the one to push back. Her school runs a program called TREP$ (TREPS EDUCATION) where kids build real businesses and sell at a marketplace. Her company: #CuteKits  DIY stationery kits: $5 Starter Kit. $8 Deluxe Kit Tagline: "Aesthetic. Affordable. Adorable." She did the work. Market research before building. Tiered pricing. Weekends spent handmaking inventory. But left unchecked?  She would've scoped herself into a kit she couldn't deliver and couldn't price. So I held the line. Not because she was wrong. Because she needed someone in her corner who would challenge her 𝘢𝘯𝘥 support her. Every visionary needs that person. The best founders I work with know this, too. My daughter let me be hers. And she crushed it. #ProudMom #Entrepreneurship #Leadership #CuteKits #TREPS #StartupLessons

"Was Tatiana crying on that call?" "No. It was facepalm." This exchange happened after a meeting, a senior leader messaged my peer to check if I was okay. I was. I was just exhausted from explaining AGAIN that buying Zendesk is not the end of the work. It's the beginning. We were about to purchase a new system. I was a product manager leading the entire work stream. And meeting after meeting I kept repeating: we will need to configure it for our support team's specific needs. We will need to integrate it into our existing infrastructure. This will take time. It won't happen the moment we sign the deal. They nodded. And then kept expecting magic 🧚‍♀️. I've implemented several third-party solutions and the story is always the same. People get excited about the new shiny thing that's supposed to solve all their pains and they don't think about what comes next. The software needs to be configured and adjusted to how your team actually works. Out of the box it knows nothing about you. Some features that looked great on a sales demo just don't work in your context. We were looking at live chat for 150 million players with no chat bot automation. Just think about that for a second. And no system exists alone in your infrastructure. You need integrations, and integrations bring development, security and scalability concerns with them. Sure. A tool can solve your problems. But it can't do it by itself.  And for sure, it won't do it on day one, right from the box. If you're a PM going through this right now, I've been there, more than once. And if you're in senior leadership, when your implementation lead is facepalming on calls, maybe stop and actually listen to what they've been telling you.

Arrête d'empiler des features dans ta roadmap. Pose ces 7 questions avant. Tu vas en supprimer la moitié. Je suis PM depuis 2012. J'ai analysé des dizaines de roadmaps. Le constat est toujours le même : La majorité des features n'auraient jamais dû exister. 7 questions à poser avant chaque feature : 1️⃣ C'est quoi le problème ? ↳ Pas la solution. Le problème. ↳ Formule-le en une phrase. ↳ Si tu peux pas, stop. 2️⃣ Tu as parlé à combien de personnes ? ↳ "Les users veulent ça" ne suffit pas. ↳ 10 personnes minimum. ↳ Sinon, tu développes sur du vent. 3️⃣ Il se passe quoi si on ne fait rien ? ↳ Parfois, la réponse c'est "rien de grave". ↳ Si c'est le cas, skip. ↳ Le mieux est souvent de ne rien faire. 4️⃣ On peut résoudre ça sans code ? ↳ Un process manuel peut suffire. ↳ Une FAQ, un mail, un workaround. ↳ Ne développe que si t'as pas le choix. 5️⃣ Combien ça coûte vs combien ça rapporte ? ↳ Estime le coût de dev en jours. ↳ Estime le gain attendu en euros. ↳ Si le ratio est mauvais, next. 6️⃣ Qui va l'utiliser chaque semaine ? ↳ Une feature utilisée 1 fois par an = inutile. ↳ Vise l'usage récurrent. ↳ Sinon c'est du décor. 7️⃣ Ça sert ta stratégie ? ↳ Ta roadmap doit servir ta vision. ↳ Pas juste répondre aux demandes. ↳ Si ça sert pas la stratégie, c'est non. Pose ces 7 questions systématiquement. Tu économiseras des mois de dev. (♻️ Aide les autres PM en partageant ce post) PS : quelle question (1-7) tu trouves la + critique ? — Abonne-toi pour recevoir mes conseils en Product 💡.

I’m pleased to publish my first whitepaper! Measuring What Matters is a framework I’ve been developing for measuring success from AI and Automation projects. An MIT study published that “95% of generative AI pilots delivered zero measurable financial return” last year. Not because the projects failed. Because the measurement was wrong. I’ve run into this first hand. Leadership ask “is this working?” and traditional metrics could not answer the question. So I wrote this framework. Seven dimensions of success. From delivery performance through to strategic impact. Built around a concept called “Value Moments”, the specific interactions where your product actually delivers value. The concept is built for a small team delivering value large scale projects at pace. It might not suit all businesses but I hope it can provide some great pointers and conversation starters. The full whitepaper is a free PDF on my blog. Swipe through for the key ideas are grab the full document below. https://lnkd.in/eMHWndFC Let me know what you think!

By 2026, we need to decide what AI actually is to our work. Build with it, enable it, power everything through it. Most organizations haven't picked a direction yet. Some are scaling into production. Others have engineering picking one path, ops picking another, leadership picking a third. Some are claiming they're AI-ready without the execution. The rest haven't even started. The question isn't whether AI matters. It's what role you want it to play in your next move. Clarity always changes everything: who you hire, decision-making, and how work gets done. Same applies whether you're building product, driving revenue, managing operations, or leading the company. What does AI actually mean for your work right now?

A founder asked me last week: "Should I hire a full-time CTO or go fractional?" Here is the math. Full-time CTO: • $180-250K salary + equity • 3-6 months to find the right one • Locked in whether the product pivots or not • Still needs a team under them Fractional CTO: • $10-20K/month • Starts next week • Scales up or down as the product evolves • Brings a team that already ships together One of our clients came to us after burning $400K on a full-time technical co-founder who built the wrong architecture. We rebuilt their MVP in 8 weeks for a fraction of that. They are now at $30K MRR and growing. The pattern I keep seeing: Non-technical founders do not need a CTO on payroll. They need technical leadership that matches their stage. Seed stage? Fractional. Pre-product-market-fit? Fractional. Scaling past $1M ARR with a proven product? Now hire. Stop over-hiring for where you want to be. Build for where you are.

I've been thinking about the line between cognitive offloading and cognitive surrender. We've always offloaded cognition. Calculators. Spreadsheets. Spellcheck. Nobody hand-calculates compound interest anymore. That's fine. Offloading the mechanical frees up brain space for the stuff that matters – judgment, creativity, pattern recognition. But something is different now. When AI writes your email, drafts or edits your strategy, prioritises your roadmap, codes your product... where's the line between "this frees me up to think deeper" & "I've stopped thinking altogether"? I know lots of folks thinking the same. The distinction isn't the tool. It's the level of handoff. Offloading is delegation. You know what good looks like. You could do it yourself, but the tool saves time. You review the output with judgment intact. The cognitive load shifts, but the thinking doesn't disappear. Surrender is abdication. You don't know what good looks like anymore. You trust the output because you can't evaluate it. The thinking disappears entirely. The space between those two is the question. I've seen founders use AI to draft pitch decks without understanding their own unit economics. Operators shipping features they can't debug because they didn't write the code. Not because they're lazy – because the tool made it so easy that learning felt inefficient. Here's an uncomfortable question: maybe that's fine? Maybe we're evolving past needing to understand every layer. Maybe "knowing how to evaluate AI output" is the skill, not "knowing how to do the thing." But I worry about the learning curve. You don't develop taste by watching someone else cook. You develop it by burning things, under seasoning things, figuring out what works through trial and error. If you never write bad code, how do you recognize good code? If you never draft a messy strategy doc, how do you know when the AI's strategy is actually coherent vs just well-formatted nonsense? The danger isn't that AI makes us lazy. It's that it makes us incompetent without realising it. You think you understand something because you can generate output. But understanding and generating aren't the same thing. Not even close. So here's my current heuristic, for what it's worth: - Offload the repetitive. Keep the generative. - Let AI handle patterns you already understand. Things you've done enough times that you know what good looks like. Where you can spot when the output is wrong or off. - Do the new stuff yourself. At least until you've built enough judgment to delegate it. Until you can look at AI output and immediately know if it's right or garbage. Not a rule. Just where I'm landing for now. The shift is real. The tools are incredible. But capability without judgment is just expensive randomness. We're all figuring out where that line is in real time. What's your line? Where are you offloading vs surrendering, and how are you deciding? Genuinely curious how others are navigating this.

I spoke with an AI consultant today who builds out openclaw for brands. I demo'd him Profasee Ultra. You could see the moment the entire conversation changed. Because what we built replaces what he sells. He charges about $5,000 a month to build custom AI workflows. But here’s the problem: AI does not want to be a consulting project. AI wants to be a product. Companies don’t want someone to build agents. They want to hire an agent. Log in. Pick PPC Agent. Pick Pricing Agent. Pick Inventory Agent. Turn it on. Done. No project plan. No roadmap. No invoices. No six-week implementation. Just outcomes. This is the shift happening right now: We are moving from “We build AI for your company” to “Your company runs on AI.” One is services. One is software. One charges retainers. One replaces agencies and headcount. And when this shift happens, industries don’t slowly decline. They disappear. The future is not custom AI. The future is AI that just works. Out of the box. Like hiring an employee. Just my $0.02 for the day

Most engineering budget reviews have a line for infrastructure. A line for headcount. Almost none have a line for coordination overhead, context-switching penalty, or knowledge reconstruction cost. These are the dominant cost categories in growth-stage engineering organizations — and they are invisible to the finance function. This is the core problem with structural technical debt: it does not appear on any invoice. It appears in your velocity metrics, if you are reading them correctly. I have spent time diagnosing this pattern across multiple teams. The burn rarely shows up dramatically. It is a slow degradation of the ratio between engineering input and product output. The team looks busy. The standups sound productive. But estimates drift upward quarter after quarter, incidents require three engineers instead of one, and a new senior hire still cannot ship independently after seven weeks. These are not operational problems. They are structural ones. The distinction matters because the interventions are entirely different. Operational burn responds to process changes. Structural burn does not. If perfect team execution would still result in slow delivery and fragile deployments, the problem is in the architecture, not the process. The clearest early signals are observable in tools you already run: - Feature estimation inflation trending upward across multiple teams over two or more quarters in Jira or Linear indicates coupling that makes system behavior genuinely hard to predict - More than 40% of incidents in a quarter requiring cross-service root cause analysis is a structural signal, not an operational one - Onboarding time exceeding 45 days for experienced engineers indicates the system is the obstacle, not the process - Integration-to-feature ratio above 30% in consecutive sprints means the architecture is consuming engineering capacity to sustain itself - The same two or three engineers appearing repeatedly in escalations means the system has created single points of human failure The common mistake I see: leadership treats these signals as team performance problems and responds with process improvements, clearer priorities, or additional headcount. Adding engineers to a structurally constrained system frequently increases coordination overhead faster than output. The team grows. The delivery does not. By the time runway calculations become alarming, the structural burn has typically been running for over a year. The capital was spent before anyone had language for the problem. The diagnostic conversation is worth having earlier — it takes four data inputs and 30 minutes with two engineers who work across service boundaries.

High-performance teams go a step further beyond hardwork by wasting less. The best teams I’ve led (across SaaS & crypto) were the most aligned, accountable, and decisive. Day-in-day out, we made it our goal to always eliminate friction. We avoided productivity hacks all the time. Here’s what we systematically removed: • Redundant work → multiple teams solving the same problem • Unclear ownership → no single person owned an outcome • Delayed decisions → I saw firsthand how waiting killed growth This eradication framework worked wonders by: ☑️ 2.1x increase in output ☑️+$7M in incremental ARR ☑️ Faster shipping, fewer meetings, stronger morale I want to debunk the narrative whereby teams thought speed was the by-product of pressure. Wrong!! It comes from clarity. I'm referring to clarity on: • What matters now • Who owns what • What success actually looks like Lock those three and you're good to go regarding momentum and compounding of execution. What’s the biggest source of waste on your team right now? Care to share? #ProductManagement #PMLeadership #Startups #SaaS #Crypto #Growth #Execution #Leadership #ProductStrategy #TechLeadership #BuildInPublic #Founders #Scaleups #HiringremotePM

🌟 Customer Success Starts with Quick Wins 🌟 The onboarding stage is the most critical part of the customer journey. Expectations are high, but when implementations drag, customers fall into the “trough of disillusionment”, frustration, doubt, and churn. 💡 The solution? Quick Wins. Delivering small, immediate nuggets of value within days or weeks builds trust, momentum, and loyalty. It’s not about waiting until the product is fully deployed, it’s about showing customers success right away. Why Quick Wins Matter: - Trigger positive emotions that keep customers engaged - Prevent churn and cancellations during onboarding - Drive retention, expansion, and revenue growth - Help customer teams demonstrate value to stakeholders early - Strengthen long-term partnerships Examples of Quick Wins (Construction Field): - Delivering initial site preparation or foundation work within the first week - Providing 3D models or architectural prototypes early to visualize progress - Installing a demo section of materials (e.g., façade, flooring) for client approval - Sharing a preliminary project report comparing current vs. planned improvements How to Roll Out Quick Wins: 1. Listen to your customers, identify early struggles. 2. Define one quick win that aligns with their needs. 3. Pilot with a small group, refine, and scale. 4. Build playbooks and assets for consistency. 5. Enable customers with training and content to achieve wins independently. 🔑 Quick Wins vs. First Value: - Quick Wins = emotional impact + early celebration (days/weeks). - First Value = measurable deliverable once product is live (longer). The secret to loyalty isn’t just “going live.” It’s about delivering success early and often. Quick wins transform onboarding from a risk zone into a growth engine. 👉 Start today. Deliver value fast. Build trust early. Watch your customers—and your business—thrive. #CustomerSuccess #Onboarding #QuickWins #Growth #Leadership #Construction

Authority is given. Responsibility is taken. ​I came across a great thought from Kevin Ha today that perfectly captures the "PM Paradox." ​In most tech organizations: Authority is visible. It’s the org chart. It’s who has the "decision rights" on paper. Responsibility is what happens when the server goes down at 2 AM or a feature launch misses the mark. ​As a Senior Product Manager, I lived in the gap between the two every single day. ​We are often told we are the "CEOs of the product," but the reality is: ​We don't manage the engineers. ​We don't manage the designers. ​We don't always hold the budget. ​We have zero formal authority, yet we carry 100% of the responsibility for the outcome. ​The secret to moving from a "feature factory" to a high-performing team isn't asking for more authority. It’s leaning into that responsibility. It’s about building trust and influence so that when "something happens," the team looks to you—not because of your title, but because you own the result. ​The takeaway? Stop waiting for the "right" to make a decision. Start taking the responsibility for the outcome. The authority will follow. ​#ProductManagement #Leadership #TechCulture #InfluenceWithoutAuthority #SeniorPM

Most 0→1 products don’t fail because of bad ideas. They fail because of unclear problems. In many enterprise environments, product teams face: • Multiple stakeholders • Conflicting requirements • No single definition of success Classic chaos. The turning point usually comes from one simple shift: 👉 Defining ONE core problem clearly For example: “Reduce turnaround time for global users.” That level of clarity changes everything: • Roadmaps become sharper • Decisions become faster • Adoption improves significantly • Efficiency gains follow Key takeaway: 👉 Clarity > Complexity 👉 Problem > Features If a product feels stuck, adding features rarely helps. Refining the problem almost always does. What’s one product lesson you’ve learned the hard way?

Most AI products fail not because of bad AI, but bad product thinking. Here's the framework I use to prevent it: STOP asking: "What can AI do?" START asking: "Which decision does the user lose confidence making?" That's where AI belongs. AI isn't a feature. It's a decision support system. One real example: A SaaS team built an "AI Insights" tab. Nobody used it. Why? Insights weren't the problem. The user's real problem: "I have 10 minutes before my board meeting. What do I say?" They rebuilt it as a 3-line AI board summary + one recommended action. Adoption went from 4% → 61% in 6 weeks. Same AI. Better problem definition. THE 5 METRICS THAT ACTUALLY MATTER FOR AI PRODUCTS: Forget accuracy rate. Nobody retained by a number the user never sees. ① Task Completion Rate — did the user accomplish what they came to do using the AI output? This is your north star. ② Time-to-Useful-Output — how long until the user has something actionable? Every second of friction is lost retention. ③ Override Rate — what % of AI suggestions do users edit or reject? Under 15% = blind trust (dangerous) Over 70% = no trust (useless) Sweet spot: 25–45%. Helpful AI, user still in control. ④ Return Intent (7-day) — do users come back to the specific AI feature within 7 days? Not the app. The feature. ⑤ Trust Trajectory — does override rate drop over time per user? If yes, trust is building. If it's flat or rising — your AI isn't improving or users found a workaround. THE ROADMAP MISTAKE KILLING AI TEAMS: Every roadmap item must answer: 1. Which user decision does this improve? 2. What's our trust KPI target this quarter? 3. What does the fallback look like if AI fails here? Question 3 is the one nobody asks. Every AI feature needs graceful degradation. Teams that think through failure states before launch are the ones with users 12 months later. "AI-powered" is no longer a differentiator. "AI that tells your AE exactly why a deal went cold and what to say next" — that's a differentiator. Specificity is your moat. Drop "FRAMEWORK" in the comments. I'll send you the one-page AI PM decision template I use on every product. Follow → Ammad Sattar for weekly AI PM frameworks.

A lot of CEOs think they need better product execution when the real issue is that the company has outgrown the way product decisions are being made. That is when the roadmap starts shifting too often, priorities get pulled in too many directions, and teams stay busy without creating enough business lift. What looks like a product problem is often a decision-making problem underneath it. If that sounds familiar, that is exactly the kind of issue I help leadership teams work through.

Klaviyo shipped something significant yesterday. One prompt. A complete multi-channel campaign: audience segments, copy, flow logic, timing, channel routing. All generated autonomously. Nothing goes live without human approval, but the AI builds everything. Their Co-CEO put it plainly: "The execution layer in software is moving from humans to agents." That's not a roadmap item. That's a live product called Composer, in beta now, available to 193,000 brands. Here's the part most will read past. He also said what matters is "having both the agents that do the work, AND the infrastructure that gives them the full picture." Those last three words are the entire problem. In a standard Klaviyo deployment, the AI can see: - Orders placed, emails opened, cart events - SMS engagement history - Basic behavioral signals What it cannot see: - Open support tickets in Zendesk - Active deal stages in Salesforce - Real-time inventory levels at SKU depth - Subscription modification reasons So Composer will autonomously build campaigns based on a partial view of every customer. The customer who filed an angry support ticket yesterday? Composer sees "recent purchaser." It queues a "Buy it again" campaign. It looks correct. It goes out. We broke down exactly what "the full picture" requires technically and the 5-stage infrastructure build sequence for getting there. https://lnkd.in/gWe3X4jV #MarketingTechnology #AI #Klaviyo #MarketingOps #DataInfrastructure

PLG didn't fail. Most companies just weren't honest about whether their product was built for it. The ones it worked for had one thing in common: users reached the aha moment on their own, fast, without a conversation. Most B2B products don't do that. They need context, onboarding, and a real human somewhere in the journey. So teams adding sales back aren't course-correcting from a failed strategy. They're finally being honest about what their product actually requires. Have you seen PLG work in a product that genuinely needed onboarding? Curious what made the difference.

My co-founder and I run a startup product studio — two people, two products (a healthtech company and RoleForge.ai, an RPG gaming platform), more in the pipeline. People ask how we manage it all. AI has changed what "two people" means. We have 20 AI agents handling business development, financial modeling, marketing, research, analytics, content, product design & engineering. We orchestrate them like a real team: defined roles, autonomy, feedback loops, accountability. They strategize with us, hand off work to each other, and proactively fix blockers. The output is hard to explain. So let me put numbers on it: Every hour of agent time produces 5-8 hours of senior human-equivalent work. We run 40-60 agent hours/week. At a conservative 5:1 ratio, that's 200-300 hours of senior-level output weekly. To match it, you'd need ~20 people: a biz dev lead, finance lead, marketing lead, market analysts, email marketer, content writer, copywriter, community manager, VP engineering, solutions architect, ML/AI engineers, engineering manager, full-stack devs, DevOps engineer, UI/UX designers, QA/security engineers. Human team: $2.5−3.5M in salary. North of $4M fully loaded + equity. Our AI team: ~$5K/month. They work 24/7 and don't spend 40% of their time in meetings about meetings. Not a marginal improvement. A fundamentally different operating model. Three layers we're learning from: AI runs the business. Agents refine growth strategy, analyze customers and demand, align product to market, write content, manage community, model unit economics. We set vision and steer. The leverage isn't 2x or 5x — it's a category shift in what a small team can attempt. AI builds the product. Architecture, frontend, backend, API, testing, infra — across two products today, more tomorrow. "Idea this morning, live in production this afternoon" is becoming normal. AI is the product. RoleForge uses AI to narrate game adventures, remember choices across weeks-long sessions, run living RPG worlds. The AI isn't a feature. It IS the experience. When AI isn't assisting a workflow but IS the workflow, design challenges are completely different. The hardest layer isn't tech. It's organizational — building roles, trusting AI with real decisions, designing handoffs between AI and human judgment. These are management problems, not infrastructure problems. Most companies will hit them as they scale AI. The cost isn't even the best part. It's the speed. 20 humans means meetings, Slack, misalignment, PTO. Our AI team executes in parallel, shares context instantly, never loses the thread. We haven't figured it all out. But building all three layers at once is compressing our learning curve fast. What I am sure of: the bottleneck is us — the humans — not them. What's the first non-coding task you handed AI that made you think "This changes everything"? http://roleforge.ai #AI #BuildInPublic #FutureOfWork

activation is tracked only 34% of the time across plg companies. that's not a tooling problem — it's a strategy problem. most teams can't measure what they haven't defined. here's what that blindspot costs you in practice. at miles education, we built miles masterclass as a top-of-funnel plg product. the temptation was the same as everywhere else: drive traffic, hit sign-up targets, declare success. we ignored that trap. instead, the first design question was: what does the 'aha moment' look like for a ca/cma aspirant who just landed in our funnel? we built the entire persona-driven, interactive mentorship experience around that single answer — not around ad spend, not around reach. user activation is a one-time event — the critical moment when a user experiences the value of a product firsthand. you can't reverse-engineer it after the fact. you have to architect for it before you write a single line of acquisition copy. the result: 30k+ users onboarded. ₹20 cr+ in revenue generated — in the first 12 months — from a product that was designed as a magnet, not a megaphone. activation is the foundation of everything else. if users don't activate, they won't retain, convert, or expand. scaling acquisition spend into a leaky activation bucket is just burning money faster. the sequence matters: instrument the aha moment → validate activation → then scale traffic. most plg teams do it backwards. they fund the top of the funnel before they've fixed the middle. and pql adoption remains low

STARTUP FAILS NOT BECAUSE OF THE PRODUCT - BUT BECAUSE OF THE PEOPLE Most founders spend time thinking about: Product 📦 Growth 📈 Strategy 🎯 But companies that last often get one thing right early: Who they build with. 🤝 Here’s the controversial truth: Talent alone rarely builds great companies. People leave when: Their ideas aren’t heard 🔇 Their work lacks meaning ❓ They feel like employees, not partners But when alignment exists… everything changes. People start to: ✔ Take ownership 💡 ✔ Solve problems early 🚀 ✔ Think long-term 📊 Many think culture means: Fancy office 🏢 Perks 💰 Events 🎉 But culture is simpler: Culture is the daily signal that people matter. Here’s the debate: 🔥 Product & Growth first ⚡ People & Culture first Which one builds stronger companies? 👇 #Founders #StartupLife #LeadershipMindset

Why The Brand With Fewer Users Always Beats The Brand With More : I watched a brand with 10,000 users crush a brand with 500,000 users. Same market. Same product category. Same year. Here’s exactly how it happened 🧵👇 Everyone is playing the more game. More users. More installs. More reach. More spend. Average influencer marketing budgets grew 171% year over year and nearly two-thirds of that came from brands cutting traditional paid channels to fund creator campaigns. The budgets are moving. The strategies aren’t. Most brands are just buying more volume. More of the wrong people ❌ Here’s the math nobody shows you 👇 Brand A: 500,000 users. Average spend per user: $3. LTV: $3. Brand B: 10,000 users. Average spend per user: $150. LTV: $150. Brand A revenue: $1.5 million Brand B revenue: $1.5 million Same top line. Completely different business. 🤯 Brand A needs to keep acquiring forever to survive. Brand B compounds — because high LTV users refer, return, and trust. Volume is a treadmill. Value is a flywheel. 🎯 The data that should change every acquisition strategy 👇 Gartner is clear: 80% of future revenue will come from just 20% of existing users. That’s not a theory. That’s a pattern playing out across every category — B2C, apps, crypto, EdTech, fintech. 92% of consumers trust peer recommendations over branded content — and that gap widens every single year. Your top 20% aren’t just your best customers. They ARE your marketing department. If you treat them right The 2026 shift happening right now 👇 The brands winning in 2026 treat creators as strategic inputs to their paid ecosystem — sourcing content, lowering CAC, improving performance, and compounding efficiency across channels. 67% of podcast listeners made a purchase directly because of a podcaster. 85% took brand action after listening. That’s not reach. That’s trust converting at scale. The question isn’t how many people you can reach. The question is: how deeply can you reach the right ones? 💡 Most brands will keep chasing volume in 2026. They’ll celebrate install milestones. They’ll post about hitting 1 million users. They’ll quietly wonder why revenue isn’t following. Meanwhile the brand next door with 10,000 users? Is building something that lasts. 📈 Stop counting users. Start counting believers. That’s the only metric that matters in 2026

One of the challenges we have in Revenue today: listening to the market and getting regular user insights without resource-heavy user interviews. We've been working on this for our core persona: “Mike,” a B2B sales professional. I want to know (in his words): - How he talks about PhantomBuster - How he perceives AI and automation in his job - Which tools are giving him a headache - What are his biggest pains today This kind of user centricity is what ultimately drives leads, conversion and retention. User interviews are incredibly valuable, but they don’t scale. So we turned to a source that's becoming critical for GTM: Reddit. Using PhantomBuster’s new Reddit Phantoms, we: - Ran targeted searches across relevant subreddits - Extracted posts and comments over a specific time range - Summarised everything with Claude to surface patterns and verbatims Within a few minutes, we had unfiltered, high-volume voice-of-customer data. Here’s how we’re turning that into revenue impact: → Landing pages: using the language Mike uses → Product: prioritising features tied to real pains → Content: focusing on topics already top-of-mind This approach lets us make data backed decisions and keeps GTM strategy aligned with what are users are saying.

What if sending people to your website is actually killing your sales? We ran Facebook Ads for a brand. Website clicks. Good traffic. Barely any sales. Most people would blame the product. We blamed the strategy. Here's what we found out 👇 The product wasn't the problem. The customer's mindset was. People weren't ready to buy. They had doubts. Questions. Hesitations. And a "Buy Now" button doesn't answer any of that. So we made one shift We changed the CTA from website to messages. Sales jumped. Instantly. Not because the ads got better. Because we met the customer where they actually were. We started clearing doubts. Explaining the difference. Telling them exactly why our product over everything else out there. That conversation became our biggest competitive edge. And those leads? They didn't just buy once. They became people we could follow up with, nurture, and sell to again. The real lesson: Before you run ads ask yourself: Does my customer need to click, or do they need to be convinced first? Understand where your buyer stands. Then meet them there. The results will follow.

There's a stage every SaaS founder hits and almost no one talks about it honestly. You have product-market fit. You have revenue. You have a team. And somehow, your growth has flatlined. Not because anything changed with the product. Not because demand dried up. Because the infrastructure underneath the business was never built to scale. Your acquisition team is duct-taped together. Your CRM doesn't reflect how your team actually works. Your data sits in three tools that don't talk to each other. And your AI strategy is a Zapier workflow someone built on a Friday afternoon. This is the plateau. Most founders think they need better people at this point. What they actually need is a rewired system. The businesses that break through aren't out-spending anyone. They're out-compounding everyone. Carve out some time this evening to take a step back and ask: what does our current growth stack look like, honestly? If this gives you anxiety, you're in the right place. Reach out today for a free consultation. 📩 team@misticktechnologies.com

Everyone’s shipping something these days - dashboards, copilots, “insight engines,” whatever the buzzword of the week is. It all looks smart - clean UI, confident language, charts pointing up or down like they mean something. Most of it has never survived contact with reality. We’ve replaced “does this work?” with “does this look right?” I fell into the same trap. Building logic. Defining metrics. Everything is neat, structured and impressive on paper. Then one question killed it: did this change anything? Silence. Because detecting patterns isn’t the hard part anymore. Even interpreting them isn’t that impressive. The only thing that matters is: Did someone do something differently because of it and did that actually improve anything? If your “AI insight” doesn’t make someone act differently and doesn’t measurably improve anything then it’s just a well-designed opinion. So I’m resetting how I build. Not scaling, not polishing, not adding features to impress others. One loop only: Detect → interpret → recommend → verify against reality. If that loop doesn’t hold, the product is fiction. Most tools today aren’t solving problems. They’re describing them more eloquently. And that’s not progress.

The profile of a successful executive is shifting...and FAST For years, we’ve valued operators. Leaders who drive execution, manage teams, and deliver outcomes through discipline and rigor. That still matters, but it’s no longer enough. AI is changing the game. The leverage point is moving from doing to designing how work gets done. The next generation of effective executives will be: • Orchestrators who align humans and AI into cohesive systems • Systems thinkers who design flows of work, decisions, and information • Force multipliers who scale output without scaling headcount The question is no longer...Can you run the play? It’s...Can you design the system that runs better plays, continuously? Real-world example From: A traditional sales leader focuses on pipeline reviews, coaching reps, and hitting quota. To: A modern sales leader redesigns the system: - AI qualifies and prioritizes leads in real time - Outreach is partially automated and continuously optimized - Customer signals feed directly into product and marketing loops - Reps focus only on high-value conversations Same goal. Completely different leverage. A metaphor: - The old model is a conductor leading a symphony - The new model is a composer + producer building the entire sound system One leads people to perform, the other designs the environment where performance compounds. That’s the shift. Operators will always be valuable, but orchestrators and system designers will define the next generation of leadership advantage #modernorgnization, #modernleadership, #systemsthinking, #leadershipdevelopment

Some people will see this dashboard… and think: “nice numbers.” That’s it. But what they don’t see is what it took to get here. Because results don’t start from numbers. They start from zero. No clarity. No guaranteed wins. Just questions, testing, and uncertainty. What actually works? Which product will move? What kind of content will convert? Which creator will deliver results? And most of the time… The first answers are wrong. So you test again. You analyze. You adjust. You rebuild. Behind every clean dashboard, there are: • failed product tests • content that didn’t perform • creators that didn’t convert • strategies that had to be reworked But that’s where the real edge is built. In the process. In the research. In the strategy. In the execution no one talks about. Because scaling is not luck. It’s alignment. When the right product, meets the right content, with the right creators, under the right system— That’s when things start to move. And slowly… Numbers start appearing. For some people, they’re just numbers. But for the ones who’ve built it— They represent: decisions, discipline, failures, and consistency. That’s the story behind every dashboard.

The role of a CMO through Start-up lifecycle stages: PRE-SEED / SEED: You are juggling knives and babies on a tightrope over a canyon filled with snakes and alligators. Money is short, time even shorter, human resources do not exist. ou ask co-founders for support, everyone does everything, at all times. Your main goal is to prove enough traction with the limited resources at your disposal to secure a follow up investment. Be ready to fail. Most startups die in this phase because they just dont have enough time to figure out their product market fit, channel mix, or understand all friction points before the runway runs out. SERIES A: Series A gives you a measure of security and lot more resources. You turn from solo crazy scientist who lives of mania and caffeine into doctor Frankenstein with a full fledged lab and minions. You give up a larger part of your hands-on responsibilities and your main goal is not to wiggle out some resemblance of success but rather to start finding patterns, create repeatable processes, experiment and build something which can live. You still dont have "Fuck you" money, and larger investment needs to count, BUT you have leeway to run more experiments, fail more often, test more channels until you are happy with a mix that works. Profitability is mostly still far off, but you are closing into the break even territory. You become the advocate between your minions and management and try to feed the lab with resources you need to have to succeed. Internal politics become a part of your day to day and stakeholder management becomes much more important. SERIES B (And Above) If you are not scaling up at this point in time something went terribly wrong and your investors have enormous belief that at some point they might see their money back.  The chaos should be gone, results predictable, internal responsibilities and KPIs clearly defined. You should by now have the right internal and external team, SOPs, a knowledge base and your work is less operative. You conduct an orchestra and make sure that stakeholders are happy. What you are trying to build is a marketing engine which generates significant profit. One of the more painful responsibilities is finding obsolete elements, cutting costs, becoming more and more efficient with less and less input so that the financial reporting numbers look awesome for an upcoming exit event. You are no longer "one of the guys / gals" in the marketing team, but rather a distant persona in charge of giving directions and making the hard decisions. Most stress you have in your day to day comes from board meetings and political shenanigans.

Some people will see this dashboard… and think: “nice numbers.” That’s it. But what they don’t see is what it took to get here. Because results don’t start from numbers. They start from zero. No clarity. No guaranteed wins. Just questions, testing, and uncertainty. What actually works? Which product will move? What kind of content will convert? Which creator will deliver results? And most of the time… The first answers are wrong. So you test again. You analyze. You adjust. You rebuild. Behind every clean dashboard, there are: • failed product tests • content that didn’t perform • creators that didn’t convert • strategies that had to be reworked But that’s where the real edge is built. In the process. In the research. In the strategy. In the execution no one talks about. Because scaling is not luck. It’s alignment. When the right product, meets the right content, with the right creators, under the right system— That’s when things start to move. And slowly… Numbers start appearing. For some people, they’re just numbers. But for the ones who’ve built it— They represent: decisions, discipline, failures, and consistency. That’s the story behind every dashboard.

As founders, we often focus on scaling products, teams, and revenue - but real leverage comes from scaling ourselves. I’m currently doubling down on self-development, with a strong focus on public speaking and communication. Because raising capital isn’t just about numbers - it’s about storytelling, clarity, and conviction. Every pitch is an opportunity to transfer belief. Every presentation is a chance to lead. The better you communicate, the faster you grow.

Agile solved many problems that Waterfall couldn’t. But let’s be honest — it also introduced new ones. • Too many ceremonies, less real output • Limited long-term architecture thinking • Dependency chaos in large programs • “Agile in name, Waterfall in disguise” So naturally, execution models are evolving again. 🚀 What’s emerging beyond Agile? 🔹 Product Operating Model (Product over Projects) Organizations are moving from delivering projects to owning products. Persistent teams, clear ownership, and a strong focus on business outcomes over feature delivery. 👉 “You build it. You run it. You improve it.” 🔹 DevSecOps (Next evolution of DevOps) Security is no longer an afterthought or a checkpoint. It is now embedded into the delivery pipeline, especially critical for regulated industries like Insurance and BFSI. 👉 “Security is no longer a gate — it’s part of the flow.” The real shift is this: We are moving from speed-centric delivery → value-centric execution. Agile isn’t going away. It’s being redefined. The question is no longer: “Are you Agile?” But: “Are you consistently delivering measurable business value?” #AgileEvolution #ProductMindset #DevSecOps #DigitalTransformation #Insurance #BFSI #Leadership

If your growth is stalling despite a strong pipeline and a category-defining customer base… you don’t have a GTM problem. You might have a product leadership gap.

Most teams treat cancellation flows like an administrative necessity — a boring page you bolt onto the back of your checkout. That’s a missed growth lever. The moment someone tries to leave your product is one of the most information-rich, emotionally charged interactions you’ll ever have with a customer. Treat it like product research, not damage control. Tactical approach you can implement this week: 1) Instrument intent. Track the precise event that led to the cancellation (usage drop, failed billing, invoice dispute, feature gap). Don’t just log “cancelled.” Capture context: last login, key feature usage, time since onboarding, NPS or support interactions. Those signals tell you whether this is a retention problem, a value mismatch, or a billing/UX failure. 2) Segment before you save. Not all cancels are equal. Segment by true intent: churn-risk (low usage), transactional cancels (billing), and exploration cancels (trial users who didn’t find value). Each segment needs different interventions — education and onboarding for explorers, flexible plans for transactional, re-engagement/playbook for at-risk users. 3) Use the cancel flow as an experiment engine. Offer options, not just a discount: pause, downgrade, invoice credits, concierge onboarding, feature-specific trial, or export data. A simple split-test (pause vs. discount) will tell you which retains more true customers. Track cohort retention, not immediate reversals — a discount that buys one month but loses long-term loyalty is a false win. 4) Automate personalization with caution. Use ML to predict churn and LLMs to summarize free text reasons and draft tailored messages — but keep human oversight for high-value accounts. AI can scale relevant offers (timing, channel, copy) but don’t use it to trap customers or hide cancellation paths. That destroys trust and increases negative word-of-mouth. 5) Close the loop. Follow up with people who left: short surveys, 15-minute exit interviews, or an automated “we’re sorry” series that asks what would make them return. Use these insights to prioritize roadmap fixes, not just marketing tactics. Controversial bit: don’t reflexively make cancelling hard. Most friction only delays churn and destroys brand equity. Make it easy, learn quickly, and use the data to build a product people don’t want to leave. That’s the real retention play. What’s one change you’ve made to your cancel flow that actually moved the needle?

A 20-minute wait time dropped to under 30 seconds, without AI. At Moxie Pest Control — a field services company handling 9,000 customer calls a day — Raj Mehta leads product and technology and draws a distinction most operations leaders never make explicit. His rule: if a process has defined steps with no meaningful deviation, it's a traditional automation problem. Scripts and rules are sufficient, you don't need a language model. If a process involves genuine variability — judgment calls, unpredictable inputs, diverging paths — that's where AI has a place. The lead response process at Moxie illustrated this exactly: A customer submits details on the website. The lead arrives as an email. Someone manually pulls the information, enters it into the CRM, assigns it based on routing logic. Every step defined. No judgment required. Pure automation. Response time dropped from 20–25 minutes to under 30 seconds — a change that contributed to a 5% conversion improvement in a business where virtually every customer is a recurring subscription. The highest-impact project in the transformation involved fixing a manual handoff nobody had gotten around to automating. "Focus in on the data, look at tiny improvements that can be scaled," Mehta told Ashley Stirrup, host of the Experimentation Edge. "Eventually they compound and start becoming pretty significant achievements." Go through your operations and separate the two categories. Which processes have defined steps sitting there unautomated? Those are almost certainly faster to fix and more impactful than any AI project currently on the roadmap. If you've been the person making this call inside an operations business — the one who knew which problems needed a script and which ones actually needed a brain — that specific judgment is exactly what the Experimentation Edge was built to capture. Apply to share yours: https://lnkd.in/ekXfcdMZ

They spent $240K on a website redesign. Launched it with champagne. Conversion rate: 1.1% Same as before. Six weeks later, we hit 2.8%. Here's what actually worked. The Setup: Series A SaaS. 22,000 monthly visitors. Beautiful new site. Terrible conversion: 1.1% The CEO: "Do we need another redesign?" Me: "You need different words, not different pixels." Week 1: The Clarity Rewrite Old hero: "Empower your team with intelligent collaboration solutions." New hero: "See which features customers actually use. Stop building what they ignore." Conversion: 1.1% → 1.6% Week 2: The Trust Placement Great testimonials existed. Buried on page 3. Moved ONE customer quote above the fold: "Reduced roadmap guesswork by 80%" — Sarah Chen, VP Product Added photo + logo. Conversion: 1.6% → 1.9% Week 3: The Form Surgery Trial form had 8 fields. Cut to 2: Email + Company. "But we need qualification data!" "Get them in first. Qualify later." Conversion: 1.9% → 2.3% Week 4: The CTA Cleanup 5 competing CTAs causing analysis paralysis. Killed everything except "Start Free Trial." Conversion: 2.3% → 2.5% Week 5: The Mobile Fix 67% of traffic was mobile. The "Start Trial" button was cut off on iPhone. Fixed the viewport. Conversion: 2.5% → 2.7% Week 6: The Value Ladder Rewrote features from: Feature → Feature → Feature To: Pain → Outcome → Feature ❌ "Advanced analytics dashboard with customizable widgets." ✅ "Your team argues about which features to build. See usage data that ends the debate. Analytics show what customers actually do vs. say." Conversion: 2.7% → 2.8% The Numbers: Before: 22,000 visitors 1.1% = 242 trials/month ~$87K monthly revenue After: 22,000 visitors 2.8% = 616 trials/month ~$221K monthly revenue Annual impact: $1.6M From traffic they already had. The Truth: We didn't make it prettier. We made it clearer. Beautiful sites with vague messaging: 1–2% Average sites with clear messaging: 3–5% Design gets attention. Clarity gets conversions. Before you spend $200K on a redesign: Show your homepage to 10 strangers. 5 seconds each. Ask: "What does this company do?" If they can't tell you clearly, your paying customers can't either. Fix the words first. Redesign later. Or don't redesign at all. When's the last time you tested your homepage clarity?

Last week I had back-to-back conversations with two teams at similar-stage companies with the same mandate: "Figure out AI." Team A carved out a few sprints to explore AI features on top of their existing product. Smart, low-risk, and they'll probably ship something useful by Q3. Team B did something uncomfortable. They froze a chunk of their roadmap, pulled senior engineers off committed projects, and asked a harder question: If we were building this from scratch today, without the constraints of the existing product, what would we actually build? Six months from now, Team A will have incremental improvements. Team B might have nothing... or they might have something their competitors can't catch. That's the real tension I keep seeing between AI mandates and boots-on-the-ground implementation. It's not that people don't believe in AI. It's that nobody changed the expectations. You're still on the hook for the roadmap you committed to before the ground shifted. So teams hedge. They bolt AI onto existing workflows, call it innovation, and move on. The leaders who break through share a common trait: they're willing to trade short-term predictability for long-term reinvention. They treat fast failure as signal, not setback. I'm not going to tell you that's the only path. But I know which bet I'm making. What about you?

Negative feedback is my favorite kind of feedback. (Not always in the moment, but give me a minute.) Because if someone is confused by your documentation, they’re not just struggling with your words, they’re struggling with the product. And if they are struggling with the product, that means they aren't using it and getting value from it. And that’s the whole job, right? Clarity. Every “this doesn’t make sense” is a roadmap. Every frustrated user is a signal. Good documentation doesn’t avoid feedback. It improves because of it. Let that sink in. So yes, please tell me what’s wrong because I really do want to know! That’s how it gets better. Agree? #feedback #gooddocumentation #clarity

🚨 Existe um gap curioso vindo com a IA (e cada vez mais perigoso) em Product Management. 👤 De um lado, Product Managers excelentes em priorização “clássica” manual, reuniões de alinhamento e feeling de negócio. 🤖 Do outro, ferramentas e times que já usam IA para priorizar em tempo real: analisando dados de uso, feedback, receita, esforço técnico e até tendências de mercado automaticamente. ❌ Poucos conseguem transitar bem entre os dois mundos. E é exatamente aí que muitas roadmaps perdem relevância — ou pior, viram uma lista de itens sem valor que ninguém usa. Porque não basta: 📊 Priorizar bem “no feeling” → se os dados reais mostram outra direção 🧠 Priorizar só com IA → se falta contexto de negócio e visão estratégica que a máquina ainda não capta 🚀 Os melhores resultados que vi aconteceram quando conseguimos conectar de verdade: Estratégia humana + Inteligência artificial com clareza de: 🎯 Objetivos de negócio claros (o que realmente move a agulha?) 📈 Dados confiáveis e atualizados (não só vaidade) 👨‍💼 Decisão final sempre com o PM como piloto. 💡 Priorização em 2026 não é mais só arte. E também não é só automação. É a capacidade de usar IA como copiloto poderoso, mas sem abrir mão do julgamento humano que transforma dados em valor real. ⚡ Produto não é só decidir o que fazer. É decidir o que fazer com base no que realmente importa, mais rápido e com mais precisão. 🤔 Você ainda prioriza majoritariamente com planilhas e reuniões… ou já está deixando a IA influenciar (ou até liderar) suas decisões de roadmap? #ProductManagement #ProductStrategy #Priorizacao #Roadmap #IAnoProduto #ProductDiscovery #GestaoDeProdutos #TransformacaoDigital #AIProductManager #ProductOwner

El problema no es que los desarrolladores no puedan liderar producto. Es que nadie les ha enseñado cómo hacerlo desde su lenguaje. El contenido de producto está diseñado para perfiles no técnicos. Frameworks para quien no construye. Teoría para quien no entiende el sistema. Pero hay un perfil que cambia eso. 👉 El Product Engineer no necesita que nadie le traduzca el roadmap. Lo lidera desde dentro. Por eso hemos organizado un webinar gratuito donde mi compañero Jesús Maldonado Fernández y yo explicaremos: — Cómo dar el salto a Product Engineer sin dejar de ser técnico — Qué errores comete todo el mundo en esa transición — Por dónde empezar si estás en desarrollo y quieres moverte hacia producto Además, los asistentes en directo tendrán acceso a códigos de descuento exclusivos en Alpha Product. 📅 22 de abril — 19:00 CEST 🔗 https://lnkd.in/eE3Wx43q #ProductEngineer #ProductManagement #SoftwareDeveloper #TechCareer #AlphaProduct

Você já parou para pensar o que faz um produto sai de uma ideia para ENTREGAR VALOR para o cliente e para o negócio? Assim como em muitas organizações, estou no desafio de contribuir para mais um planejamento trimestral, onde a ideia é sairmos com um plano de execução claro para o segundo TRI de 2026. A resposta da pergunta acima não está apenas na tecnologia, mas na sintonia fina entre duas figuras centrais: o Product Management (PM) e o Product Owner (PO). Entender as competências de cada um deles é o primeiro passo para entregar valor real. O PM é o estrategista que olha para o horizonte. Ele precisa de uma soft skill mestre: a Visão Sistêmica. É a capacidade de conectar o que o cliente deseja com o que a diretoria espera. O PM não foca no "como" o código é escrito, mas no "porquê" aquela funcionalidade deve existir. Ele usa o Storytelling para convencer stakeholders de que aquele roadmap é o caminho para o lucro e para a satisfação do usuário. O PO é o guardião do valor no dia a dia. Enquanto o PM olha para a estratégia, o PO olha para as dores e necessidades dos clientes de perto. Sua competência essencial é a Comunicação Assertiva. Ele traduz a visão macro do PM em algo que o time consiga executar sem dúvidas. O PO precisa de uma resiliência de ferro e Poder de Negociação para priorizar o que realmente importa, garantindo que o esforço do time não seja desperdiçado em detalhes irrelevantes. E como tudo isso é concatenado? É durante este evento que chamamos de PI Planning que essa parceria ganha vida. A PI Planning é o momento onde a estratégia e a execução se abraçam. É um evento onde as pessoas saem com um plano alinhado. Sem o entrosamento dessas duas competências, a PI Planning vira apenas uma reunião cara e confusa. O PM entra com a clareza do "Porquê". A influência do PM é sentida na apresentação da Visão. Ele precisa inspirar os times, mostrando o impacto real do que será construído. Sua competência de liderança é o que mantém todos os "trens" correndo na mesma direção, evitando que a energia da empresa se pulverize em iniciativas que não se conversam. O PO entra com o refinamento do "O Quê". Se o PM inspira, o PO viabiliza. Sua influência na PI Planning é garantir que as histórias de usuário tenham Critérios de Aceite claros. Ele atua removendo dúvidas de negócio em tempo real. Se o time hesita sobre uma regra de negócio, o PO decide. Ele é o ponto focal que traz segurança para que os desenvolvedores planejem com confiança. O segredo não é a divisão de tarefas, mas a união de competências. Quando um PM estratégico e um PO tático trabalham em simbiose, a PI Planning deixa de ser burocrática e vira uma vantagem competitiva. O resultado? Clientes satisfeitos com soluções que resolvem seus problemas e um negócio que cresce com escalabilidade e previsibilidade. E na realidade de vocês? Vocês sentem que a estratégia e a execução estão falando a mesma língua? Vamos trocar ideias e experiências nos comentários!

🎉 Excited to share that I just earned my badge in Generative AI Overview for Project Managers! At the principal and program level, we're constantly juggling complexity; aligning stakeholders, managing dependencies across workstreams, and making strategic decisions with incomplete information. Generative AI is changing the game for all of it. This course sharpened my thinking on how AI can elevate the work we do at a senior level: 🔹 Synthesizing large volumes of project data into executive-ready insights 🔹 Accelerating roadmap development and scenario planning 🔹 Streamlining cross-functional communication and reporting 🔹 Freeing up bandwidth to focus on strategy, not just execution As Principal PMs, Program Managers, and Product Managers, we set the standard for how our teams operate. Embedding AI literacy into our practice isn't just a nice-to-have, it's a competitive edge. If you're operating at a senior PM level and haven't explored how GenAI fits into your workflow, now is the time! #GenerativeAI #ProjectManagement #ProgramManagement #ProductManagement #PrincipalPM #Leadership #AI #ContinuousLearning

Most product roadmaps are wish lists. 47 items. Engineering can deliver 12. Sales wants 20. The CEO just added 3 more from a conference. The result? Features get built by whoever yells loudest, not by what moves revenue. We wrote the playbook on how mid-market companies fix this: https://lnkd.in/de9j5kQB