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"It is insane. Executives met at an offsite. They tried to create OKRs. All the OKRs were obviously AI generated (at the offsite)—looked ok, until further analysis. All that great context lost, translated into a bunch of slop Then those AI generated OKRs somehow made it into AI generated 1-pagers, PRDs. Then those AI generated PRDs became AI generated stories and tasks. Which become AI generated markdown files. Which become AI generated PRs...."

Once a SaaS company raises money, pressure increases, and product focus decreases. (Here is why) Suddenly: • Sales wants enterprise features • Marketing wants more positioning angles • Investors want expansion • The roadmap triples Funded SaaS doesn’t fail because of a lack of resources. It fails because product discipline disappears. The best teams protect: – Clear positioning – Ruthless prioritization – Single north-star metric – Strong product leadership Use your leverage wisely. Follow along to level up your SaaS product and growth strategy.

𝙋𝙧𝙤𝙙𝙪𝙘𝙩𝙤 𝙨𝙞𝙣 𝙚𝙨𝙩𝙧𝙖𝙩𝙚𝙜𝙞𝙖 𝙚𝙨 𝙨𝙤𝙡𝙤 𝙙𝙚𝙨𝙖𝙧𝙧𝙤𝙡𝙡𝙤. Puedes tener: • Un equipo técnico sólido • Un backlog perfectamente organizado • Sprints impecables • Releases constantes Y aun así… no estar construyendo un gran producto. Porque sin estrategia, solo estás entregando funcionalidades. La diferencia está en responder antes estas preguntas: 🎯 ¿Qué problema de negocio estamos resolviendo realmente? 📊 ¿Cómo mediremos impacto más allá de métricas de vanidad? 👥 ¿Qué segmento genera mayor valor? ⛔ ¿Qué vamos a decidir NO hacer? He visto productos crecer en complejidad… pero no en impacto. Y casi siempre el problema no es técnico. Es estratégico. Un roadmap no es una lista de tareas. Es una declaración de apuestas. Cada decisión de producto es una inversión de tiempo, talento y presupuesto. Y cuando lideras producto, no gestionas features. Gestionas riesgo. Gestionas foco. Gestionas dirección. Ahí es donde se define la madurez de un Product Manager. 🚀 Ahora te pregunto: ¿Qué pesa más en tu experiencia: la ejecución impecable o la estrategia clara? #ProductManagement #Estrategia #Liderazgo #DesarrolloDeProducto #TomaDeDecisiones #CrecimientoProfesional

Excited to share that I’ll be speaking at the Break the Pattern: 2026, International Women’s Day Virtual Summit hosted by Your Next Win, and by the awesome Angelina Millare! 👉 My session is titled: “When Everyone Can Build… Who Decides What’s Worth Building?” AI has lowered the barrier to building almost anything. The real leverage now isn’t just execution. It’s judgment. - Who defines the problem? - Who sets the constraints? - Who weighs the tradeoffs? - Who decides what shouldn’t be built? Contrary to the hype, the best PMs I see aren't trying to take over the engineer's role, they are using AI to make their entire Product Development Lifecycle more efficient, more visual, to help with easier decision making and alignment, and more clear communication. ✅ That’s the conversation I’ll be leading — especially as the PM role evolves in this AI-first era. 🔥 This global summit brings together speakers from 7 countries who are helping people rethink career paths, experiment with AI, and break patterns that keep them stuck. 🗓 On-demand sessions begin March 15 ⚡ Good Vibes Buildathon: April 1–18 🌍 Live summit: April 17–19 🎤 My talk: April 18th 💙 If you’re thinking about how AI is reshaping careers and leadership, this will be a powerful set of conversations. The sign up page isn't open yet for my talk, but once it is, I hope you'll join us for the conversation. Conference page link in the comments 👇 #IWD #yournextwin #productwithpri #leadership

One thing I’ve been noticing in product teams is how easily discovery gets compressed when delivery pressure increases. In theory, discovery is where teams spend time understanding the problem, validating assumptions, and exploring options. In practice, once timelines tighten or stakeholders push for movement, that phase often gets shortened or skipped. The team moves faster, but with less clarity on what actually needs to be solved. Some organizations try to protect discovery with dedicated phases or frameworks. Others integrate it directly into delivery and rely on tighter feedback loops. Both approaches can work, but they create very different tradeoffs in terms of speed, risk, and rework. Curious how other product leaders are balancing discovery and delivery when pressure to move quickly increases. #ProductLeadership #ProductManagement #Leadership

Chegou a hora de construir minha própria história! Nos últimos anos fui CPTO e Fractional CPO em empresas de tecnologia que já tinham time, produto e receita - e queriam fazer visão virar execução - mais recentemente na Letrus e Gruppy. Cortamos portfólio pela metade sem perder receita. Reduzimos cycle time em até 5 vezes. Substituímos operação humana por IA e economizamos milhões por ano. Fechamos o maior contrato B2G da história de uma das empresas. Não com mais gente. Com sistema melhor. Agora estou formalizando esse trabalho com um nome, um endereço, muito foco e disposição. Se o seu negócio já tem time, produto e receita — mas a visão não está chegando na execução, o time entrega sem mover resultado, ou a IA ainda não está onde deveria estar — o Rumbo foi feito para isso.

Most products don’t fail. They dilute. They usually start with a clear idea, a real opportunity, and a capable team. Then nothing actually breaks. The roadmap fills in, constraints show up, and decisions get made to keep things moving. Each decision makes sense. An engineering tradeoff, a feature adjustment, a simplification to hit cost. All reasonable. But they’re made locally. So the product doesn’t break—it drifts. The original intent gets softer with every step. Harder to explain, harder to differentiate, harder to choose. From the inside, it still looks like progress. From the outside, it feels weaker. That’s the dangerous part. Because it doesn’t look like failure. It looks “good enough.” Execution doesn’t fix this. It amplifies it—faster and more expensively. The issue isn’t effort. It’s translation. If no one is actively holding what the product is supposed to mean, the system will optimize for convenience. And convenience erodes intent. Before pushing forward, you need to see where that drift is already happening. Context → Narrative → Reality. That’s DEFI.

64% of companies say AI is enabling innovation, yet only 39% report an enterprise-level impact on EBIT (McKinsey, The State of AI: Global Survey 2025). That gap tells you something important. A lot of companies now have AI on their roadmap. Some have already shipped it. And yet the market still does not see them as an AI company, or even as having a meaningfully stronger product because of AI. I think this is a product strategy problem, not an issue of delivering the wrong feature set. If the company is not clear on what AI is supposed to make materially better, the roadmap starts to fill up with disconnected things. A copilot. Summarization. Recommendations. Workflow automation. None of those are bad ideas on their own. But that does not mean they add up to a stronger product position. The real question is simpler and harder: what is this supposed to make stronger? More specifically, what part of the product should become more valuable, more defensible, or harder to replace because of AI? If there is no sharp answer to that, the company may still ship useful features. But it usually does not change how buyers understand the product. The roadmap gets busier. The story gets fuzzier. That is a big part of why the innovation story is running ahead of the business impact story right now. So, are you enlarging your moat, or just adding some more towers? Photo by Russell Butcher: https://lnkd.in/ghsYG_ZV

Many SaaS companies now have AI on their roadmap. Far fewer have a precise answer to what that AI is supposed to make stronger. That is where many AI product strategies start to break down. Teams add AI features and expect the market to see a more differentiated product. Usually, that is not what happens. The feature set expands, but the product story does not really get sharper. This gets treated as a messaging issue all the time. In many cases, it is a strategy issue. If there is no clear view of what AI is reinforcing, each new capability stays somewhat isolated. It may be useful. It may make the product feel current. But it does not necessarily strengthen the part of the product that customers depend on most. That is the real test. The better question is not, “Where can we add AI?” It is, “What core value in this product should become stronger because of AI?” That usually leads to a very different roadmap. And a much clearer product story. AI can absolutely create separation. But only when it is reinforcing a real product thesis, not just adding to the feature list. Enlarge your moat, don't just add more towers. Photo by Russell Butcher: https://lnkd.in/gc969Yy2

We recently got an RFI asking for our 3-5 year product roadmap. The honest answer? We don’t have one. Which I realize is not the most comforting sentence to write as the CTO at Struct PIM 🙈 But it is not because we don’t think long-term, but because in SaaS, detailed long-term roadmaps tend to be fiction. Things move too fast: 🔹 Technology shifts 🔹 Customers evolve 🔹 New opportunities appear What we do have is a clear long-term strategy. But how we execute on that strategy is intentionally short-term and adaptive. Most of what we ship comes from: 🔹 Customer feedback 🔹 Partner input 🔹 Patterns we observe across the market We release every week. Over the last 6 months, we’ve shipped 500+ improvements across the product. We didn't plan those improvements three years ago - we built them because we stayed close to the problems our users were facing today. In our experience, this leads to better and more relevant products than committing to a plan that will be outdated in 6 months. Curious how others handle this: Do you build long-term roadmaps or optimize for adaptability? 🤔

AI is making it very expensive to not have a real operating model. I’ve been consulting for more than 10 years. This week I finally built the website. CMC Associates — fractional CPO work and operating model consulting for companies of any size navigating the gap between what they want to build and what their organization is actually ready to execute. After more that 20 years in product and technology leadership, I have a pretty specific point of view on where that gap usually comes from. It’s almost never the product team. It’s almost always the operating model around it — how decisions get made, who has clarity, whether the roadmap means anything to anyone outside the standup. I’ve seen roadmaps that stopped at the product team. Roadmaps that didn’t exist at all. Roadmaps that were a beautifully maintained work of fiction that everyone quietly agreed not to question. What I haven’t seen very often is a roadmap that actually functions as an operating system for the whole company. That’s always been the work worth doing. In the AI moment, it’s more important than ever. → cmcassociates.us Is your roadmap actually guiding your organization — or is it something else entirely?

Rich Mironov has lived through more phases of product management and software delivery than almost anyone else in the industry. But rather than remain stuck in theory, Rich has “smoke-jumped” directly into the fray as a fractional CPO. He’s been in the lone voice in the room able to have the crucial conversations necessary to buy the product teams the time and focus to deliver business impact over features. I would argue that the failure of every “transformation” over the past 30 years – Web, Mobile, Agile, Digital, Design Thinking – has been precisely because everyone one of the practitioners, after learning about or getting certified in one of the powerful enabling frameworks, used it to justify a form of “Dunning-Kruger” effect, where they dismissed business leaders and their focus on delivering measurable business value to a customer. “These business executives and middle managers just don’t get it,” they would say. “You need to be Agile, not Waterfall.” But the failure of these methods has been their inability to articulate their value in delivering business results and speaking the language of business. Rich’s “Money Stories” shows that basic business savvy doesn’t have to take two years and hundreds of thousands of dollars to learn. An absolute must-read.

Most CEOs and founders are still asking the wrong question. They ask: “How long will it take to build?” The better question is: “How long will it take us to decide?” Because software isn’t slow anymore in the places that matter. What’s slow is: - getting a real answer out of stakeholders - settling scope before the third rewrite - deciding what “done” means - agreeing on what you’re willing to not build And that’s why the same teams keep saying building is expensive. They’re not paying for code. They’re paying for indecision. Here’s the truth I’ve watched play out again and again: AI didn’t make software cheap. It made ambiguity expensive. When teams had to manually grind through every line, ambiguity was hidden inside “engineering effort.” Now, the build can move quickly… and all the delays show up where they’ve always been: Upstream. In the thinking. In the tradeoffs nobody wants to own. That’s why some companies are “shipping faster” while others keep signing bigger SOWs and still missing dates. It’s not talent. It’s not tooling. It’s not even “complex integrations” half the time. It’s that one group is operating with a clear constraint: “We will ship X by Y, and we’ll cut anything that threatens it.” And the other group is operating with a wish list: “Let’s keep options open.” Options feel safe. Options also kill timelines. So here’s the new reality: The cost of building is dropping. The cost of not knowing what you want is rising. That shift changes what leadership has to do. Your advantage is no longer “a bigger team.” It’s having leaders who can: turn fuzzy ideas into testable slices make hard calls early, protect the team from thrash and ship something real while everyone else is still debating “requirements” That’s also why fractional leadership works when done right. A strong fractional CTO, CPO, or CAIO doesn’t show up to add more activity. They show up to remove the ambiguity tax. The teams that win won’t be the ones who “build the most.” They’ll be the ones who decide the best. If you’re being honest, what slows your delivery down more right now: engineering effort… or decision-making?

Need a 2026 version with "We _just_ have to blah blah blah AI"

"The core CSM problem has always been the tension between personalization and coverage. You want every customer to feel like they’re your only customer. But you have 200 customers and a team of 4 CSMs. Something has to give, and usually it’s personalization. AI agents break that tension."

"People don't understand executive calendars. I describe an executive's calendar as like a strobe light going off. You wake up at 8AM, you've already got a huge list of urgent things going on. You go from a meeting with finance on a budget, to an interview for another executive, to a people problem, to a legal problem, to a product review. And the product manager coming to that product review, who's trying to make a pitch thinks I've been prepping for this meeting for two weeks. But the executive coming into that session hasn't thought about you since." — Jessica Fain

Jessica Fain's best product ideas kept dying, and she couldn't figure out why. So at eight and a half months pregnant, she pitched Slack's CPO April Underwood on becoming her Chief of Staff. She wanted to see how executive decisions actually get made from the inside. What she learned changed everything she knew about influencing execs. People don't realize that an executive's calendar is like a strobe light going off. Budget meeting, a people problem, a legal issue—then your product review. You've been prepping for three weeks. They haven't thought about you since the last meeting. They may not have gone to the bathroom today. And most people walk into that meeting chasing a quick yes. Instead, she learned to treat execs like she treats her users—with the same curiosity and empathy. Jessica has since led product teams at Slack, Box, brightwheel, and now Webflow. In our tactical conversation she shares: 🔸 The 60-second meeting opener most PMs skip 🔸 Why "that's so interesting, what led you to believe that?" can help you disarm an exec 🔸 How to align your pitch with what your exec is actually scared about 🔸 "Stewart plus two more"—her playbook for responding to a CEO's feedback 🔸 Why killing your own project is the ultimate trust-building move Listen now: https://lnkd.in/gRxck6zh Thank you to our wonderful sponsors for supporting the podcast:  🏆 Omni — AI analytics your customers can trust: https://omni.co/lenny 🏆 Lovable — Build apps by simply chatting with AI: https://lovable.dev/ 🏆 Vanta — Automate compliance, manage risk, and accelerate trust with AI: https://vanta.com/lenny Also available on: • Spotify: https://lnkd.in/gJff2_Bm • Apple: https://lnkd.in/g8YvAAn5

Everyone talks about what AI can do for designers. Not what it quietly stops us from doing. The optimistic story says AI elevates designers from execution to strategy. The uncomfortable truth is that most of us are using the extra time to do more execution, faster. I've watched designers spend few hours getting AI to generate perfect wireframes for a product that was solving the wrong problem. Nobody stopped to ask whether the problem was worth solving. That's what I wrote . On speed, judgment, and what the thinking actually demands. Read here: https://lnkd.in/gSdwXquH

Product leadership is not measured by the number of frameworks introduced, the workshops conducted, or the terminology adopted across the organization. It is measured by outcomes. Did the product create measurable business value. Did customer trust increase or decline. Did engineering clearly understand priorities and direction. Did the business see growth, adoption, and results. Introducing discovery processes, roadmaps, and design rituals does not automatically build a product organization. Those are tools. Tools without measurable outcomes become theater. Strong product teams are built through accountability and clear impact on the business. If revenue declines, trust erodes, and teams lose clarity, then something fundamental is missing regardless of how advanced the language of product management sounds. You might believe that a product framework was built. You might believe product design maturity was introduced. But the real question is simple. What do the numbers say.

What really drives company growth—strategy, product, or incentives? In the Grow Rogue Podcast, Reade Milner sits down with Steve Linowes, former Microsoft marketing leader and scale operator, to unpack the real levers behind scaling. Steve breaks down: ▶️ Going from $700M to $1.4B ▶️ Why most founders misunderstand product-market fit ▶️ How incentives shape outcomes more than tactics ▶️ The role of self-funded employers in healthcare innovation If you're building, leading, or marketing something that matters, this conversation is for you. Watch the full episode here: https://lnkd.in/epVr-ydS #GrowthStrategy #FounderAdvice #ProductLedGrowth #ScaleUp #MarketingLeadership

AI doesn’t speed up teams. Great teams speed up with AI. That’s the distinction most companies miss. AI can absolutely compress timelines. It can turn a 12 week build into 3. It can shrink research, testing, and iteration cycles. But AI doesn’t create leverage on its own. It multiplies whatever operating system you already have. If your team is clear, disciplined, and has strong judgment, AI feels like jet fuel. If your team is misaligned, under-led, or unclear on ownership, AI turns into chaos at a higher speed. And I’m seeing this everywhere right now. Teams ship an AI feature quickly, then spend months cleaning up what they didn’t think through: data access, retention policies, who approves outputs, how decisions get audited, what happens when the model is wrong. That’s not a tooling problem. That’s a leadership problem. Because the biggest risk with AI isn’t that it fails. It’s that it works just well enough to get embedded before anyone asks the hard questions. So here’s the real shift happening: AI adoption is no longer about tools. It’s about people who can operationalize tools responsibly. The teams that win will have leaders who know how to: - set boundaries on what AI should and should not do - control what data touches the model - validate outputs before they become decisions - create audit trails before procurement demands them - build trust into the system, not after the fact In other words, speed is available now. But only to teams with judgment. This is also why fractional CTO, CPO, and CAIO leadership is rising. Not as advisory. As leverage. The right fractional leader doesn’t “bring AI.” They bring the operating discipline that makes AI usable at speed. The competitive edge won’t be access to AI. It’ll be whether your org can use AI without creating risk you can’t explain later. ❓What's the biggest blocker in your org right now tooling, talent, or operating discipline?

Three months ago I published the Product OS pillar piece. I said the deep-dives on each layer would follow starting with Product Vision. They have, just slower than planned. The honest reason: since that post I've taken on two fractional CPO engagements. Real companies, real problems, real product work that couldn't wait. Which is, I suppose, the best possible reason for the delay. The second article in the series is now out. It's on product vision and why most of them don't actually work. Not because they're badly written. Because nobody told the people writing them what a vision is supposed to do. My view is that a vision that can't guide a decision in the field isn't a vision. It's a slogan with good formatting. The article covers how to build one that actually functions as a practical instrument, including the eleven criteria I personally use to stress-test them, a real anonymised example, and an honest look at three well-known visions (LinkedIn, Instagram, Netflix) scored against the same criteria. Link in the comments. Like, subscribe and share to other product leaders working in real product companies.

Product and Engineering conflict is rarely about personalities. It’s about governance. When roadmaps trigger the same debate every quarter — scope, timelines, feasibility — the real issue is usually undefined decision rights and invisible capacity. Alignment doesn’t come from more meetings. It comes from structure. Alignment is a system, not a relationship. https://lnkd.in/e5UMBEFG

I’ve always been drawn to human-centered design. When we build products, the starting point is the user — their problem, their experience, and the context around it. From there, the real work is designing the system that solves it. That system might be a product, a platform, or even a company. The more companies I look at, the more convinced I am that execution problems are often system design problems. Here’s my latest thought on it. —— I’ve said the phrase “operating model” to enough quizzical faces that I’ve started explaining it a new way: The Company’s OS I don’t mean culture decks, meeting cadences, or KPI dashboards. That’s the tactical process layer above it. I mean the underlying layer — ownership, decision rights, governance — interacting with the org architecture as an integrated system. Everyone debates strategy, for good reason. But strategy needs to run on a system. If that system is weak or outdated, even good strategies can go sideways in execution. So take a moment to ask yourself: What OS is your company running?

I always ask people who want to build some nonsense: - how will you know that it worked? - what can you do if it didn't? The answer is usually: we won't, and nothing. It never occurs to them that the idea might not be an inevitable success, despite ALL prior experience of ideas failing to be an inevitable success and instead immersing the team into a months-long boondoggle.

Time to Marie Kondo your operating system. Your roll-ups are not sparking joy. They are sparking confusion. I see teams make this mistake all the time: confusing managed entities with basic labels. They fall into the roll-up trap, weaving real things together with narrative buckets. The result is bloated hierarchies where no one is really accountable for anything.

Today, an exec says to me, "Must be easy to have a newsletter that's all guest posts." I hope Lenny Rachitsky doesn't mind, but I told him this story: Early last December, Lenny reached out to me about something, and I pitched him on an idea for a guest post. We went back and forth on ideas and settled on something. I spent a week over the holiday break writing a big, messy 8,000-word essay on positioning stuff and lobbed it back at him. Lenny does a first deep pass on it, and we go back and forth for a week or so. Next, he loops in one of his editors, who did a structural edit (publishers do this with books - this edit makes sure the structure works and it flows for readers). Her feedback was great, and I spent another couple of weeks rewriting it to incorporate it. The editor, Lenny, and I then go back and forth for about a month or so - adding and removing sections, messing with titles, and rewriting the confusing bits (aside - there are many confusing bits 😂). Lenny thought a graphic might help, so a graphics person was pulled in, and we messed around with that for a couple of weeks. We are now in late February and ready for a proper copy edit. A copy editor was added to our group, and we had a few iterations on the wording before we reached a final version. We spent another week or so messing around with titles and subtitles, and then it's ready to go. Three months start to finish - that's how you make it look "easy." Lenny's got a great podcast out today talking about how he grew the newsletter to 1.2 million subscribers - it's worth a listen. The finished product here - https://lnkd.in/gabnxcRK Lenny's podcast on how he grew the newsletter here - https://lnkd.in/gRSiKW_k

➰ Prototype -> AI Interview -> User Feedback -> Apply To Prototype How to speed up validation... If you are building more but validating less, it's just going to lead to Product Debt. Lots of unused product surface area that creates massive costs. Prototyping has become a superpower. But validation also stems from quickly iterating on user feedback. We just launched Prototype Testing in Reforge Research to enable a powerful loop. 1. Generate a prototype in Reforge Build or your favorite prototype tool. 2. Set up an AI Interview for your prototype in Reforge Research. No schedule, manual synthesis, etc needed. Just send a link to your audience. 3. Get auto-synthesized feedback in 10X less time. 4. Take that feedback and give it back to Reforge Build to apply to your prototype. You can shortcut this process even further with Synthetic Users to get instant feedback before sending to users. The discovery deficit is the gap between how fast your team can build and how fast it can validate what's worth building. Closing that gap doesn't require a bigger team or a slower build process. It requires validation tools that move at the same speed as the rest of your workflow. More info below.

I wasn’t at IFA last week. While the franchise industry was discussing marketing, growth, and brand expansion, I was at my desk building the thing I’ve been writing about. Over the past month I’ve been sharing what I believe is happening to Customer Success. The function is restructuring. Automation is taking over the operational layer. And CS will either own Net Revenue Retention or slowly lose relevance. But writing about that shift is the easy part. The harder question is what you actually change in how you work while you are still inside the system. Because the real transition is already happening: 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 𝐢𝐬 𝐦𝐨𝐯𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐭𝐨 𝐫𝐞𝐯𝐞𝐧𝐮𝐞 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧. Here is what I’m changing in how I work. 1️⃣ I stopped treating health scores as the truth. They are a proxy. Nothing more. The real signal sits underneath the score: what the champion stopped saying, what stakeholders stopped asking for, what the account quietly stopped doing. Health scores summarize the past. Revenue risk appears in behavior first. 2️⃣ I started owning the commercial narrative of my accounts. If Customer Success owns NRR, relationship management is not enough. I need to understand the expansion logic, renewal economics, budget cycles, and the priorities of the CFO long before the renewal conversation happens. Champion happiness is useful. But it is not a revenue strategy. 3️⃣ I protect raw customer input before AI touches it. Every customer conversation gets unedited notes first. Only then do summaries happen. Because summaries compress nuance. And nuance is where churn risk, product gaps, and expansion opportunities actually appear. If we automate away the raw signal, we automate away the insight. 4️⃣ I treat orchestration as the real job. Not coordination. Not support escalation. Orchestration. Moving the right people, at the right moment, with the right context across Sales, Product, and Leadership. That is where Customer Success creates leverage. 5️⃣ I think more about the economics of attention. Not every account should receive the same human effort. Automation will run large parts of Customer Success. Which means the human role becomes more valuable where complexity exists: interpreting signals, shaping expansion narratives, and aligning internal teams around the customer. The future of CS is not more activity. It is better allocation of attention. None of this is written in a job description yet. But it is what the next version of the role looks like. And I would rather start building that version now than spend the next three years defending the old one. Because the real risk for Customer Success is not AI replacing it. It is the function refusing to evolve while the revenue model already has. What are you actually changing in how you work right now? Not what you are observing. What you are doing differently.

The easiest way for a company to hide its problems is simple: “Blame the sales team.” Targets get missed… Pipeline slows down… Deals start slipping… And suddenly every department becomes confident about one thing: “Sales isn’t doing their job.” But look closely at the picture. The sales team is standing inside the boat, waist-deep in water, desperately trying to throw buckets out to keep the company afloat. The rest of the company is sitting comfortably on the dry side saying, “Good thing the hole isn’t on our side.” This is the quiet reality inside many organizations. Sales becomes the shock absorber for every mistake made upstream. If the product solves a weak problem, sales must convince harder. If pricing doesn’t match the value, sales must negotiate more. If marketing attracts the wrong audience, sales must filter the noise. If leadership misunderstands the market, sales must face the rejection. And when things go wrong, the easiest narrative appears: “Sales performance is the issue.” But sales is the department that faces the market every single day. They hear the hesitation in a prospect’s voice. They hear when competitors are winning deals. They hear when the product promise sounds exciting in a presentation but confusing in real conversations. Sales doesn’t create the reality. Sales exposes it. Think about how a company actually reaches a customer: Product builds the solution. Marketing tells the story. Leadership sets the direction. Sales carries that entire story into the market. If there is a crack anywhere in that chain, sales is the first place where the pressure shows up. That is why struggling sales numbers often reveal something deeper: * A positioning problem * A product-market fit issue * A pricing gap * A weak ICP definition * Messaging that sounds good internally but collapses in real conversations Sales is simply where the market pushes back. Great companies understand this. They treat sales conversations as intelligence, not excuses. They ask: “What are prospects actually saying?” “What objections repeat every week?” “Where do deals break down?” Because inside those conversations sits the truth about the business. When sales starts drowning, the smartest companies don’t shout instructions from the dry side of the boat. They grab a bucket and start fixing the hole.

The fastest way to undermine a product team isn’t poor talent. It’s constant shifts in direction from leadership. Monday: “We need to focus on growth.” Two weeks later: “Let’s prioritize enterprise.” A month later: “AI needs to be part of everything.” So the team adapts. The roadmap changes. The backlog gets rewritten. Work gets rescoped. Again. And again. From the outside, it looks like momentum. Inside the team, it feels like confusion. Because great product teams don’t need more ideas. They need fewer priorities that survive long enough to matter. Product leadership isn’t about having the smartest strategy in the room. It’s about having the discipline to stay with one long enough to actually learn something. Most companies don’t lose because the strategy was wrong. They lose because they changed it before it had a chance to work.

One thing I see constantly in go-to-market teams is how quickly people turn a constraint into a philosophy. A rep says the market is saturated. A manager says prospects do not answer phones anymore. A leader says outbound is broken. An account goes quiet and suddenly the product is the problem. Pipeline slows and the story becomes that buyers have changed, cold calling is dead, attention spans are gone, competition is everywhere, and nothing works like it used to. Some of that is occasionally true. A lot of it is just commercial self-soothing. In most cases, what is actually happening is less ideological and more uncomfortable. Activity is inconsistent. Message quality is average. Lists are weak. Follow-up collapses too early. Managers tolerate low standards. Teams confuse personal discomfort with market reality. And organisations build entire narratives around constraints they have not really earned the right to claim. That pattern does not just show up in prospecting. It shows up all the way through the customer lifecycle. Usage dips and people say value is falling. Tickets rise and they say the product is failing. Stakeholders get quieter and the story becomes political danger. Again, sometimes that is right. But often it is just another example of people mistaking their first interpretation for the truth. That is why I keep coming back to a simple distinction: observation, perception, perspective. What do we actually know? What story are we telling ourselves about it? And what else could plausibly explain the same facts? Without that discipline, teams end up making strategic decisions based on mood, folklore, and whatever lazy trope is most socially acceptable in the moment. The cold calling world is full of this. “Nobody picks up.” “People hate being interrupted.” “The data is bad.” “It only works in certain markets.” “Senior buyers do not take calls.” Most of the time those lines are not insights. They are cover. Cover for poor execution, weak relevance, low repetition, fragile belief, and the very human desire to explain away difficulty rather than confront it. Markets do change. Constraints are real. But too many teams hide inside them. The stronger operators I know do something different. They treat constraints as variables to work through, not slogans to retreat behind. They do not confuse friction with impossibility. And they are careful not to turn a bad week, a hard patch, or a few bruised egos into a theory of the market. That applies just as much in retention and expansion as it does in new business. If you misread the cause, you run the wrong play. And once a bad interpretation gets repeated enough inside a team, it starts sounding like strategy. Usually it is not strategy. Usually it is avoidance with better branding...

18 marca będę w Warszawie na Product HIVE. Masz produkt, który już złapał trakcję, ale organizacja zaczyna być wąskim gardłem? Decyzje na czucie, chaos w priorytetach, zespół bez kultury produktowej — znam ten moment od środka. Przez lata budowałem organizację produktową od zera w STS. Od małej firmy na skraju upadku, przez debiut na giełdzie, po przejęcie za £700M i integrację na trzech rynkach. Dziś działam jako fractional CPO i buduję własny startup. Jeśli jesteś na tym etapie — napisz w komentarzu lub prywatnie. Umówimy się na kawę. PS. Łap kod -10% na bilet: MICHAL

The real cost isn’t headcount. It’s ambiguity. Many organisations debate whether functions like HR are too large. In my experience, that debate often misses the real issue. Structural ambiguity. When accountability is assumed rather than designed, coordination costs rise across functions. Headcount grows to manage friction that clearer structure would have prevented. This is rarely visible at first. It shows up gradually in slower decisions, cautious execution and missed momentum. I’ve explored the structural mechanics behind this in a new article in The Elevatexec Brief.

A founder told me: “We just need a fractional CPO to move faster.” On paper, it made sense. Backlog was messy. Roadmap unclear. Sprints chaotic. But I asked him: “Do you need speed… or direction?” He paused. Most companies hire fractional product leaders to: “Fix execution.” “Run better ceremonies.” “Ship more features.” Strategic companies hire them to: Align product with revenue. Pressure-test market assumptions. Connect roadmap to growth. Same role. Different expectation. Here’s the reality: Execution problems are visible. Alignment problems are expensive. In one company, the fractional leader improved delivery velocity. More features shipped. Nothing changed in revenue. In another, the fractional leader cut 40% of the roadmap, refocused the ICP, realigned pricing. Velocity slowed. Revenue grew. They didn’t need more output. They needed better decisions. Fractional product leadership in 2026 isn’t about filling gaps. It’s about creating leverage. Quick question: Are you hiring for execution… or for transformation?  #ProductManagement #FractionalLeadership #ProductStrategy #ChiefProductOfficer #StartupLeadership

Early in my career, I thought product management was about strategy. Over time, I realized it’s mostly about communication. Roadmaps don’t usually fail because the plan was bad. They fail because different people understood it differently. Deadlines don’t slip because people are not doing their job. They slip because expectations weren’t clear. And “misalignment” is just a polite word for “we assumed instead of asking.” When I started working as an Associate Product & Project Manager, I believed that if I built the perfect PRD and timeline, execution would follow naturally. It doesn’t. Clarity isn’t created inside documents alone. It’s created in conversations. Hard ones. Repeated ones. Sometimes uncomfortable ones. Here’s what managing a team taught me- You can have the best product strategy in the room. But if engineering interprets it one way, design sees it another way, and stakeholders expect something else… You don’t have a strategy. You have confusion with a deadline.

AI reduces the cost of serving customers. It doesn't fix the cost of serving the wrong ones. There's a temptation right now — understandable, but dangerous — to believe that because AI can do more with less... bad revenue is less of a risk. Automate the support. Scale the onboarding. Handle the volume. But bad-fit customers don't become good-fit customers because you can serve them cheaper. The strategic misalignment is still there. The product gaps are still there. The drain on your roadmap, your team's energy, your brand — all still there. If anything, AI raises the stakes. Bad revenue spreads faster now. A churned enterprise customer doesn't just talk to their peers — they're leaving reviews, feeding competitive intelligence tools, and showing up as a case study in your competitor's next sales deck. The reputational damage that used to take months to surface now moves in weeks. Here's what I think actually changes in an AI world: → You have better signals earlier. AI surfaces churn risk faster — which means you can see a bad-fit customer struggling sooner. If you're willing to look. → The bar for "we can make it work" gets higher. Automation creates a false sense of coverage. A customer can receive 100 touchpoints and still be the wrong fit. → The definition of bad revenue expands. Customers who won't adopt, can't integrate, or pull your product toward a segment that doesn't serve your core market — those are bad bets now too. Good revenue in an AI world still looks the same: customers who succeed, grow, and make your product better. The shortcut hasn't changed. It just got faster. How is your team thinking about fit as the cost of serving customers goes down?

SEO is not dead. But if it's all you're doing, you're playing one instrument in a three-piece band. Here's the difference between SEO, AEO, and GEO - and why D2C brands need all three in 2026. (1) SEO - Search Engine Optimization  → You already know this one. Rank high-intent PDPs and category pages on Google. Build topical authority through ingredient guides, founder stories, and comparison content. → Own the "best [product] for [concern]" queries that precede purchase. → Still the foundation. It drives compounding traffic with high purchase intent and fuels repeat-purchase discovery organically. (2) AEO - Answer Engine Optimization Get chosen by AI Overviews, People Also Ask, and voice search. This is about structuring your content so AI engines surface your brand as the answer, not just a result. The key plays for D2C: → Add FAQ schema to ingredient and formulation pages → Answer buyer questions directly: "Is niacinamide safe during pregnancy?" "How much protein per day?" → Structure short answers for zero-click visibility on category queries (3) GEO - Generative Engine Optimization Get cited, recommended, and sold by AI agents. This is the new frontier. GEO is about building entity presence on the sources LLMs trust most, so when a shopper asks an AI to find the best product, your brand shows up. The key plays for D2C: → Write entity-rich product descriptions LLMs can parse and cite → Seed presence on Reddit (r/SkincareAddiction, r/Supplements), Healthline, Byrdie, and Wirecutter-style review sites → Get cited in DTC newsletters and aggregator roundups AI trusts Here's the uncomfortable truth: You can rank #1 on Google and still be completely invisible to ChatGPT. I've seen it with D2C brands spending $15K+ per month on SEO. Page 1 for dozens of high-intent keywords. Great reviews. Strong formulations. But ask an AI agent to recommend the best collagen supplement or creatine for women? Their competitors show up. They don't. The fix isn't choosing one over the other. The fix is the D2C Priority Roadmap: SEO First → Product schema, site speed, PDP keyword targeting. Capture buyers at the "best [product] for [need]" moment. Underpins everything. AEO Layer → Win AI Overviews and PAA with ingredient FAQs. Own the zero-click layer as buyers research formulas, safety, and comparisons. GEO Now → Seed Reddit, review sites, and trusted DTC publications. Become the brand AI recommends when shoppers ask agents to "find the best." Most D2C brands are still running single-player SEO in a three-layer game. The brands winning in 2026 are running all three simultaneously.

Most Director-level interviews go one way: the company evaluates you. The best ones go both ways. Here are the five questions I’d ask to assess any platform organization. 1. “Who owns the platform roadmap today?” A clear, specific answer signals organizational maturity. Hesitation, or “it’s kind of shared,” tells me ownership is unclear at the leadership level. That gap will show up everywhere downstream. 2. “What does a developer do when the platform blocks them?” Strong orgs have a defined path: a ticket, a channel, a clear SLA. Weak orgs say “they usually just ask around” or “they ping someone on Slack.” Tribal knowledge is not a support model. It’s a retention risk. 3. “When did the platform last cause a production incident, and what changed afterward?” I’m looking for a specific answer. Specificity signals a learning culture. Vagueness signals one that buries failures. 4. “How does the platform team measure its impact on product velocity?” Most teams measure uptime and deployment frequency. Few measure how much time they save product engineers per week. If they don’t measure it, they don’t know if the platform is helping or just existing. 5. “What’s the one thing the platform team can’t get prioritized, and why?” This question surfaces organizational blockers faster than any other. The answer tells me where power sits, where tradeoffs get made, and whether platform work is a cost center or a capability investment. Vague answers to these questions don’t mean the leader is uninformed. They mean the organization hasn’t had to answer them yet. That’s the real assessment. Most platform failures aren’t technical. They’re organizational.

"And here’s the real kicker: all these AI Agents don’t talk to each other. Not really. When we ran a price promotion for SaaStr AI Annual this week, we had to manually update five different agents with the same context."

Backlog organizado não significa produto estratégico. O papel do Product Owner vai além da organização de demandas. É preciso conectar cada entrega à visão de futuro e aos objetivos do negócio. Produto forte é aquele que evolui com intenção, não apenas com volume de entregas. 👉 Seu roadmap está guiando decisões ou apenas registrando pedidos? #ProductOwner #GestãoDeProduto #Roadmap #Estratégia

BOA | The thing founders won't admit You don't actually believe your teams are aligned most of the time. You just haven't admitted it's expensive enough to staff against it. So you keep solving for it with better managers. Tighter communication. Clearer goals. And none of it works because no one is accountable for the thing you're trying to fix. Alignment isn't a byproduct of good leadership. It's a function. And right now that function is split across 11 people who all have other jobs. Your chief of staff is trying to keep the executive team connected. Your people ops lead is trying to close engagement gaps. Your PMO is trying to make sure projects don't collide. But no one is making sure your product team's roadmap is connected to the close rates your AEs are being held to. No one is making sure your IC in Austin understands how her work ladders to the board's new priority. No one is tracking whether your manager of six actually knows what high performance looks like after the definition just changed. You're running a 2500 person org with no one responsible for making sure the work being done today connects to what matters today. And you're calling the gap culture. The gap isn't culture. The gap is operational. And it's costing you more than you think. Listen to The Business of Alignment podcast: https://lnkd.in/e9hJ9Xb2

How a $2.4M savings projection became a $3.1M remediation cost.

AI makes building cheaper. It doesn't make learning cheaper. AI didn't just reduce the cost of code. It reduced the cost of producing almost everything: • Product prototypes • Landing pages • Sales decks • Marketing videos • Documentation • Entire "good enough" apps More people can build. More teams can ship. Which means the moat isn't the ability to produce anymore. The most dangerous failure mode isn't a team that can't execute. It's a smart team executing brilliantly on something that shouldn't exist. AI doesn't create this failure mode — it amplifies it. The default way things go wrong isn't "it doesn't work." It's: it works… and it's the wrong thing. Wrong tradeoff. Wrong edge case. Hidden compliance risk. Fragile abstraction. Quietly irreversible decisions. The constraint hasn't changed: you still have to talk to users, choose the right problem, and have the judgment to throw work away. When output is abundant, the advantage is knowing what to ignore. I've seen "faster shipping" hide the fact we weren't learning. How I apply this: I treat AI output like an untrusted PR — small diffs, explicit constraints, evals/tests where possible, and real user feedback before scaling. Anything that can create irreversible downside (security, privacy, compliance) gets hard gates. Execution is being democratized. Judgment isn't.

I don't have a catchy statement like "Ai will kill news product management" but for a while now I’ve been thinking a lot about what (gen) AI is doing to the role of product in journalism (and working on it at the NPA). News Product Management as a discipline has grown in the past few years but we know organizational conditions needed for product work to succeed are still missing in many newsrooms (particularly clear ownership, leadership alignment, and consistent use of audience insight in decision-making). And yet, we already need to start thinking how it needs to change to meet the moment because the role of product is evolving alongside technological developments and generative AI. The classic product lifecycle is compressing. We can now build prototypes and ship products much faster, put them in front of real people earlier, and learn much more quickly. That shortens the learning loop dramatically: launch faster, learn faster, iterate faster. And, in theory, this also makes product managers more powerful. With new (and often fantastic) tools, a NPM can prototype, test, and validate ideas in ways that previously required much more resources. The catch is that in many organizations, this increased capability doesn’t translate into more resources or stronger product teams. Often the opposite happens: because individuals can “do more,” teams get smaller. So the role expands while the support around it shrinks. And with this, a few structural challenges are still unresolved: 👉 Yes, we now have the ability to learn and put those learnings into practice much faster than before. But that also requires absorption capacity within the organization: the ability to collect data, interpret it, and make decisions at the same speed. And that is precisely where many news organizations still struggle the most. 👉 Additionally, we can build MVPs incredibly quickly now. But scaling them is a different story. Infrastructure, cost, and adoption remain real constraints, especially in newsrooms. And when workflows rely heavily on LLMs, maintaining reliability at scale becomes complicated. These tools work best when they are used by subject-matter experts who can recognize when something is missing, wrong, or incomplete. So, it seems that AI drives product teams to shrink when product strategy is needed the most. We can do many things very quickly now, but that exercise is futile if we can't connect what we are doing to audience needs and develop a clear path to value exchange. The ability to identify opportunities based on audience data, define direction, prioritize effectively, and navigate uncertainty is becoming more valuable than ever. Those are the "strategic parts of product work". Then, the real question isn’t whether AI will change product work in news (it already has) but whether news organizations can evolve their understanding of it fast enough to take advantage of the speed without losing direction.

A Fun Thought Experiment for Executives in the AI Era Imagine a software company that ships every single customer feature request. No filtering. No prioritization. Just building what is asked. Customers would never know this policy exists (to avoid manipulation of the policy like what happened with Anthropic's vending machine). Results Short term, loyalty spikes. Customers feel heard. Deals close faster. Churn drops. Then the second-order effects show up. The product grows fast and uneven. New customers struggle to understand it. Existing customers feel the product bending too far toward edge cases that aren’t theirs. The company survives by over-indexing on feature flags and hyper-personalized onboarding. Every customer effectively gets their own product. AI makes that barely feasible, but maybe more feasible as it evolves. QA becomes the bottleneck. Dependencies explode. Testing turns into a graph problem instead of a checklist. AI helps, but only if quality is treated as a first-class system. Sales changes entirely. There’s no roadmap selling. Every conversation becomes “yes, we can do that.” Proof would have to replace verbal promises. Demos would become live builds. This company would feel magical early. Chaotic later. And strangely aligned with where AI is pushing us. Maybe it's the right future?

Mastery: Staying long enough to master one domain mastery doesn’t come from exposure. It comes from depth. Depth means: • Solving the same type of problem 50 times • Seeing edge cases others ignore • Refining systems instead of replacing them • Staying when it gets boring The market doesn’t reward curiosity alone. It rewards applied depth. Anyone can learn a new tool in 30 days. Very few can extract leverage from one domain for 3+ years. The real advantage? Staying long enough to: → Understand the fundamentals → Build your own frameworks → Predict outcomes Breadth makes you interesting. Depth makes you valuable. Stay. Build. Refine. Mastery is sustained focus Context switching can be advantageous, but only under certain conditions. It’s not always “bad” — it depends on the goal. Here’s a structured way to see it: 🟢 When Context Switching Helps Cross-Pollination of Ideas Switching between unrelated domains can spark innovation. Example: Applying SEO patterns to frontend UI design might reveal unexpected efficiencies. Breaking Mental Blocks Stuck on a tough problem? Switching tasks can let your subconscious work on it. Example: Coding a tricky React component Or Analazing a highly competitive keyword research pattern → take a break and do some system design thinking → solution emerges. High-Level Strategy / Prioritization Switching helps you connect dots across domains. Example: Seeing how marketing funnels affect product adoption, rather than focusing on just one silo. Energy Matching Some tasks require high focus; some require low cognitive effort. Switching to low-effort tasks when energy dips prevents burnout. Learning Multiple Skill Sets switching helps discover strengths and interests.

You are close to the product, close to the decisions, close to every hire, and often close to every problem. That proximity builds speed in the beginning. But if you don’t evolve as the company grows, the behaviors that once fueled momentum quietly become the ceiling. The first sign you’re still leading like it’s Day One is that you’re in every decision. If approvals and escalations all route through you, that isn’t excellence. It’s a bottleneck. Scaling requires distributed ownership. If your team cannot move confidently without you, you’ve built dependence, not leaders. Ask yourself: What decisions could you permanently remove yourself from this quarter? If the answer is “not many,” that’s your growth edge. The second sign is caring more about the product than the people building it. Passion may have gotten you here. But growth shifts your responsibility from refining output to developing leaders. Products do not scale themselves. People do. If most of your energy still goes toward fine-tuning deliverables instead of strengthening capability, your leadership is misallocated. The third sign shows up in your one-on-ones. If they are mostly status updates and deadline reviews, you’re managing tasks, not building alignment. At scale, belonging drives performance. Sustainable growth happens when people understand how their work connects to who they are becoming. The fourth sign is reactivity. Early intensity can look like drive. At scale, it feels like instability. Your tone sets culture. If you process frustration in real time, you teach caution instead of confidence. Scalable leadership requires steadiness. The fifth sign is subtle. Your team executes, but the energy feels flat. Metrics are met, but connection is thin. That is rarely a strategy issue. It is a belonging issue. Here is the shift: Day One leadership proves the idea. Scaling leadership builds the people. If your company doubled tomorrow, would your team feel directed or deeply aligned? That answer tells you whether you’ve grown with your company. If this resonated, subscribe to the Limitless Leader newsletter for practical strategies on evolving your leadership as your company scales. https://lnkd.in/eSrNFdFc #productivity #strategy #virtualassistant #management #leadership #entrepreneurship #ceo #business #team #technology #inspiration #startups

Caution Zero Emojis thread, read at your own risk ;) The biggest mistake I see is treating CX as a department. Some companies have evolved past that and now treat it as a strategy, or a set of behaviours, or a communication style. Better, but still not right. Customer Experience is an ecosystem! It lives in your pricing model, your product design, your hiring decisions, your internal culture, your technology stack, your employee experience, and the way your finance team writes an invoice. ✕ No single department owns all of that.  ✕ No behaviour training can fix all of that. ✕ No single strategy can govern all of that. Here is what that means in practice or i would say in practically: ▪︎ If your CX team is excellent but your operations are broken, you don't have a CX problem → you have an ecosystem problem. ▪︎ If your frontline staff are warm and empathetic but your digital experience is clunky and cold, you don't have a training problem → you have an ecosystem problem. ▪︎ If leadership talks about putting the customer first but makes decisions based purely on short-term cost reduction → no CX initiative in the world will save you. Customer Experience brings return on its investment when it’s embedded it into the DNA of every function, every process, and every decision. That shift requires a different kind of leadership conversation: ▶︎ It requires bridging silos that have been comfortable for decades. ▶︎ It requires measuring things that are harder to quantify than handle time and ticket resolution. ▶︎ It requires the honesty to accept that your Customer Experience is not what you say it is, but it is what your customers feel it is. ( my fav line ) The main question to ask here is: Do we actually understand the ecosystem we are asking our customers to live inside? What else would you add? #cx #customerexperience #customerrelations #talksoncustomercentricity

I've spent two decades building and leading product teams. One challenge I'm seeing right now is different from anything that came before it. The PM team is talented. They're working hard. The roadmap is full. Sprints are completing. Features are shipping. And yet when leadership asks "what moved ARR this quarter?" or "why did retention improve?" the room goes quiet. Not because the team isn't capable. Because somewhere along the way, the definition of success became Output rather than Outcome. Here's what this looks like in practice. A PM synthesizes customer feedback, writes a thorough PRD, partners with engineering to ship on time. But nobody stopped to ask: which specific business metric does this feature move? By how much? And how will we know within 90 days if it worked? Without that thread from roadmap decision to measurable business outcome, even the best execution is essentially a bet placed without odds. The result is a team that feels perpetually busy but can't always explain why the business is better for what they built. This isn't a capability problem. The problem is structural. Most product organizations are still measuring themselves on the metrics that made sense when software development was the bottleneck: velocity, story points, roadmap completion. Those metrics made sense when shipping fast was the hard part. Shipping fast is no longer the hard part. The hard part is deciding what to ship and being able to connect that decision explicitly to ARR growth, retention, adoption, or margin expansion before a single line of code is written. Leading large product organizations, I've had to solve this directly - and the answer wasn't a new framework. It was a different operating model. Built around one question: not what are we building, but what specifically are we trying to move, and how will we know we moved it? Every team I led that made that shift delivered differently, and the business results reflected it. AI is making this more urgent, not less. As AI tools accelerate execution, faster research, faster drafting, faster synthesis, the teams that will pull ahead aren't the ones moving fastest. They're the ones who've built the clearest methodology for deciding what to move toward. Speed without direction just gets you lost faster. I'm curious where this lands for people who are in the middle of this transformation right now. When your team makes a prioritization decision, how explicitly is that decision connected to a specific business outcome? Do you have a defined process, or is it more intuitive than anyone fully acknowledges?

Prioritization sounds clean in theory. Score the features. Estimate the impact. Stack rank the roadmap. Then the trade-offs become real. One initiative has an executive sponsor. One has a loud customer behind it. One has already consumed three months of engineering time. One shaped last quarter’s narrative and no one wants to unwind it. Now you’re weighing politics, sunk cost, credibility, and risk. A simple framing helps: 𝗞𝗲𝗲𝗽. 𝗞𝗶𝗹𝗹. 𝗣𝗶𝘃𝗼𝘁. Keep — because the evidence holds and the exposure is understood. Kill — because the downside is larger than we admitted. Pivot — because the underlying need is valid, but the current solution misses it. Most teams default to keep. Some have the discipline to kill. Very few slow down long enough to consider pivot. This is where 𝘊𝘢𝘵𝘢𝘭𝘺𝘵𝘪𝘤 𝘊𝘶𝘴𝘵𝘰𝘮𝘦𝘳𝘴 can be of real service. These aren’t power users or cheerleaders. They’re experienced participants in the category. They have enough context to recognize patterns. They care about utility. And they’re willing to say, constructively, when something doesn’t hold up. When you run a feature through that lens, a few things happen: They expose weak logic behind a “keep.” They make it easier to justify a “kill” with credibility. They often point to the more grounded “pivot” you hadn’t fully articulated. Prioritization rarely fails because teams lack frameworks. It fails because assumptions stay unchallenged until late. If you bring the right customers in early, before the roadmap hardens, keep, kill, or pivot becomes a sharper decision. And sharper decisions tend to stick. _________ 𝗣𝗦: If your next roadmap review is coming up and some calls still feel murky, this is exactly when Catalytic Customers make the difference. I help product leaders bring them in before the window closes — while they can still pivot with confidence instead of defend with spin.

BOA | The role you're not hiring for You've got 15 people touching revenue in some way. Product. Marketing. AEs. Customer success. Ops. Finance modeling the assumptions. Every one of them is optimized for their function. None of them are optimized for each other. And no one is responsible for making sure the work they're doing connects to the same truth at the same time. That's not a meeting problem. That's not an OKR problem. That's a structural gap. And until someone owns it the way someone owns your close rate, you're going to keep wondering why execution feels so expensive and so slow. Alignment is operational. It's not HR. It's not comms. It's the work of making sure energy spent today connects to what the business needs today. Not last quarter's plan. Not the roadmap you built before the market shifted. Today. Listen to The Business of Alignment podcast: https://lnkd.in/e9hJ9Xb2

The economics of software just changed. That’s a problem. For decades, product organizations were structured around scarcity. Engineering capacity determined roadmap scope.   Market size determined whether something was “worth it.”   Standardization was mandatory because customization didn’t scale. Everything bent around that constraint. Now the economics of software are shifting. The cost and time required to build are compressing. More initiatives clear ROI math.   More markets look defensible.   More customization becomes viable. But most orgs are still structured as if capacity is the bottleneck. When build stops being scarce, the constraint doesn’t disappear. It moves. You see it in quarterly planning. The roadmap doesn’t get smaller. It expands. Initiatives that would’ve been killed two years ago now survive because they’re “cheap enough.” The hard problem isn’t building. It’s deciding. What not to build.   What not to fund.   What not to prioritize, even when you can justify it. Judgment becomes the new bottleneck. And most product operating models weren’t designed for that. So the real question for CPOs isn’t: “How do we use AI to build faster?” It’s: "If judgment is now the constraint, is my organization structured around it?" The constraint moved. Has your org design?

𝐓𝐡𝐞 𝐇𝐢𝐝𝐝𝐞𝐧 𝐂𝐨𝐬𝐭 𝐨𝐟 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐂𝐨𝐧𝐬𝐞𝐧𝐬𝐮𝐬 At scale, roadmap planning becomes a negotiation. Engineering has constraints. Sales wants deal-closing features. Leadership wants board-ready bets. Legal has flags. Finance has questions. By the time a roadmap survives that process, the sharp edges are gone. What's left is a list of things nobody objected to, which is a very different thing from a list of things that will move the product forward. This is how large tech companies end up shipping features nobody asked for, maintaining products nobody loves, and losing ground to a 10-person startup with a clear point of view. The best PM leaders I've seen do something counterintuitive, they protect the roadmap from the stakeholders who should theoretically be closest to it. They kill the question "does everyone agree?" and replace it with "does this make the product meaningfully better for the user who matters most?" Here's the uncomfortable truth: Consensus means you found something everyone can live with. But users don't need a product everyone can live with. They need a product someone was willing to fight for. 𝐒𝐚𝐟𝐞 𝐫𝐨𝐚𝐝𝐦𝐚𝐩𝐬 𝐝𝐨𝐧'𝐭 𝐛𝐮𝐢𝐥𝐝 𝐜𝐚𝐭𝐞𝐠𝐨𝐫𝐲 𝐥𝐞𝐚𝐝𝐞𝐫𝐬. 𝐂𝐨𝐧𝐯𝐢𝐜𝐭𝐢𝐨𝐧 𝐝𝐨𝐞𝐬 #ProductManagement #ProductStrategy #TechLeadership #Roadmap #BigTech #EnterpriseProduct

User-Centric vs User-Led Between the Roadmap and Reality | A Series Lessons from building and leading products at scale How do you handle the tension between listening closely and deciding strategically? This is a tension I’ve felt more than once. You listen to users. Really listen. You hear the frustration. The urgency. The “this is critical for us.” And you want to fix it. That’s what being user-centric feels like. But leadership gets harder when you zoom out. Because while one customer needs this solved now, another segment needs something entirely different. And the business is carrying constraints most users never see. The uncomfortable part? Not every valid request becomes a roadmap item. Users should influence the product. Deeply. They help us see blind spots, friction, and unmet needs. But they aren’t responsible for: • Portfolio trade-offs • Market positioning • Long-term scalability • Opportunity cost We are. Sometimes being user-centric means saying “I understand why this matters to you.” And also “We’re not doing this right now.” That balance isn’t cold. It’s stewardship. Between the roadmap and reality, the work is holding empathy and discipline at the same time. #BetweenTheRoadmapAndReality #ProductLeadership #ProductManagement #Strategy #UserCentric

🦉 Ever look at a sprint report where every metric looks healthy, but somehow the product still feels stuck in the same place? 👔 What it looks like inside real teams • Story points trending up • Velocity charts looking healthy • Commits happening daily • Standups feel productive Yet when leadership steps back and looks at the bigger picture, something feels off. The product roadmap hasn’t meaningfully advanced. The strategic goals still feel just as far away as they did a few months ago. Everyone is busy, but the outcomes are harder to point to. 🧠 Why this happens Most engineering metrics were designed to answer a simple question: Is the team active? They were never designed to answer the harder one: Did anything meaningful actually change? Over time teams get very good at optimizing the things that are measured, as is human nature. If activity is what shows up on the dashboard, activity is what the system naturally rewards. 🧭 What experienced leaders start watching instead Instead of only looking at how much work moved through the system, the conversation slowly shifts toward impact. • Did this release meaningfully move the product forward? • Did customers notice the improvement? • Did the change remove friction that used to slow teams down? Those questions tend to produce quieter dashboards, but far clearer signals. 🛠 Where AI makes this even more interesting AI tools are about to amplify this problem. When code can be written faster, reviewed faster, and shipped faster, traditional signals like commit counts or velocity will become even noisier. Activity will explode, but meaningful progress will still require leadership judgment. 🎯 The takeaway Metrics are useful since they help teams see patterns and trends, but every leader eventually runs into the same realization: Numbers can tell you a lot about motion, but they can't always tell you whether the organization is actually moving forward. Sometimes the most important leadership question is simply: Are we measuring what matters, or just what is easy to count?

Many organizations adopt OKRs (Objectives and Key Results) with the hope of creating alignment, focus, and measurable progress. On paper, the framework is simple: set ambitious objectives and define clear, measurable key results. Yet in practice, something often breaks along the way. The first breakdown usually happens at the strategy level. Leadership may define high-level objectives, but if they are vague or disconnected from real priorities, teams struggle to translate them into meaningful action. “Improve customer experience” sounds inspiring, but without clarity, it becomes a catch-all phrase that leads to scattered initiatives rather than focused execution. The second issue is misalignment. Teams frequently create OKRs in isolation, optimizing for their own targets without understanding cross-functional dependencies. Marketing may chase lead volume, while sales prioritizes deal size, and product focuses on feature velocity. Individually, these OKRs look strong. Collectively, they may conflict. Another common failure is treating OKRs as a performance evaluation tool rather than a learning framework. When compensation or job security is tightly tied to hitting 100% of key results, teams become conservative. They set safe targets instead of ambitious ones. The spirit of experimentation and stretch goals disappears. Finally, many organizations underestimate the cultural shift required. OKRs demand transparency, regular check-ins, and honest reflection. Without consistent review cycles and leadership modeling vulnerability, OKRs become a quarterly paperwork exercise rather than a living system. When OKRs fail, it’s rarely because the framework is flawed. It’s because clarity, alignment, trust, and discipline are missing. The organizations that succeed with OKRs understand that they are not just setting goals—they are building a system of focus, accountability, and continuous learning.

Revenue Without Self-Betrayal  How to Scale a Values-Led Business Without Losing What Made It Worth Building There is a version of success that looks exactly right from the outside and feels subtly wrong from the inside. Revenue is up. The team is growing. You are winning clients you would have dreamed about three years ago. And yet there is a quiet feeling. It surfaces on Sunday evenings, or in the gap between meetings. Or in the moment just after a win when you expected to feel most proud and instead felt... nothing. That something has been lost in the process. The thing that made your business yours. The playbook that hollows businesses out. There is an entire industry built on telling you how to scale. Productize everything. Systematize relentlessly. Remove yourself from the operation. The advice sounds reasonable. But it was designed for businesses that treat identity as a marketing layer, not for businesses where identity is the product. When you systematize without intention, you replace the things that made you distinctive, your point of view, your way of working, the energy clients describe when they explain why they chose you, with things that are easier to replicate but harder to care about. Over time, the business becomes more efficient and less alive. That quiet Sunday evening feeling is your nervous system telling you the growth model you are following was not designed for a business like yours. Scaling and soul are not opposites, but sequence matters. The most durable businesses I have worked with became more concentrated as they grew, more specifically themselves, more clear about what they will and will not do. This is the result of founders who treated their values not as marketing language but as operational infrastructure, encoding them into how they hire, how they price, and how they respond when growth asks them to compromise. Revenue without self-betrayal is not a philosophy. It is a methodology. Three questions to test your alignment. Does your current client base represent the work you want to be known for, or the work you said yes to because you needed the revenue? If you described your business today to the version of yourself who started it, would they recognise it? Are the decisions driving your growth made with explicit reference to what you stand for, or by committee, by feel, or by what competitors are doing? If the gap makes you uncomfortable, good. That discomfort is the first honest signal in a long time. What we believe. The Tamashi Collective exists because we believe the most commercially successful businesses of the next decade will treat their values as their primary competitive advantage. Not as a constraint on growth, but as the thing that makes their growth irreplaceable. Your soul is not in tension with your revenue. It is the reason your revenue is defensible. Business is your soul at work. Build accordingly.

“If someone commits fraud in your company, it’s not their fault. It’s yours.” It sounds harsh and not entirely right but when I stepped into the leader of company, I can see clearly that scaling a company is not just about growth. If you ask Bvarta leaders what scaling means and you might get three different answers. For Martyn, the biggest challenge in scaling maybe is revenue. For Azby, maybe it’s tech and product foundation. For me? It’s systems and people. Because revenue can grow. Tech can evolve. Product can improve. But if the system is weak and the wrong people are operating inside it, growth only magnifies the cracks. We learned this the hard way. There was a period when fraud happened. Not because we lacked ambition. But because the system was poorly designed. It was built without sufficient controls. The people selected were not fully capable or not aligned with the roles they were given. And there was no proper cross-check mechanism. A flawed system, built by misaligned people, without accountability loops, will eventually fail. Scaling is not speed. Scaling is structure. Revenue is the outcome. Product is the vehicle. But systems and people determine whether growth is sustainable or fragile. Leadership is not about assuming everyone will act right. It’s about building an environment where even if someone wants to act wrong, the system makes it difficult. That’s what real scaling means to me.

Yes, SaaStr is now 3 humans, 20+ AI Agents, and a dog. But we have budget for ... 1-2 more humans If you know how to help us in an AI-first sales, marketing, content, community, events etc. role ... email me. Most of your colleagues will be AI Agents, though. Let's be clear on that.

Just in: the Head of Design at Anthropic said something most senior designers won’t want to hear. In her interview Jenny Wen says: the most overlooked design hire right now is the cracked new grad. Not because they’re cheaper. Because they don’t carry baggage. The traditional design process is breaking down. Engineers spin up coding agents and ship working versions before a designer finishes exploring options. That discover-diverge-converge loop? Too slow. So companies panic-hire seniors who can “hit the ground running.” But what if experience is part of the problem? Seniors bring muscle memory for workflows that are going obsolete: long research phases, design reviews, multi-year roadmaps. I learned that playbook too - I just never had time to get attached. Every company I’ve been at moved too fast. So I adapted early. My first product role was at an AI startup. I’ve only worked in AI since. I didn’t enter before the wave - I entered during it. According to Jenny, that’s the profile companies are sleeping on. The designer who wins now isn’t the one who mastered the old way. It’s the one who never stopped adjusting.

The “SaaS is dead” / “SaaS is alive” debate is funny. It's a clarity problem. The tech is impressive, but it doesn't change whether leadership actually understands what the hell their product does and who it’s for. 🏎️ A badass car still needs a driver who knows where to go. 🤖 The most advanced quantum computer still waits for an input. Direction is the bottleneck. Ask a simple question: What are the goals, in order of priority? You’d be surprised how often that breaks a room. Let’s say the answer is: 💠 Revenue. 💠 Retention. 💠 Speed. Okay ... 💠 How does everything build toward those? 💠 How does the product support it? 💠 How does marketing reinforce it? 💠 How does the team execute against it? AI might replace the need to know certain things. It does not replace the need to want something specific. 💠 It doesn’t create ambition. 💠 It doesn’t create taste. 💠 It doesn’t create vision. It reminds me of a genie. Incredibly powerful but completely dependent on the quality of the wish. It's not a matter of whether SaaS survives AI, it's whether leaders can articulate: 💠 Who it’s for 💠 What pain it solves 💠 Why it matters AI will happily build the wrong thing faster.

𝗟𝗲𝗮𝗻 𝗦𝘁𝗮𝗿𝘁𝘂𝗽 𝘃𝘀. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗘𝗿𝗮: 𝗖𝗼𝗻𝘃𝗶𝗻𝗰𝗲 𝗺𝗲 𝗠𝗩𝗣 𝗶𝘀 𝘀𝘁𝗶𝗹𝗹 𝗲𝗻𝗼𝘂𝗴𝗵. In 2010, during my first startup — YAssume — I made a leadership mistake. We kept refining the product based on feedback to the idea. We never let customers experience something real. Development was busy. Velocity looked healthy. But we were building what I thought customers wanted. When we pivoted to MakeMyDabba.com, my co-founder Akkiraju Bhattiprolu (for the second time — thank you for the trust 🙏) handed me The Lean Startup by Eric Ries. It was humbling. This time, we validated first. Facebook Ads. Google Forms. No portal. That lesson shaped me. Later, during the IoT wave at Happiest Minds, MVP became our edge. Enterprises embraced disciplined experimentation. We won logos because we knew how to ship small, learn fast, and reduce capital risk. MVP worked because building was expensive. Iteration reduced risk. Scope discipline was survival. But something has shifted — dramatically — in just the last few months. Recently, while building AI-First solutions for Institutes and Universities, our roadmap was ready. The first feature was almost done. Marketing was gearing up. In standup, someone asked: “What should we be pitching?” A year ago, the answer was obvious. Pitch the MVP. This time, I paused. With AI-assisted engineering and agentic design patterns, the remaining modules aren’t quarters away. They’re weeks away. Sometimes days. By the time marketing sets up the first serious call, the roadmap has already moved. And that’s when it hit me: 𝗪𝗵𝗲𝗻 𝗯𝘂𝗶𝗹𝗱 𝗰𝘆𝗰𝗹𝗲𝘀 𝗰𝗼𝗺𝗽𝗿𝗲𝘀𝘀, 𝘀𝗺𝗮𝗹𝗹 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗼𝘂𝗻𝗱𝘀 𝗳𝗮𝘀𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗱𝗲𝗯𝘁. Lean thinking taught us how not to waste capital. AI-native thinking must teach us how not to waste possibility. We talk a lot about technical debt. But in 2026, something else may compound faster: 𝗔𝗺𝗯𝗶𝘁𝗶𝗼𝗻 𝗱𝗲𝗯𝘁. The gap between what’s possible… and what we dare to design. MVP asks: What’s the least we can build to test viability? The Agentic Era may require us to ask: What’s the fullest coherent system we intend to become — and how fast can we close the gap? This is not Big Bang chaos. It’s not vaporware theatre. You still validate. You still ship in increments. You still manage risk. But you architect for the full world from day one. You design for inevitability — not just viability. 𝗜𝘀 𝗠𝗩𝗣 𝘀𝘁𝗶𝗹𝗹 𝗲𝗻𝗼𝘂𝗴𝗵 𝗶𝗻 𝗮𝗻 𝗔𝗜-𝗻𝗮𝘁𝗶𝘃𝗲 𝘄𝗼𝗿𝗹𝗱? Or are we quietly accumulating ambition debt? Convince me. #AI #ProductStrategy #LeanStartup #Innovation #Leadership #HappiestMinds

Salesforce bought Momentum last week. That's 10 acquisitions in 6 months, and nearly all of them are about filling holes in Agentforce. Momentum is the interesting one. The idea is to take what people say on calls (eg: Zoom and Google Meet) and turn it into data that AI agents can use. Because right now, Agentforce only knows what a rep typed into Salesforce after the call (which, if you've ever managed a sales team, like me, you know less than half of the important information makes it into the CRM). For the short-term investor narrative, this is great: "We're buying our way to a complete AI-native revenue stack" is exactly the kind of thing that gets an analyst to raise a price target. And it might work! Ten acquisitions that all fit together on a PowerPoint slide is a pretty compelling narrative. The problem is that customers don't care about investor narratives/slides. They care about how a product will help them win. And the reality is, when a product makes ten acquisitions in six months, that means ten different engineering teams figuring out who reports to who. Ten code bases. Ten sets of gtm leaders jockeying for headcount. Ten product roadmaps that need to become one product roadmap, except nobody agrees on what that one roadmap should look like. The people who are best at navigating that kind of internal complexity are rarely the same people who built the original product. Salesforce has run this exact experiment before. In 2022 they bought Troops.ai, a Slack-based tool that helped reps update their CRM without leaving Slack. Good product, real users, compelling pitch. A couple of years later, Salesforce killed it, migrated everyone to something called Slack Elevate, and Troops basically ceased to exist. The thesis was right. The execution wasn't. I don't think this means Salesforce is making a mistake. That said, I do think that the nature of being a platform this large makes it almost impossible to ship fast and stay close to users after absorbing a smaller company. There are simply organizational physics at play. The gravity of a 70,000-person company bends everything toward internal consensus and away from the best interests of the end users. Which is why I'm even more bullish on my friends over at Attention (Anis Bennaceur). Attention plays in the exact same space as Momentum. Same problem, same category of customer, same bet that conversation data is the context layer that makes AI agents useful. The difference is that Attention doesn't have to spend the next 18 months figuring out how to fit inside Salesforce. They get to keep doing the thing that made them good in the first place: talking to customers, shipping product, and not spending their Mondays in integration planning meetings. Momentum getting acquired actually validates the entire market Attention has been building in. The question for revenue teams is pretty simple. Do you want a LEGO castle? Or an agile innovator still operating independently?

I’ve been thinking a lot about what “good” enterprise SaaS selling actually looks like on the other side of this AI inflection point. Not pitching features. Not promising roadmaps. Not managing expectations. But noticing a problem, shaping the solution with the customer, and being able to say “that’s done” instead of “we’ll look at it”. That only works if you’re comfortable sitting at the intersection of product, UX, and execution. And if you’re excited by adapting the product in real time, not just talking about it. This is becoming less about persuasion and more about judgement, trust, and timing. It’s a very different job from classic enterprise sales. The people who are excited to work this way will create opportunities rather than wait for them. And I think they’ll have an unfair advantage over the next few years.

"We *just* need to agree on what the words mean!" Epics, Initiatives, Bets, Goals, Projects, Pillars, Priorities, Opportunities, Spikes, Workstreams, Value Streams, Operational Value Streams, etc. etc. etc. I love digging into what people *really* mean (implicitly, explicitly) with the words they throw around. Here's a tool I use to spark discussions about the intent behind the words.

The fastest way to kill a good strategy? Ignore the system it depends on. I’ve seen ambitious roadmaps collapse — not because the vision was wrong, but because the constraints were invisible. “Just a small change.” Until it touched five interconnected services. “Two months should be enough.” Until complexity surfaced halfway through. “Let’s support all use cases at once.” Before the core workflow was even stable. None of this happens because people lack intelligence. It happens when strategy floats too far above implementation. Technical depth doesn’t mean writing code every day. It means understanding — or having someone at the table who understands: • Architectural constraints • Where complexity hides • How data really flows • What is small — and what only looks small When those realities are surfaced early, trade-offs improve. Timelines stabilize. Roadmaps become credible. Strategy doesn’t break because ambition is high. It breaks when ambition isn’t grounded. You don’t need every product leader to be deeply technical. But you do need technical depth close enough to strategy that assumptions don’t survive too long. Otherwise, the cost shows up later. And it’s always higher. Where have you seen strategy drift too far from technical reality?

Your 12-month product roadmap is basically fiction by month 3. Here's the thing: Slack's CPO says roadmaps are the wrong artifact. In 2026, successful teams focus on outcomes, not outputs. They prototype rapidly with AI and kill dead ends without crying about it. Static plans in a fast-moving market are like bringing a map to a hurricane. Impressive dedication, mostly pointless. https://lnkd.in/dYd4thdh #ProductManagement #Agile #ProductStrategy #AIPrototyping #OKRs #ProductPrinciples

Over the past few years, I’ve seen delivery capability improve across enterprises—better teams, better tooling, faster execution—yet outcomes continue to disappoint and costs continue to rise. This article reflects on why that happens, and why the real bottleneck is no longer delivery, but decision-making upstream. Agile rarely fails in execution. It falters earlier—when intent, ownership, and economic clarity arrive late or incomplete. Sharing this as a reflection for leaders, product, and delivery practitioners navigating Agile at scale.

Are you building a faster company, or a more capable one? That’s the question underneath most AI roadmaps. In leadership meetings, I frame it as: does this AI increase the value of human expertise, or does it replace it? Sharing the full report here: “Building pro-worker artificial intelligence” (Acemoglu, Autor, Johnson) from The Hamilton Project at The Brookings Institution. Most AI roadmaps optimize for productivity. This report asks a better operating question: 𝗔 𝗾𝘂𝗶𝗰𝗸 𝗼𝘃𝗲𝗿𝘃𝗶𝗲𝘄 𝗼𝗳 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼𝗿𝘁 𝗰𝗼𝘃𝗲𝗿𝘀:  • A 5-part taxonomy of technologies (labor-augmenting, capital-augmenting, automation, expertise-leveling, and new task-creating) that helps explain why “AI adoption” can push outcomes in opposite directions  • The key idea: new task-creating tools are the most reliably pro-worker, because they expand what people can do (and what skills matter) rather than just accelerating existing output  • Practical examples of “pro-worker” designs (assistants for skilled trades, service work, teachers, decision support, accessibility) that feel closer to product strategy than policy debate  • Why these systems are underbuilt today: incentives often favor automation-first ROI, even when collaboration-first tools would produce more durable capability A simple test for 2026 planning: label each initiative as task-replacing or task-creating. If you can’t name the new tasks, you’re likely scaling output, not building capability. When you look at your AI pipeline right now, what’s the mix: task-creating vs task-replacing?

Strategy is Easy. Resource allocation is where careers are made. I've sat in rooms where everyone agreed on the strategy. The real fight started when we had to fund it. Because here's what no one tells you about operating roles: You will have to say NO to things that are actually good. 1)A headcount request you fully agree with 2)A product fix customers are begging for 3)A market opportunity that genuinely makes sense 4)A leader whose initiative is critical, just not right now That's the job. Not the vision decks. Not the all-hands. Not the strategic narrative. The job is deciding what doesn't get funded. Most companies don't die from a lack of ideas. They die from funding too many of them at once. Focus isn't a culture thing. It's not a value you put on a wall. Focus is a financial discipline and most companies are terrible at it. The operators I respect most aren't the ones who build consensus around exciting bets. They're the ones who can look a smart person in the eye and say: "You're right. And we're still not doing it." Then concentrate everything: money, people, attention on the one thing that actually has to win. That's not leadership that gets you applauded in the moment. But it's the only kind that builds something durable. What's the hardest resource allocation call you've had to make? #Strategy #OperatingModel #Leadership #ChiefOfStaff #BusinessDiscipline

I'd like to talk about the F-word. No, not that F-word. Fractional. More specifically, Fractional Product Leadership. If you're building an early or growth-stage company, it might be the smartest hire you're not making. For early and growth-stage companies, product leadership is one of the highest-leverage investments you can make, and one of the hardest roles to justify hiring full-time. You need someone who can shape product strategy, align the organization, and ensure your teams are building the right things. Someone who can work with customers, guide discovery, establish priorities, and connect product decisions to business outcomes. You need senior product leadership. Someone who has scaled products, built roadmaps, led discovery, managed engineering relationships, and knows how to translate vision into velocity. But you're not ready, financially or structurally, for full-time CPO and the associated overhead. So what happens? Either the CEO carries the product burden (alongside everything else), a junior PM operates without real guidance, or the company charges forward, building the wrong things faster. None of those are good outcomes. This is exactly where Fractional Product Leadership changes the game. Fractional product leaders provide companies access to experienced executive-level product thinking at the moment they need it most: when decisions matter, and mistakes are expensive. A seasoned fractional CPO or VP of Product brings: • Immediate strategic clarity • Honest product counsel without internal politics • Pattern recognition from scaling multiple products at multiple stages • The ability to hire, mentor, and uplevel your existing team • Experience, without the full-time overhead They can: • Align founders, executives, and investors around priorities • Focus teams on outcomes instead of output • Build effective product organizations • Avoid wasting time and capital on building the wrong things • Prepare the organization for a full-time product executive For founders: You get a thought partner who has been in the rooms you're about to walk into. For CEOs: You get product accountability and leadership, providing immediate leverage without long-term risk. For VCs: Your portfolio companies get enterprise-grade product leadership at seed and Series A economics. That's a multiplier on every dollar you've invested. Fractional isn't a compromise. It's a strategy. Many early product decisions have multi-year consequences. Waiting until the company is “big enough” for product leadership is often the most expensive choice you can make. The best companies don't wait until they can afford great leadership; they find creative ways to access it now. Not forever. But when the foundational decisions still matter most. The F-word isn't something to be afraid of. It might be exactly what your company needs. If you're a founder, CEO, or investor trying to scale a product organization or avoid expensive product mistakes? Let’s talk.

One Meta PM on the Zoom call still has hope left. You can see it in their eyes, as if their belief alone might hold the roadmap together. Then they ruin their day: “Are we still good to launch by the end of the half?” The PM hangs their question in the air like a piñata. The room takes turns destroying it. Three engineers reply as if the PM asked three separate questions. Design wants to revisit principles they revisited last Tuesday. Privacy says they “haven’t formed an opinion yet,” which always means a future opinion will be catastrophic. Someone from Legal has joined the call late and clearly confused this project with another one. The PM writes all this down without blinking, nodding the way a hostage nods when the camera is rolling. On paper, the PM job is straightforward: define what we’re building, why we’re building it, and which metrics will prove it worked. In practice, the PM spends months negotiating a mission statement only to have a director in a completely separate org casually redefine it in a product review. What was once a clean narrative becomes a diplomatic crisis with an attached Google Doc. A PM at Meta is responsible for an outcome while owning neither the people nor the systems required to achieve it. They run meetings where engineers glare at them as if asking, “Why are we doing this?” while their leadership glare at them as if asking, “Why isn’t this done yet?” The PM role at Meta is the only job where you are fully accountable for outcomes and fully dependent on people who do not report to you and may not believe the outcome should exist. A PM can ask for a deadline extension and receive a reply consisting entirely of a thumbs-up emoji and total non-compliance. But many PMs at Meta somehow survive this. Some even thrive. They become experts at manufacturing the illusion of consensus. They learn which director stops paying attention after slide 8,  which TL only trusts charts with p-values, and which data scientist expects a small amount of ego maintenance before they’ll green-light a launch. Their documents become diplomatic instruments, rewritten so every stakeholder can point to a sentence and say, “Yes, that part was my idea!” They can make a coalition align for just long enough to ship something into the world. Something real. Something millions (maybe billions?) of people use. Something the PM tiptoed through minefield of veto points.

Two new pieces on AI strategy are telling a very consistent story today. Bain & Company’s “AI Enterprise: Code Red” argues that the real risk isn’t falling behind on models—it’s treating AI as a tech project instead of a rewiring of how the enterprise creates value and makes decisions. The Harvard Business Review/Boston Consulting Group (BCG) article, “Look for New Ways to Create Value When Deploying Gen AI,” shows that across 800 U.S. firms, productivity gains from gen AI are already being competed away in many sectors; margins aren’t moving because most companies are just doing the same work, slightly faster. Put together, the message is pretty blunt: ▪️ Efficiency is table stakes; it rarely delivers durable advantage. ▪️ Gen AI that doesn’t change the design of work, products, and business models will quietly commoditize you. ▪️ The real opportunity is using AI to unlock new value pools—new services, new ways to bundle and price, new experiences—not just cheaper versions of existing ones. If you’re leading an AI agenda right now, a useful gut check might be: 👉 How much of your AI roadmap is focused on cost and throughput vs. creating new value you couldn’t credibly offer two years ago? Linking both articles in the comments—worth the read if you’re still pitching AI as “productivity” instead of “strategic re-architecture.” #GenerativeAI #DigitalTransformation #ValueCreation #Leadership #BusinessStrategy Image by: Towfiqu Barbhuiya

You closed your seed round 4 months ago. You hired 6 engineers. Velocity increased. Roadmap expanded. Board updates look impressive. But you are already 3 months late on your first commercial milestone. The prototype works. Procurement conversations are “progressing.” Pricing is still being “refined.” Meanwhile burn is €120k/month. That’s €360k since closing. And revenue validation hasn’t materially strengthened. This is where capital distortion begins. Not because engineering failed. Because sequencing drifted. Engineering accelerated. Commercial validation did not. That gap compounds quietly. Most founders only see it when the next investor conversation becomes uncomfortable. How to fix it: Freeze roadmap expansion until one commercial uncertainty is structurally reduced. Redefine the next milestone in commercial terms, not technical ones. Pressure-test pricing under real procurement conditions, not pilot enthusiasm. Align hiring decisions to validated revenue sequencing, not technical ambition. How to prevent it: Before accelerating engineering velocity, explicitly map: • What commercial uncertainty this next sprint reduces • How it strengthens pricing credibility • Whether it shortens funding risk If product momentum accelerates faster than revenue logic strengthens, sequencing needs correction. Capital efficiency is a structural discipline. Not a financial afterthought.

There are moments in product management when everything looks right on paper.... and still something feels wrong. The feature is approved The roadmap is aligned The data supports the decision — and yet you hesitate. Not because the team can’t build it, but because the real question hasn’t been fully explored- should we build it? Working in fintech products over the years — from lending platforms to financial data and bank statement analysis systems — I’ve realised that product management is not just about translating requirements into releases. It is about exercising judgment when speed, automation, and business pressure are all pushing forward at the same time. At FinEye, while building products around bank statement analysis, OCR, and account aggregator integrations, many decisions directly influence how credit is evaluated and how risk is interpreted. This becomes even more critical when you are serving financial institutions that process millions of records containing sensitive personal and financial data. In such environments, shipping fast is important, but shipping responsibly is non-negotiable. Over time, I’ve also realised that “fail fast” was never meant to mean move fast. It was meant to mean learn early, while the cost of being wrong is still small. And learning only happens when teams feel safe enough to question assumptions, pause when needed, and ask uncomfortable questions — even when everything looks correct on paper. As product managers, we don’t just ship features. We shape systems that make decisions at scale. And that means we own the consequences too. In a world where we can build almost anything, good product management is often about knowing when to slow down. Curious to hear from other product managers — Have you ever paused a feature even when everything looked correct? #ProductManagement #Fintech #ProductLeadership #Fineye #Agile #ProductThinking #DataResponsibility

In Nigeria, a broken product doesn’t get feedback. It gets abandoned. No long email. No feature request. People just quietly go back to “their guy.” That reality changed how I built CarryGo AI. Because here: Fuel finishes at 9pm and you need gas. Water suppliers promise “on the way” for 3 hours. Logistics is unpredictable. Trust is fragile. So after designing the app, the real question wasn’t: “What cool features can we add?” It was: What cannot fail in this market? Because if: – The AI misunderstands “12.5kg gas, abeg fast.” – A supplier isn’t properly verified. – Payment hangs on weak network. – Tracking feels fake. You don’t get a second chance. In Nigeria, reliability is the product. So I built the Work Breakdown Structure around risk. Not excitement. Phase 1 isn’t about going nationwide. It’s about surviving Lagos. – Make the AI truly understand Nigerian English. – Lock down supplier verification. – Make ordering simple enough for tired users at 10pm. – Make delivery feel predictable in an unpredictable system. No expansion. No “we’ll fix it in v2.” No scaling chaos. Because here, chaos is expensive. And Nigerian users don’t tolerate experiments with their essentials. Most people think product management is about features and growth. In this market? It’s about sequencing trust. What must work before anything else matters? That’s the question I keep asking. And honestly, that question is harder than any design decision. Tomorrow I’ll share the one assumption about Nigerian logistics that forced me to reorder my entire Phase 1 plan. #NigeriaTech #BuildingInPublic #ProductThinking #Startups #CarryGoAI

📉 Hewlett-Packard + Autonomy Corporation (2011): $11B Marketed as a bold move into enterprise software. A pivot away from low-margin hardware. A signal to Wall Street that HP could compete at a higher multiple. Within a year? 💥 $8.8B write-down. Yes, accounting irregularities were cited. But that wasn’t the whole story. Behind the scenes: • Leadership turnover during integration • Conflicting product roadmaps • Sales teams unsure how to position Autonomy • Cultural mismatch between Silicon Valley hardware and UK enterprise software • No clean alignment between systems and go-to-market execution Strategy looked sharp in the boardroom. Execution fractured in the field. The problem wasn’t just valuation. It was post-merger integration discipline. When product vision, systems architecture, and incentives aren’t unified fast — confusion compounds. Revenue stalls. Talent exits. Narrative collapses. And once the write-down hits? The market decides the deal “failed.” M&A doesn’t implode overnight. It erodes through slow integration drag.

Agile is dead. Not because it failed. Because speed made it irrelevant. When iteration cycles were 2-week sprints, "move fast and learn" made sense. You had time to course-correct. Time to discover what you should have known before you started. That luxury is gone. AI compressed the build cycle from weeks to hours. Ship a feature before lunch. Run 40 experiments by Friday. Deploy a quarter's work in an afternoon. The paradox nobody's talking about: The faster you can execute, the more dangerous it becomes to not know where you're going. Welcome back, Waterfall. Sort of. Not the bureaucratic 200-page-spec version. A sharper one. Forces you to think before you move. To articulate not just what you're building — but what you're deliberately NOT building. And why. Eric Ries said Build-Measure-Learn. Reid Hoffman said Blitzscale. Annie Duke said Think in Bets. All three are still right. But the sequence has flipped. Old world: Build → Measure → Learn → Adjust the bet. New world: Place the bet first → Build only what the bet demands → Learn whether your thesis was right. And you're no longer placing one bet at a time. You're managing a portfolio. Some safe, some bold, all intentional, mapped like an investor maps positions. Understanding the critical path across all of them simultaneously, not sequencing them into neat little sprints. You plan for pivots before you write the first line of code. Because the old model had a trap nobody talks about. Iterate fast, pivot and inherit every shortcut you took getting there. Three pivots in and your architecture is a house of cards. Tech debt compounding with every "agile" decision. The new model demands architecture designed for pivots at scale. Systems that absorb strategic shifts without collapsing under yesterday's quick wins. Here's the real shift: Building isn't the bottleneck. Deploying isn't. Testing isn't. The only thing limiting validation now is marketing budget. You can build five product variations in the time it used to take to argue about one PRD. The constraint moved from engineering capacity to distribution spend. From "can we build this?" to "can we get this in front of enough people fast enough to learn?" Your product strategy is no longer a deck forgotten in a shared drive. It's an operating manual read by AI agents. Parsing your strategy. Following your prioritisation frameworks. Using your "what we won't do" list as actual constraints. Every vague sentence becomes a vague output. Every missing "no" becomes an accidental "yes." Lean Startup said embrace uncertainty. Blitzscaling said embrace speed. The next era demands we embrace clarity. Agile gave us permission to not know. That permission just expired. #ProductStrategy #ThinkingInBets #ProductLedGrowth #AIStrategy #LeanStartup

From Sprints to Days: How AI Is Rewriting the Product Build Cycle For years, product development revolved around sprints. Two weeks. Planning cycles. Backlog grooming. Engineering capacity as the primary constraint. That cadence shaped capital allocation, hiring plans, and investor expectations. Today, that constraint is moving. With modern AI tooling, product managers can move from concept to working prototype in days. Backend logic, workflows, integrations, data transformations. What once required a full sprint commitment can now be validated before formal engineering cycles even begin. This is about shifting where leverage lives inside the organization. When early system creation becomes dramatically faster, product managers sit at the nexus of idea formation and execution. They are no longer handing off static requirements. They are building working artifacts, testing assumptions, and reducing ambiguity before meaningful engineering investment is deployed. For CEOs preparing for funding or acquisition, this matters. When build cycles compress, the bottleneck becomes clarity. Investors are not evaluating how quickly you can ship a feature. They are evaluating whether your organization can consistently allocate capital toward the right problems. AI-enabled product managers can validate demand before scaling engineering spend, demonstrate working systems instead of slides, and shorten the feedback loop between customer signal and product response, reducing rework before production hardening begins. Engineering becomes more leveraged, not less relevant. Instead of spending cycles on early scaffolding, teams can focus on production resilience, system architecture, data integrity, security, and long-term scalability. The quality bar rises as the experimentation layer accelerates. As companies approach funding rounds or liquidity events, the question shifts from "Can you build it?" to "Can you make disciplined decisions about what deserves to be built?" When iteration moves from sprints to days, capital efficiency improves. Risk surfaces earlier. Strategic alignment becomes visible. The organizations that benefit most from this shift will treat AI tooling as a force multiplier for product judgment, not as a shortcut around discipline. From sprints to days is not just a speed story. It is a capital allocation story. That distinction is what separates companies that raise on strength from those that scramble to explain their burn.

Product Management Is Systems Thinking (Even If We Don’t Call It That) Many people think Product Management is about: • Roadmaps • Backlogs • Standups • Shipping features That’s just execution. Good PMs execute well. Great PMs understand systems. A Product Is Not a List of Features A product is a system. It includes: • Users • Incentives • Metrics • Constraints • Human behavior • Company politics Everything is connected. When one thing changes, something else moves. When a Feature Fails… It’s rarely because the feature was “bad.” Usually, it’s because: – You improved one metric but hurt another – You fixed a small problem and created a bigger one – You ignored second-order effects – You underestimated how people would react Example You increase discounts. Conversions go up. But then: Retention goes down. Customers expect discounts. Paid growth becomes necessary. Pricing power drops. Brand perception weakens. That’s not a feature problem. That’s a system problem. 2026 Reality: Execution Is Cheap AI can now: • Build faster • Prototype instantly • Run experiments at scale Speed is no longer the advantage. Execution is becoming a commodity. The Real Differentiator: What will matter is system awareness. Because AI can build features. But it still struggles with: – Incentive misalignment – Organizational politics – Tradeoffs – Long-term consequences – Human irrational behavior The future Product Manager isn’t just a backlog manager. The future Product Manager is a system designer. #ProductManagement #SystemsThinking #ProductStrategy #AI #FutureOfWork #ProductLeadership

Dear CXOs, quick question. Is your AI strategy aligned with your people’s incentives? In the middle of all this AI rush, the real context war is not just vendors vs vendors. The thing is vendor vs vendor competition speeds things up. But vendor vs the people closest to the workflow slows it down just as much. I did not fully get this until recently. Earlier, most AI products I built and worked on were for people who were already quite ahead on the tech curve. They might sometimes wonder how their role would evolve, but they could also see the upside. Because they understood the system deeply, they were usually the best people to make AI actually useful. But in a lot of operational environments, it feels very different. The people closest to the workflow know that most AI efforts start by extracting their tribal knowledge. And that tribal knowledge is literally their leverage today. So when the system starts learning it, the obvious question becomes, where does that leave me? And this is where things slow down. Not in loud, obvious ways. But in small things. Extra skepticism. Harsh judgments based on one random demo. "AI cannot really do this." And leadership ends up hearing more doubt than possibility. To be clear, this is not irrational. It is human. If I could not see how I grow in an AI first world, I would probably resist it too. Which makes me think the context war is not just about who has the best tech. It is also about who helps the people closest to the workflow see a future for themselves in it. Genuinely curious, for leaders, how are you navigating this inside your orgs? And for people building or deploying AI, are you seeing this too? #AI #ArtificialIntelligence #EnterpriseAI #IndustrialAI #FutureOfWork #AIAdoption #Leadership #Startup #Founders

Section analysed 4,500 AI use cases and found that most organisations give employees AI licenses and ask them to discover their own use cases. On top of their actual jobs. But use case development cannot be a personal responsibility for employees. When we hand someone an AI tool and ask them to "figure out where it fits," we're offloading strategy. Adoption measured by access and frequency stopped being a meaningful signal the moment every employee got a license. The real question isn't "are people logging in?" It's "do people know what they're supposed to accomplish with this?" Section recommends: 1. Build role-specific use case libraries. A focused set of applications tied to what an engineer, a product manager, a marketer, or a financial analyst actually does every day, not generic prompts, but starting points grounded in real work. 2. Make use case development a manager accountability. Every manager should identify and track at least three meaningful AI applications per direct report. Tie it to performance expectations. If it isn't measured, it gets deprioritised. 3. Create feedback loops so effective use cases can spread. A lightweight process for employees to share what's actually working with their teams.  Link to the full post in comments.

𝗪𝗵𝗲𝗿𝗲 𝘀𝗲𝗻𝗶𝗼𝗿 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗮𝗿𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘀𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗔𝗜 We talk a lot about scaling AI. But this HBR research highlights something more fundamental - senior leaders are struggling not with tools, but with 𝘤𝘭𝘢𝘳𝘪𝘵𝘺. The biggest friction points aren’t technical: • Translating AI ambition into specific business priorities • Redesigning workflows - not just layering AI on top • Defining ownership across product, tech, and operations • Measuring real impact beyond pilots and demos What stands out to me: adoption stalls when AI is treated as an “initiative” instead of an operating model shift. AI is not a feature. AI is not a lab experiment. AI is not a side project for innovation teams. It’s a cross-functional transformation that forces leaders to answer uncomfortable questions about incentives, org design, and decision rights. The companies pulling ahead are doing three things differently: • Starting with measurable business problems, not abstract capability • Embedding AI into core processes early • Investing in evaluation and change management as first-class priorities The hard part of AI adoption isn’t model quality anymore. It’s leadership alignment. #AIrevolution #ArtificialIntelligence #LargeLanguageModels #GenerativeAI

The Founder Illusion Nobody Talks About There’s a quiet illusion that traps smart founders. “If I just push a little harder, this will scale.” At Yo! No Code, we’ve seen this mindset repeatedly especially with capable operators who’ve already proven they can build. The product works. Revenue exists. Users return. But scale refuses to accelerate. So the instinct is: • Improve the UI • Add one more feature • Experiment with another channel • Launch something “bigger” Yet none of those moves address the real constraint. The Real Constraint Growth doesn’t stall because of effort. It stalls because of invisible structural ceilings. Here are three we see constantly: 1️⃣ Decision Saturation Too many decisions still require the founder’s judgment. That caps speed. 2️⃣ Monetization Leakage Revenue exists but pricing isn’t tightly anchored to the repeat outcome. Margins erode quietly. 3️⃣ Distribution Fragility Acquisition works but only when the founder drives it personally. Remove them, and the engine weakens. None of these are product failures. They are architecture failures. A Pattern We Noticed In one evaluation, we found: • The founder personally handled 80% of sales objections • The core workflow delivered 90% of value • 70% of roadmap items were edge-case requests • Automation existed but wasn’t trusted The business wasn’t underperforming. It was structurally constrained. Once the repetitive logic was embedded, pricing tightened around the repeat value, and messaging standardized, growth didn’t spike. It stabilized. And stability is what allows acceleration. There’s a difference between momentum and compounding. Momentum feels exciting. Compounding feels controlled. One burns energy. The other builds inevitability. The question isn’t: “How hard am I pushing?” It’s: “What still depends on me?” #YoNoCode #founders #systems #productarchitecture #scale #automation

We went from zero to 20+ AI agents in 9 months. But how to really manage them is the one thing nobody really tells you about. The numbers sound great: → $2.75M+ booked by AI SDRs → 5-7% outbound response rates (2x industry average) → 150,000+ customer chats handled → Eight-figure revenue with single-digit headcount But every single agent requires weeks of training and daily management. There’s no magic button. Every agent has hallucinated at some point. Quality requires constant calibration, not perfection. Managing 20+ agents now consumes 30% of our Chief AI Officer’s time. The 5 biggest mistakes executives make with AI agents: 1. Running 8-10 vendor bakeoffs instead of picking one and going deep 2. Expecting automation and getting a 30%-of-your-time management job 3. Scaling too fast — you can only absorb ~1.5 agents per month effectively 4. Ignoring soft costs — “quiet can be lonely” when AI replaces most of your team 5. Judging vendors by demos instead of talking to the person who actually deploys your agent The agents that “don’t work” are the ones nobody trained. The first agent is YOUR job — not an agency’s, not a consultant’s. 30 days of hands-on work. No shortcuts. But if you’re willing to invest the effort, the competitive advantage is massive. We’re their #1 performing customer at both Artisan and Qualified. That didn’t happen by accident.

The most interesting AI conversations are not happening online. They are happening quietly inside companies. A CTO testing agents on internal workflows. A founder replacing 6 hours of meetings with one AI dashboard. A product team discovering their roadmap moves twice as fast with async decisions. 𝗡𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲𝘀𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗽𝗼𝗹𝗶𝘀𝗵𝗲𝗱 𝗲𝗻𝗼𝘂𝗴𝗵 𝘁𝗼 𝗽𝗼𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝘆𝗲𝘁. 𝗕𝘂𝘁 𝘁𝗵𝗲𝘆 𝗮𝗿𝗲 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆𝘄𝗵𝗲𝗿𝗲. Which is why the AI shift feels confusing from the outside. 𝗣𝘂𝗯𝗹𝗶𝗰 𝗱𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻 𝗶𝘀 𝘀𝘁𝗶𝗹𝗹 𝗱𝗲𝗯𝗮𝘁𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀. 𝗣𝗿𝗶𝘃𝗮𝘁𝗲 𝘁𝗲𝗮𝗺𝘀 𝗮𝗿𝗲 𝗿𝗲𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗵𝗼𝘄 𝘄𝗼𝗿𝗸 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗺𝗼𝘃𝗲𝘀. Less coordination. More architecture. Less meetings. More systems. 𝗧𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄 𝗶𝘀 𝗻𝗼𝘁: “What AI tool should we use?” It is: “𝗪𝗵𝗮𝘁 𝘄𝗼𝗿𝗸 𝘀𝗵𝗼𝘂𝗹𝗱 𝗻𝗲𝘃𝗲𝗿 𝗿𝗲𝗾𝘂𝗶𝗿𝗲 𝗮 𝗺𝗲𝗲𝘁𝗶𝗻𝗴 𝗮𝗴𝗮𝗶𝗻?” Curious what others are seeing. 👇What experiment inside your company quietly changed how work happens?

I watched a leadership team debate resource allocation for 90 minutes last Thursday. Everyone agreed on the priorities. Everyone nodded at the timeline. Everyone committed to the deliverables. Three weeks later, I saw the same team in crisis mode. Operations thought "accelerate product development" meant hire faster. Sales thought it meant compress the launch timeline. Product thought it meant reduce feature scope. Finance thought it meant maintain the current burn rate. Same words. Four different executions. The breakdown didn't show up in their weekly dashboards. No red flags in the KPIs. No variance alerts. Just quiet drift that compounded until delivery stress hit. Most leadership teams think alignment means agreement. But agreement without shared interpretation creates execution gaps that don't surface until the damage is done. The friction lives in the space between "we all heard the same thing" and "we all understood the same thing." What interpretation gap have you noticed only after execution started?

Ask a product manager what their job is, and they’ll say strategy, customer insight, prioritisation. Ask what they did last week, and you'll hear: stakeholder alignment meetings, Jira ticket grooming, customer escalations, roadmap presentations for executives who want dates. The gap between “what product management should be” and “what it actually is” comes from the same structural forces. The organisation and the PMs don’t carve out time for the hard work: understanding customer needs independently of what sales is asking for and what engineering wants to build. So the strategic work gets squeezed into Friday afternoons. If it happens at all.

لما كل إطلاق feature يحتاج CSM يترجمه للناس… اعرف إن الـ Product مش ماسك المنتج بجد. في شركات SaaS كتير… فريق Customer Success بيتحول تدريجيًا لـ Product Owner في الظل. تلاقيه هو اللي بيعمل: • توثيق للمشاكل أو الـ bugs • ترتيب أولويات ملاحظات العملاء أو الـ feedback • ترجمة أحتياج العميل لمتطلبات واضحة للفريق • شرح تفاصيل المنتج داخليًا • سد فجوات المعرفة بين الفرق المختلفة ليه ده بيحصل؟ عشان فريق Customer Success هو أكتر حد فاهم المنتج وعايش يوميًا مع العملاء. بس هنا في نقطة مهمة: لما فريق الـ CS يبقى هو المسؤول عن وضوح المنتج… يبقى في مشكلة في تصميم النظام الداخلي للشركة. دور Customer Success هو إنه يكون صوت العميل. ودور فريق Product هو امتلاك خطة التطوير أو الـ roadmap. الاتنين مختلفين تمامًا. فريق الـ CS المفروض يقدم رؤية استراتيجية جاية من السوق. وفريق الـ Product هو اللي يملك التنفيذ، وضبط الجودة، وتنظيم الـ Releases. لما الخط الفاصل ده يختلط: • فريق الـ CS يشتغل بطريقة تفاعلية أو reactive طول الوقت • الـ Releases تبقى عشوائية ومتوترة • والوقت الاستراتيجي مع العملاء يضيع شركات SaaS القوية بتتعامل مع كل ميزة جديدة كأنها إطلاق منتج صغير، وده بيشمل: • وجود ownership واضحة • عملية review process محددة • التزام بمواعيد الـ Release • تنسيق كامل بين فرق Product و Marketing و Customer Success و Support فريق الـ CS مش المفروض يبقى طبقة الربط البشرية بين كل الفرق. لو ده بيحصل… تبقى دي مشكلة في تصميم السيستم. وأي سيستم ينفع يتعاد تصميمه بشكل أذكى.

Boris Cherny, creator of Claude Code at Anthropic, says coding as we know it is effectively “solved” and predicts the title “software engineer” could begin to fade. He argues AI tools are already pushing engineers into broader “builder” roles focused on product strategy and systems thinking. Cherny says Claude Code now writes all of his production code. Given the scale of AI disruption, he believes the broader societal implications “shouldn’t be up to us,” and that society must lead the larger conversation about the future of work. 🔗 Read the full story in the bio. ➡️ https://lnkd.in/gY95-C6A #AFROTECH

The Software Team of the Future Won’t Look Like Today. At AppifyDevs, we’re experimenting with something radical. We built an AI Tech Team. Not a single assistant. Not a code generator. An entire Multi-Agent AI Workforce working together like a real company. A Product Manager Agent defining scope. A Technical Lead Agent reviewing architecture. An Architect Agent designing scalable systems. Backend & Frontend Agents building features. A QA Agent stress-testing every edge case. A DevOps Agent preparing deployment pipelines. A Marketing Agent preparing launch strategy. All collaborating. All reviewing each other. All coordinated by an AI Orchestrator. This is not automation. This is structured AI collaboration. We are currently testing this multi-agent model internally on selected products to explore: ⚡ Faster product cycles ⚡ Autonomous validation loops ⚡ Built-in quality control ⚡ AI-driven architectural thinking ⚡ Parallel execution at scale The future of software won’t be: Developer → QA → DevOps → Launch. It will be: Human Vision → AI Workforce → Accelerated Execution. We don’t believe AI replaces engineers. We believe AI multiplies high-performing teams. And this is just the testing phase. The real shift? Companies won’t compete based on team size. They’ll compete based on how intelligently they design, orchestrate, and deploy AI agents. Welcome to the era of AI-native companies. We’re not just adapting to the future.We’re building it. #AppifyDevs #AIWorkforce #MultiAgentAI #FutureOfSoftware #AIEngineering #BuildTheFuture #StartupInnovation #ArtificialIntelligence #TechLeadership #AutomationRevolution

BY 2026, HAVING A HUMAN PRODUCT TEAM WILL BE CONSIDERED A CHARITY PROJECT FOR INEFFICIENT FOUNDERS. 🛑📉 Stop treating your smartest people like "Human APIs" for Jira. Writing 50-page PRDs and "circling back" on Slack isn't product management—it’s digital manual labor that burns your runway and kills your momentum. The era of the INFINITE PM AGENT is here. 🤖✨ --- 1️⃣ THE SPEC-CRUSHER 📑 Stop waiting weeks for documentation no one reads. This agent generates 50,000 pages of technical specs in seconds and sends the CEO a single "Stonks" emoji summary while you’re still pouring your morning coffee. 2️⃣ THE BLINK-AND-DONE PM ⏱️ Stop wasting an hour on Zoom watching people struggle with the "Unmute" button. This agent conducts 500 simultaneous standups in 0.05 seconds, planning a 3-year roadmap before your team can even say "alignment." 3️⃣ THE INSTANT ARCHITECT 🎨 Stop begging designers for "one more iteration" on a wireframe. This bot ships 10,000 high-fidelity mockups before your brainstorming meeting starts, ending the "feedback loop" forever. --- Stop hiring humans to act like data-entry robots. ⚡💼 --- 👇 WHICH OF THESE 3 AGENTS WOULD YOU HIRE RIGHT NOW? DROP A 1, 2, OR 3 IN THE COMMENTS! 👇 #DelegateAI #AgenticAI #FutureOfWork #Automation #TheAITalkshow

Why Most Startups Confuse Momentum With Direction Momentum feels amazing. Slack is buzzing. Features are shipping. Revenue is ticking upward. The roadmap is packed. It looks like progress. But motion is not the same as meaning. And speed is not the same as strategy. Many startups move quickly because they can, not because they’re certain where they’re going. They build what customers ask for. They chase what seems to convert. They expand into segments that appear adjacent. Each decision makes sense in isolation. Collectively, they drift. Momentum requires energy. Direction requires judgment. Momentum says, “We’re growing.” Direction asks, “Are we growing toward something defensible?” Momentum compounds activity. Direction compounds advantage. Without direction, speed just gets you lost faster. The uncomfortable truth: Most teams don’t slow down long enough to examine their underlying thesis. Who exactly are we for? What are we uniquely positioned to win? What are we deliberately choosing not to do? Direction is defined more by what you refuse than by what you pursue. And refusal feels risky. So instead, companies default to movement. Because movement feels safe. The companies that endure aren’t always the fastest. They’re the most coherent. Their product, positioning, pricing, and strategy reinforce the same core belief. They don’t just build momentum. They build alignment. For founders, operators, and investors alike, the question is simple: Are we accelerating toward a clear idea of the future? Or are we just accelerating? Momentum is visible. Direction is intentional. Only one of them builds something that lasts.

Sales and Product are arguing again this quarter. Same debate. Different words. I've been in that room when it cost us six weeks and a seven-figure enterprise deal. Everyone called it "misalignment." It wasn't. It was missing decision rights. Sales wanted the feature to close the deal. Product wanted to protect the roadmap. Both were right. No one owned the tie-breaker. So it went to the CEO. Then back to Product. Then back to Sales. Three exec meetings. Two board updates. Zero decision. That's not collaboration. That's theatre. Here's the uncomfortable truth: If Sales and Product are debating the same trade-off every quarter, leadership has failed to define authority. Alignment workshops won't fix that. Better communication won't fix that. Only ownership will. Assign a DRI (Directly Responsible Individual). One person makes the call when they disagree. They hear both sides. They own the trade-off. They log the decision. In one place the team can see. Not Slack. Not email. So when it reopens, you point to what you already decided. If it reopens, it goes back to the same DRI. Not to the CEO. Not to you. Most exec teams say they value speed. But they design for consensus. You don't need more alignment. You need fewer referees. Who owns the tie-breaker at your company?

Yesterday I was reading the Block story. The company said it would cut over 4,000 jobs, nearly 50% of its workforce- a devastating week for thousands who were impacted. But in contrast, the stock rose 25% in after-hours trading after the announcement. The sentiments have changed. The definition of scale is changing: From more people, more functions, more layers to fewer people, better tools, stronger systems. That changes hiring and org design. That changes what makes someone valuable inside a company. And I think it changes product too. For a long time, product teams were built around execution scarcity. Limited engineers. Limited design bandwidth. Limited time. But if AI keeps reducing the cost of building, prototyping, and iterating, then the bottleneck shifts. Not to effort. To judgment. The real advantage is no longer just shipping more. It is: knowing what matters, running better experiments, learning faster from users, and finding signal in a much larger set of possibilities. Maybe that is where product is headed. Less roadmap policing. More clarity, taste and speed. More responsibility for deciding what deserves attention in a newer world where almost anything can be built. How do you see the role of product and engineering changing as AI lowers the cost of execution?

GOOD TO KNOW: Open AI recruits consulting firms. Open AI announced this week that the "limiting factor for seeing value from AI in enterprises isn’t model intelligence, it’s how agents are built and run in their organizations." With the launch of its Frontier product (https://lnkd.in/ernQvjmU), Open AI has made a strategic move to fill the gap between the technology and the corporations applying it. They have recruited McKinsey, BCG, Accenture and CapGemini to work alongside their Forward Deployed Engineering (FDE) team, combining OpenAI’s research and product expertise with deep transformation experience and global delivery teams. Each partner is investing in dedicated practice groups and building teams that will be certified on OpenAI's technology. BCG and McKinsey will help customers deploy AI across their organizations - building the strategy, operating model, and change management plan needed for sustained impact. Accenture and Capgemini will advise on strategy and then help wire Frontier into the systems and data enterprises actually run on - securely and reliably. Article: https://lnkd.in/eNVDnWXN

Most founders don’t wake up and think, “I need support.” They think, “I just need to push harder.” But the early signals that support is needed are rarely emotional. They’re structural. Here are three patterns I consistently see before a company hits a ceiling. 1. Every meaningful decision still routes through you. You’ve hired smart people. You trust your team. And yet priorities stall or shift the moment you’re not directly involved. That’s not a talent issue. It’s a decision design issue. If progress only moves at the speed of your availability, you are not leading a scalable system. You are functioning as the system. Over time, that becomes product development risk — not just founder fatigue. 2. Your roadmap is shaped by urgency, not filters. A loud customer request reshuffles priorities. An investor suggestion suddenly becomes “strategic.” A new opportunity feels too promising to ignore. Without clear evaluation criteria and governance, everything can sound important. The result isn’t lack of effort. It’s diluted focus. When strategy doesn’t have filters, momentum doesn’t compound. 3. You feel indispensable — and quietly uneasy about it. You know the company depends on you. You’re proud of that. But you also know that if you stepped away for a few weeks, execution would wobble. When clarity, trade-offs, and escalation all depend on one person, growth becomes directly tied to that person’s cognitive load. That’s not strong leadership. That’s structural fragility. Needing support at this stage doesn’t mean you’re failing. It means complexity has outgrown your current operating model. This is why I don’t describe my work as coaching. I don’t sit on the sidelines asking reflective questions. I step in as a fractional product partner and we redesign the structure together — decision rights, roadmap filters, governance rhythms, and founder role clarity. Focus90 is built for this exact moment. Over 90 days, we identify the core bottlenecks creating risk in your product and leadership model, and we implement the structural shifts required to remove them. Not in theory. In practice. Together. If you’re recognizing your company in one of these signals, message me “Focus90” and I’ll share how it works. You don’t need to push harder. You need a stronger system.

How I’m Making Product Decisions in the AI Era If judgment is becoming the differentiator, then decision-making needs to get sharper. Here are the filters I’m using before building anything: 1. If AI disappeared tomorrow, would this still matter? If the value only exists when execution gets cheaper, it’s fragile. 2. Is this solving a painful problem, or just showcasing capability? Most AI features are demos disguised as strategy. Noise is loud right now, real pain is often quieter. 3. Does automation reduce friction or remove responsibility? Automation that erodes user agency isn’t innovation. It’s dependency. Just because AI can compress a workflow doesn’t mean it should. Humans still need to be in the loop for judgment calls. 4. Are we building because we can or because we should? What behavior are we reinforcing? Speed? Dependence? Confidence? Skill erosion? Clarity? Capability is not a roadmap. AI has reduced the cost of building. It hasn’t reduced the cost of building the wrong thing. Bad decisions are expensive, and we’ll see the fallout if product, design, and engineering teams don’t have streamlined processes and guardrails. Speed used to be the advantage. Now discernment is. What are you saying no to today? Which filters are you using in your decision-making?

This morning was starkly (pun intended) different from 6 months ago. I spun up an agentic swarm, built, tested and deployed a full Customer 360 with VOIP and AI integration into sandbox for my core client. Then knocked out a complete go-to-market strategy for a product, 20 message variants, 4 customer segments, deep psych profiling, top performers scored, $12K–$18K impact roadmap. Done. All before 8am. With headphones on and coffee going. We're in the age of Jarvis. You're the Iron Man. The AI isn't the hero, you are. It just amplifies what you're capable of. And I'm just ONE person on our team. My teammates are literally building software that had $500M valuations a year ago. From their home offices. What would you build with your personal Jarvis? #AI #Agentics #AIStrategy #FinanceLeadership #Innovation