Scaling Innovative Ideas

My team and I get pitched 5–10 new businesses every week. Mostly from entrepreneurs trying to raise money. If you want your message or pitch to stand out to investors, do this: 1. Start with the problem, not the product. If I don’t feel the pain, I won’t value the solution. 2. Be brutally clear. My team should understand your business in 10 seconds or less. 3. Show traction, not just vision. Even if it's small, show me that the market wants it and you know how to deliver. 4. Tell me why you’re the one. I’m investing in you as much as the idea. Show conviction, not just ambition. 5. Make it a conversation, not a monologue. Curiosity builds trust. Ask good questions and make it collaborative. Keep it simple.

We scaled Chatbase from a side project to a $6M ARR startup. No sales team, no VCs, just product‑led growth. Here is the full strategy for scaling to millions purely through product-led growth. 1. 𝗣𝗶𝗰𝗸 𝗮𝗻 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝘄𝗶𝘁𝗵 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀. Look for time sinks, spreadsheets, and hacked-together workflows that people already pay to solve. Don't try to invent smth never seen before if this is your first startup. You're either a genius or it's not going to work, and it's most likely the latter. 2. 𝗦𝗵𝗶𝗽 𝗮𝗻 𝗠𝗩𝗣 𝗶𝗻 3 𝗱𝗮𝘆𝘀. Your only goal here is to have a Stripe button on a landing page. Anything more is just procrastination. 3. 𝗕𝘂𝗶𝗹𝗱 𝗶𝗻 𝗽𝘂𝗯𝗹𝗶𝗰 𝗮𝗻𝗱 𝗳𝗿𝗮𝗺𝗲 𝗶𝘁 𝗮𝘀 𝘀𝗵𝗮𝗿𝗶𝗻𝗴, 𝗻𝗼𝘁 𝘀𝗲𝗹𝗹𝗶𝗻𝗴. Talk like a friend showing progress, not a founder pitching. 4. 𝗠𝗮𝗸𝗲 𝘀𝘂𝗿𝗲 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝘄𝗼𝗿𝗸 𝗳𝗹𝗮𝘄𝗹𝗲𝘀𝘀𝗹𝘆 𝗯𝗲𝗳𝗼𝗿𝗲 𝗮𝗱𝗱𝗶𝗻𝗴 𝗻𝗲𝘄 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀. This will reduce churn of your users and increase long term trust. Your MVP should be very small and very reliable. 5. 𝗠𝗮𝗻𝘂𝗮𝗹𝗹𝘆 𝗳𝗶𝗻𝗱 𝘆𝗼𝘂𝗿 𝗳𝗶𝗿𝘀𝘁 100 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀. DM people in niche communities who've complained about the exact problem you solve. Create value-first posts: "Built this tool that [solves X problem], looking for 5 testers..." 6. 𝗠𝗶𝗻𝗶𝗺𝗶𝘇𝗲 𝗰𝗹𝗶𝗰𝗸𝘀 𝘁𝗼 𝘁𝗵𝗲 “𝗮𝗵𝗮” 𝗺𝗼𝗺𝗲𝗻𝘁.  Every extra click is a tax on conversion. Simplify the path from signup → value. 7. 𝗚𝗶𝘃𝗲 𝗮𝗺𝗮𝘇𝗶𝗻𝗴 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝘀𝘂𝗽𝗽𝗼𝗿𝘁. Users willing to talk are basically paying to be your focus group. Treat them well. 8. 𝗦𝗼𝗺𝗲𝗼𝗻𝗲 𝗯𝗼𝘂𝗴𝗵𝘁? 𝗧𝗮𝗹𝗸 𝘁𝗼 𝘁𝗵𝗲𝗺 (𝗮 𝗹𝗼𝘁). Jump on calls, watch them screen‑share, ask why they almost didn’t buy. 9. 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝘃𝗶𝗿𝗮𝗹 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀. Partner with the influencers other influencers copy.  Talk about your growth for more growth. 10. 𝗦𝗘𝗢 𝗶𝘀 𝗮 𝗯𝗲𝗮𝘂𝘁𝗶𝗳𝘂𝗹, 𝗰𝗼𝗺𝗽𝗼𝘂𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗶𝗻𝗴. Blog today so Google sends users tomorrow, next month, and next year. FYI, PLG doesn't mean staying small. You can always add a sales team and move upmarket later. This will be much easier with all the learnings from self-serve customers. This is what we're doing now on our way to $100M ARR.

⚡ 𝐀𝐫𝐞 𝐠𝐨𝐯𝐞𝐫𝐧𝐦𝐞𝐧𝐭𝐬 𝐚𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐬𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐭𝐫𝐚𝐧𝐬𝐢𝐭𝐢𝐨𝐧𝐬 — 𝐨𝐫 𝐡𝐨𝐥𝐝𝐢𝐧𝐠 𝐭𝐡𝐞𝐦 𝐛𝐚𝐜𝐤?⁣ ⁣ Our new open-access article in 𝘌𝘯𝘷𝘪𝘳𝘰𝘯𝘮𝘦𝘯𝘵𝘢𝘭 𝘚𝘤𝘪𝘦𝘯𝘤𝘦 𝘢𝘯𝘥 𝘗𝘰𝘭𝘪𝘤𝘺 shows that governments don’t just “support” or “block” change — they take on 𝐬𝐢𝐱 𝐝𝐢𝐬𝐭𝐢𝐧𝐜𝐭 𝐫𝐨𝐥𝐞𝐬 in how they respond to emerging sustainable alternatives (“niche innovations” — new technologies, practices, and ideas that challenge the status quo):⁣ ⁣ • 𝐂𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐨𝐫 — actively resisting or disadvantaging new alternatives to protect incumbents⁣ • 𝐂𝐨-𝐨𝐩𝐭𝐞𝐫 — adopting niche practices into mainstream systems, sometimes altering their intent⁣ • 𝐂𝐨-𝐞𝐱𝐢𝐬𝐭𝐞𝐫 — allowing new alternatives to exist alongside the dominant system without major change⁣ • 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐨𝐫 — working directly with innovators to nurture and scale new practices⁣ • 𝐂𝐨𝐦𝐩𝐫𝐨𝐦𝐢𝐬𝐞𝐫 — negotiating between incumbents and innovators to find middle ground⁣ • 𝐂𝐨𝐥𝐥𝐚𝐩𝐬𝐞𝐫 — deliberately phasing out old systems to create space for new ones⁣ ⁣ Using cases from the Dutch agri-food system, we demonstrate how governments at times protect incumbents and resist innovation, while in other moments they collaborate, negotiate, or even create space for new practices to thrive.⁣ ⁣ 👉 𝘙𝘦𝘢𝘥 𝘵𝘩𝘦 𝘧𝘶𝘭𝘭 𝘢𝘳𝘵𝘪𝘤𝘭𝘦: https://lnkd.in/g-ybVAYA ⁣ This typology offers both researchers and policymakers a new lens to reflect on government roles:⁣ 🔎 What role is your government playing right now?⁣ 🔎 And what role should it be playing to accelerate transitions?⁣ ⁣ 💡 Our findings suggest governments are more likely to play positive roles when they work across silos, respond to societal pressures, and make deliberate choices to nurture alternatives.⁣ ⁣ Many thanks to Hens Runhaar for leading on this article. ⁣ Sophie van Doorm Laura Bello Cartagena Sabine de Graaff Charlotte Offringa Laura van Oers Nataliia Pustilnik Evelien Remorie Willem Lageweg Copernicus Institute of Sustainable Development, Utrecht University Monash Sustainable Development Institute Sustainability Transitions Research Network (STRN)

In Bangladesh, where catastrophic flooding now affects millions of households annually, an organisation called Shidhulai Swanirvar Sangstha has been operating solar-powered floating schools for over two decades — but the model is gaining renewed international attention in 2026 as climate adaptation moves from policy documents into infrastructure design. The boats function as classrooms, libraries, digital learning centres, and clinics, reaching children who would otherwise lose months of schooling each year to seasonal flooding. ⛵📚 The scale of the problem these schools address is significant. Bangladesh is one of the world's most flood-vulnerable countries, with over 17% of its land at risk of permanent inundation by 2050 under moderate climate projections. During monsoon season, rural communities in the northern and central river delta regions can be cut off for weeks. Conventional school buildings simply stop functioning. Children — particularly girls — are disproportionately affected when schools close and families prioritise boys' continued education. 🌊👧 The floating school model inverts the infrastructure assumption: instead of building land infrastructure that floods will damage, build infrastructure that operates on the water. The boats are solar-powered for both propulsion and onboard electricity. Digital tablets, satellite connectivity, and trained local teachers bring curricula aligned to Bangladesh's national standards. Women and girls also receive training in climate-adaptive agriculture, water management, and financial literacy aboard designated floating training centres. ☀️📱 In 2026, international development organisations including UNICEF and World Bank are studying the model for replication across other flood-affected regions — the Mekong Delta in Vietnam, low-lying areas of Nigeria's Niger Delta, and parts of Pakistan's Indus floodplain. The challenge is not the concept — it is the local organisational capacity and funding continuity required to sustain fleets of boats over decades. 🌏💡 The structural lesson Bangladesh offers to the world is this: climate adaptation is not only about seawalls and early-warning systems. It is about redesigning core social infrastructure — schools, clinics, markets — to function under the climate conditions that are already here, not the ones we hope to avoid.

After creating hundreds of thousands of presentations, Nancy Duarte discovered a framework in 2010 that changed her life. She mapped it over Martin Luther King's "I Have a Dream" speech and Steve Jobs introducing the iPhone. Both aligned perfectly. She cried in her office - the pattern she'd been desperate to find was real. See, most founder pitches fail the same way. You stack all the customer pain points at the start, then demo your product at the end. By the time you reach your solution, people have already decided if they're interested. They tuned out at slide 8. Duarte's Sparkline does the opposite. You alternate between “what is” and “what could be” throughout the entire pitch. Pain, solution. Pain, solution. The pattern works because contrast commands attention and open loops create psychological discomfort. The brain needs recurring tension to stay engaged: - MLK toggled between injustice now and "I have a dream" repeatedly. - Jobs contrasted clunky smartphone limitations with iPhone capabilities throughout the 80-minute presentation. - JFK alternated between the US’s space limitations and “we choose to go to the Moon in this decade.” Each toggle made staying in the current state unbearable. The execution: 1. Make your customer the hero by using their exact words Interview five target customers or investors before you build slides. When they describe frustrations, use their language verbatim. This proves you understand their reality before pitching your solution. 2. Paint “what could be” with sensory detail Not better accommodations. Instead: a family arrives in Paris, their Airbnb host left fresh croissants and a handwritten neighborhood guide on the kitchen table. They feel like locals, not tourists. Concrete outcomes stick. Abstract benefits are forgotten. 3. Alternative problem/solution throughout - never batch Pain 1, solution 1, pain 2, solution 2, pain 3, solution 3. Never group all problems then all features. Batching lets investors and customers mentally check out before you finish. 4. End with an immediate next step (24-48 hours) For investors: “By Friday, confirm the partner meeting date and three references you want to call.” For customers: “By tomorrow, send three use cases and I'll record a custom demo by Wednesday.” Make the decision immediate and concrete. Watch for these signals mid-pitch: You're losing them when investors lean back, check phones, or pivot to questions about your burn rate and competition. You're winning when customers interrupt to describe their specific use case, ask about implementation timeline, or want to loop in their team immediately. When every startup in your category has similar features, the pitch that creates unbearable tension wins the round, the sale, and the talent.

Many amazing presenters fall into the trap of believing their data will speak for itself. But it never does… Our brains aren't spreadsheets, they're story processors. You may understand the importance of your data, but don't assume others do too. The truth is, data alone doesn't persuade…but the impact it has on your audience's lives does. Your job is to tell that story in your presentation. Here are a few steps to help transform your data into a story: 1. Formulate your Data Point of View. Your "DataPOV" is the big idea that all your data supports. It's not a finding; it's a clear recommendation based on what the data is telling you. Instead of "Our turnover rate increased 15% this quarter," your DataPOV might be "We need to invest $200K in management training because exit interviews show poor leadership is causing $1.2M in turnover costs." This becomes the north star for every slide, chart, and talking point. 2. Turn your DataPOV into a narrative arc. Build a complete story structure that moves from "what is" to "what could be." Open with current reality (supported by your data), build tension by showing what's at stake if nothing changes, then resolve with your recommended action. Every data point should advance this narrative, not just exist as isolated information. 3. Know your audience's decision-making role. Tailor your story based on whether your audience is a decision-maker, influencer, or implementer. Executives want clear implications and next steps. Match your storytelling pattern to their role and what you need from them. 4. Humanize your data. Behind every data point is a person with hopes, challenges, and aspirations. Instead of saying "60% of users requested this feature," share how specific individuals are struggling without it. The difference between being heard and being remembered comes down to this simple shift from stats to stories. Next time you're preparing to present data, ask yourself: "Is this just a data dump, or am I guiding my audience toward a new way of thinking?" #DataStorytelling #LeadershipCommunication #CommunicationSkills

WHY DO MOST INNOVATIONS NEVER SCALE? Organisations celebrate pilots. They reward new ideas. Yet very few innovations travel far enough to change a system. Because scaling innovation is not about the brilliance of an idea. It is about building the conditions where many people can contribute to it. That is the central theme of “Genius at Scale: How Great Leaders Drive Innovation” by Linda Hill, Emily Tedards, and Jason Wild, published by Harvard Business Review. The authors describe three roles leaders must play if innovation is to scale. First. Design organisations where experimentation is possible, and people feel safe challenging the status quo. Second. Connect silos. Many good ideas die simply because they cannot travel across teams, partners, or institutions. Third. Mobilise an ecosystem so innovation moves beyond a project and becomes a movement. What struck me most while reading the book is this. We often look for individual genius. But the real challenge for leaders is enabling collective genius. The book illustrates this through examples from organisations around the world, including Sampark Foundation. A useful reminder. INNOVATION MAY START WITH AN IDEA. BUT IT SCALES ONLY WHEN MANY PEOPLE MAKE IT THEIR OWN. The real question for leaders is simple. Are we celebrating ideas, or building systems that allow ideas to scale?

🚀 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐆𝐞𝐧𝐀𝐈: 𝐓𝐡𝐞 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐈𝐦𝐩𝐚𝐜𝐭 To make GenAI successful at scale, we need more than just good ideas; we need robust technical setups that can grow with demand. Here are some key lessons we’ve learned in our recent projects: ☁️ 𝐂𝐥𝐨𝐮𝐝 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐟𝐨𝐫 𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲 We’ve moved to a cloud-native setup, allowing us to adjust resources based on demand. Imagine being able to turn up or down the power needed for GenAI, like adjusting the volume on a speaker, so we’re always prepared without wasting resources. 🔄 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐌𝐨𝐝𝐞𝐥 𝐔𝐩𝐝𝐚𝐭𝐞𝐬 𝐚𝐧𝐝 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 GenAI models aren’t static; they need regular tuning to stay effective. We’ve set up automatic model retraining so models get updated as new data comes in. It’s like scheduling regular maintenance on a car to keep it running smoothly without manual effort each time. 🔐 𝐁𝐮𝐢𝐥𝐭-𝐢𝐧 𝐃𝐚𝐭𝐚 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐟𝐨𝐫 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 For GenAI to work responsibly, data security is key. We’ve embedded data governance controls right into our workflows, so every new GenAI function automatically checks for data quality and compliance with regulations, keeping everything secure as we grow. ⚙️ 𝐑𝐞𝐮𝐬𝐚𝐛𝐥𝐞 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐟𝐨𝐫 𝐅𝐚𝐬𝐭𝐞𝐫 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 We’re building a library of API-based microservices - think of these as plug-and-play GenAI functions that can be used across different projects. This way, instead of building from scratch each time, we simply connect the pieces we need, making deployment faster and more consistent. Scaling GenAI requires more than just strong models; it’s about having the right technology backbone to keep everything running efficiently and securely. 👇 𝐇𝐨𝐰 𝐢𝐬 𝐲𝐨𝐮𝐫 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐩𝐫𝐞𝐩𝐚𝐫𝐢𝐧𝐠 𝐭𝐨 𝐦𝐞𝐞𝐭 𝐭𝐡𝐞 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐨𝐟 𝐬𝐜𝐚𝐥𝐢𝐧𝐠 𝐀𝐈? #GenAI #TechInnovation #CloudComputing #ModelMaintenance #DataSecurity #APIMicroservices #AIAtScale ¦ Deloitte

I often use Glean to understand what’s top of mind for enterprise buyers. Recently, I asked: How are enterprise leaders approaching AI change management today versus a year ago? A year ago, many leaned on pilots and PoCs. They doubled as a “change-management” strategy: a safe way to expose teams to AI in controlled doses and signal progress without tackling the harder realities of implementation and scale. But buyers are increasingly aware of the limits of this approach. Pilots can show a tool works without proving it creates business value. And because they’re often run in isolation, they can dodge the hardest challenges, like driving broad adoption, ensuring governance and permissions, and integrating with legacy systems. Over the past six months, our conversations with enterprise buyers have shifted: less about whether to pilot, and more about how to commit to intentional rollouts by defining clear business outcomes, investing in role-based enablement, and building networks of “AI champions”— employees who are early adopters and model effective use and spread best practices across the organization. AI pilots can give a comforting illusion of progress, but too often they’re used as an excuse to postpone the real work of adoption, integration, and scale. The better path is to do the research up front, ask for tailored demos, and partner with proven vendors. Then commit. Start small with specific teams or departments if you need to, but always with scale in mind: embed AI into real workflows, anchor on business outcomes, and use real change-management strategies like mobilizing your internal AI champions.

Clinicians don’t trust your HealthTech product. And they’re right not to. You think you’re selling innovation. But they’re seeing liability. When a doctor uses your product, they’re not just clicking a button. They’re staking their license, reputation, and someone’s life on a tool they didn’t build… Made by someone who’s never stepped inside an operating theatre. This is the Clinical Trust Chasm. Most HealthTech companies never cross it. They win pilots, not trust. Investors, not integration. Press, not protocols. Trust in medicine isn’t earned with features. It’s earned with consequences. Ask any surgeon why they use a specific tool. It’s not because it’s cutting-edge. It’s because it’s predictable under pressure. They’ve seen it fail, and seen what happens next. They know it's blind spots. They know when not to use it. You can’t shortcut that with UI polish and a few endorsements. If you want your HealthTech product to be adopted, not just trialled: You have to reverse the trust equation. Here’s how I’ve seen it work: - Put the clinician in control - Stop “automating decisions”. Start augmenting judgement. - Build fail-safes, override paths, audit trails. Trust starts when you acknowledge what you don’t know. Design for blame Assume someone will get hurt using your product. Will they say: “We knew this tool. We trusted it. We stood by it.” Or: “They promised it would work.” Over-communicate uncertainty No one’s ever said, “That medical device was too transparent.” Show the confidence intervals. Flag the edge cases. Clinicians are trained to work with ambiguity, just not surprise. Many HealthTech founders think clinicians are “resistant to change”. IMO they’re not. They’re allergic to risk they didn’t consent to. They don’t need to understand your model. They need to understand how it breaks, and what happens when it does. Build for that moment. That’s where real adoption begins.