Platform Engineering Insights

💫2026: How GenAI’s Role Changes in Insurance Simple efficiency gains are done. The basic productivity phase of GenAI is largely complete. What comes next is not smarter AI. It is exposed operations. ⸻ 🔍 What GenAI really surfaces in 2026 As GenAI moves beyond basic automation, it begins to reveal operational gaps insurers have lived with for years: 　⚠️ processes that work only because people fill in the blanks 　⚠️ decisions based on tacit understanding rather than explicit rules 　⚠️ operations that can be explained, but not consistently repeated This is not a technology issue. It is an operational reality check. ⸻ ⚙️ Why this matters now In 2026, GenAI removes the human buffer that used to absorb ambiguity. What was once: • “handled case by case” • “managed by experienced staff” • “good enough in practice” becomes visible, inconsistent, and hard to scale. ⸻ 🎯 Executive takeaway GenAI does not change insurance operations. It reveals which operations were never fully defined in the first place. GenAI is no longer an efficiency tool. It is an operational stress test. #GenAI #Insurance

The Untapped Risk in Marketplace-First Brands... Marketplace Dependency Risk — and it quietly compounds as you scale. Here’s the math: 1️⃣ Topline Fragility  If 70–80% of your revenue comes from Amazon, Flipkart, or Myntra... One algorithm tweak, penalty, or policy shift — and your revenue can drop by 30–40% overnight. 2️⃣ Pricing and Margin Squeeze  Marketplaces push for discount parity.  They want the lowest prices and commissions.  You can’t easily raise prices, but your costs (logistics, returns, ads) keep rising quietly. Margin compression isn't a phase. It's structural. 3️⃣ No Consumer Ownership  Even after selling 10,000+ units, you don’t own the customer data.  You can’t remarket. You can’t build loyalty.  You are permanently renting traffic—on someone else’s terms. 4️⃣ Working Capital Traps  Longer payment cycles + return risks = working capital nightmares.  Every rupee stuck in the system delays scale. 5️⃣ Exit Valuation Hit  Brands with over 60% marketplace dependence often get lower valuations.  Investors penalize the "platform risk" by adjusting down the revenue multiple. This is the advice I've seen the smart founders share: - Balance marketplace sales with your own website D2C channel. - Invest in brand-building early—even when marketplace sales look tempting. - Build retention engines (email, WhatsApp) off-platform. - Negotiate smarter platform deals once you have leverage. 📌 What's one thing a founder should do to de-risk their channel dependence? Picture - Inc42 FAB MAVEN

It's not AI that's delivering the biggest impact for developers today. There's a proven path that's been forgotten. According to the Atlassian 2025 DevEx survey, developers reported major improvements in: - Application scalability & reliability - DevEx & productivity - Time-to-market - Operational costs Sound familiar? These are the exact benefits AI promises but hasn’t delivered (yet). So what is delivering? Platform Engineering. Remember that? 98% of developers said they’re either using an IDP or planning to. Platform engineering is doing the heavy lifting, helping teams to deliver higher-quality software faster. It's interesting that on one hand, we have a new technology that promises to deliver benefits, and on the other, we have something that is delivering these benefits, but most investment goes into the promise and not the (almost) guarantee. In an ideal world you would do both. It's an interesting situation at the moment. I'm interested to know how your platform engineering initiatives are going? There seems to be far less talk about it these days. #PlatformEngineering #DeveloperExperience #AI 

Developer and user experience (DX and UX) are critical to the success of any data platform/product. I and Cristiano Rocha delivered a talk few days back with some focus on it's importance. Here’s how they play key roles in data platform engineering based on my experience: 👍🏽 Ease of Onboarding and Adoption > A well-designed platform with intuitive interfaces, clear documentation, and streamlined workflows allows developers to get started quickly and reduces the learning curve. > If developers find the platform cumbersome, adoption rates will drop, even if the platform is technically robust. 💪 Productivity Boost > Platforms that are user-friendly, with features like notifications, clear error messages, and integrated debugging tools, help developers work more efficiently. > Automated processes, such as schema validation, pipeline creation, or infrastructure management, save time and reduce cognitive load. 🐞 Error Reduction > A platform with good UX minimizes opportunities for human error. This includes: > Clear, actionable error messages. > Guardrails to prevent common mistakes. > Role-based access control to avoid unauthorized changes. 🤝 Improved Collaboration > A platform that prioritizes DX often includes features like version control, shared dashboards, and collaborative workspaces, fostering better teamwork among data engineers, data analysts, data scientists and other stakeholders. 🤗 Flexibility and Customization > Developers appreciate platforms that provide flexibility to customize workflows and integrate with existing tools. For instance: > Modular APIs for extending functionality. > Compatibility with diverse programming languages or frameworks. ➰️ Feedback-Driven Iterations > A positive developer experience fosters regular feedback, which can guide iterative improvements in the platform, making it more aligned with user needs. ⏳️ Time to Market > A seamless experience reduces the time spent on debugging, learning new tools, or setting up infrastructure, enabling faster delivery of data products. ☘️ Long-term Sustainability > Happy users are more likely to advocate for the platform, reducing the need for extensive change management. > Platforms with a good DX have lower maintenance costs due to fewer bugs, support tickets, and the need for developer training. 🤷♂️ Practical Examples in Data Platform Engineering: 🚀 Data Ingestion Pipelines: Integrated Development Platform, pre-built tables, and reusable templates enhance the developer experience. 🔍 Monitoring and Debugging: Real-time dashboards and detailed logs empower developers and support teams to quickly identify and resolve issues. 🔗 APIs: Clear and well-documented APIs enable seamless integration with external applications. #DataPlatformEngineering #DataProducts #DeveloperExperience #Automation

𝗧𝗼𝗱𝗮𝘆'𝘀 𝗖𝗹𝗼𝘂𝗱𝗳𝗹𝗮𝗿𝗲 𝗖𝗿𝗮𝘀𝗵 𝗪𝗮𝘀 𝘁𝗵𝗲 "𝗪𝗵𝗮𝘁 𝗜𝗳" 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗜 𝗪𝗮𝗿𝗻𝗲𝗱 𝗔𝗯𝗼𝘂𝘁 When I insisted on building our own CAPTCHA instead of relying solely on Cloudflare or Google, a client's expert challenged me: "Why rebuild what already works?" 𝘔𝘺 𝘢𝘯𝘴𝘸𝘦𝘳: "What if they change their pricing model? What if they alter their rules and break our UX? What if they simply stop the service?" Today's massive Cloudflare outage, which took down platforms like ChatGPT and Discord, validates that exact thinking. While I didn't predict 𝘁𝗵𝗶𝘀 𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗼𝘂𝘁𝗮𝗴𝗲, the principle remains: Over-reliance on any single external service is a strategic risk. This is why we engineered for independence: - Built our own checkpoint as a fallback - Designed for graceful degradation: if a third-party service fails, core functionality remains - Avoided vendor lock-in on critical path services The lesson isn't to avoid cloud services altogether. It's to architect with the understanding that any external dependency can fail whether technically, financially, or operationally. Your resilience strategy shouldn't just account for servers going down, but for business models changing, APIs being deprecated, and yes centralized providers having global outages. What's the one external service your product couldn't function without tomorrow?

Founder chat got spicy when someone asked about platform risk. Hope my response wasn't too brutal... OMG guys, let me tell you about when Twitter suddenly BLOCKED our API access at Sendible. No warning. Zilch 😱 Customers freaking out. Team in panic mode. Me? On hols in Mallorca trying to save the business from hotel wifi. Nightmare! Why? All bc we built on someone else's platform. They didn't like our white label offering. Just like that, access GONE. This is what nobody warns you about with API-dependent businesses. You're literally one policy change away from disaster. After 13+ yrs of that stress, my rule is simple: if your core business depends on someone else's API, your success isn't determined by customers loving you - it's determined by platforms tolerating you. Especially at scale. The bigger you get, the bigger the target on your back. That's why Swarm and StoryPrompt are built differently. No mission-critical integrations. Own your infrastructure. Own your destiny. (Anyone else been burned by platform dependencies? Drop your stories 👇) — Follow me (Gavin Hammar) for weekly tips on scaling a profitable bootstrapped SaaS business.

Fresh from Nairobi: Who Owns the Rails? Last week, I had the privilege of joining the Gates Foundation convening on Social Commerce and Women’s Livelihoods in Nairobi, a gathering that brought together builders, innovators, and policy shapers deeply committed to scaling women-led enterprises. The energy in the room was electric, real stories, sharp insights, and shared urgency about how digital platforms are transforming livelihoods across Africa and Asia. And yet, one discussion stood out for all of us: platform dependency. We saw how entire sales funnels, customer support, and training flows are now anchored to a few apps; how discovery, messaging, payments, and even identity verification ride on a single provider’s APIs; how a tweak to pricing or policy can change margins overnight; and how an outage or rule change in one channel can stall hundreds of micro-businesses at once. The takeaway was simple: concentration risk isn’t theoretical, it shows up as missed orders and lost income. Just 2 days later, the news broke that Meta will remove ChatGPT and other external AI chatbots from WhatsApp starting January 15, 2026. That means 50 million people who rely on WhatsApp for AI-powered access and support will lose it overnight. This isn’t a story about AI competition. It’s about control and about who owns the digital rails that livelihoods increasingly depend on. For those of us building digital ecosystems for youth and women entrepreneurs, this moment is a reminder to design for resilience: (1) Diversify channels: treat WhatsApp, SMS, IVR, Telegram, and web as a portfolio, not a single bet. (2) Build portability: let users and data move easily across platforms. (3) Abstract the AI layer: route through multiple providers to stay operational even when policies shift. (4) Plan for volatility: build redundancy and response playbooks into the core architecture. When platforms change, livelihoods shouldn’t collapse. If our goal is inclusion and dignity, resilience isn’t optional. It’s the foundation. #AIForGood #WomenInBusiness #SocialCommerce #PlatformEconomy #BuildForDignity #YouthAgripreneurs #KuzaOneNetwork Kuza One (Kuza Biashara)

You can do great work and still lose a client. Trust doesn’t work the way most people think. In BD, trust doesn’t disappear all at once. It erodes in small, often well-intentioned moments. Most professionals never notice them in real time. They feel helpful. Efficient. Even reasonable. Then the client goes quiet, and you’re left trying to piece it together. But when you look back, the signals were there. Here are 7 common ways trust breaks down: 1. Protecting them from bad news ↳ You wait because you’re trying to help. ↳ They experience it as a late surprise. 2. Prioritizing the work over the relationship ↳ You focus on execution and outcomes. ↳ They miss feeling personally connected. 3. Never asking what you could do better ↳ They had feedback. ↳ You never made space for it. 4. Checking the task, not the person ↳ You track progress closely. ↳ You overlook how they’re actually doing. 5. Failing to guide the transition ↳ You stayed involved in the work. ↳ They weren’t sure what changed or what to expect next. 6. Rounding up on the invoice ↳ The amount isn’t the issue. ↳ The surprise is. 7. Going quiet after the work ends ↳ The project is complete. ↳ The relationship doesn’t have to be. The encouraging part? None of these requires better work. They require better presence. Clearer communication. More intention in the in-between moments. That’s how strong relationships are built. And how good work turns into long-term trust. ♻️ Valuable? Repost to help someone in your network. 📌 Follow Mo Bunnell for client-growth strategies that don’t feel like selling. Want the full infographic? Sign up here: https://lnkd.in/e3qRVJRf 

What Happens When Your Tech Dependency Becomes a Strategic Liability? In today’s interconnected business world, the technology that powers your competitive advantage may also be your greatest vulnerability. For years, technology has been the enabler of scale, speed, and innovation. But as businesses around the world become more deeply reliant on digital tools, platforms, and infrastructure, an uncomfortable question has emerged: What happens when the tech you depend on is no longer available—or no longer aligned with your values, strategy, or geopolitical reality? This isn’t a hypothetical for the future. It’s a present-day consideration. Disruptions—from trade restrictions to cloud outages, software and service licensing changes to supply chain bottlenecks—are already forcing organizations to rethink what resilience really means. Whether it’s cloud platforms, AI models, collaboration tools, or even personal computing hardware, over-dependence on any one provider or ecosystem can quietly turn from a strategic shortcut into a systemic risk. The efficiency gains from standardization must be weighed against the resilience benefits of diversification. It’s not about abandoning integration; it’s about making smarter, risk-aware choices when selecting your technology partners and platforms. And yet, in our drive for seamless integration and rapid delivery, many of us have built tech stacks that are deeply entwined with a single country’s innovation pipeline or a single company’s roadmap. I’m not suggesting we retreat from global collaboration or stop using excellent technology from wherever it comes. So here’s the real question: Are we paying enough attention to where our technology comes from—and what it would take to adapt if it were suddenly unavailable? I’ve spent much of my career focused on creating human-centered, resilient systems—ones that don’t just work, but keep working when conditions change. That requires more than good tech. It requires asking better questions: • Have we mapped our critical dependencies beyond first-tier suppliers? • What triggers would prompt us to activate alternative technology pathways? • How do we balance standardization efficiencies against diversification resilience? • Do we have meaningful alternatives—or just backups? • Are our dependencies conscious and intentional, or just convenient? • What role should leadership play in regularly revisiting these decisions—not just leaving them to procurement or IT? Ultimately, resilience isn’t just a technical attribute. It’s a leadership choice. I’d love to hear from others around the world: How are you thinking about your organization’s technology dependencies? How are you building optionality into your future? #TechnologyResilience #Leadership #DigitalStrategy #BusinessContinuity #GlobalLeadership #HumanCenteredTech #SupplyChainResilience #TechDiversification #StrategicRiskManagement #AI - Human-made, AI-assisted -

𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐰𝐚𝐧𝐭𝐬 𝐀𝐈. 𝐕𝐞𝐫𝐲 𝐟𝐞𝐰 𝐢𝐧𝐬𝐮𝐫𝐞𝐫𝐬 𝐚𝐫𝐞 𝐩𝐫𝐞𝐩𝐚𝐫𝐞𝐝 𝐟𝐨𝐫 𝐰𝐡𝐚𝐭 𝐀𝐈 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐞𝐱𝐩𝐨𝐬𝐞𝐬. I see this pattern everywhere right now. Insurance leaders rush to deploy AI models for underwriting, claims, or pricing. The models perform well in testing. Everyone celebrates the innovation. Then reality hits. AI doesn't hide data problems. It amplifies them. That clean dataset you thought was ready? AI finds the gaps instantly. Those manual overrides your team made for years? AI reveals the inconsistencies. That tribal knowledge sitting in someone's head? AI exposes how much you've been depending on it. Here's what most insurers miss: AI implementation isn't a technology project. It's an organisational mirror. When AI starts making recommendations, it forces uncomfortable questions: • Why do we have three different definitions for the same risk factor? • Why does our data quality drop after the first renewal? • Why can't we explain this pricing exception from 2019? • Why do different teams use completely different assumptions? These questions existed before AI. We just didn't have to answer them. The insurers winning with AI in 2026 aren't the ones with the fanciest models. They're the ones willing to fix what AI reveals. They treat AI deployment as a forcing function for organisational clarity. Before launching the next AI initiative, ask yourself: • Are we ready to face what our data actually looks like? • Can we handle the transparency AI will create? • Do we have the discipline to fix foundational issues before scaling? AI won't transform your business if your business isn't ready to transform itself first. What's the hardest truth AI has revealed in your organisation? #AIinInsurance #InsuranceLeadership #InsurTech #DigitalTransformation #DataStrategy