SLASOG: Leaders are Readers

Hosted by Ritavan
  • 5.0 (1)
  • TECHNOLOGY

Ritavan speaks with outstanding operators and seminal thinkers to unpack first principles system rethink for data-driven value creation. slasog.substack.com

Episodes

  1. JAN 9

    Brian Potter - The Origins of Efficiency: Scaling Production

    Modern culture treats efficiency as inevitable. Give smart people incentives, add technology, wait…and productivity appears! Brian Potter’s The Origins of Efficiency dismantles this assumption. The book’s central claim is that efficiency is not a default outcome of markets or technology, but a historically rare achievement that requires deliberate intent and execution. Efficiency emerges only when a specific set of conditions align, often over decades. Where those conditions are absent, even advanced societies stagnate. Efficiency Is an Emergent Property Potter treats efficiency as a property of systems rather than individuals, firms, or technologies. Improvements depend on whether a system can reliably convert inputs into outputs at scale, not on isolated acts of ingenuity. This perspective explains why: * Societies with sophisticated tools can remain inefficient * Early industrial firms were often chaotic and wasteful * Some modern sectors show little productivity growth despite heavy investment Efficiency requires coordination across many actors and time periods, which makes it difficult to achieve without supporting institutions. Efficiency is not something a system can simply “adopt.” It is an emergent property that appears only when multiple layers reinforce one another. Four conditions recur throughout Potter’s historical analysis: * Legibility: the system can be measured and compared over time * Repeatability: work can be standardized and reproduced reliably * Energy: power is cheap, predictable, and abundant * Authority: someone has the right to change how work is done Remove any one of these layers and optimization collapses. This is why pre-industrial societies stagnated despite ingenuity, why early factories were chaotic and wasteful, and why modern sectors like construction and healthcare remain stubbornly inefficient. Efficiency is not a trait of people or machines. It is a property of systems that can see themselves clearly and act on that information. The Continuous Flow Process The Core Mechanism Behind Sustained Efficiency One of Brian Potter’s most important frameworks in The Origins of Efficiency is his emphasis on the continuous flow process as the foundation of durable productivity gains. Rather than treating efficiency as a general outcome of industrialization, Potter shows that it depends on a specific way of organizing work. What Are “Continuous Flow Processes”? A continuous flow process is one in which: * Work moves through a system in a steady, predictable sequence * Tasks are decomposed into discrete, repeatable steps * Inputs arrive at a regular rate and outputs leave continuously * Interruptions, batching, and handoffs are minimized The defining feature is not speed, but regularity. The system is designed so that work rarely stops, accumulates, or resets. This contrasts with: * Craft production * Project-based work * Batch-and-queue systems In those systems, work advances in bursts, with frequent pauses and reconfiguration between stages. Why Continuous Flow Enables Efficiency Potter argues that continuous flow is the organizational precondition for sustained efficiency improvements. It enables several reinforcing mechanisms. First, it makes processes measurable. When work proceeds in a stable flow, it becomes possible to observe throughput, identify bottlenecks, and compare performance over time. Without flow, variation dominates the signal. Second, it enables learning-by-doing. Small improvements compound only when the same process is repeated continuously. If each unit of output is produced differently, lessons do not accumulate. Third, it supports standardization. Continuous flow requires consistent inputs, fixed task sequences, and controlled variability. These constraints are what allow systems to improve incrementally rather than reset with each job. Fourth, it lowers coordination costs. Instead of relying on ad hoc human judgment to manage transitions, the system itself governs timing and handoffs. Why Some Sectors Resist Continuous Flow Potter uses continuous flow to explain persistent productivity gaps across sectors. Continuous flow is difficult where: * Outputs are highly customized * Work is site-specific * Demand is irregular * Responsibility is fragmented across firms * Regulation constrains process redesign Construction is the canonical example. Each project resets the process, preventing stable flow and cumulative learning. As a result, productivity improvements remain local and temporary. Continuous Flow vs. Optimization A key point in the book is that continuous flow often precedes formal optimization. Flow creates the conditions under which optimization becomes meaningful: * Stable baselines * Repeated cycles * Observable effects of changes Attempts to optimize before flow exists usually fail because variation overwhelms improvement signals. Efficiency, in this framework, is not achieved by better decisions within chaotic systems, but by reducing chaos first. Thanks for reading SLASOG: Leaders are Readers! Subscribe for free to receive new posts and support my work. Share the post with someone who will benefit from it. Subscribe here to to be first to know when the next episode drops: https://www.youtube.com/@SLASOG For more of my thoughts, follow me on LinkedIn. Get my book Data Impact for a pragmatic take on data-driven value creation for business. Thanks for reading SLASOG: Leaders are Readers! Subscribe for free to receive new posts and support my work. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit slasog.substack.com

    1h 7m

  2. 11/27/2025

    Dr. Chris Pedder - Chief Data Officer: The Broken CDO Playbook

    The modern Chief Data Officer was created to bring rigor, clarity, and discipline to how companies use data. Instead, the role frequently collapses under structural weaknesses that are obvious once stated out loud. The CDO is an “unstable particle” in the corporate C-Suite, with a short half-life. Dr. Chris Pedder, a former string physicist and experienced CDO, describes the job as “a startup inside a corporation” with all the associated fragility but none of the autonomy. The global failure rate for digital and “AI” transformations is roughly 80-90% percent based on surveys and studies published by global consultancies and business schools. That’s like betting heads on a coin that almost always turns out tails. Yet many companies continue deploying the same broken approach and hope for a different outcome. Why the CDO Role Fails The typical CDO playbook is outdated. It prescribes centralizing all data, hiring a large team, buying an expensive platform, and expecting value to magically appear. In practice, the opposite happens. Chris notes that although CDOs are usually told they have three years, the actual window before the board demands results is twelve months or less. Unfortunately, most CDOs spend that early time inside a technical bunker far from the levers that drive business outcomes. The deeper failure is organizational. Companies do not understand what the data function is for. As a result, the CDO’s org and reporting line get passed from CTO to CFO to COO and back again. Each reassignment signals that the company lacks a principled view of what problem it expects data to solve. Any role without a clear mandate, authority, decision rights, and goals will fail regardless of who occupies it. Groupthink Is the Real Enemy Many firms compound the problem by hiring for conformity. Head-hunters are instructed to provide candidates with “ten years of experience in the industry”. If the industry has not meaningfully used data for the past decade, this requirement becomes logically impossible to satisfy. You either get a candidate steeped in industry groupthink, or you hire someone incapable of rewriting the playbook. As I pointed out in the discussion, this is equivalent to indexing your returns. You will never outperform your market if you copy incumbents who are not winning with data to begin with. Chris puts it perfectly: “Good CFOs follow best practice. Great CFOs do not. The same applies to data leaders.” Only contrarian, first principles thinking produces value. The successful CDOs, Chris has observed, reject the standard playbook entirely. They operate like founders. They embed their teams directly into business units. This is similar to Palantir’s model of “forward-deployed engineers”. When data teams sit with marketing, sales, or operations, they see the real constraints, define the real KPIs, and produce work that is immediately useful. This approach creates pull rather than push. When Marketing presents credible ROI improvements at the leadership meeting, Sales starts requesting similar support. The transformation becomes demand-driven rather than compliance-driven. The Distraction of Superintelligence A separate but related failure mode is the industry’s obsession with superintelligence. Chris calls the discourse equivalent to “16-year-olds doing philosophy in their bedrooms”. The concept of surpassing human intelligence is impossible to define because human intelligence is not a static, measurable threshold. There is not even an agreed-upon definition of intelligence itself. Superintelligence debates consume time and energy from leaders who should be focusing on real, solvable problems. Chris Pedder’s Data Playbook A practical data strategy requires five elements. * Ignore superintelligence debates. Tools do not create value. Decisions do. * Treat the data function as a startup. Build quickly, validate quickly, and shut down what does not work. * Forward deploy teams into the business. Proximity drives accuracy and velocity. * Centralize goals and decentralize execution. Define a North Star metric and allow teams to innovate toward it. * Reduce complexity continually. Simplicity compounds and accelerates learning. The CDO role does not fail because of the individuals in it. It fails because the industry keeps recycling a playbook that is inconsistent with reality. Thanks for reading SLASOG: Leaders are Readers! Subscribe for free to receive new posts and support my work. Subscribe here for early access to future episodes. Get my book Data Impact for a pragmatic take on data-driven value creation. For more of my thoughts, follow me on LinkedIn. Thanks for reading SLASOG: Leaders are Readers! Subscribe for free to receive new posts and support my work. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit slasog.substack.com

    1h 7m

  3. 10/31/2025

    Nicholas Goubert - Veteran CPO : Product Leadership in Traditional Industries

    Stop Saying “We Tried That Before” You can tell how dead a company is by how fast someone says, “We tried that before!” That line is the canary in the coal mine. It’s what legacy firms say when they want the world to think they’re evolving, but all they’re really doing is protecting what’s left of yesterday. I had Nicholas Goubert on the podcast, a veteran product leader who’s spent decades years in traditional industries like automotive, insurance, health, maritime. The most dangerous sentence in business “We tried that before” is the corporate version of “don’t make me think.” It signals the end of inquiry. It assumes the past was right and the context is frozen. It lets people feel wise while staying stuck. Nicholas’s antidote is deceptively simple: keep asking WHY?.Why didn’t it work? Was it the idea or the execution? Why can’t it work now, in a new environment, with better tools and better people? Traditional industries don’t fail because they’re incapable. They fail because they stop asking questions. Once curiosity dies, so does everything else. Collaboration without conformity Most organizations swing between 2 bad extremes: the “lone genius” myth and the “everyone must agree” paralysis. Both kill great products. Real product work lives in tension. The leader defines the why and clears the path. The team debates the what and how. Disagreement is not dysfunction. If everyone nods in the meeting, you’re either terrifying them or wasting your time. And here’s a quick test: count how many “top initiatives” your company has. If it’s more than 5, you’re spreading talent and resources way too thin. Focus is an executive priority, it won’t happen bottom-up. The growth illusion Every company says it wants growth. Few stop to ask what kind? Growth has become a job title, a department, a set of dashboards or reports. Growth isn’t something you hire; it’s something you earn by serving customers well. Legacy companies already hold cards startups don’t: existing customers, trust, distribution, data, capital. Yet they waste those advantages chasing new, shiny ideas that add no value. His rule: fix the leaks before adding water. Keep your current customers first. Expansion without retention is just expensive churn. Do fewer things, do them better. For example double down on your killer feature — the thing you’re genuinely world-class at — and add a small magic feature that keeps customers coming back. What strong product leadership looks like Leading product in a traditional sector isn’t about startup style disruption it’s about disciplined approach with first principles thinking. Here’s how Nicholas approaches it: * Ban “we tried that before.” Every idea gets a fresh first-principles review. * Push for argument, not agreement. Collaboration means contribution, not consensus. * Cut the noise. More than 5 “strategic priorities” is just chaos and distraction. * Redefine growth. Measure outcomes customers feel — not vanity metrics. * Protect focus. If it doesn’t tie directly to your vision for your customer, cut it. Leadership isn’t about heroics. It’s about clearing the path so smart people can do meaningful work, in the best way possible. Traditional industries don’t need saviors from Silicon Valley. They need disciplined builders who can separate legacy from dead weight. Because the truth is, most of what the world calls “innovation” is just old ideas done well, by people who refused to accept “we tried that before.” Subscribe here for early access to future episodes. Get my book Data Impact for a pragmatic take on data-driven value creation. For more of my thoughts, follow me on LinkedIn. Thanks for reading SLASOG: Leaders are Readers! Subscribe for free to receive new posts and support my work. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit slasog.substack.com

    1h 2m

  4. 10/16/2025

    Lou Franco - Swimming in Tech Debt: Rethinking Technical Debt as Resistance

    According to veteran software engineer Lou Franco, the tech debt metaphor is holding us back. According to this metaphor, engineers took a shortcut to deliver a feature faster. They borrowed from the future. And now, unless they pay it back, they’ll incur interest payments when they try to add more features. This metaphor is flawed in two ways. The first is that most technical debt just happens because a project is successful and long-lived, and the world changes. Or, our ideas get better. Or, we learn more about the problem. Or, our ambitions grow. Those early decisions, or “shortcuts”, may have been critical for us to get here. We might have problems, but whether those problems were caused by shortcuts or rational, appropriate decisions won’t help us fix them. The original intention is not germane to the decision of what to do now. The second is that in the real world, debts are obligations. But a problem in our codebase may not be our most pressing issue. We might be able to live with it. When your mindset is “debt”, you feel guilty and pay more than you should. A New Metaphor: Resistance So, instead of debt, Lou thinks of tech debt problems like trying to swim upstream. In the water, you feel the resistance pulling you back. But also, only the coders who are swimming in the codebase feel it. To the rest of the business, you just look slow. Using a resistance metaphor helps you make decisions about what to do about technical debt. By defining it as a relationship between the swimmer and water, we focus on problems in the codebase, not the running software. By recognizing that others might not see it, we can try to couple it with things they can see or find ways to make our internal issues visible. Finally, by looking at its primary effect, slowing us down, we see that its main cost is developer productivity. When we feel it, we can use that as a trigger to make a proportional improvement. Applying the SLASOG Framework to Tech Debt To save, businesses with legacy codebases should not rewrite systems just because they are old. Old code isn’t necessarily bad. Instead, realize that this code may have valuable solutions embedded in it that we risk losing in a careless rewrite. Leveraging that code to adapt it to a new context is a way for a legacy business to have an unfair advantage. In the podcast, Lou talks about his personal experience in moving a DOS application written in the 80s to become a web application in the 90s by layering an API on top of legacy code. But some code does cause developer productivity issues, so Lou recommends aligning our engineers around leading indicators that help us understand when that is happening. To guide them, he suggests establishing a budget that encodes the “commander’s intent” of how much time the CTO wants to spend fixing those issues. With that in place, Lou suggests simplifying the problem to targeted areas that would have the biggest effect. He proposes a system that scores each problem along cost-benefit dimensions that relate to resistance to growth, visibility, productivity, project size, and the risk of destroying value. To find those areas, Lou suggests quarterly meetings where the engineers apply their budget in a systematic way that can be optimized. In these meetings, he suggests starting with a retrospective of what was accomplished so far and how it has moved our leading and lagging indicators. We can use that to make a better plan for the next quarter. We can check that we are aligned by tracking our actual spend vs. the budget and by supplying visualizations of our progress back to the CTO. Finally, if we concentrate on fixing problems that resist our best ideas for growth, the productivity we have gained will speed up our implementation of those ideas. You can find out more at: https://loufranco.com/tech-debt-book The Problem with the “Tech Debt” Metaphor * Most tech debt isn’t from shortcuts.It arises naturally as projects evolve, succeed, and adapt to changing conditions, and not because of bad decisions or “borrowing from the future.” * Debt implies obligation and guilt.Viewing tech issues as “debts” pressures teams to fix things prematurely or excessively, even when they don’t meaningfully hinder progress. * Past intent doesn’t matter.Whether the code came from shortcuts or smart trade-offs, what matters now is how it impacts today’s development speed and goals. A Better Metaphor: “Resistance” * Resistance is what developers feel when code slows them down.Like swimming upstream — the harder you swim (e.g., build features), the more resistance (e.g., code complexity) pushes back. * Only developers feel it.To the business, you just look “slow,” so engineers must find ways to make resistance visible and connect it to outcomes others understand. * Focus on productivity, not morality.The “cost” of tech resistance is lost developer velocity. Fix it proportionally when it measurably slows you down. Subscribe here for early access to future episodes. Get my book Data Impact for a pragmatic take on data-driven value creation. For more of my thoughts, follow me on LinkedIn. Thanks for reading SLASOG: Leaders are Readers! Subscribe for free to receive new posts and support my work. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit slasog.substack.com

    1h 6m

  5. 10/01/2025

    Michael Gackstatter & Artem Demchenkov - CEO & CTO TODAY: Peace of Mind at Scale

    Pension systems across the West are under pressure. Governments are retreating, and traditional public safety nets are straining under aging populations. In Germany, for example, the pension and care insurance systems are showing cracks that experts predicted years ago. Meanwhile, financial advisors and insurance agents—the very professionals responsible for bridging this gap—are overworked, under-appreciated, and in many cases, considering leaving the profession. One study found that one in three insurance brokers in Germany is contemplating leaving the industry due to high regulatory and administrative burdens. Advisors spend up to a full day per week on paperwork alone, often for returns that barely justify the effort. At the same time, the profession ranks as the second least respected occupation in the West, a mismatch between the societal importance of their work and the recognition they receive. Enter TODAY, a tech startup that aims to transform the economics and experience of financial advising. Co-founded by Michael Gackstatter and Artem Demchenkov—veterans behind InsurTech unicorn Clark and FinTech Billie and backed by Lucid Capital and 14 angels from insurance, finance, and AI—TODAY is attacking the problem on two fronts: reducing administrative overhead and enhancing advisory performance. Michael Gackstatter explains the company’s mission as “peace of mind at scale.” By combining AI with human expertise, TODAY allows advisors to extend their reach, provide more personalized guidance, and focus on what truly matters: the client. “We’re making it easy to scale the knowledge of the advisor, educate customers, and make advisors available at scale,” Michael notes. The result is what he calls an “unfair advantage”—perfect memory, performance coaching, and administrative efficiency that frees advisors to engage deeply with their clients. For Artem, the problem is deeply personal. He grew up in a city of miners, with a grandfather from a village of farmers and miners who knew the value of hard work but also the insecurity that comes with relying on physically demanding jobs. In such communities, pensions are not abstract financial products; instead, they are the difference between dignity and hardship in old age. As Artem puts it, “when you see families that have literally built their lives on the strength of their backs, you understand why pension systems matter so much, and why making access to good financial advice sustainable is critical.” That ethos underpins TODAY’s work: ensuring financial security for millions who, like Artem’s family, have given decades of labor to society and their communities. The implications are profound. For advisors, it’s a way to reclaim time, improve client outcomes, and earn recognition for their expertise. For insurance companies, it’s an opportunity to modernize operations, reduce bottlenecks, and meet rising customer expectations for personalization and 24/7 availability. For investors, TODAY represents a scalable solution to an industry ripe for disruption: a legacy sector facing a “silver tsunami” as experienced advisors retire, leaving knowledge gaps that, if unaddressed, will widen the insurance accessibility gap. TODAY’s approach is not about replacing humans with AI; it’s about amplifying human expertise. Michael recalls his early days at Clark, where the difference between average and exceptional service was relentless customer obsession and execution. “It’s not enough to say the insurance industry is huge and under-technologized. You have to bring the work ethic and execution to the street,” he emphasizes a philosophy that permeates TODAY. As the West grapples with aging populations, stressed pension systems, and evolving customer expectations, solutions like TODAY are emerging as critical enablers of financial security. By empowering advisors, leveraging AI for administrative efficiency, and maintaining a human touch, TODAY is not just improving productivity; it’s ensuring that millions of people can access the advice they need to protect their futures. The question for investors and insurance leaders is clear: who will rise to solve the systemic advisor challenge, and who will watch the gap widen? TODAY is positioning itself to answer that question decisively. If these insights resonate with you, do share this post with someone who will get value from it. * Subscribe here to be first to know when the episode drops * For more of my thoughts, follow me on LinkedIn * Get my book Data Impact, for a pragmatic take on data-driven value creation for business Thanks for reading SLASOG: Leaders are Readers! Subscribe for free to receive new posts and support my work. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit slasog.substack.com

    1h 8m

  6. 09/16/2025

    Prof. Daniel Trabucchi - The Digital Phoenix Effect: Legacy Innovation with Platforms

    For decades, legacy companies have been trapped in a linear world: buy, make, sell: * Optimize processes. * Reduce costs. * Scale production. But the data-driven digital paradigm doesn’t just reward scale. It rewards leverage: turning what you already have into nonlinear growth engines. Daniel Trabucchi and Tommaso Buganza’s book The Digital Phoenix Effect isn’t about startups or chasing Silicon Valley trends. It’s about legacy firms using platform thinking to create exponential value from assets they already own: relationships, physical assets, know-how, and data. For business leaders, this is not optional, it’s the new strategic imperative. 1. Platforms Aren’t Digital Toys Too often, executives mistake a digital service for a platform. Airbnb. Uber. Facebook. All platforms—but the common misunderstanding is that “digital equals platform.” Daniel Trabucchi breaks down this myth: “From a business model perspective, a platform is a different way to create value compared to a linear value chain. If there are at least two distinct customer sets generating value for each other—and network effects kick in—you have a platform.” The insight for leaders: Platforms go beyond the traditional linear value chain. It is about matching different groups of users. Transactions become feedback loops. Network effects compound over time. Linear metrics like EBITDA will lie to you because they don’t measure the real dynamic. Instead, track engagement, transactions, retention, and opportunity costs by focusing on staying relevant to your ecosystem. 2. Idle Assets Are Your Special Weapon Startups build platforms because they have nothing. Legacy firms? They’re sitting on a goldmine of idle assets: * Spare manufacturing capacity. * Dormant customer relationships. * Proprietary datasets. * Decades of expertise. The principle is simple: ask not “What new can we create?” but “What existing asset can generate new value?” * A hotel chain can turn unused rooms or facilities into new offerings. * A manufacturer can expose APIs for external innovation. * A retailer can transform supplier relationships into data-driven partnerships. Airbnb and Uber discovered idle assets out of necessity. Legacy firms already have them. Platform thinking is not about invention, it’s about leverage. 3. Solve Problems, Don’t Chase Cool Here’s the trap most executives fall into: platforms are sexy, so we’ll build one. Wrong. Platforms are tools, not toys. They are frameworks for solving complex problems more efficiently than linear approaches. Daniel identifies five areas where platforms outperform traditional models: * Operational inefficiencies – streamline interactions, reduce friction, eliminate bottlenecks. * Evolving customer demands – anticipate needs that linear models cannot serve effectively. * Innovation bottlenecks – unlock external skills, knowledge, and solutions. * Data underutilization – aggregate, anonymize, and monetize across ecosystems. * Strategic blind spots – extract predictive insights from networked interactions. For leaders, the takeaway is clear: platforms are not about “being cool.” They are about non-linear value creation and capture. 4. Start with Core Interactions, Scale with Agility Legacy firms love to overplan. Infinite slide decks. Multi-year roadmaps. Predictable KPIs. Platforms don’t work that way. Daniel’s advice: “Start with a simple, core interaction. Define your value proposition for all sides. Then iterate.” Amazon didn’t launch AWS, Kindle Direct Publishing, Twitch, and Prime Video overnight. It started with a marketplace, modularized complexity over decades, and continuously adapted to emerging customer behaviors. Platforms are marathons, not sprints. They compound exponentially, but only if you start and iterate deliberately and rigorously. 5. Leadership Resilience Is Non-Negotiable Building a platform in a legacy firm is not easy. It challenges culture, threatens existing linear revenue, and forces multi-sided thinking. Success requires: * Evangelist leadership – the champion must translate abstract network effects into tangible value for skeptical stakeholders. * Agility in execution – monitor continuously, adapt relentlessly, evolve based on ecosystem feedback. Without these, platforms stagnate, and legacy inertia wins. 6. Metrics That Matter Are Different Forget EBITDA, gross margin, or quarterly profit. Platform success requires metrics aligned with network value creation: * Transaction volume and frequency * Engagement across all customer sets * Recurring usage and stickiness * Opportunity costs prevented (how much market share or latent demand are you retaining?) For legacy firms, the hidden value often lies not in immediate revenue, but in protecting existing customers from competitors and opening new growth avenues. 7. Counterposition: A Legacy Advantage In my book Data Impact, I argue that counter positioning as a legacy business is a critical advantage when competing against a challenger startup. To build on The Digital Phoenix Effect, this is how platforms enable counterposition: using your existing linear business as a springboard for nonlinear expansion. Consider Walmart: its stores, once seen as a linear cost structure, became a platform advantage. By integrating online data, loyalty programs, and supplier insights, it created new digital revenue streams and hardened its moat against pure-play e-commerce competitors. For legacy firms, this is the ultimate strategic lever: turn what looks like fixed cost into a compounding asset. 8. Real-World Inspiration The Digital Phoenix Effect has three compelling examples show the breadth of platform thinking: * Siemens Accelerator – hardware transformed into an innovation platform, enabling third-party software development and marketplace services. Siemens evolved from purely hardware to hybrid digital. * AXA Digital Commercial Platform – B2B ecosystem connecting clients with risk mitigation services. Expanded value without reinventing insurance. * ENI Supplier Platform – supports supplier sustainability transformation while solving procurement inefficiencies and fostering external innovation. The takeaway: platforms don’t need to reinvent your business. They can enhance operations, unlock latent value, and solve real problems. 9. Thinking Bigger Requires Empirical Rigor Platforms are not about buzzwords or shiny new products, they are about empirically validated leverage, iterated rigorously and deliberately over time. Leaders must: * Define a clear vision: the core interaction. * Measure relentlessly: track engagement, retention, and ecosystem growth. * Scale iteratively: start small, compound slowly, avoid unnecessary complexity. * Cultivate a flexible mindset: continuously expand customer sets, refine interactions, and capture latent demand. Legacy firms often underestimate the latent demand they can capture. Every idle asset, dataset, and relationship is a potential platform lever. 10. The Leadership Challenge The Digital Phoenix Effect is ultimately a leadership test. It asks: * Can you shift your mental model from linear optimization to nonlinear leverage? * Can you invest in long-term, compounding growth while managing short-term skepticism? * Can you make your legacy an asset rather than a liability? For leaders willing to embrace these questions, the reward is profound: not just survival, but strategic resurrection—rising like a phoenix from your legacy business. Bottom line: Stop chasing startups. Stop mimicking tech trends. Look at your assets, your data, and your customers, and ask: Where can value multiply instead of just flow linearly? That’s the Digital Phoenix Effect in action. Share the post with someone who will benefit from it. Subscribe here to to be first to know when the next episode drops: https://www.youtube.com/@SLASOG For more of my thoughts, follow me on LinkedIn. Get my book Data Impact for a pragmatic take on data-driven value creation for business. Thanks for reading SLASOG: Leaders are Readers! Subscribe for free to receive new posts and support my work. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit slasog.substack.com

    51 min

  7. 08/27/2025

    Dr. Marco Adelt & Daniel Glaremin - CEO & COO LOYAGO: Solving the German Insurance Problem

    Note: this podcast episode is in German, but the article below is in english. 500 Million Contracts, Increasingly Less Care, and a $Billion Opportunity The German insurance industry holds roughly 500 million active insurance policy contracts. On paper, a goldmine. In reality, a ticking liability. Two-thirds of insurance brokers and agents are over 50; 10% are past retirement. A decade ago, one broker or agent managed 1,700 contracts. Today, it’s 2,700. No human can provide proper customer consultation and service at that scale. Most policyholders have a touchpoint with their insurer once a year: at invoice time. Insurers are legally obliged to provide ongoing service (Insurance Contract Act), but the gap between regulation and reality is massive. This is not a customer-experience issue—it’s a structural crisis that costs the industry billions in latent value. LOYAGO’s Mission Dr. Marco Adelt and Daniel Glaremin founded LOYAGO to unlock that latent value. Adelt, Co-Founder of CLARK, one of the few global InsurTech unicorns, has built digital insurance solutions at scale in the past decade. Glaremin started working in the insurance sector at the age of 12 and has decades of customer-facing experience, and most recently was VP Operations & Customer Service at CLARK. Adelt and Glaremin both share a lifelong passion for insurance customers and believe the role and responsibilities of an insurance broker/agent are critical to a well-functioning insurance industry. They both saw insurers obsess over acquiring new customers at rising costs, while existing policyholders received almost no attention. LOYAGO bets on the contracts insurers already own. Idle policies can become active assets through targeted engagement, tailored content, and frictionless service. This is where growth, loyalty, and trust intersect to turn into a high-value compounding growth flywheel. Commoditization and Misaligned Incentives The German insurance sector has unfortunately become a purely price-driven commoditized industry. Price comparison portals have conditioned policyholders to view policies as interchangeable. Insurers followed suit, cutting prices until differentiation disappeared. The result: thinner margins, broker/agent burnout, and eroded trust. Not to mention a poor customer experience and service. As Dr. Uwe Stuhldreier points out, before chasing the latest AI fad, insurers should master the basics: ensure they have the email and contact details of all their customers! This sounds surreal, but in aggregate, the insurance sector in Germany has email addresses for only 15% of customers. If you live in Germany, you're used to getting postal communication in paper form from your insurers. Most of the industry is chasing shiny trends while ignoring fundamentals. The LOYAGO Approach LOYAGO focuses on three levers that deliver measurable ROI: Relevance – Engagement only matters if content is personalized and relevant. By using portfolio and behavioral data, LOYAGO triggers outreach that solves problems. Engagement rates are multiple times higher than average campaigns. Ease of Use – Complex processes cost time, money, and trust. LOYAGO simplifies interactions, making insurance feel modern, transparent, and reliable. Knowledge – Policyholders want actionable context, not generic messaging. Tailored communication builds trust, turning passive contracts into active relationships and trusted brands. Executive Imperative Legacy insurers are trapped in a high-cost cycle: rising customer acquisition costs, underserved portfolios, and shrinking margins. LOYAGO shows a different path. Growth comes from activation and relevance, not discounts or mass marketing. Look at other industries: Amazon cut $500 million in launch advertising to fund the early customers ordering on Prime, generating viral adoption. Octopus Energy is differentiated by transparency and profit-sharing in a commodity market. LOYAGO applies the same principle: serve your existing customer base better, and growth follows naturally. The ROI of service over acquisition is massive. A modest improvement in customer consultation and service across just 10% of the 500 million contracts could unlock billions in lifetime value while reducing churn and reputational risk. That’s the scale of opportunity insurers ignore. Unlocking Billions, Building Trust Marco Adelt puts it bluntly: “Concentrate on the fundamentals—data, simplification, and delighted customers. Get that right, and value creation will take care of itself.” LOYAGO isn’t chasing buzzwords. It addresses the structural problem: millions of contracts, millions of policyholders, and almost no customer consultation and service. For boards and executives, the question is simple: continue wasting billions on acquisitions, or unlock the latent value in the contracts you already hold. The choice is clear. What would you do? Watch the full episode with Dr. Marco Adelt and Daniel Glaremin: Thanks for reading SLASOG: Leaders are Readers! This post is public so feel free to share it. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit slasog.substack.com

    1h 8m

  8. 08/19/2025

    Adrian Stoch - CEO Americas at Hai Robotics: Challenging the Status Quo in Automation

    In this conversation, Adrian Stoch, a phenomenal supply chain leader, discusses his journey from South Africa to becoming a CEO in the robotics industry, speaks with Ritavan, author of the bestseller Data Impact. They speak about Adrian's insights in the context of Ritavan's SLASOG framework. Adrian emphasizes the importance of actionable strategy, clear communication, and the need to challenge the status quo in business practices. Adrian shares insights from his experiences in leadership, particularly drawing parallels with sports and military strategies. He highlights the significance of leveraging unique advantages in the robotics market, countering larger incumbents, and fostering a customer-centric approach. The discussion also touches on the necessity of embracing uncertainty and complexity in supply chains, as well as instilling an abundance mindset within organizations for long-term success. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit slasog.substack.com

    43 min
  • 8 Episodes

Ratings & Reviews

About

Ritavan speaks with outstanding operators and seminal thinkers to unpack first principles system rethink for data-driven value creation. slasog.substack.com

Information

  • Creator
    Hosted by Ritavan
  • Years Active
    2025 - 2026
  • Episodes
    8
  • Rating
    Clean
  • Copyright
    © Bavarian Tiger Ventures Gmbh
  • Show Website
    SLASOG: Leaders are Readers