Business Model Transformation

The same people hunting for discounts on Myntra are paying ₹1,500 for instant fashion on Zepto. This isn't just another retail trend. It's a complete reversal of how we understand fashion buying. Urban consumers have started treating fashion like groceries, demanding immediate delivery for immediate needs. Think about it. That Saturday evening party outfit can't wait three days.  The campus event tomorrow needs the perfect look today. Quick commerce understood this shift before traditional retail even noticed and quick commerce platforms are specifically targeting trend-conscious urban customers and Gen Z. Why? Because they're willing to pay ₹500 to ₹1,500 on Zepto or ₹1,400 to ₹1,600 on NEWME for 25 to 60 minute delivery. The implications for fashion brands are staggering. Expanding inventory to new regions now requires: → Tech-led demand prediction systems → Understanding hyperlocal preferences → Building distributed warehouses → Tracking regional buying patterns Brands studying fashion demand must consider completely new factors. Weekend travel creates spikes in metro cities. Festive seasons hit differently across regions. Occasion-based purchases drive impulse buying. Each locality has its own style DNA. Traditional retail spent decades perfecting central warehouses and seasonal collections. Quick commerce demands the opposite. Small inventory points everywhere. Weekly design drops. Regional customization. Fashion has entered the 10-minute economy, and there's no going back. What's one fashion emergency that made you wish for instant delivery?

𝗠𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝘁𝗵𝗶𝗻𝗸 𝗮𝘂𝘁𝗼𝗺𝗼𝗯𝗶𝗹𝗲 𝗱𝗲𝗮𝗹𝗲𝗿𝘀𝗵𝗶𝗽𝘀 𝘄𝗶𝗻 𝗯𝘆 𝘀𝗲𝗹𝗹𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝘃𝗲𝗵𝗶𝗰𝗹𝗲𝘀. 𝗜 𝗱𝗶𝘀𝗮𝗴𝗿𝗲𝗲. The dealerships that will dominate the next decade will not be the ones with the biggest showrooms... They will be the ones with the smartest systems, strongest execution, highest retention, and most disciplined operations. Because the dealership business is changing rapidly. --- A dealership today cannot survive by functioning only as: 👉 A showroom 👉 A sales outlet 👉 A discount-driven business 👉 A transaction center The future belongs to dealerships that operate as: ✔ Customer intelligence platforms ✔ Data-driven operating systems ✔ Capital-efficient businesses ✔ Experience-led retail ecosystems ✔ Operationally scalable enterprises --- 𝗜𝗻 𝗺𝘆 𝘃𝗶𝗲𝘄, 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 𝗳𝗼𝗿 𝗳𝘂𝘁𝘂𝗿𝗲-𝗿𝗲𝗮𝗱𝘆 𝗱𝗲𝗮𝗹𝗲𝗿𝘀𝗵𝗶𝗽𝘀 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀: 𝗣𝗛𝗔𝗦𝗘 𝟭 - 𝗦𝗵𝗶𝗳𝘁 𝗳𝗿𝗼𝗺 𝗧𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 Winning dealerships will focus on: • Customer lifetime value • Service loyalty • CRM intelligence • Repeat business • Long-term engagement Because profitability starts after vehicle delivery. Not before it. --- 𝗣𝗛𝗔𝗦𝗘 𝟮 - 𝗥𝘂𝗻 𝗗𝗲𝗮𝗹𝗲𝗿𝘀𝗵𝗶𝗽𝘀 𝗟𝗶𝗸𝗲 𝗗𝗮𝘁𝗮 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 Tomorrow’s strongest dealerships will master: ✔ Inventory intelligence ✔ Demand forecasting ✔ Lead conversion analytics ✔ Workshop productivity ✔ Financial visibility ✔ Digital integration The future of dealership growth will depend on operational intelligence. Not intuition. --- 𝗣𝗛𝗔𝗦𝗘 𝟯 - 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗛𝗶𝗴𝗵-𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗖𝘂𝗹𝘁𝘂𝗿𝗲 Great dealerships focus on: • Leadership development • Accountability • Team productivity • Operational discipline • Skill-based training Because dealership growth is ultimately a people-performance business. --- 𝗣𝗛𝗔𝗦𝗘 𝟰 - 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗕𝗿𝗮𝗻𝗱 𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗢𝗘𝗠 The next decade will reward dealerships customers trust for: ✔ Transparency ✔ Service quality ✔ Consistency ✔ Ownership experience ✔ Leadership vision --- The industry is no longer driven only by: • Footfalls • Discounts • Monthly targets It is increasingly driven by: ✔ Customer retention ✔ Financial discipline ✔ Operational scalability ✔ Team productivity ✔ Digital transformation That’s why I always say: 𝗜 𝗱𝗼𝗻’𝘁 𝗷𝘂𝘀𝘁 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗮𝘂𝘁𝗼𝗺𝗼𝗯𝗶𝗹𝗲 𝗱𝗲𝗮𝗹𝗲𝗿𝘀𝗵𝗶𝗽 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀... 𝗜 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗚𝗔𝗠𝗘 𝘁𝗵𝗮𝘁 𝘄𝗶𝗹𝗹 𝗱𝗲𝗳𝗶𝗻𝗲 𝗶𝘁𝘀 𝗳𝘂𝘁𝘂𝗿𝗲. #DealerPrincipal #AutomobileIndustry #AutomotiveRetail #DealershipManagement #BusinessTransformation #OperationalExcellence #AutomotiveLeadership #BusinessStrategy #CustomerRetention #FutureReady

90% of Car Dealerships Will Disappear by 2030. The traditional car dealership model is facing an existential crisis. Here's what's changing, the dealership model was built for a world that no longer exists. In 1950, local dealers were essential because: - Cars required constant maintenance, - Financing was complex and local, - Vehicle information wasn't readily available, - Test drives were the only way to experience a car. Fast forward to 2025, and every single one of those pillars has crumbled. The perfect storm hitting dealerships: → EVs need 70% less maintenance than gas cars → Online financing is faster and more transparent → Virtual showrooms provide better information than sales floors → Direct-to-consumer brands are proving the model works But here's the real dealbreaker: Customer experience. Recent studies show that 87% of car buyers would prefer to complete their entire purchase online. The dealership visit isn't a valued feature, it's the friction point buyers are trying to avoid. Tesla eliminated dealers entirely. Rivian followed suit. Lucid confirmed the strategy works. Now legacy automakers are watching their franchise agreements like ticking time bombs, knowing change is inevitable. The 10% that make it won't be "dealerships" as we know them. They'll evolve into: - Service and experience centers, - 0EV charging hubs with retail integration, - Subscription and fleet management specialists, - Augmented reality showrooms with same-day delivery. By 2030, the suburban mega-dealership with 200 cars on the lot could be as outdated as Blockbuster Video. The real estate costs alone will force massive consolidation. The question isn't IF this transformation happens. It's HOW FAST the industry adapts to meet evolving customer expectations.

Beyond the Hype: How Ontologies Can Unlock the Potential of Large Language Models for Business This post delves into the transformative capabilities of Large Language Models, such as GPT-4, and examines the crucial role that the structured intelligence of ontologies can play in deploying LLMs in production environments. 🔵 The Power of LLMs: LLMs have remarkable capabilities; they can craft letters, analyse data, orchestrate workflows, generate code, and much more. Companies such as Google, Apple, Amazon, Meta, and Microsoft are all investing heavily in this technology. Everything indicates that LLMs have enormous disruptive potential. However, there is a problem: they can hallucinate. So, how can serious businesses ever use their full power in production? 🔵 The Structure of Ontologies: Ontologies provide a formal and structured way to represent knowledge within specific domains. They enable computers to understand and reason in a logical, consistent, and controlled manner. Ontologies are also human-readable, editable, and auditable artefacts that can be kept under source control. 🔵 It's All 'Just' Semantics: The combination of LLMs and ontologies creates a powerful synergy. This synergy allows organisations to harness the capabilities of LLMs within the guardrails of a controlled structure. This collaborative partnership establishes a reinforcing feedback loop of continuous improvement. Ontologies provide context to prompts and validate the LLMs' responses, while the LLMs help extend ontologies with missing concepts. 🔵 Bringing It All Together in a Working Memory Graph: LLMs and Ontologies can be combined in a design pattern that I call the 'Working Memory Graph'. Within a Working Memory Graph (WMG), LLM embedding vectors, ontologies, facts from the Knowledge Graph, and graph-based analytics are all integrated. The WMG uses ontologies to help build prompts, translates natural language questions into a graph structure, employs Graph Retrieval-Augmented Generation (GRAG) to gather facts, and uses LLM-based GML to perform graph-based analytics. In summary, Large Language Models represent not merely a passing fad but a paradigm shift that progressive organisations can ill afford to ignore. Ontologies offer a versatile yet disciplined framework, enabling organisations to move forward with LLMs while remaining in control. ⭕ Working Memory Graph: https://lnkd.in/eQF4PE27 ⭕ Continuous and Discrete: https://lnkd.in/ex8HA_Nj ⭕ Vectorizing Your Knowledge: https://lnkd.in/eDDd3MAz ⭕ LLM-based Node Classification: https://lnkd.in/e_YzTg_V

RIP Tableau Tableau is a business intelligence tool owned by Salesforce. For years it was part of how we worked at Voi. In the beginning it felt powerful, but over time it turned into what many legacy SaaS tools become: expensive, clunky and slow. Every ad hoc request ended up in an analyst backlog. Local teams across our 100 plus cities were left waiting for insights, costs kept going up and speed disappeared. So we ripped it out, saved at least 500k EUR, potentially millions (from speed). The direct savings are hundreds of thousands of euros in licenses. The indirect savings are even bigger since analysts can now focus on high impact work instead of repetitive reporting. The biggest shift is speed. What once took weeks now happens in seconds. Here is how we made it possible: 1. We fixed the foundations. Years of work on data governance. Every metric has an owner, quality checks, semantics and definitions. Everyone in the company knows what a number means. With that in place, self serve became possible, which is essential when local teams in 100 plus cities need the right data at the right time. 2. We defined what we need, not what we paid for. A single source of truth, real time data streaming and self serve for non technical users. Analysts no longer spend their days on small one off requests. 3. We used LLMs as the bridge. Together with a design partner we built a UI that supports continuous business intelligence, and we created an AI data analyst that lives inside Slack and Sheets. LLMs translate natural language into SQL, query the warehouse and return insights or visuals in natural language again. This step is what unlocked true self serve at scale. But LLMs alone are not enough. In an enterprise setting you need strict guidelines and guardrails. Without governance you risk inconsistent answers, wrong definitions or even compliance issues. The combination of solid data governance with the power of LLMs is what makes this work. The results are clear: 1. Millions saved on SaaS and labor 2. One source of truth for all key metrics 3. Self serve for everyone in the company within clear constraints 4. Up to 100x faster time to insight and decision making LLMs made this shift possible. Strong governance made it safe. RIP Tableau. And it will not be the last legacy SaaS tool we replace.

Every dealer I talk to right now — large groups, single points, franchise and independent — is saying the same thing: Business is getting tougher. Margins are shrinking. Affordability is the biggest challenge on both new and used. For the first time in years, the market feels a lot like 2018 again… maybe tougher. Here’s what I’m seeing across the country: 🔹 Affordability has hit a ceiling. Interest rates, insurance, and high transaction prices have pushed payment-sensitive buyers to the limit. 🔹 New-car gross is tightening fast. Inventory is back, OEM programs are ramping up, and competition for qualified buyers is intense. 🔹 Used-car margins are compressed. Acquisition costs are high, negative equity is rising, and the fight for trades and consumer cars is fierce. 🔹 The “COVID gross era” is over. We’re back in a market where discipline, process, and leadership matter more than ever. But here’s something a lot of people aren’t talking about: We don’t just have an inventory or affordability problem. We have a people and training problem. And this is where strong operators pull ahead: ✔️ Salespeople need to be trained to prospect again. Many don’t even understand the word. They’ve never been taught how to build a book of business, make outbound calls, promote themselves on social media, or create their own opportunities. That skillset is becoming essential again. ✔️ Service advisors and techs need to be retrained on the basics. Proper multipoint inspections… reviewing recommended maintenance… adding lines and hours per RO… communicating value to the customer. The service lane is the heartbeat of the dealership — and it needs consistent coaching. ✔️ Sales managers need situational awareness. What’s happening on the lot, in the showroom, and in the digital showroom. Who’s aging. Who’s waiting. Who needs a follow-up. Managers can’t spend the whole day behind a desk anymore — they need to lead from the front. ✔️ Dealers must acquire aggressively from consumers. Service drive, equity mining, instant cash offer funnels — everything matters right now. ✔️ Recon must be tight, disciplined, and fast. Speed to market is non-negotiable. ✔️ Older, affordable inventory is critical. More customers are payment buyers. Sub-$20k retail is a must. ✔️ Expense control has to return to 2018 levels. The 2021–2022 expense model doesn’t work anymore. ✔️ Technology and AI are becoming competitive advantages. The dealers who adopt it first will win on efficiency, training, follow-up, and acquisition. The market has shifted — and it’s not shifting back anytime soon. This is the moment where real operators separate themselves. If you’re feeling the pressure, you’re not alone. And if you’re doubling down on fundamentals, training, and accountability, you’re already ahead.

The future of car dealerships? Here’s my (unpopular) take. If I were to build one today—it wouldn’t have a million-dollar coffee bar or excessive square footage. It would be a command center. A boutique-style showroom. Two cars. Strategically located in a high-traffic area. Sleek. Frictionless. Elegant. The service center? A well-oiled warehouse. Out of sight, but highly efficient. Pick up. Drop off. Done. AI would be at the core. Robotics, automation, smart systems—designed not to impress Wall Street, but to serve Main Street. Because let’s be real: Customers don’t care about ceiling tiles. They care about speed, convenience, and control. Many businesses today are optimized to look impressive—but not necessarily to be profitable. The lower your break-even point, the stronger your model. Virtual is where it’s at. Test drives are being replaced with tailored experiences. “Bring the car to me” is the new standard. This mindset applies across industries. Maybe even yours. AI. Automation. Smart operations. That’s not just the future of dealerships— That’s the future of business.

𝗬𝗼𝘂𝗿 𝗗𝟮𝗖 𝗕𝗿𝗮𝗻𝗱 𝗦𝗲𝗹𝗹𝘀 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀. 𝗔𝗺𝗮𝘇𝗼𝗻 𝗦𝗲𝗹𝗹𝘀 𝟳 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗧𝗵𝗶𝗻𝗴𝘀. 𝗛𝗲𝗿𝗲'𝘀 𝗪𝗵𝗮𝘁 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗦𝘁𝗲𝗮𝗹. Amazon's trailing twelve-month revenue hit ₹57.6 lakh crore ($691 billion). Product sales? Only 42%. The other 58% is a revenue diversification playbook every D2C founder should copy. 𝗛𝗼𝘄 𝗔𝗺𝗮𝘇𝗼𝗻 𝗠𝗮𝗸𝗲𝘀 𝗠𝗼𝗻𝗲𝘆 Online Store: 42% (₹24.2L cr) Third-Party Services: 23% (₹13.3L cr) AWS: 16% (₹9.2L cr) Advertising: 7.5% (₹4.3L cr) Subscription: 7% (₹4L cr) Physical Stores: 4% (₹2.3L cr) 𝗪𝗵𝗮𝘁 𝗬𝗼𝘂𝗿 𝗗𝟮𝗖 𝗕𝗿𝗮𝗻𝗱 𝗖𝗮𝗻 𝗟𝗲𝗮𝗿𝗻 𝐀𝐝𝐝 𝐚 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐏𝐥𝐚𝐲: 23% of Amazon's revenue comes from others selling on their platform – zero inventory risk. Could you let complementary brands sell through your site? Commission-based revenue scales infinitely. 𝐋𝐚𝐮𝐧𝐜𝐡 𝐚 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧: Prime generates ₹4L cr and makes members spend 2-3x more. Weekly boxes, exclusive access, VIP perks – recurring revenue beats one-time sales every time. 𝐌𝐨𝐧𝐞𝐭𝐢𝐳𝐞 𝐘𝐨𝐮𝐫 𝐓𝐫𝐚𝐟𝐟𝐢𝐜: Amazon makes ₹4.3L cr from advertising. You have traffic and audience attention. Start with affiliate links, then sponsored placements, brand partnerships. Traffic is an asset – stop giving it away free. 𝐁𝐮𝐢𝐥𝐝 𝐇𝐢𝐠𝐡-𝐌𝐚𝐫𝐠𝐢𝐧 𝐎𝐟𝐟𝐞𝐫𝐢𝐧𝐠𝐬: AWS is 16% of revenue but 50%+ of profits. What's your high-margin play? Online courses, coaching, tools, SaaS products related to your niche. 𝐄𝐧𝐚𝐛𝐥𝐞, 𝐃𝐨𝐧'𝐭 𝐉𝐮𝐬𝐭 𝐒𝐞𝐥𝐥: Amazon's biggest wins came from building infrastructure others need – fulfillment, cloud, payments. What does your industry struggle with that you could solve and monetize? The lesson? Revenue diversification isn't a nice-to-have. It's how you survive when CAC spikes, margins compress, and competition intensifies. Stop selling one thing. Start building seven revenue streams. Picture: Respective Owner #amazon #D2C #revenue #growth #strategy

Everyone’s watching Salesforce cut thousands of jobs. Nobody’s talking about why growth stalled at the same time. Salesforce didn’t suddenly forget how to run a software business. It’s one of the most industrial-grade selling engines ever built. Yet they just posted the slowest revenues in their history during the same period they bet hard on AI as the answer for expansion. That’s not an AI story. It’s what happens when the market evolves faster than the playbook - when the vendor stays seller-centric while the buyer quietly rewrites the rules. And it’s not just Salesforce. Across enterprise tech, the real buyer has shifted. Deals get bigger, budgets get tighter, and suddenly the CFO, COO, & business-unit leaders, not just functional champions make the call. Vendors keep pushing features. Executive buyers demand clarity, control, and measurable outcomes. I worked with one enterprise team stuck in that exact gap. Their strategy was automation everywhere - AI for service tickets, AI for data entry, AI for workflows. I asked how they mapped and supported executive priorities in their largest accounts. There was nothing: • No buyer map • No decision architecture • No way to show finance and operations how risk would drop or control would rise Business stakeholders felt pushed, not supported. You can’t automate trust. And you can’t scale a system that isn’t aligned with how enterprise executives actually make decisions. So we rebuilt the fundamentals, not the tech stack, the buyer logic: ↳ Mapped decision criteria across finance, operations, & transformation. ↳ Reframed value: risk, control, P&L. ↳ Shifted “selling the platform” to help buyers build cases for change. ↳ Moved account teams off short-cycle pressure & onto long-arc value Three months later, deals didn’t just close. Stalled opportunities resurfaced. Expansions grew. Renewal risk dropped. Same product. Same accounts. Different posture. The Salesforce story isn’t about automation or AI productivity. It’s a signal that enterprise sales stalls when vendors double down on selling while buyers double down on caution. Effectiveness now starts with buyer-centricity at the executive level: Outcome clarity before feature talk. Mutual control before vendor pressure. That’s how predictable enterprise growth is built, & how you keep pace when the buyer changes faster than your roadmap. #DontSell #EnableBuying #EnterpriseDeals

Legacy on-premise IT systems have a stranglehold on telco innovation. The AI-first future demands speed and agility that traditional software systems simply can't deliver. In the latest Telco in 20 podcast episode, Vodafone's Dr. Lester Thomas and I dive into how a radical new approach to IT is breaking down the barriers that have stalled telecom progress. While most operators debate whether cloud-native transformation is realistic, Vodafone is demonstrating not only is it doable — it's absolutely critical. We cover: • How Vodafone moved 17 petabytes of data from 600 Hadoop servers into Google Cloud to create their foundation for AI adoption • The company’s strict "cloud native" definitions have resulted in 80-90% of digital workloads being truly cloud native • The three principles Vodafone's Open Digital Architecture is based on: machine-readable standards, open-source collaboration, and proof-of-concept testing • Why AI is forcing complete software redesign at Vodafone, and how their AI Booster platform democratizes access while maintaining governance The operators who thrive won't be the ones doing IT the way it’s been done over the last 20 years. They'll be the ones bold enough to do the heavy lifting of truly becoming cloud-native and work to create a data platform that’s usable by AI so they are able to push the boundaries of what's possible in telecom. This is THE conversation to watch before you head to TM Forum’s DTW Ignite event in Copenhagen! If you missed the LinkedIn Live event you can watch the conversation on demand or listen to the audio only version on your favorite podcast player! Links in the comments. #Vodafone #telecommunications #cloudnative #AI #digitaltransformation