AI-Driven Business Models

Just published a framework for building an AI services company, going after the $20 trillion dollar industry powered by human-driven, low-tech businesses where legacy incumbents have built their brands on historical reputation and prestige rather than measurable performance. Here is the full framework & analysis: https://lnkd.in/gAEZ_Gq3 While many focus on AI making existing software more efficient, the true revolution is happening as AI pushes directly into domains previously exclusive to human experts: strategic negotiations, creative problem-solving, and high-stakes decisions. These critical domains have remained stubbornly resistant to software automation until now. We're witnessing a fork that will split the professional services landscape into two distinct futures: 🌑 Legacy Professional Services: - Dominated by established incumbents with century-old processes - Knowledge siloed by human experts with limited documentation - Services bottlenecked by human cognitive and time constraints - Premium pricing models based on artificial scarcity 🌕 Elite AI-Driven Professional Services: - AI-native firms delivering demonstrably superior outcomes - Expertise amplification across entire organizations - Services that scale beyond traditional human constraints - Value-based pricing tied to measurable outcomes This transformation represents perhaps the largest opportunity in the AI landscape today. For entrepreneurs and investors, the $20 trillion market of pure human expertise is now accessible in ways previously unimaginable. Let me know if you're building in this space. The future belongs to systems that truly learn from experience.

What kind of AI-native company are you building? We talk a lot about model quality, UX, and moats. But behind the best AI startups today is something more fundamental: A business model that’s designed for how AI systems actually work in the wild. Over the last year, we’ve seen four distinct business models gain traction. They differ in interface depth, ops intensity, margin structure, and entanglement with customer reality. 👇 Dive into the full breakdown below: Here’s a quick primer: 🔹 Model 1: Product-Only Distribution compounds faster than AI models decay. These companies win by embedding into daily workflows—with UX, trust, and distribution that outlasts any one AI model. Examples: Cursor, Perplexity, MotherDuck 💡 Cursor isn't winning on model access. It's winning because it mirrors how devs context-switch, debug, and flow through large codebases. 🔹 Model 2: Product + Embedded Engineering You don’t build the spec in the lab. You build it in the field. These companies embed engineers alongside customers—not to consult, but to co-develop domain-specific systems that actually hold up. Examples: Harvey, Adaptional, CurieTech AI 💡 Harvey doesn’t sell “legal AI.” It builds copilots with Am Law firms, tuned to real workflows and risk psychology. 🔹 Model 3: Full-Stack Services: Where AI is embedded Customers aren’t buying tools. They’re buying outcomes. These companies offer AI-powered services—not software—with control over data, execution, and continuous feedback. Examples: LILT AI, Town 💡 Lilt delivers global localization as a managed service, blending human expertise with AI at every step—from content routing to tone correction. 🔹 Model 4: Roll-Up + AI Don’t start from zero. Start from ops. Infuse with AI. These companies acquire expert-heavy physical businesses (e.g. warehouses, pharmacies) and embed AI into labor, logistics, and trust loops. Examples: stealth roll-ups in logistics, healthcare, robotics 💡 A warehouse roll-up using AI to route robotic arms, triage edge cases, and compound labor—not replace it. Across all four models, one truth keeps surfacing: AI is not the product. It’s the substrate. The best companies aren’t “AI-powered tools.” They’re systems—engineered for throughput, refined in production, and impossible to unbundle. Huge thanks to Ashish Thusoo, Jordan Tigani, Suril Kantaria, Dylan Reid, Jocelyn Goldfein, and Annelies Gamble for sharing insights, counterexamples, and lived experiences.

AI is finally making services businesses scalable—and—exciting to VCs. The global services market is in the trillions of💰s, far larger than today’s software market. Yet, services businesses haven’t been the darlings of venture capital, as they were perceived to lack rapid scaling potential. 𝗔𝗜 𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝘁𝗵𝗮𝘁. By blending AI seamlessly with human expertise, there is an opportunity to get into much larger markets with models that have the potential to scale in ways services - or even SaaS businesses - can't. For example, instead of offering a marketing SaaS, an AI-powered Service-as-Software business can deliver what the customer really wants: high-quality leads or compelling content. We’ve seen this potential firsthand through Emergent Ventures’ investments in multiple AI-powered companies that leverage humans-in-the-loop. These models resonate with B2B customers because they offer faster, clearer paths to value—reliable outcomes delivered with greater efficiency. For many customers, it’s a significant upgrade over traditional agency or service-provider relationships. While the potential is huge, only a fraction of AI-powered services startups will scale. 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗱𝗲𝗽𝗲𝗻𝗱𝘀 𝗼𝗻 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝗳𝘂𝗹 𝗲𝗮𝗿𝗹𝘆 𝗰𝗵𝗼𝗶𝗰𝗲𝘀 𝗮𝗻𝗱 𝗲𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻. Here’s what we have learned works well: 𝟭. 𝗔𝗜-𝗛𝘂𝗺𝗮𝗻 𝗦𝘆𝗻𝗲𝗿𝗴𝘆: AI and software should do the heavy lifting, with humans involved strategically— e.g. for validating AI output, edge cases, enabling adoption, or acting on AI insights. Over time, reduce human input as the AI learns, and models improve. Target 60%+ initial gross margins, with a path to SaaS-like 75%+ margins over time. 𝟮. 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗛𝘂𝗺𝗮𝗻 𝗜𝗻𝘃𝗼𝗹𝘃𝗲𝗺𝗲𝗻𝘁: The dependency on hiring & training humans should not constrain scale and economics. Have a path to tapping into freelancers or agency partners. Leverage human experts in a high-talent location such as India. 𝟯. 𝗥𝗲𝗰𝘂𝗿𝗿𝗶𝗻𝗴 𝗥𝗲𝘃𝗲𝗻𝘂𝗲: Focus on high-value, recurring use-cases to ensure subscription-based revenue with strong net revenue retention (NRR). 𝟰. 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗣𝗼𝘄𝗲𝗿: Iterate to a solution that can command higher pricing, and a model that aligns incentives with customers, e.g. based on outcomes. 𝟱. 𝗗𝗮𝘁𝗮 𝗠𝗼𝗮𝘁𝘀: Build solutions that improve with use, creating compounding competitive advantages over time. 𝟲. 𝗠𝗼𝗱𝘂𝗹𝗮𝗿 𝗧𝗲𝗰𝗵: Architect a stack that can evolve with AI advancements. 𝟳. 𝗙𝘂𝗹𝗹-𝗦𝘁𝗮𝗰𝗸 𝗧𝗲𝗮𝗺: A founding team that has the technical expertise to build and rapidly improve complex AI-powered solutions, and deep operational acumen. A rare combination. These are complex businesses to build, and the right playbooks are yet to be perfected. But where this works, 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀-𝗮𝘀-𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗔𝗜 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 𝘄𝗶𝗹𝗹 𝗿𝗲𝗱𝗲𝗳𝗶𝗻𝗲 𝗺𝗮𝗻𝘆 𝗕𝟮𝗕 𝗰𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝗲𝘀 📈 #EnterpriseAI #startups #vc #SaaS

While everyone debates tactics, a quiet revolution is happening in B2B startups. Early-stage founders drown in contradictory advice: perfect your sales funnel, build remarkable culture, master your tech stack, nail your positioning. Beneath this noise lies a surprisingly consistent set of founder responsibilities that transcend changing markets, technologies, and business models. These fundamentals haven't changed. What has changed is how founders can execute against them. As AI drives building costs toward zero, I'm noticing B2B startups adopting operational models that look remarkably similar to agencies: 1️⃣ Small, specialized teams 2️⃣ Custom solutions built on AI foundations 3️⃣ Revenue from day one 4️⃣ Products evolving into managed services 5️⃣ Capital efficiency as a competitive advantage This isn't the "service business trap" founders were once warned about. It's a deliberate hybrid model leveraging AI to achieve product-like margins while maintaining agency-like flexibility. What makes this shift particularly interesting is how it emphasizes—rather than diminishes—the fundamental disciplines: Deep customer understanding isn't just important; it's existential when you're engaged with clients from day one. Trust-building isn't a marketing function; it's your entire business model. Pricing power doesn't come from optimization strategies; it emerges from truly unique value. Capital efficiency isn't a bootstrap necessity; it's a strategic advantage in markets where AI has collapsed the cost of experimentation. Leadership isn't about managing scale; it's about defining problems worth solving. Tomorrow's B2B winners will operate at the intersection of fundamental disciplines and AI-powered execution. They won't be distracted by tactical debates or rigid business model orthodoxy. They'll recognize that whether you're building a traditional SaaS company or an AI-enhanced service business, the core challenges remain remarkably consistent. Success will be increasingly about seeing through tactical noise to the eternal questions of value creation. #startups #founders #growth #ai — Are you building an expertise-led business? I’d love to help you gain more traction. Let’s connect — I’m always eager to learn from fellow founders!

AI is dramatically reshaping business models. This framework is the foundation of my new LinkedIn Learning course "AI-Driven Business Model Innovation". See below for a brief summary of the 6 domains of AI’s impact on value creation, together with the major driving forces and the capabilities required as business models rapidly evolve. Link to the course - free for LinkedIn subscribers - in comments. DRIVING FORCES 🧠 Driving Forces of AI Evolution We’re at a structural shift in business. AI capabilities are accelerating, costs are falling, and data is becoming a strategic asset. These forces are reshaping the foundations of value creation — demanding that leaders rethink not just what their business does, but how it evolves. SIX DOMAINS OF AI-DRIVEN BUSINESS MODEL INNOVATION ⚙️ Scalable Efficiency AI enables organizations to operate at a new scale — automating tasks, streamlining decisions, and amplifying productivity. This isn’t just about cost-cutting and efficiency — it’s augmenting talent for higher-value work and building systems that continuously learn and improve. 🎁 Enhanced Value Propositions AI enhances what you offer — and how it’s experienced. From smart, adaptive products to deeply personalized services, it allows you to deliver more relevance, utility, and meaning to every customer. The frontier of value lies in customer responsiveness and learning at scale. 💞 Shifting Customer Relationships AI transforms how we engage with customers — not just improving service, but enabling co-creation, building trust, and responding to individual needs in real time. The most successful companies will be those that become embedded in customers’ lives through intelligent, trusted relationships. 🏗️ Redesigning Organizations Organizations must evolve from static hierarchies to adaptive systems that blend human and AI capabilities. This means rethinking workflows, decision-making, and structures to be more fluid, responsive, and innovation-driven. AI is not a bolt-on — it enables dramatic reconfiguration of value creation. 🧑💻 The AI Agent Economy AI agents are becoming participants in the economy — acting on behalf of users, negotiating, coordinating, and executing tasks. This shift calls for new strategies, where businesses design for agents as well as humans, and where trust and interoperability become core to competitive advantage. 🌐 AI in Platforms and Ecosystems The most powerful business models today are built around data-rich ecosystems. AI turns data into action, unlocking new platform value and shared innovation. Success increasingly depends on how well you participate in — or build — dynamic, intelligent ecosystems. CAPABILITIES 🚀 Capabilities for AI Evolution Thriving in this landscape requires more than tools. It demands vision, adaptability, experimentation, and the ability to work across boundaries — human, organizational, and technical. These capabilities are the foundation of tomorrow's business models and success.

SaaS startup founders have countless playbooks to guide them, but AI-enabled services founders are charting new territory. These pioneers combine AI with human expertise to deliver faster, better, and cheaper outcomes than legacy service providers. Many companies adopting this model have had compelling early traction. But here's the catch: founders are trying to force-fit traditional SaaS strategies onto these service-based businesses. There are some similarities but also major differences. We've identified 5 critical lessons for building an iconic AI-enabled service company. Consider this a contribution to the new playbook: 1) Bring on a domain expert early. They're even more critical than before In traditional SaaS, you're selling a product. In AI-enabled services, you're selling yourself. Domain authority isn't just important, it's existential. It also unlocks access to high-quality talent channels, which enables the rapid staffing that you may need while your AI is still maturing. 2) Beware Mirage PMF PMF is a different beast in AI-enabled services. Strong revenue growth and NDR can mask a lack of true AI enablement, i.e. "Mirage PMF." Real PMF in AI-enabled services requires proving you can scale non-linearly relative to your costs. To get there, your AI must quantifiably improve cost, quality, or speed—or ideally, all 3. 3) Develop partnerships early on — they can be a key growth accelerator. Incumbents offer immediate market credibility, established distribution, and access to proprietary datasets, which can be crucial early on while your data corpus is small. To take advantage, smart service startups are exploring partnership models that are well beyond the traditional SaaS revenue-share approach. 4) Leverage new pricing models — they can help unlock higher contract values. AI-enabled service contracts have two different models, each with unique benefits and risks: → Labor-Based: Priced by labor hours.  Guarantees early margins, but limits upside as automation scales. → Outcome-Based: Priced by delivered value. Value aligned and can unlock very high margins over time, but risks early profitability with nascent AI. We’ve found that it's typically best for AI-enabled service vendors to start with a labor-based approach while learning how to deliver their service. Just set clear timelines to transition to an outcome-based model. 5) It’s the demo, stupid! In this case, founders should borrow directly from the SaaS playbook and build a “wow” demo for their tech. Ditch the deck; a strong demo boosts customer confidence and accelerates sales conversations. If you’re exploring AI-enabled services, we’d love to learn alongside you. Share your thoughts—we’re all figuring out this new model together. P.S.- Thank you to Arjun Chopra, Medha Agarwal, Wayne Hu, James Currier, Zachary Bratun-Glennon, Wenz Xing, Nic Poulos, and Kent Goldman, for helping me put this together.

🚀 AI-Driven Business Strategies: The 2025 Playbook Every Leader Needs The future of business isn’t just digital—it’s intelligent. By 2025, AI isn’t a “tool” in your strategy; it’s the foundation. Here’s how top companies are rewriting the rules: 🔑 Key Trends Shaping 2025 1. Hyper-Personalization at Scale       → AI deciphers customer behavior to deliver tailored experiences, boosting loyalty and revenue.     2. Decision Intelligence       → Predictive analytics + AI = automated, real-time decisions (supply chains, pricing, logistics).     3. Generative AI Revolution       → From marketing copy to synthetic data testing, GenAI slashes time-to-market by 70%+.     4. Operational Overdrive       → AI optimizes routes, inventory, and workflows—cutting costs while scaling efficiency.     5. Ethical AI & Cybersecurity       → Trust is non-negotiable. Leaders bake transparency into AI and deploy it to combat threats.     Why This Matters🌍 - $1.3T Market by 2032: Early adopters are already outpacing competitors. - Dynamic Pricing: AI shifts pricing from static to behavior-driven, maximizing margins. - Swarm Learning: Cross-department AI collaboration unlocks innovation silos.  🛠️ The Execution Gap Success isn’t about tech—it’s about strategy: ✅ Start small, iterate fast. ✅ Invest in AI fluency (teams and tools). ✅ Prioritize data quality like it’s oxygen. The Bottom Line: Companies that treat AI as a strategic partner—not a cost center—will dominate. The rest? Still debating ChatGPT prompts.

Most AI businesses fail because they solve fake problems. After talking to many AI business owners, I’ve seen a pattern. Here’s what I’ve discovered: There are 5 AI business models that actually generate real revenue: 1. AI-Powered Service Business — Use AI to deliver services 10x faster than your competitors. 2. Personal Media Company — AI creates content; you build the audience. 3. AI Consulting & Implementation — Help businesses adopt AI smoothly. 4. AI-Enhanced Coaching — Use AI to scale your expertise and reach more people. 5. AI Agent Integrations — White-label AI agents into niche markets to do the boring work humans don't need to do. Pick one. Master it. Scale it. The key skills AI can’t replace are what make us human: Leadership, strategic thinking, relationship building, creative problem solving. Focus on those. Let AI handle execution. Right now, AI is powerful enough to build real businesses. But it’s not saturated enough to eliminate opportunity. This window won’t last forever. Use it now. Most founders overcomplicate AI: Start simple. Identify one repetitive task AI can handle. Measure the time you save, not just new features. Test with existing tools before building custom solutions. The winners are solving real problems people will pay for. The AI gold rush is happening now. But unlike past tech waves, this one rewards execution more than innovation. Stop overthinking. Start building. Enjoy this? Repost it to your network and follow me for more insights. Want to future-proof your founder brand with AI systems?