Business Models for AI Innovation

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

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.

The biggest AI trend is NOT what you might think. You may have heard of "Boring Businesses" before, but now there's "Boring AI" But for those solving these problems, there's nothing boring about the incredible growth and profits they're seeing. While everyone's chasing the next consumer AI app, I've analyzed 100+ companies using AI... Companies solving mundane problems are generating revenue 4X FASTER than those building "revolutionary" AI. I call it "Boring AI" - solutions for tasks nobody wants to do but everyone must pay for. As an investor who's grown companies from 0 to 7 figures, 6 to 8 figures, and 9 to 10 figures, I've seen this pattern repeatedly. The psychology is fascinating: • Boring problems = desperate customers • Boring problems = less competition • Result = perfect business opportunities    Here's my P.A.I.D. framework for identifying these opportunities: P - Predictable (follows consistent patterns) A - Automatable (possible with current tech) I - Invisible (crucial but overlooked) D - Durable (will remain necessary) The most profitable "boring AI" opportunities hiding in plain sight: • Document processing • Data entry/validation • Meeting summarization • Email management • Scheduling coordination    None require breakthrough research—just deep problem understanding. Want to build a successful boring AI business? Follow this playbook: 1. Start ultra-narrow but exceptional 2. Prove ROI in days, not months 3. Use "AI + Human" hybrid approach 4. Price based on value, not cost 5. Focus on distribution     This is the trillion-dollar opportunity nobody's talking about. While everyone's distracted by the sizzle, focus on the steak. Comment below: What's the most boring, repetitive task in your industry that's ripe for AI automation?

93% of Fortune 500 CHROs now use AI in HR operations yet 70% of employees still lack clear AI guidelines at work according to Gallup... The AI revolution in People Operations isn't coming, it's here and accelerating exponentially. While executives race to deploy AI solutions, a massive execution gap is creating both opportunity and chaos. Today we analyzed the five strategic AI deployment models that elite organizations are using to transform their workforce architecture. 🎯 The Five AI Models Reshaping People Strategy: 1.) AI Co-Pilots Digital assistants to boost manager effectiveness and decision-making. Humans drive culture, engagement, and leadership. Think Lattice, Culture Amp, Leapsome. 2.) AI Solutions Purpose-built platforms for HR data, compliance, and talent management. You focus on aligning strategy and enabling teams. Think HiBob, Personio, Workday. 3.) AI Workers Hire digital recruiters, engagement bots, or predictive models instead of adding headcount. You handle human oversight and fairness. Think pymetrics (now Harver), Hirevue, SeekOut. 4.) AI Workflow Builders Platforms to automate HR processes and connect systems. You manage HR tech engineering and integration. Think Zapier HR, Tability, n8n 5.) DIY on LLMs Build with APIs to create custom HR apps and analytics. You drive innovation in HR data and workflows. Think OpenAI HR APIs, Hugging Face (HR-focused). The breakthrough insight? Top-performing organizations don't choose one model. They orchestrate all five strategically, creating AI-powered People Operations that deliver measurable competitive advantage. But here's the reality check: Only 15% of employees understand their company's AI strategy, and 76% of HR leaders believe organizations without AI adoption in the next 24 months will lag behind competitors. 🚀 The Strategic AI Acceleration Playbook → Map Your AI Stack: Audit current processes against the five models to identify integration gaps → Layer Smart: Start with operational solutions, then add co-pilots and custom development based on workforce maturity → Build AI Governance: Establish frameworks for ethical deployment and bias monitoring in people decisions This transformation is creating a new category of competitive advantage. At PeopleAtom, we're building the community where visionary CXOs, founders, and People leaders collaborate to navigate this complexity and turn AI adoption into market differentiation. Ready to architect your AI-powered workforce strategy? Join forward-thinking CXOs, founders, and People leaders who are shaping the future of work (Apply for an invite, link in comments) What's your biggest AI opportunity in workforce transformation right now? Love the evolution, Joe

These traditional business models are becoming obsolete. (Even YC's guide needs an update for the AI era) Many founders are still building companies using outdated frameworks. Here's what they're missing: The end of "pure play" models → Anthropic charges subscriptions + usage + enterprise deals → Midjourney blends consumer subscriptions with enterprise licensing → OpenAI's hybrid model: API usage + enterprise + consumer subs The old "pick one model" playbook is dead. Speed is the new moat → Hugging Face added enterprise offerings while maintaining open-source growth in about 18 months → Stability AI launched commercial products in about a year → Traditional 3-5 year GTM timelines don't work anymore Companies that stick to old rollout schedules are being left behind. Distribution is built in → Vercel’s growth strategy creates a pathway for users to become enterprise customers, with a focus on high-potential prospects → Replicate provides tools for developers to monetize their AI models, enabling marketplace-like opportunities → The line between product and distribution is disappearing The reality? These traditional models worked in a world of slower innovation cycles. But AI is forcing a complete rethink of how value is created and captured. What actually works now: → Building multiple revenue streams from day one → Launching fast, then adding enterprise features → Using the product itself as the go-to-market strategy The fundamentals of business still matter. But the playbook for executing them has changed entirely. Curious: Are you still following traditional business models, or have you adapted your approach for the AI era? #ai #business #entrepreneurship #startups 

Should investors and founders warm up to 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬 ? In comparison to Software, Services usually get a bad rap - they’re lower margin, tougher to scale, lower multiples, etc. BUT, if you believe in AI, you should also believe in AI’s potential to make Services more attractive and remedy these drawbacks. Blending Software, AI, and Services should now get a lot more interesting and open the door for exciting new business models. It’s no longer just Software 𝘈𝘚 a Service, instead we’ll see more Software 𝘈𝘕𝘋 a Service. We recently wrote about this concept and how it will often manifest as “Diagonal Software” ↗ What do we mean by 𝘋𝘪𝘢𝘨𝘰𝘯𝘢𝘭 𝘚𝘰𝘧𝘵𝘸𝘢𝘳𝘦 ? Blending Software & Services will typically also blend Horizontal and Vertical approaches, hence Diagonal. I’m excited about 3 new Diagonal business models gaining traction with the aid of recent AI innovations: 1) 𝐂𝐨𝐫𝐞 𝐃𝐢𝐚𝐠𝐨𝐧𝐚𝐥 - typically a Vertical Software product core with a Horizontal Services distribution 2) 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐚𝐬 𝐚 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 - more of a Horizontal Software core that startups will often deliver with a Vertical or segmented distribution focus (at least at first) 3) 𝐑𝐨𝐛𝐨𝐭𝐢𝐜 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬 - Robotics and AI go hand in hand and Robots are often trained by imitating human actions. What better way to improve data, training, and feedback loops than to own the frontline human services they will augment? AI should make Services more attractive by improving margins and scalability. Services can also strengthen AI with better training and data capture. This symbiotic relationship will open up more unique business models that leverage Services in creative ways. Services can also help to: 1) 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞 𝐆𝐓𝐌 - use Services as a ‘rocket booster’ to gain share 2) 𝐄𝐱𝐩𝐚𝐧𝐝 𝐭𝐡𝐞 𝐓𝐀𝐌 - Services + a Diagonal model can flip a business from selling niche software in a tough market to owning the distribution and addressing a massive market We’ve invested in creative companies that blend Software & Services, like Glencoco Juniper Square Cafe X Warby Parker, and others. We’re seeing other unique full stack startups like Sora Schools Monumental Metropolis Technologies and Hadrian pop up more. I commend founders like Ingwon Chae Jason Bao Henry Hu Chris Power Garrett Smiley Salar al Khafaji Alexander Israel Neil Blumenthal Alex Robinson and others that have boldly reinvented traditional business models by fusing services. I think it’s still early days to blend Software & Services, and AI should change the game for Diagonal models. Check out our article for more examples and let me know what you think. If you’re a founder that wants to reimagine and redefine how Software & Services work together, reach out, we’d love to chat. https://lnkd.in/efib6Ntp

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.