The Power and Promise of Vertical AI

While the AI headlines obsess over foundation model fundraises and hyperscaler spending, a quieter revolution is generating real, measurable returns. In this episode of AI to ROI: The Big Story, Ray Rike and Peter Buchanan break down why vertical AI companies may be building the most durable and valuable businesses in the history of enterprise software, and why most people aren't paying attention yet.

What's covered in this episode:

• Defining Vertical AI: What separates vertical AI from horizontal tools like Microsoft Copilot or Google Workspace AI, and why the distinction matters for buyers and investors alike
• A fundamentally different business model: Why vertical AI companies target labor budgets (10x the size of enterprise software budgets) rather than IT spend, and how outcome- and consumption-based pricing is replacing the traditional per-seat model
• The funding explosion: Vertical AI investment grew from $8B in 2023 to $22B in 2024 to $42B in 2025, with unicorn counts in the sector jumping nearly 6x in just two years
• Harvey (Legal AI): How this $8B+ valuation company grew ARR from $100M to $190M in just four months by orchestrating multiple AI models across legal workflows and embedding deeply into law firm operations
• Abridge (Healthcare AI): How a cardiologist-founded company reached a $5.3B valuation by turning physician-patient conversations into structured clinical documentation in real time, with deep Epic EHR integration across 150+ health systems
• Sierra (Customer Experience AI): How Brett Taylor's enterprise AI platform hit $100M ARR in just 21 months and crossed the $10B decacorn threshold, raising the question of whether the agent era could produce the first trillion-dollar enterprise software companies
• MaintainX (Industrial/Manufacturing AI):How this maintenance management platform is tackling $1.4 trillion in annual equipment failure costs across 11,000 customers and 11 million assets — with a 34% reduction in unplanned downtime for customers
• Why vertical AI moats are so durable: Proprietary data that compounds with every transaction, embedded institutional knowledge that makes switching costs higher than any legacy ERP migration, and a model architecture that gets stronger as foundational models improve
• Advice for enterprise buyers: Why 2026 is the year to evaluate vertical AI vendors, insist on outcome-based pricing, and start with one workflow before expanding

Interested in reading the details on the Vertical AI industry and trends? Check out the AI to ROI Newsletter providing even more detail by clicking here.

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