Vertical AI
Vertical AI is artificial intelligence built for one industry or business function, using that domain's data, workflows and rules. General-purpose models will take on any task. Vertical AI goes deep on a narrow set of problems, like clinical documentation, contract review or financial analysis, where general models lack context.
The term covers two layers. Analysts use it for models trained or fine-tuned on domain data, and investors use it for the applications built on top of them. Buyers are usually operators in regulated or specialized fields who need answers they can defend. Since the label gets used loosely, any product claiming it deserves a test. Does it use domain data a general model lacks, sit inside the real workflow, and own an outcome?
What Is Vertical AI?
Gartner describes specialized models as those "trained or fine-tuned on industry or business process-specific data", and forecasts 2025 spending on them at $1.1B of $14.2B total GenAI model spending. By 2027, it predicts more than 50% of enterprise GenAI models will be industry- or function-specific, up from about 1% in 2023.
These domain models can be smaller, need less compute and carry less hallucination risk. Scope is debated. Menlo Ventures saves "vertical" for industries like healthcare and finance, and calls role-specific tools such as sales software departmental AI. This article uses Gartner's broader industry-or-function framing.
Vertical AI vs. Horizontal AI
Vertical AI vs horizontal AI comes down to depth versus breadth. Menlo Ventures found that of $19B spent on AI applications in 2025, horizontal tools took $8.4B, departmental $7.3B and vertical $3.5B.
| Dimension | Vertical AI | Horizontal AI |
|---|---|---|
| Scope | One industry or function, deep | Many tasks, broad |
| Data | Proprietary, domain-specific | General, web-scale |
| Buyer | Industry or functional operator | IT or company-wide |
| Risk | Lower hallucination risk in its domain, narrow range | Flexible, but weaker on specialist tasks |
Real Vertical AI or a Wrapper?
Plenty of products are just general models with an industry label. Gartner warns of "agent washing" in agentic AI, estimating only about 130 of thousands of vendors are real.
A credible vertical product uses data a general model lacks. It sits where the work actually happens. And it's accountable for a measurable result. If a well-written prompt on a general model could replace it, it's a wrapper.
Vertical AI vs. Vertical SaaS
Vertical SaaS is industry software for records and workflows. Vertical AI does the work itself, which pulls spend out of software budgets and into labor and services. Andreessen Horowitz argued in late 2024 that AI could raise vertical SaaS revenue per customer "by up to 10x" by replacing labor spend.
Bessemer Venture Partners reported in September 2024 that LLM-native vertical AI SaaS companies had reached about 80% of traditional vertical SaaS contract value, with roughly 400% annual growth.
Examples of Vertical AI by Industry and Function
- Healthcare. Menlo Ventures estimates healthcare took about $1.5B, roughly 43%, of 2025 vertical AI spend. Abridge's ambient scribes turn visit audio into clinical notes.
- Legal. TechCrunch reported in December 2025 that Harvey reached an $8B valuation and passed $100M ARR.
- Finance. BloombergGPT, a 50B-parameter model trained on financial data, outperformed existing models on financial tasks.
- Sales and GTM. AI SDR tools automate prospecting and outreach, and sales intelligence systems surface account and buyer data.
Vertical AI for Revenue Teams
A June 2025 Gartner survey found 61% of B2B buyers prefer a rep-free buying experience, and 73% avoid suppliers that send irrelevant outreach.
That gap gets sharpest when selling to technical buyers. Developers show intent in code repositories, forums and Q&A sites. Firmographic data rarely captures any of it. This piece on GTM for software infrastructure companies explains why that calls for domain data. A GTM intelligence platform pulls these signals together for sales and marketing. For more, see vertical AI for revenue teams.
FAQ
What is the difference between vertical AI and horizontal AI?
Horizontal AI handles many tasks across industries using general, web-scale data. Vertical AI goes deep in one industry or function, with proprietary domain data, embedded workflows and domain rules. Vertical systems are more accurate on specialist tasks. Horizontal systems adapt more easily to new use cases.
What is vertical AI SaaS?
Vertical AI SaaS is industry-specific software where AI does work that used to require people, instead of only storing records or managing workflows. Because it replaces labor, it competes for services and headcount budgets, not just software budgets. Andreessen Horowitz calls it the next wave of vertical software.
What are vertical AI agents?
Vertical AI agents are autonomous systems that plan and finish multi-step tasks inside one domain's workflow, such as drafting clinical notes or researching accounts. Gartner cautions that many products sold as agents are rebranded chatbots or automation. For a sales view, see this overview of AI agents in technical-buyer GTM.
What are examples of vertical AI?
Abridge generates clinical documentation from patient visits. Harvey supports legal research and drafting for law firms. BloombergGPT is a language model trained on financial data. In sales, AI SDR and sales intelligence tools apply the same idea to prospecting and account research.
How do you tell real vertical AI from a wrapper?
Run three checks. It should use domain data a general model doesn't have, sit inside the real workflow instead of beside it, and own a measurable outcome. If a well-crafted prompt on a general-purpose model could replace the product, you're looking at a wrapper.