Harvey AI just raised $300 million in Series E funding at a $5 billion valuation, cementing its position as the highest-valued legal AI startup. This isn’t an isolated case. Across industries, specialised AI applications are capturing investment dollars at rates while horizontal platforms struggle to maintain their competitive edge.
This change represents more than a new direction in how AI delivers business value. While horizontal platforms like ChatGPT and Claude serve broad audiences with general-purpose functionality, vertical AI companies are achieving 80% of traditional SaaS contract values with 400% year-over-year growth and 65% gross margins.
This transformation creates both opportunity and urgency for technology leaders. Your strategic choices around AI investment will determine whether your organisation captures this wave or watches competitors pull ahead. The question isn’t whether to invest in AI—it’s whether to build vertical capabilities or rely on horizontal solutions.
We’ll examine why vertical AI applications attract more investment, which companies lead their respective sectors, and how you can evaluate whether building vertical AI capabilities makes sense for your business. You’ll discover the defensive advantages that specialised solutions create and get a framework for transforming existing SaaS products into vertical AI applications.
What Are Vertical AI Applications and How Do They Differ from Horizontal AI Platforms?
Vertical AI applications are industry-specific AI solutions built for particular sectors like legal or healthcare, while horizontal AI platforms serve multiple industries with general-purpose functionality. Vertical solutions offer deeper specialisation and stronger defensible moats through domain expertise and proprietary data.
Vertical AI refers to AI applications and platforms purpose-built for specific industries, leveraging large language models and generative capabilities to solve industry-specific problems. Unlike traditional vertical SaaS that digitises existing workflows, vertical AI automates complex, repetitive language-based tasks that were previously impossible to address cost-effectively.
Harvey AI exemplifies this approach. Built atop leading large language models like ChatGPT and Claude, Harvey combines these foundational models with data and workflows designed specifically for and by lawyers. This specialisation enables Harvey to serve 337 legal clients across 53 countries with functionality that general-purpose AI simply cannot match.
Horizontal platforms like ChatGPT, Claude, and Gemini provide broad capabilities across multiple use cases and industries. They excel at general tasks but lack the deep integration and domain-specific optimisation that drives enterprise value. These platforms often become commoditised as “wrapper” applications proliferate, making differentiation difficult.
The technical implementation differences are significant. Vertical AI applications integrate deeply with industry-specific systems, regulatory requirements, and professional workflows. They require specialised data collection, model training, and user interface design that reflects how professionals actually work. This depth of integration creates switching costs and network effects that horizontal platforms cannot replicate across multiple verticals simultaneously.
Which Vertical AI Companies Are Leading Investment Rounds in 2025?
Harvey AI ($300M at $5B valuation), Tandem Health ($50M Series A), PathAI (acquired by Tempus), EvenUp (financial services), and Axion Ray (manufacturing) represent successful vertical AI investments across legal, healthcare, and industrial sectors.
Harvey AI secured its position through laser focus on legal workflows, serving 337 clients across 53 countries with specialised document review, contract analysis, and legal research capabilities. The Series E round was co-led by Kleiner Perkins and Coatue, making it the highest public valuation of any legal AI startup, surpassing competitors like Ironclad ($3.2 billion) and Clio ($3 billion).
Healthcare represents another high-growth vertical. Companies like Abridge turn patient-doctor conversations into clinical notes, while ClinicalKey AI provides AI-powered medical search platforms. In healthcare, providers are adopting solutions such as Abridge — which turns patient-doctor conversations into clinical notes — and ClinicalKey AI — an AI-powered medical search platform. PathAI’s acquisition by Tempus Labs demonstrates the ongoing consolidation in AI-powered diagnostics and pathology analysis.
Manufacturing and industrial applications are gaining traction through companies like Axion Ray, which helps manufacturers by analysing large volumes of product data across IoT & telematics, field failures, production, and supplier data. These solutions address previously uneconomical automation opportunities in complex industrial environments.
Financial services vertical AI includes companies like EvenUp, which automates demand letter generation, and JusticeText, which automatically reviews hundreds of hours of camera footage to help public defenders build their cases. These applications demonstrate how AI can tackle high-value, time-intensive tasks that generate immediate ROI.
Kleiner Perkins’ partner Ilya Fushman notes that Harvey “sets the blueprint for how a vertical AI enterprise company can build and execute”, highlighting the company’s performance across all business facets. These valuations reflect investors’ confidence in the defensibility and growth potential of industry-specific AI solutions.
Why Are Vertical AI Applications Attracting More Investment Than Horizontal Platforms?
Vertical AI applications create stronger defensible moats through proprietary industry data, deep workflow integration, and domain expertise that horizontal platforms cannot replicate. This specialisation enables higher customer retention, premium pricing, and protection from competition while serving previously uneconomical market segments.
The investment attraction stems from superior unit economics and market dynamics. Vertical AI companies maintain expansion of total addressable markets (TAM): AI unlocks markets once considered too niche or small for SaaS, extending serviceable markets and boosting margins. These solutions can serve functions and industries previously unreached by traditional software due to high manual labour inputs or implementation costs.
Competitive advantages emerge from three sources. First, proprietary industry-specific data collection creates network effects that strengthen over time. Second, deep product integration with existing industry systems generates high switching costs. Third, domain expertise and regulatory compliance understanding represent barriers that horizontal platforms cannot economically replicate across multiple industries.
Vertical SaaS players maintain an average S&M-to-revenue ratio of 17%, compared to 34% for horizontal vendors, demonstrating more efficient customer acquisition through targeted marketing and industry-specific value propositions. This efficiency translates directly to better margins and faster growth.
Horizontal platforms face commoditisation pressure as major technology companies release competing general-purpose AI capabilities. As “wrapper” accusations persist, focus should be on building robust moats via sector-specific knowledge and integration with industry systems. Vertical solutions avoid this commoditisation trap through deep specialisation that cannot be easily replicated.
AI makes possible or affordable tasks previously done poorly or not at all, especially by automating data-intensive workflows. This creates new revenue streams and addressable markets that traditional SaaS could never access profitably.
What Makes Vertical AI Applications More Defensible Than Horizontal Solutions?
Vertical AI creates defensibility through three key advantages: proprietary industry datasets that improve over time, deep integration with specialised workflows that increase switching costs, and domain expertise that horizontal platforms cannot economically replicate across multiple industries.
Data network effects provide the strongest defensive moat. Defensibility stems from domain expertise: integrations, data moats, and multimodal interfaces built for vertical-specific needs. Industry-specific data collection improves model performance through continuous usage patterns, creating competitive advantages that compound over time. This proprietary data becomes valuable as it captures nuanced industry patterns that generic datasets cannot replicate.
Workflow integration creates switching costs by embedding AI capabilities into mission-critical business processes. The strongest teams quickly move beyond fine-tuning and into deep, verticalized utility, developing solutions that become integral to how professionals complete their daily work. Migration complexity increases when AI systems are deeply integrated with industry-specific tools, regulatory compliance systems, and established professional workflows.
Domain expertise represents an economic barrier that horizontal competitors cannot overcome. Key differentiators include proprietary data, depth of product integration, and economic value delivered. Building this expertise across multiple industries would require investment in industry specialists, regulatory knowledge, and specialised feature development that dilutes focus and resources.
The competitive landscape dynamics favour specialisation. Horizontal platforms must serve the lowest common denominator across industries, limiting their ability to develop deep functionality for specific use cases. Vertical solutions can optimise for their target market, creating user experiences and capabilities that horizontal platforms cannot match without sacrificing their broad appeal.
The best-positioned startups will have strong technical moats, customer traction, and embedded workflows that make them hard to replicate. This combination of technical, operational, and market positioning advantages creates multiple defensive layers that reinforce each other over time.
How Can CTOs Evaluate Whether to Build Vertical AI or Use Horizontal Platforms?
You should assess market fragmentation, technology adoption rates, available domain expertise, data quality, and implementation complexity. Build vertical AI when serving specialised workflows with proprietary data advantages; use horizontal platforms for general productivity tasks without industry-specific requirements.
Market assessment forms the foundation of this decision. Evaluate industry fragmentation levels, technology adoption readiness, and growth potential through comprehensive TAM analysis. Traditional SaaS players face a stark choice: evolve or become obsolete as AI-native startups push deeper into industry-specific workflows. Industries with high fragmentation, regulatory complexity, and willingness to pay premium prices for specialisation present the strongest vertical AI opportunities.
Your technical capability evaluation determines feasibility and resource requirements. Assess internal AI/ML expertise, data availability and quality, integration complexity with existing systems, and development timeline requirements. You often have to work with limited resources and must make decisions on build vs buy, which stack to choose. The decision hinges on whether you can develop competitive advantages faster than market incumbents or emerging competitors.
Business case development requires ROI projections for build versus buy scenarios. ROI is clear from day one and there’s no Excel spreadsheet needed to explain it to the user. Vertical AI investments should demonstrate immediate value through productivity improvements, cost reductions, or revenue growth. Calculate customer willingness to pay for specialisation, competitive differentiation potential, and long-term strategic value alignment.
Implementation timing matters. For many, the fastest path to innovation is acquisition, particularly when market leaders have already established strong positions. Consider partnering or acquiring existing vertical AI capabilities when building in-house would take too long or require expertise your team lacks.
The decision framework should include risk assessment and mitigation strategies. Evaluate technical risks, market acceptance uncertainty, and competitive response scenarios. Success metrics and measurement approaches must be established before development begins to ensure accountability and course correction capability.
What Core Industries Offer the Best Vertical AI Investment Opportunities?
Legal, healthcare, manufacturing, financial services, and construction represent high-opportunity verticals due to language-intensive workflows, regulatory complexity, data availability, and willingness to pay premium prices for specialised AI solutions that automate expensive repetitive tasks.
The three markets that score highest on our criteria are construction, manufacturing, and healthcare, which are predicted to experience meaningful increases in vertical software adoption. These industries combine high-value workflows, regulatory requirements, and data richness that create ideal conditions for AI automation.
Legal services demonstrate vertical AI potential through document-intensive workflows and premium pricing acceptance. Law firms, which rarely even use CRMs, have already begun adopting co-pilot based solutions for contracting, demand summary generation, case intake, and other time-intensive tasks. The combination of high hourly rates, repetitive document work, and regulatory compliance requirements creates automation value.
Healthcare represents opportunity through clinical workflow automation and diagnostic assistance. Providers adopt solutions like Abridge for converting conversations into clinical notes and ClinicalKey AI for medical search platforms. Strict regulatory environments favour specialised solutions that understand compliance requirements and medical workflows.
Manufacturing and industrial applications benefit from IoT data abundance and operational complexity. Predictive maintenance, quality control, and supply chain optimisation represent high-value automation opportunities with clear ROI calculations. Companies in this space analyse IoT data, field failures, production metrics, and supplier information to optimise operations.
Construction project management, education personalised learning platforms, agriculture precision farming solutions, and real estate transaction automation represent emerging opportunity sectors. We anticipate a wave of consolidation in high-service, regulated industries like healthcare, logistics, financial services, and legal tech. These industries share characteristics of regulatory complexity, high service costs, and fragmented market structures that favour vertical AI solutions.
How Can Existing SAAS Companies Successfully Pivot to Vertical AI Applications?
Successful SAAS-to-AI pivots require identifying language-intensive workflows in your existing customer base, developing AI capabilities for core industry tasks, building domain expertise through customer collaboration, and creating proprietary data advantages that increase switching costs and competitive differentiation.
Customer base analysis provides the starting point for identifying pivot opportunities. Map existing customer workflows and pain points, focusing on language-intensive and repetitive tasks that consume time and resources. Core workflows: Tasks central to the profession (e.g., contract drafting for lawyers, financial modeling for bankers) offer the highest value, while supporting workflows like marketing for dentists or procurement for shippers often face less resistance and deliver higher ROI.
AI capability development strategy requires decisions about building versus buying versus partnering for technology components. Vertical SaaS leaders continuing to serve businesses with software solutions need to incorporate AI if it hasn’t been incorporated already. Data collection and model training approaches must align with existing product architecture while enabling quality assurance and performance monitoring.
Domain expertise development accelerates through customer collaboration and strategic hiring. Vertical SaaS providers leverage specialised, proprietary data that better reflects industry patterns, resulting in more accurate and valuable AI models for specific use cases. Building regulatory compliance capabilities and creating proprietary data collection mechanisms strengthen competitive positioning and customer retention.
Market entry strategy should focus on wedge products that demonstrate immediate value. Find markets ripe for innovation – pursuing an industry that previously lacked access to software is the most common approach. Target specific industry pain points where horizontal solutions cannot provide comprehensive answers, implementing a value-first approach that delivers measurable productivity improvements.
The “land and expand” strategy is especially effective in vertical markets, where deep industry knowledge enables natural upsell and cross-sell opportunities. Success depends on optimising customer retention and expanding into adjacent workflow areas that leverage existing domain expertise and data advantages.
What ROI Can CTOs Expect from Vertical AI Application Investments?
Vertical AI applications typically achieve 65% gross margins, 400% year-over-year growth, and 80% of traditional SaaS average contract values. You can expect 10x productivity improvements in targeted workflows and 18-24 month payback periods for successful implementations.
Financial performance benchmarks demonstrate investment returns. Vertical AI companies are achieving 80% of the average contract value of traditional SaaS, posting ~400% year-over-year growth, and maintaining ~65% gross margins. These metrics exceed traditional SaaS benchmarks, particularly in growth velocity and margin sustainability.
Productivity improvements provide immediate operational value. These tools unlock 10x productivity, reallocate labour to higher-value work, reduce costs, or drive topline growth. The value is immediate, not a “nice to have”. Specific examples include developers completing 21% more tasks and merging 98% more pull requests with AI adoption, and product companies experiencing cycle time reductions from 6.1 to 5.3 days with 7% output increases.
Investment and payback analysis reveals attractive economics for well-executed implementations. Time-saved calculations demonstrate value creation: 2.4 hours saved per engineer per week across 80 engineers generates 768 hours monthly, translating to approximately $59,900 in value versus $1,520 in tooling costs—representing roughly 39x ROI. These calculations assume successful implementation and user adoption across target workflows, with realistic scenarios showing more modest but still attractive returns.
Long-term strategic value extends beyond immediate productivity gains. Exit activities, such as significant acquisitions, signal increasing market acceptance and opportunity. Market share protection, competitive differentiation sustainability, and platform expansion revenue potential create additional value streams that compound over time.
Projections suggest that at least five Vertical AI firms will reach $100M+ ARR in the next 2-3 years, with the first IPOs expected soon. This trajectory indicates exit opportunity valuations and market validation for successful vertical AI implementations.
FAQ Section
What is the difference between vertical AI and traditional SaaS?
Vertical AI automates complex, language-intensive tasks using AI capabilities, while traditional SaaS primarily digitises and streamlines existing workflows. AI-native solutions can tackle previously impossible automation challenges that generate immediate productivity improvements rather than incremental efficiency gains.
How long does it take to build a successful vertical AI application?
Timeline varies based on industry complexity and team capabilities, but most successful implementations require 12-18 months from concept to initial market traction. Companies typically spend 2-3 years developing deep domain expertise and market-ready solutions before achieving scale.
What are the biggest risks of investing in vertical AI development?
Technical risks include model performance and integration complexity, while market risks involve customer adoption rates and competitive response. The primary mitigation strategy involves starting with specific, high-value use cases that demonstrate clear ROI before expanding scope.
Can horizontal AI platforms eventually compete with vertical solutions?
Horizontal platforms struggle to match the depth of industry integration and domain expertise that vertical solutions provide. The economic challenge of developing specialised capabilities across multiple industries while maintaining competitive pricing makes this scenario unlikely.
What technical skills are required to build vertical AI applications?
Teams need AI/ML expertise, domain knowledge specialists, and integration architects familiar with industry-specific systems. The most critical capability is combining technical AI skills with deep understanding of target industry workflows and regulatory requirements.
How do you measure success in vertical AI application development?
Success metrics include user adoption rates, productivity improvements in target workflows, customer retention, and revenue growth. Key performance indicators should focus on workflow efficiency gains rather than traditional software usage metrics.
What are common mistakes when building vertical AI?
The most frequent errors include underestimating domain expertise requirements, focusing on technology rather than workflow integration, and attempting to serve too broad a market initially. Successful implementations start narrow and expand systematically.
How do you choose between building in-house vs acquiring vertical AI capabilities?
The decision depends on time-to-market requirements, available technical talent, and competitive positioning. Acquisition makes sense when market leaders have established strong positions and building in-house would take too long to capture market opportunity.
Conclusion
Vertical AI applications represent a shift in how technology creates business value, moving beyond general-purpose tools to industry-specific solutions that automate complex, high-value workflows. The investment momentum behind companies across legal, healthcare, and manufacturing sectors reflects their ability to create defensible competitive advantages through proprietary data, deep workflow integration, and domain expertise.
For technology leaders, the strategic choice between building vertical capabilities or relying on horizontal platforms will define competitive positioning over the next decade. Success requires careful evaluation of market opportunities, technical capabilities, and resource allocation to capture the ROI potential that vertical AI offers. The companies that move decisively now will establish the data advantages and market positions that become difficult to replicate over time.
