The Case for Vertical AI

While general-purpose AI models like ChatGPT generated tremendous excitement and investment in recent years, the natural evolution is innovation at the application level. Specialized AI companies are building sustainable businesses by applying AI to solve specific industry challenges. 

Unlike Horizontal AI, which offers broad applications across industries, Vertical AI integrates deep domain expertise with advanced machine learning to solve industry-specific challenges. This specialization creates greater defensibility, faster adoption, and higher value capture, particularly in sectors like professional services, healthcare, financial services, logistics, and real estate.

Horizontal AI attempts to solve a key problem across many types of industries and businesses; vertical AI solves that problem for a specific type of customer, allowing for customized features, unique data for training and a more targeted go-to-market approach.

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The Business Case for Vertical AI

The vertical AI market, valued at $10.2 billion in 2024, is expected to grow at 21.6% annually through 2034 ($69B or more)¹ But raw market size only hints at the opportunity.

What makes these companies particularly interesting to investors is their capital efficiency. While foundation model companies regularly raise nine-figure rounds just to cover training costs, vertical AI startups typically raise modest rounds of $2-8 million at pre-seed and seed stages, then quickly convert this capital into paying customers and revenue growth.

According to Bessemer Venture Partners, early-stage companies in this space are experiencing rapid funding and revenue growth.² The increasing need for regulatory compliance and risk mitigation is further driving demand, as businesses pursue AI solutions customized to industry-specific standards rather than broad, one-size-fits-all models. Leading firms such as NEA, and Sequoia Capital have actively backed startups in this space, attracted by their market potential and differentiated value propositions.³

The operational metrics are equally impressive. Leading vertical AI companies demonstrate:

• 400% year-over-year revenue growth
• 65%+ gross margins
• 80% of the average contract value of traditional vertical SaaS

These aren't theoretical projections—they're results from companies already in market. Industry analysts predict at least five vertical AI companies will reach $100M ARR within the next 2-3 years, with the first vertical AI IPO likely in the same timeframe.

Finally, while vertical software-as-a-service solutions compete for software spend (1% of US GDP), vertical AI solutions, expected to replace end-to-end workflows, will compete for services spend (13% of US GDP).

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Business Models in Vertical AI

Every founder needs to decide: are they enabling existing providers or replacing them?

That seemingly basic choice determines everything from customer acquisition costs to exit multiples. The six resulting business models offer investors dramatically different risk-reward profiles.

The table below maps the six dominant business models that have emerged in this space:

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Jason Shuman, a General Partner at Primary, emphasizes the strategic importance of understanding these distinctions: "Understanding whether to enable service providers or to be a service provider is essential for founders navigating the Vertical AI space. The chosen model significantly influences a company’s growth trajectory and its potential impact on the industry.”⁴ 

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Enabling Service Providers

• “Do it for me” Co-pilot
  
  •  These companies provide AI-native tools that automate  work for businesses, offering high margins, recurring revenue, and typically shorter sales cycles. Comaies in this category are particularly effective for companies serving small business customers.
• AI Native Service Provider
  
  •  These firms function as outsourced partners, completing specific tasks for other businesses using AI,  enabling companies to improve efficiency without developing in-house capabilities.
• AI Native Marketplace
  
  •  Operating as managed marketplaces, these platforms use AI to match service providers with customers, facilitating transactions and improving service delivery across the ecosystem.

Being Service Providers

• Vertically Integrated Services Firm
  
  • These comprehensive service providers integrate AI directly into their clients’ core operations, effectively replacing traditional service providers with more sophisticated alternatives.
• AI Enabled Roll-up
  
  • This strategic approach involves acquiring existing service businesses and integrating AI to enhance efficiency– a consolidation play that leverages established client relationships while introducing technological transformation. 
• AI Franchise
  
  • Perhaps the most scalable model, these companies expand by franchising their business model, providing franchisees with AI tools and processes that ensure service quality and operational excellence across the network. 
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Transformation in Action: Industry Applications 

Across sectors, Vertical AI is addressing pain points that general AI models simply cannot solve:

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Why Vertical AI is a Compelling Investment

Beyond its technical and operational advantages, Vertical AI presents unique investment opportunity with several key benefits:

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1. High Barriers to Entry

Unlike general AI models that can be applied across industries, Vertical AI is built for specific sectors. This specialization makes it significantly harder for new entrants to compete. 

A critical advantage comes from proprietary data–often difficult to access or replicate. Many startups secure exclusive partnerships or develop unique datasets that create defensible competitive moats. Without this data, competitors struggle to deliver comparable accuracy and performance.

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“In AI, access to proprietary data isn't just an advantage—it's a barrier to competition. Vertical AI companies that secure exclusive datasets and integrate deeply into industry workflows build moats that are nearly impossible to cross.”⁵

- Bessemer Venture Partners

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Once embedded in operations, these solutions become difficult to replace. Businesses are hesitant to switch providers due to the challenges of retraining models, helping early entrants retain customers and strengthen their market position.
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2. Clear Value Proposition & Faster Adoption

Vertical AI is designed to tackle real-world problems from day one. This focused approach accelerates adoption as businesses quickly experience gains in efficiency, accuracy, and cost savings.  Its seamless integration into existing workflows minimizes disruption while maximizing impact. Whether automating manual tasks, improving decision-making, or optimizing resource allocation, these industry-specific AI solutions deliver measurable returns in a shorter timeframe.^6
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3. Large Addressable Markets

The TAM within these sectors is immense:⁷

• Healthcare – A $4 trillion industry in the U.S. alone, where AI is revolutionizing diagnostics, personalized medicine, and operational workflows

• Supply Chain & Logistics – Projected to reach $40 trillion globally by 2030, with AI driving advancements in demand forecasting, inventory management, and route optimization

These industries, along with others rapidly adopting specialized AI solutions, represent multi-trillion-dollar markets where Vertical AI can enhance efficiency at unprecedented scale.
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4. Synergies with Emerging Technologies

Vertical AI  thrives when integrated with other cutting-edge technologies like IoT, blockchain, edge computing, and automation. These combinations enhance efficiency, fortify security, and create deeper competitive moats, unlocking entirely new revenue streams. Companies that embed AI within broader technological ecosystems make their solutions harder to replicate, strengthening their market position and long-term value.^8
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5. Strong Pricing Power and Value Capture

By addressing mission-critical industry challenges, these AI-driven solutions justify premium pricing models and secure long-term contracts. Their deep integration into business operations results in high switching costs, making them indispensable to customers and ensuring recurring revenue.⁹

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Market Trends & Future Outlook

Look ahead three years and the vertical AI landscape will be dramatically different. Industry analysts expect at least five companies to cross the $100M ARR threshold by then – a milestone that separates the serious players from the merely interesting startups. The first vertical AI IPO will likely hit public markets in that same window, bringing broader investor attention to the category.

The timing couldn't be better. Healthcare, legal services, and financial firms face crippling labor shortages with no quick fix in sight. Vertical AI isn't just offering incremental efficiency – it's becoming essential infrastructure for industries that can't hire enough skilled professionals to meet demand.

What's curious is how relatively underfunded this category remains despite the clear potential. While founders chasing the next ChatGPT attract billion-dollar valuations on little more than technical demos, companies solving actual business problems with domain-specific AI are raising at valuations that still make fundamental sense.

• This won't last. The disconnect between value creation and investment attention represents one of the more obvious arbitrage opportunities in today's venture landscape.

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The Venture Capital Perspective

The contrast in funding efficiency is stark. Training another large language model might require $100M+ just to cover compute costs, with commercialization still a distant hope. Meanwhile, vertical AI startups typically raise $2-8M at early stages and convert that capital directly into customer acquisition and revenue.

For investors, this presents a compelling proposition: back businesses that can achieve profitability without the infrastructure costs of general AI. The capital efficiency translates to lower dilution for founders and investors alike, while the faster path to revenue reduces overall investment risk.

The potential returns look particularly attractive as these solutions gain traction across major industries. Companies that establish leadership positions now will be difficult to dislodge later, creating powerful first-mover advantages for early investors.

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Agentic AI Is the Future of Execution

Artificial intelligence is evolving from a system centered on data analysis and recommendation generation to one that plays a more dynamic and capable role in supporting complex tasks. The latest shift in artificial intelligence is the rise of agentic AI systems, which act more like capable collaborators than traditional tools. Instead of waiting for commands and offering suggestions, these agents act autonomously, plan ahead, adapt in real time, and complete complex tasks with minimal human input. 

For example, in a business setting, an agentic system might be given a goal like “increase customer retention.” From there, it could identify the causes of churn, design and run targeted campaigns, measure results, and refine its strategy automatically, all without someone needing to oversee every step.

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Vertical vs. Agentic vs. Combined

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Industry Use Cases for Agentic AI

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Real-World Examples

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Ada – Agentic AI in Customer Support

Ada illustrates the potential of agentic AI. Unlike traditional chatbots limited by scripted responses, Ada’s platform autonomously handles tier-1 and tier-2 support across industries such as fintech, e-commerce, and SaaS. With direct access to backend systems like Salesforce, Shopify, and Zendesk, Ada agents can perform actions, not just provide answers. They manage user issues end-to-end, reducing reliance on human agents and improving through continuous learning.

Key agentic capabilities include:

• Autonomous execution
  
  • Ada can issue refunds, reset passwords, modify subscriptions, and trigger backend processes automatically
• Smart escalation
  
  • The system identifies complex or ambiguous cases and escalates only when necessary, improving triage efficiency
• Continuous learning
  
  • Using reinforcement from past interactions, Ada refines its resolution logic, improving both accuracy and tone
• Omnichannel orchestration
  
  • It operates across web, mobile, social, and in-app environments with consistent performance

The results speak for themselves: companies using Ada report a 30–50% reduction in support ticket volume, increased customer satisfaction scores (CSAT), and faster time-to-resolution across thousands of interactions.

Flexport Flow – Agentic AI in Logistics

Flexport Flow is reshaping supply chain operations by automating critical decisions across freight, customs, and warehousing. It monitors delays, pricing, and bottlenecks in real time, adjusting routes, rebooking freight, and updating stakeholders without manual input. This operational autonomy reduces dependency on human intervention and enhances resilience in an industry where precision and speed are essential.

Key agentic functions include:

• Dynamic rerouting based on disruptions like port congestion or weather
  
  
• Autonomous carrier selection and rebooking to optimize for cost, timing, or capacity
  
  
• Proactive notifications to customers, vendors, and internal teams
  
  
• Efficiency gains, including lower transit times and reduced carbon emissions

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The Bottom Line: Durable Value Will Be Built by Vertical and Agentic AI

The future of AI isn’t general — it’s purpose-built, embedded, and autonomous. Vertical and agentic systems are more than technical innovations; they’re new business models with real traction and real moats. They solve industry-specific challenges with speed, accuracy, and efficiency — unlocking ROI from day one. For investors, the signal is clear: the next wave of enterprise value is forming at the intersection of intelligence and specialization. Quietly. Rapidly. And with lasting impact.

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^1. Global Market Insights. "Vertical AI Market Size, Share & Growth Report, 2024-2034."Global Market Insights, 2024, https://www.gminsights.com/industry-analysis/vertical-ai-market‍

^2. Bessemer Venture Partners. The Future of AI Is Vertical." Bessemer Venture Partners, 2024, https://www.bvp.com/atlas/part-i-the-future-of-ai-is-vertical.

^3. "Tomorrow's Titans: Vertical AI." New Enterprise Associates (NEA), https://www.nea.com/blog/tomorrows-titans-vertical-ai

^4. Shuman, Jason. "Vertical AI vs. Software: How Founders Are Navigating the Rapidly Evolving Landscape." LinkedIn, 11 Mar. 2025, www.linkedin.com/pulse/vertical-ai-software-how-founders-navigating-rapidly-evolving-shuman-vuaze/

^5. Bessemer Venture Partners. "The Future of AI Is Vertical." Bessemer Venture Partners, https://www.bvp.com/atlas/part-i-the-future-of-ai-is-vertical

^6. "Vertical AI Market Trends." Global Market Insights, https://www.gminsights.com/industry-analysis/vertical-ai-market/market-trends.

^7. "Artificial Intelligence Market Size | Industry Report, 2030." Grand View Research, https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market.

^8. Pandey, Vijay. "Financial Technology Trends and Vertical AI Technology." Forbes, 19 Feb. 2025, https://www.forbes.com/councils/forbesfinancecouncil/2025/02/19/financial-technology-trends-and-vertical-ai-technology/

^9. "Artificial Intelligence (AI) Market." MarketsandMarkets, https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-market-74851580.html

^10.Ada: AI Customer Service Automation Platform https://www.ada.cx

^11.“How Agentic AI Is Revolutionizing Supply Chains.” https://kanerika.com/blogs/agentic-ai-in-supply-chain/