Learning from Vertical SaaS: The Need for Industry-Specific AI
There’s been considerable discussion recently about whether vertical AI applications are simply “thin wrappers” on top of large foundation models. But let’s consider a parallel: was the rise of multi-billion-dollar vertical SaaS companies merely a wrapper over a cloud database? Hardly.
Companies like Veeva in life sciences, Epic and Cerner in healthcare, Yardi in real estate, Procore in construction, and others, thrived precisely because they addressed complex, industry-specific demands with tailored functionalities, deep integrations, and specialized workflows.
Today, AI’s emergence brings a similar need for highly customized, sector-focused solutions. Yet, the major infrastructure and horizontal players are not equipped-to or focused-on addressing these specialized needs, leaving space for vertical AI applications to provide targeted value that goes well beyond a basic model interface.
An Opportunity To Shift Billions Of Dollars From Wages to Software
The opportunity in Vertical AI isn’t necessarily about replacing or reinventing Vertical SaaS. Given the embedded nature and “stickiness” of these systems, they aren’t easily displaced. Instead, Vertical AI is about augmenting these platforms and, critically, about replacing specific employee functions with agentic workflows rather than replacing software ecosystems. Sourcing from U.S. labor statistics, we identify roughly 50 million employees whose roles have minimal physical requirements, or knowledge workers. Their collective wages amount to around $3.8 trillion. Over time, this represents a significant opportunity to reallocate a portion of that spend to Vertical AI solutions, driving productivity and efficiency gains at scale. By automating tasks that traditionally require human expertise, Vertical AI can boost productivity and efficiency, freeing employees to focus on high-impact work.
Prime candidates for Vertical AI automation include repetitive, industry-specific tasks, particularly administrative roles where optimization is a priority. Areas like claims processing, medical billing, documentation, and customer support involve routine processes and regulatory compliance, making them ideal for AI-driven efficiencies. These roles not only carry significant labor costs but also stand to benefit from AI’s consistency and precision, positioning them well for early adoption.
From General to Specialized: Layering Vertical AI for Industry Needs
Looking ahead, success in Vertical AI will be driven by distinct dynamics compared to foundational model companies. While foundational AI players focus on advancing technology and pushing technical boundaries, Vertical AI will center on applying those state-of-the-art tools to meet the strict requirements of each industry.
Take healthcare customer support as an example. At the base layer, foundational language models (LLMs) from companies like OpenAI provide the general-purpose language capabilities needed to understand and generate human-like responses. Built on top of these foundational models, companies like Sierra and Decagon add horizontal customer-support frameworks and conversation management, creating adaptable tools that can handle customer interactions across multiple industries. To make AI truly effective in customer support for healthcare, a final vertical-specific layer is essential (built by players such as Eleos Health, Abridge, Hyro, and others). This layer incorporates clinical expertise, compliance with healthcare regulations, integrations with electronic health records (EHRs), and workflows designed specifically for healthcare administration. Without this tailored vertical layer, the AI solution would lack the necessary understanding of healthcare nuances and regulatory constraints, making it impractical to deploy in real-world healthcare settings.
In Vertical AI, each layer builds on the last, with the industry-specific top layer turning a powerful but generic AI into a specialized solution ready for deployment.
Winning In Vertical AI
So, what does it take to win in Vertical AI? If the moat isn’t about underlying deep tech, then the competitive edge lies in product and distribution. The companies that will succeed are those that can build defensible positions through deep industry alignment and effective go-to-market strategies. Key points of defensibility include:
- Industry-Specific Model Adaptation: Fine-tuning AI models with specialized knowledge and terminology unique to the industry, enhancing their relevance and accuracy; often leveraging unique and proprietary data.
- Task-Specific Logic: Incorporating industry-specific workflows and decision-making logic that align with established processes, ensuring that the AI solution can seamlessly support complex, role-specific tasks.
- Seamless Integration into Existing Systems and Processes: Ensuring AI solutions can plug directly into the existing software and workflows that teams already rely on, minimizing disruption and maximizing usability.
- Regulatory Compliance: Building AI products that adhere to strict industry regulations, especially in heavily regulated fields like healthcare, finance, and legal, where compliance is non-negotiable.
- Strong Distribution through Industry-Specific Channels: Leveraging trusted industry channels and partnerships to reach target users effectively, with credibility and insight into each industry’s buying behaviors.
These markets also tend to follow a “winner-takes-most” dynamic, making it essential to scale rapidly and capture market share early. We saw this play out in the last wave of vertical SaaS companies, many of which rose to dominance soon after the advent of cloud computing.
Market Landscape
There are already many exciting players emerging in this area, building these critical vertical layers across various industries. Below is our market landscape highlighting these innovators:
The Breakout Is On The Horizon
As we approach 2025, Vertical AI is poised for a breakout year, not only due to advancements in capabilities and maturity of the technology, but also because sentiment across industries - traditional sectors included - is shifting toward greater willingness to adopt these solutions. With the potential for significant impact and a strong return on investment, Vertical AI is rapidly gaining traction as businesses recognize the value in automating specialized roles and streamlining complex workflows.
