Vertical AI: The Key to Unlocking Underserved Industries

The rise of vertical SaaS over the past decade has demonstrated the power of industry-specific software, producing numerous success stories such as Toast, Shopify, Procore, and ServiceTitan. Despite these achievements, many foundational industries remain underserved by vertical SaaS due to intrinsic barriers like unstructured data, constrained total addressable markets (TAMs), slow sales cycles, low annual contract values, and entrenched incumbents. Additionally, emerging sectors undergoing significant transformations, such as the electrification of energy, have also been overlooked.

However, two key developments are now making it possible to build software that serves these underserved industries: 1) the rise of artificial intelligence capable of handling unstructured data, and 2) the redefinition of vertical SaaS as vertical software. This article explores why a vertical AI approach is crucial for building enduring applications in previously technologically underserved markets.

Why it Matters

The traditional vertical SaaS model has primarily served companies with modern tech stacks that can handle clean, structured data. Foundational industries, on the other hand, are heavily reliant on unstructured data, including contracts, records, and multimedia files across text, audio, and images. This reliance has historically limited their ability to adopt advanced software solutions.

The advent of large language models (LLMs) has changed this paradigm. LLMs can now handle workflows involving unstructured data, enabling AI to bridge the gap between technologically underserved industries and modern software capabilities. The impact of this shift is significant: an estimated 80% of enterprise data is unstructured, according to MIT Sloan Management Review.

Key Risks

While the potential for vertical AI is vast, several risks must be considered:

1. Data Quality and Availability: Unstructured data can be messy and inconsistent, requiring robust data cleaning and preprocessing techniques.
2. Regulatory Compliance: Industries like healthcare and finance are heavily regulated, necessitating stringent compliance measures to avoid legal issues.
3. User Adoption: Convincing traditionally conservative industries to adopt new technologies can be challenging, especially if they have existing legacy systems in place.
4. Scalability: Ensuring that AI solutions can scale effectively as more data is generated and more users come on board.

The Opportunity

The opportunity for vertical AI lies in its ability to transform foundational industries by providing tailored software solutions that address specific pain points. Here are a few areas where vertical AI can make a significant impact:

1. Healthcare: AI can improve patient care through better data analysis, personalized treatment plans, and streamlined administrative processes.
2. Legal: Legaltech companies can use AI to automate document review, contract management, and compliance tasks, reducing costs and increasing efficiency.
3. Manufacturing: AI can optimize production lines, predict maintenance needs, and enhance supply chain management.
4. Energy: As the energy sector transitions to renewable sources, AI can help manage grid operations, forecast demand, and reduce waste.

Case Studies

• Toast: A leader in restaurant technology, Toast has leveraged vertical SaaS to provide integrated solutions for point-of-sale (POS), online ordering, and kitchen management.
• Procore: In the construction industry, Procore offers project management tools that streamline workflows and improve collaboration among stakeholders.
• ServiceTitan: Focused on home services, ServiceTitan provides comprehensive software for managing customer relationships, scheduling, and billing.

Conclusion

The rise of vertical AI represents a significant shift in how we approach software development for foundational industries. By leveraging the power of artificial intelligence to handle unstructured data, companies can now build tailored solutions that address specific industry needs. This not only opens up new markets but also enhances the overall efficiency and effectiveness of these industries. As more sectors undergo digital transformation, the role of vertical AI will become increasingly crucial.