AI & its Impact on Vertical SaaS: The Gates of Distribution have Flung Open

🌈 Abstract

The article discusses the recent boom in vertical SaaS companies driven by advancements in large language models (LLMs) and AI capabilities. It explores the new capabilities that LLMs are unlocking, the impact on improving human efficiency, and the opportunities for distribution and defensibility for vertical SaaS companies.

🙋 Q&A

[01] The Impact of LLMs on Vertical SaaS

1. What are the new capabilities that LLMs are unlocking for vertical SaaS companies?

• Data structuring: LLMs can take unstructured data like invoices, contracts, and referral documents and turn them into structured, readable data that can be systematically analyzed.
• Analyzing: LLMs can analyze the structured data to understand the content, such as identifying clauses in a contract or pointing out differences from a standard template.
• Recommendations: LLMs can provide recommendations based on the analyzed data, such as suggesting potential diagnoses based on patient symptoms or improvements to human-written code.
• Taking action: LLMs can take actions on their recommendations, such as reaching out to a supplier to check on progress or calling a patient's insurance to verify eligibility.

2. How can these LLM capabilities impact workflows and improve human efficiency?

• Even if LLMs don't fully automate tasks, they can still significantly improve human efficiency by speeding up processes and making traditional workflow software more delightful to use.
• Examples include analyzing customer agreements to surface renewals, auto-categorizing financial transactions to streamline month-end close, and structuring unstructured data to save manual effort.

[02] Unlocking Distribution for Vertical SaaS

1. What factors are contributing to the declining friction in sales for vertical SaaS companies?

• AI-enabled products are becoming more delightful and useful, but this is not the only reason for declining sales friction.
• The broader adoption of integrated software workflows, where AI-enabled tools are core to the user's daily work, is a key driver.
• The author believes that AI has unlocked distribution, as potential users are actively seeking out AI solutions, making this a uniquely promising time to build vertical SaaS companies.

2. How has AI changed customer sentiment in traditionally difficult-to-penetrate verticals like legal?

• Lawyers, who are typically late adopters of technology, are now quickly moving to schedule demos and launch pilots to implement new AI products.
• This illustrates the drastic change in sentiment, as customers across various verticals are more open to testing and adopting emerging AI-powered solutions.

[03] Achieving Defensibility in Vertical SaaS

1. What factors can lead to long-term defensibility for vertical SaaS companies?

• The combination of successfully building distribution early and building truly embedded workflows for users is key to building enduring businesses.
• The proprietary, vertical-specific data that a company accumulates over time can allow it to improve a generic AI solution to be even more effective for its specific use case, creating a defensibility flywheel.

2. How can the data accumulated by vertical SaaS companies contribute to their defensibility?

• While a horizontal LLM may be good at structuring data, the proprietary data a vertical SaaS company accumulates can enable it to analyze and understand the specific nuances of its industry, such as common issues and the best technicians to service them.
• This vertical-specific data can drive improvements in the company's AI capabilities, creating a more meaningful product moat in the medium term that should drive further distribution and lock-in.