SaaStr 837: 10 Things To Do Right Now to Become AI Native with...

Full Title

SaaStr 837: 10 Things To Do Right Now to Become AI Native with Filevine's CEO & Founder

Summary

The episode discusses how SaaS companies can transition to being AI-native by fundamentally changing their architecture rather than just adding AI features.

It emphasizes that this requires a strategic reevaluation of existing systems, talent acquisition, rebranding, and a focus on building truly valuable AI products.

Key Points

• To become AI-native, SaaS companies must fundamentally alter their system architecture, not just add AI as a layer on top of existing functionality.
• The transition requires making difficult decisions about which parts of the existing codebase to keep and which to tear down, guided by a matrix that considers competitive advantage and system speed.
• SaaS companies have an advantage in the shift to AI because they possess existing data and systems of record, which are crucial for context-aware AI agents, unlike AI-only competitors.
• The value proposition is shifting from content (data itself) to context (how AI uses data to take action), and SaaS applications are well-positioned to leverage this shift.
• To attract AI talent, companies need to demonstrate a commitment to AI-native development, offering access to rich data and opportunities to work with other AI-native professionals.
• Acquiring companies with existing AI talent and technology can be a viable strategy for rapid team expansion and integration.
• Rebranding with intent is crucial for signaling a company's shift to AI-native status, both externally to customers and internally to employees.
• SaaS companies should obsess over usage data for their AI products to ensure they are valuable and gain traction, as this data also reveals promising areas for further development.
• Leveraging existing high gross margins from SaaS allows companies to price AI products competitively and gain market share against AI-only competitors who struggle with profitability.
• Building "hard things" and creating new categories, such as "legal operating intelligence systems," is key to differentiating and leading in the AI-native landscape.

Conclusion

Companies must embrace a fundamental architectural shift to become truly AI-native, rather than simply adding AI features.

Leveraging existing data and systems of record gives SaaS companies a significant advantage in building context-aware AI solutions over AI-only competitors.

Strategic decisions regarding talent, rebranding, pricing, and product development are paramount for successful AI-native transformation and long-term market dominance.

Discussion Topics

• What are the biggest challenges SaaS companies face when deciding which parts of their existing architecture to tear down for AI integration?
• How can existing SaaS companies effectively brand themselves as AI-native to attract top AI talent and win customer perception?
• In what ways can a SaaS company's established system of record and data provide a decisive competitive advantage against pure AI-play startups?

Key Terms

AI-native

Describes applications or companies designed from the ground up with artificial intelligence as a core component, rather than retrofitting AI into existing systems.

SaaS

Software as a Service, a software distribution model where a third-party provider hosts applications and makes them available to customers over the Internet.

ARR

Annual Recurring Revenue, a metric used to track the predictable revenue a company expects to receive from its customers over a year.

Gross Revenue Retention (GRR)

The percentage of revenue retained from existing customers over a period, excluding upsells and expansions.

Net Revenue Retention (NRR)

The percentage of revenue retained from existing customers over a period, including upsells and expansions, and accounting for churn.

APIs

Application Programming Interfaces, sets of rules and specifications that software programs can follow to communicate with each other.

CRUD app

An application that can perform Create, Read, Update, and Delete operations on data.

ML engineers

Machine Learning engineers, professionals who design, build, and deploy machine learning models and systems.

Audit logging

The process of recording actions performed by users or systems within an application to track activity and ensure accountability.

Daily Active Users (DAU)

The number of unique users who interact with a product on a given day.

Weekly Active Users (WAU)

The number of unique users who interact with a product within a seven-day period.

Monthly Active Users (MAU)

The number of unique users who interact with a product within a 30-day period.

Timeline