To transition to AI, leaders must ruthlessly dismantle parts of their existing, money-making codebase that are not competitively differentiating or slow down AI development. This requires overcoming the team's justifiable pride and emotional attachment to legacy systems they built.
Top AI talent wants to work for AI companies, not legacy SaaS businesses. To compete, sell them on your unique advantages: a massive, proprietary dataset for model training and an existing distribution channel that ensures their work gets used by thousands of customers on day one—something AI-only startups lack.
Simply adding an AI layer on top of a traditional SaaS stack will fail. A true AI-native architecture requires an "AI data layer" sitting next to the "AI application layer," both controlled by ML engineers who need to constantly tune data ingestion and processing without dependencies on the core tech team.
When AI startups demand access to your platform's data via API, turn the tables. Gate your APIs and, during negotiations, agree to their request on the condition that you get reciprocal access to the AI outputs they generate from your data. This reframes the power dynamic and protects your moat.
Incumbent SaaS companies can leverage high-margin core products to price new AI features below what pure-play AI competitors can afford. This "savage" strategy allows them to absorb a lower margin on AI products to rapidly gain market share while maintaining a healthier blended gross margin overall.
To fully commit to an AI-native future, Filevine made the bold decision to stop selling its core SaaS product to new customers who won't also buy their AI products. This forces a unified product vision, eliminates the complexity of supporting non-AI users, and ensures the entire company builds for one AI-centric future.