The traditional SaaS model of locking customer data within a proprietary ecosystem is dying. Workday's move to integrate with Snowflake exemplifies the shift. The future value for SaaS companies lies in building powerful AI agents that operate on open, centralized data platforms, not in being the system of record.
In an AI-driven ecosystem, data and content need to be fluidly accessible to various systems and agents. Any SaaS platform that feels like a "walled garden," locking content away, will be rejected by power users. The winning platforms will prioritize open, interoperable access to user data.
The most advanced GTM teams are abandoning traditional CRMs like Salesforce as their primary interface. Instead, they use data warehouses (Snowflake, Databricks) for flexible data storage and push curated insights to reps directly within their workflows (Slack, email, Notion), eliminating the need for manual data entry and retrieval.
Traditional SaaS switching costs were based on painful data migrations, which LLMs may now automate. The new moat for AI companies is creating deep, customized integrations into a customer's unique operational workflows. This is achieved through long, hands-on pilot periods that make the AI solution indispensable and hard to replace.
Customers now expect DaaS vendors to provide "agentic AI" that automates and orchestrates the entire workflow—from data integration to delivering actionable intelligence. The vendor's responsibility has shifted from merely delivering raw data to owning the execution of a business outcome, where swift integration is synonymous with retention.
A key differentiator is that Katera's AI agents operate directly on a company's existing data infrastructure (Snowflake, Redshift). Enterprises prefer this model because it avoids the security risks and complexities of sending sensitive data to a third-party platform for processing.
Companies struggle to get value from AI because their data is fragmented across different systems (ERP, CRM, finance) with poor integrity. The primary challenge isn't the AI models themselves, but integrating these disparate data sets into a unified platform that agents can act upon.
Creating a basic AI coding tool is easy. The defensible moat comes from building a vertically integrated platform with its own backend infrastructure like databases, user management, and integrations. This is extremely difficult for competitors to replicate, especially if they rely on third-party services like Superbase.
By connecting to services like G Suite, users can query their personal data (e.g., 'summarize my most important emails') directly within the LLM. This transforms the user interaction model from navigating individual apps to conversing with a centralized AI assistant that has access to siloed information.