Traditional API integration requires strict adherence to a predefined contract. The new AI paradigm flips this: developers can describe their desired data format in a manifest file, and the AI handles the translation, dramatically lowering integration barriers and complexity.

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.

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 major hurdle for enterprise AI is messy, siloed data. A synergistic solution is emerging where AI software agents are used for the data engineering tasks of cleansing, normalization, and linking. This creates a powerful feedback loop where AI helps prepare the very data it needs to function effectively.

Databricks is raising massive rounds to build an AI offering that rivals cloud giants like AWS. This shifts the primary competitive landscape from a focused battle with Snowflake to a broader war for the enterprise AI agent market, explaining their aggressive fundraising and strategy.

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.

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.

Value in the AI stack will concentrate at the infrastructure layer (e.g., chips) and the horizontal application layer. The "middle layer" of vertical SaaS companies, whose value is primarily encoded business logic, is at risk of being commoditized by powerful, general AI agents.