In 2026, enterprises will be deciding who to partner with to operationalize AI across their organizations, and those decisions will hinge on who can deliver deep integrations across complex infrastructures and tailor solutions to each organization's unique environment. We built our platform and operating model around that reality, and the demand we're seeing globally reflects it.
Anthropic is expanding its push into the enterprise market with a new set of "coworker" plug-ins designed to embed its Claude AI directly into tools used by investment bankers, HR teams, and engineers, signaling a shift from standalone assistants toward AI agents that operate inside core business workflows.
"Snowflake is committing up to $200 million to purchase access to OpenAI's frontier models and ChatGPT Enterprise over the course of the multi-year agreement," Baris Gultekin, Snowflake's vice president of AI, told The Register. "This reflects Snowflake's conviction that providing OpenAI technology to our enterprise customer base, at scale and with enterprise-grade reliability, is strategically important. It is a commercial commitment anchored in real AI consumption by Snowflake customers, not a speculative or symbolic partnership."
Large language models (LLMs) are much the same. They carry vast general knowledge yet lack the specific context that makes them immediately valuable to your organization. Just like new hires go through the onboarding ropes, LLMs need structured access to your business's data, tools, and workflows to become truly useful. That's where Model Context Protocol (MCP) comes in. MCP enables communication between AI applications, AI agents, applications and data sources.