Snowflake, a cloud data platform, is accelerating its expansion beyond data infrastructure into AI services aimed at enterprise users.

Snowflake introduced Snowflake Intelligence last year for non-developer business staff inside companies, and on March 19 released a research preview of its desktop AI assistant, Project Snowwalk.

Snowflake Intelligence is an AI chatbot interface that helps users ask questions about company data and gain business insights. Snowwalk is a solution focused on work performance.

With Snowflake Intelligence and Snowwalk, Snowflake appears to be seeking a larger share in the first interface enterprise users face when using AI, competing against major tech companies in AI.

Until now, non-developer business users in companies have not had many chances to use the Snowflake platform directly. They often consumed it indirectly through BI tools. With Snowflake Intelligence and Snowwalk, Snowflake expanded an environment in which non-developer business users can use the Snowflake platform. That means it has grown its role from a back-end platform into front-end productivity tools.

According to the company, Project Snowwalk supports employees across departments by safely carrying out multi-step tasks when they simply request the work through natural-language, conversational prompts. For example, it autonomously executes end-to-end workflows ranging from simple tasks to complex workflows, including writing reports such as forecast slides for board reports, spreadsheets to identify churn risk, and retention strategy proposals, as well as identifying supply chain bottlenecks.

Snowflake highlighted as a differentiator that Project Snowwalk is directly embedded and managed within the Snowflake platform and is based on enterprise data and context.

The company said it is built on a single enterprise-wide source of truth equipped with governance-applied metrics, shared business definitions, cross-cloud interoperability, and security and audit functions, enabling it to support business outcomes beyond simply improving productivity.

Christian Kleinerman (크리스티안 클레이너만), Snowflake's senior vice president of product, said at a press briefing hosted by Snowflake Korea on March 19 that it defined skills for Project Snowwalk tailored to enterprise field roles such as project managers, sales and finance. He stressed it is a good example showing the future of work that agentic AI brings.

Snowflake's AI strategy covers both developers and business staff. Its product portfolio is structured that way. Cortex AI and Cortex Code target technical experts, including developers, while Snowflake Intelligence and Project Snowwalk target non-developer enterprise users.

Cortex AI is the infrastructure that serves as the foundation for Snowflake's AI products, and Cortex Code, a coding AI service, was also developed on Cortex AI. Snowflake Intelligence helps business users ask questions about the data that supports the Snowflake platform and gain insights. Kleinerman said Snowwalk reflects various roles and contexts within enterprises and combines the strengths of Snowflake Intelligence and Cortex Code.

Snowflake also unveiled its overall strategy aimed at the era of agentic AI at the briefing, beyond Snowwalk. Ki-young Choi (최기영), head of Snowflake Korea, cited the emergence of dominant AI protocols, the advancement of open-source foundation models, the rise of micro-agents and the advancement of core technologies as major issues in agentic AI.

Choi also highlighted that, in the enterprise market, it is important to start building AI agents even on a small scale rather than going for a single big win, and to keep expanding and connecting various AI agents. He also stressed that an environment to do this is already in place.

Snowflake also pointed to practical problems companies face in adopting AI. It cited an MIT survey finding that 95 percent of AI initiatives fail because companies are tied to legacy processes and tools, and Deloitte data analyzing that 30 percent of AI projects are halted due to insufficient context and hallucinations. To address this, Snowflake presented an integrated data foundation, understanding business logic and context, and AI integration across workflows as key elements.