Snowflake unveils Project SnowWork to automate business workflows with AI agents

Snowflake Introduces Project SnowWork to Transform Enterprise Operations

Snowflake has unveiled Project SnowWork, an autonomous AI platform designed to help business users automate business workflows through conversational prompts1

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. The enterprise AI platform, currently in research preview, represents a significant shift in how companies can deploy AI agents to execute complex tasks rather than simply query data. According to CEO Sridhar Ramaswamy, users can ask SnowWork to build pitch decks, and the platform will pull data from multiple sources, organize it, and draft accompanying emails—no coding required2

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. This approach aims to bring AI to data rather than forcing organizations to move data to separate AI environments, a strategy Snowflake has consistently pursued through recent partnerships including its $200 million deal with OpenAI and its acquisition of Observe1

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How AI Agents Execute Multi-Step Tasks Within Governed Environments

Project SnowWork operates by orchestrating multi-step tasks such as querying datasets, analyzing results, generating reports, and preparing presentations within a single interaction3

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. The platform runs on governed enterprise data stored within Snowflake's AI Data Cloud and integrates business context including metrics, definitions, and access policies3

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. Bala Kasiviswanathan, Snowflake's VP of Developer and AI Experiences, explained that the system is most powerful when working with governed data, defined workflows, and an understanding of how the business operates3

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. The platform offers pre-built, persona-specific AI profiles tailored to functional roles in finance, sales, marketing, and operations, reducing onboarding time for enterprise users4

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. Christian Kleinerman, Executive Vice President of Product at Snowflake, noted that these profiles personalize enterprise context to specific roles with the right data, skills, and insights5

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Data Governance and Security Controls Address Enterprise Concerns

Every action Project SnowWork takes inherits role-based access controls, data policies, and audit logging automatically, ensuring the AI assistant can only act on data users are permitted to see1

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. This security framework makes every step fully traceable, allowing enterprises to inspect actions, validate outputs, and maintain control over how and when tasks are executed3

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. The platform automatically enforces Snowflake's role-based access controls, masking policies, and audit logging, ensuring compliance and traceability as AI workloads run4

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. Snowflake has also implemented mechanisms to reduce the risk of hallucinations when the system performs complex operations3

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. For regulated industries such as pharma, legal, and finance, the governance story of knowing where data resides and who accessed it resonates strongly, according to Gary McConnell, CEO of solution provider VirtuIT1

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Snowflake's Strategic Push to Connect Enterprise Data With AI Capabilities

The launch demonstrates how Snowflake is aggressively responding to a workplace reshaped by AI, providing the "last mile" to connect enterprise data and work systems through AI agents on behalf of employees2

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. McConnell noted that Snowflake's recent moves point to a consistent thesis: your data platform should be where AI work actually happens, not just a source you export from1

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. This represents a meaningful shift for organizations that historically had to stitch together a data warehouse, a feature store, and a separate AI/ML environment1

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. The company has been aggressive with its feature roadmap, recently partnering with Google to bring the Gemini model into Snowflake's Cortex AI, announcing plans to acquire Observe for observability capabilities, and signing a $200 million partnership with OpenAI to develop custom AI solutions1

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Internal Testing Shows Real-World Applications Across Business Functions

Snowflake is using Project SnowWork internally with its sales teams, which can now generate data-backed quarterly business reviews, pitch decks, and customer emails from one place1

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. Executives receive personalized intelligence feeds with metrics tailored to their roles, and the company has begun automating its earnings preparation using SnowWork to ease what was previously a weeks-long, cross-team effort1

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. Kasiviswanathan emphasized that the system is grounded in each customer's own data, definitions, and workflows, with teams able to layer in their own logic1

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. The platform improves through usage and feedback, functioning less as a fixed persona and more as a starting point that quickly becomes specific to how each company operates1

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Growing Customer Base Validates Platform Consolidation Strategy

Snowflake has added thousands of customers annually, growing from 7,800 in January 2023 to 13,330 this January—a 70 percent increase over three years1

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. Enterprise customers from the Forbes Global 2000 grew from 573 in 2023 to 790 as of January 2026, contributing 43 percent of the company's $4.7 billion in revenue during the most recent fiscal year1

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. In Korea, Snowflake has signed approximately 80 percent of the country's top 10 conglomerate groups, with local platform consumption growing more than ninefold over four years5

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. E-commerce operator Lotte ON used Snowflake's platform to break down data silos and power AI-driven use cases, delivering a 32-percent reduction in operating costs and a 40-percent improvement in overall performance5

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. The company is also expanding its India operations with a 500-plus person team in Pune and offices in Delhi and Bengaluru, according to Ramaswamy4

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