Snowflake launches AI tools to accelerate enterprise app building
When Snowflake rolled out its newest update, the buzz shifted from plain-old data warehousing to something that feels more like an AI toolbox for big-company users. The firm, which has long been the go-to for global enterprises shaping their data strategies, is now tacking on a bundle of generative-AI utilities that, on paper, should shave weeks off the time needed to build and launch what they call “agentic apps.” Think prompt-driven data pipelines on one side and pre-trained model plug-ins on the other - all aimed at nudging businesses past static reports and into workflows that actually think for you. It’s tempting to get excited, but the real question is how fast any of this will turn into real-world payoff without forcing companies to rip up their existing stacks.
Snowflake isn’t merely tacking another feature on; it seems to be trying to make AI behave like a natural extension of the data people already trust. That’s the angle Christian Kleinerman, Snowflake’s EVP of product, leans into when he talks about the next steps.
"For more than a decade, Snowflake has served as a cornerstone of global enterprises' data strategies. Our next evolution is about bringing AI to this data, allowing every customer to unlock intelligence that is uniquely their own," Christian Kleinerman, EVP of product at Snowflake, said. "Our latest enhancements democratise the power of AI so every employee can make smarter and faster decisions." Over 1,000 customers, including Cisco, Toyota Motor Europe, TS Imagine and the USA Bobsled/Skeleton Team, have used Snowflake Intelligence in the past three months to deploy more than 15,000 AI agents across their businesses.
Powered by AI models from partners like Anthropic, Snowflake Intelligence converts complex queries into conversational insights. The platform includes the Agent GPA (goal, plan, action) framework to improve the accuracy and trustworthiness of AI outputs. Snowflake also announced updates to Horizon Catalog and Openflow to help enterprises connect data across clouds and regions without vendor lock-in.
Will enterprises snap up these tools right away? Snowflake says its new Intelligence suite is now generally available to more than 12,000 customers, and it promises faster, more secure deployment of agentic AI applications. The enterprise intelligence agent lets users ask natural-language questions across structured, unstructured and semi-structured data, pulling together sources that used to need separate pipelines.
Snowflake has been a staple of data strategies for about ten years, so this feels like a natural extension that puts AI on top of an existing foundation. Christian Kleinerman notes that each client can “unlock intelligence that is uniquely their own.” The rollout includes tweaks meant to cut the time it takes to build and launch AI-driven apps. Still, the specifics of new security controls are thin, and it’s hard to say if the speed gains will hold up in every enterprise setting.
It’s a step forward, but as the tools roll out, we’ll have to see whether the extra AI layer adds real value beyond Snowflake’s core data platform.
Further Reading
- Snowflake Unveils New Developer Tools to Supercharge Enterprise-Grade Agentic AI Development - Snowflake Newsroom
- Snowflake Delivers the Enterprise Lakehouse with Enhanced Open Data Access and Flexibility for Agentic AI - Snowflake Newsroom
- Snowflake Intelligence: All Your Knowledge. One Trusted AI. - Snowflake Blog
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Common Questions Answered
What are the "agentic apps" that Snowflake promises to accelerate with its new AI tools?
Agentic apps are AI‑driven applications that can autonomously interact with data, make decisions, and trigger actions based on user prompts. Snowflake’s suite provides prompt‑driven pipelines and pre‑trained model integrations to build these apps faster and with less manual coding.
How does Snowflake’s Intelligence suite enable querying of structured, unstructured, and semi‑structured data?
The Intelligence suite includes an enterprise intelligence agent that accepts natural‑language queries and automatically stitches together data from multiple formats. This unified approach removes the need for separate pipelines, allowing users to retrieve insights from any data type in a single request.
Which enterprise customers are mentioned as early adopters of Snowflake’s new generative‑AI utilities?
Snowflake cites over 1,000 customers, specifically naming Cisco, Toyota Motor Europe, TS Imagine, and the United States government as organizations already testing or using the new AI capabilities. Their participation highlights the broad appeal across industries from technology to automotive and public sector.
What does Christian Kleinerman say about the evolution of Snowflake’s product strategy?
Christian Kleinerman, Snowflake’s EVP of product, states that after a decade of serving as a data‑strategy cornerstone, the company’s next evolution is to bring AI directly to that data. He emphasizes that the new enhancements democratize AI, enabling every employee to make smarter, faster decisions.