Snowflake’s latest agentic AI suite aims to reimagine how organizations unlock data value at scale. Photo Credit: NYSE

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Artificial intelligence has entered its infrastructure era. The most meaningful breakthroughs are no longer happening in research labs or benchmark leaderboards, but in the systems that connect AI reasoning with real enterprise data. This shift, from raw model performance to integration intelligence, is defining a new competitive frontier for the world’s largest data and cloud platforms.

Across the data technology landscape, companies are racing to embed agentic AI, reasoning systems capable of planning, adapting, and acting autonomously, into the core of enterprise operations. From Microsoft’s Azure Synapse and Google’s BigQuery to Databricks and Amazon Redshift, the data intelligence battle is now shifting towards building platforms where AI agents can interpret business workflows, access live metrics, and execute decisions without constant supervision from data teams.

Snowflake, long recognized as a pioneer in cloud data warehousing, is emerging as one of the most compelling contenders in this new phase. The company’s current strategy centers on building governed data ecosystems that don’t just store information but reason over it and systems designed to think like humans while enforcing the discipline enterprises demand.

Instead of chasing larger model integrations, Snowflake CEO Shridhar Ramaswamy is steering the company toward a platform vision where agentic AI empowers every employee, from analysts to executives, to reason with data directly across the entire organization.

“We’re entering a new era where every employee can become an analyst, strategist, and innovator. AI has progressed tremendously, from generative to increasingly agentic systems. At Snowflake, we are taking a deliberate and gradual approach to agentic AI integration for both structured and unstructured data,” Ramaswamy told me in a video interview. “When every decision-maker can interact with their data in natural language, you create an organization where insight and action happen at the speed of thought. That’s the foundation of the AI-augmented enterprise.”

The company is moving beyond simply storing and querying data to interpreting it through context-rich AI agents that operate natively within Snowflake’s secure governance perimeter. The shift underscores a broader industry reality: data without context is inert, and intelligence without governance is risky.

Snowflake’s newly launched Snowflake Intelligence platform aims to embody this evolution. It is designed to transform data into decisions by enabling employees to ask questions in natural language and receive verified, explainable answers grounded in enterprise truth. Unlike traditional dashboards that stop at the “what,” Snowflake Intelligence is betting on uncovering the “why”, turning analytics into reasoning.

“Snowflake Intelligence gives people the ability to ask complex questions and get trustworthy answers instantly, without needing to write a single line of code. This amplifies, not replaces, the power of human expertise in data analytics,” said Ramaswamy. “What we consistently hear from them is that once their data resides in our platform, it becomes significantly more AI-ready.”

He asserts that early adopters are already seeing results. Toyota Motor Europe has cut development cycles from months to weeks, Wolfspeed reduced failure analysis from hours to minutes, and Fanatics is using Snowflake Intelligence to sharpen audience targeting and speed cross-sell opportunities.

Ramaswamy also revealed that inside Snowflake, a prototype AI agent known as “Raven” is already at work. Functioning as an internal sales assistant, Raven gives employees a hands-on view of how context-aware AI agents can retrieve information, anticipate needs, and support decisions in real time—a preview of the agentic intelligence the company aims to scale next across enterprises.

“Whenever a new technological wave comes along, whether it’s generative AI, agentic AI, or something else, our first instinct is not to integrate it for the sake of hype,” he explained. “Our approach has always been to ensure that whatever we build or integrate has a clear application story. We don’t always get every technology decision perfect, but our guiding principle is to align innovation with customer outcomes and to partner where collaboration creates the most value.”

Building Trustworthy Enterprise Intelligence Agents at Scale

Snowflake’s leadership believes enterprise AI must first solve the problem of trust. Data scattered across departments or processed in external tools often loses both context and control.

“Without a coherent data strategy, you’re building AI on a foundation of sand. Snowflake Intelligence exists because we saw that gap. It allows organizations to confidently deploy AI, because they know precisely what data is fueling their insights, who has access to it, and that it’s compliant. To scale AI responsibly, you bring the intelligence to where the data lives, not the other way around,” said Ramaswamy.

The company is also betting heavily on “enterprise intelligence agents”. A cornerstone of this expansion is Cortex AISQL, which lets developers run multimodal AI tasks on text, audio, images, and video data directly in SQL. Moreover, the platform now supports modular ML workflows and one-click deployment of pre-trained models from Hugging Face.

However, skeptics argue that while Snowflake is making a credible effort to bridge the gap between experimental AI and production-ready systems, many organizations still lack the semantic and governance foundations required to deploy AI safely and at scale.

“Multi-cloud users could find flexibility limited. The real enabler of scale is open semantics that travel with the data, not the vendor. Snowflake’s long-term viability will rest on how freely those definitions can move beyond its own walls,” said Nic Riemer, CEO of Invigilator. “Snowflake’s blend of multimodal processing, adaptive scaling and built-in cost controls brings it closer to parity with Databricks’ machine-learning stack and Microsoft Fabric’s unified data fabric. The competition now centres on openness, interoperability and cost predictability.”

Unshaken Amid Growing Data Intelligence Competition

As enterprise intelligence continues to evolve, Snowflake faces increasing competition from cloud leaders and emerging specialized startups. Yet CEO Shridhar Ramaswamy remains confident, emphasizing the platform’s strength in its trusted data layer and long-standing credibility in governance and data management. The company views competition not as a threat but as validation that the market is shifting toward agentic intelligence built on reliable data foundations.

His vision is simple: the future of enterprise AI isn’t about who has the flashiest model integrations, it’s about who can guarantee trust and compliance at scale.

“The AI landscape is evolving quickly, and competition is healthy,” Ramaswamy said. “There are many strong players in this space, and we welcome that. We pay close attention and often collaborate with others, including Microsoft, AWS Redshift, and additional partners. Our goal is to be the easiest platform to use, with seamless integration and continuous innovation.”

In 2025, Snowflake commands an estimated 18.33% share of the data intelligence and cloud data warehousing market, positioning it as the sector’s largest player ahead of rivals. Databricks, the next major competitor, holds roughly 8.67%. Financially, Snowflake reported $942.1 million in total revenue, up 28% year over year, with product revenue reaching $900.3 million, a 29% increase from the previous year.

Industry experts suggest that Snowflake holds a more defensible position amongst competitors within its existing ecosystem, strengthened by the powerful network effects it has built around its data platform.

“Enterprises have invested heavily in Snowflake infrastructure, trained teams on it, and built workflows around it; those switching costs matter more than specific technical features. Snowflake’s emphasis on model interoperability and openness is smart positioning, but they’ll need to prove it in practice,” Chad Burnette, CTO and co-founder of Wayfound, told me. “Can they maintain trust without becoming too restrictive or too expensive? That’s the challenge as AI tools proliferate and complexity multiplies. Snowflake has the opportunity to make governance feel like a tool rather than a constraint, but that’s exceptionally difficult to execute at scale.”

Snowflake’s strategy suggests a broader industry realignment. As businesses push AI deeper into operations, the emphasis is shifting from creation to comprehension, what AI can generate to what it can reliably explain.

“The future of enterprise AI is open, interoperable, and deeply data-driven. Our vision is to make Snowflake the connective tissue of that ecosystem where every enterprise can bring together best-in-class models, from providers like Anthropic, OpenAI, and Google, with their own proprietary data,” said Ramaswamy. “What differentiates us is trust and context.”