‘Snowflake believes there is no sustainable AI strategy without a strong data strategy’: Rangarajan Srirangam, Senior Regional VP of Solution Engineering – India, Snowflake – APAC Media
By: Rajneesh De
‘Snowflake believes there is no sustainable AI strategy without a strong data strategy’: Rangarajan Srirangam, Senior Regional VP of Solution Engineering – India, Snowflake
The opportunity for India is to move AI from pilot to production at scale. But enterprise AI only works if it is grounded in trusted, governed data, and that is what Snowflake does for a living, asserts Rangarajan Srirangam, Senior Regional VP of Solution Engineering – India, Snowflake in an exclusive conversation with Rajneesh De, Group Editor, APAC Media & CXO Media. The Snowflake tools are becoming proactive teammates of enterprises, with shared skills and memory, so teams build on each other’s work. The goal is not AI for its own sake but measurable business outcomes, Rangarajan reiterates.
What solutions and services are currently in the Snowflake portfolio in India and what are their use cases?
As enterprises move from traditional analytics to AI-driven and increasingly agentic workloads, the priority is no longer simply bringing data together, but creating a trusted foundation that allows AI to securely understand, reason over and act on enterprise data. At the core of this shift is the Snowflake AI Data Cloud, which brings data, analytics, AI, applications, governance and collaboration into a unified platform.
Increasingly, we are building for what we call the Agentic Enterprise, where AI is generating insights, reasoning over enterprise context and helping take action across workflows. This requires four essential components: enterprise data and context, AI models, applications, and an agentic control plane that coordinates them.
That translates into capabilities for different users and workloads. Snowflake CoWork gives business users a governed way to work with enterprise data and move from questions to action, while Snowflake CoCo helps developers and data teams build pipelines, applications and AI workflows using natural language.
Horizon Catalog provides the governance and shared business context required to make those AI interactions trusted and consistent. Importantly, our approach is cloud and model agnostic, giving customers the flexibility to use the infrastructure and AI models that best suit their workloads without compromising governance or security.
Our latest collaboration with Oracle reinforces that strategy, with the Snowflake AI Data Cloud set to become available as a native, first-party service on Oracle Cloud Infrastructure, giving enterprises greater choice in how they bring their data and AI environments together.
Our objective is to provide one open, interoperable and governed foundation for enterprise data and AI, so organizations can move from fragmented data and AI experiments to production-scale, agentic applications with greater choice and control.
How does Snowflake position enterprise cloud data migration from legacy on-premise systems as the foundational step required to unlock analytics and AI?
Moving off legacy on-premise systems is often the first step towards AI. Many enterprises still have their most valuable data locked in ageing systems that are expensive to run, hard to scale and isolated from each other. Moving that data to the cloud frees it. But migration on its own does not make an organization ready for AI.
At Snowflake, we believe there is no sustainable AI strategy without a strong data strategy. What creates value is what happens after migration: bringing fragmented data together, governing it consistently and adding the business context that people and AI systems need to understand it. AI quickly exposes duplicated records and conflicting definitions that traditional reporting could live with.
That is why we help customers modernize with automated tools like SnowConvert and open formats like Apache Iceberg, so they avoid being locked into a single system. As enterprises move towards AI agents that reason and act on their own, the AI Data Cloud gives them a trusted foundation, with the right data, context and permissions, to turn migration into measurable business results.
What measures is Snowflake undertaking to deploy intelligent tools Snowflake Co-work and Snowflake CoCo for coding/pipelines, and governed multimodal AI across industries?
Enterprise AI only works if it is grounded in trusted, governed data, and that is what we do for a living. Snowflake CoWork and Snowflake CoCo are two complementary ways into the Agentic Enterprise. CoWork helps business users investigate questions, automate workflows and take action. CoCo helps builders go from a prompt to production pipelines, apps and AI workflows. Both run on the same data and governance foundation, so agents follow the same access controls, permissions and audit trails our customers already rely on.
The momentum is real. As of Q2 of fiscal 2027, more than 9,100 accounts use CoCo and 5,800 use CoWork. With CoCo Cloud Agents and Automations, agents build, run and fix pipelines, and engineers approve every change before it ships. We are also making more data AI-ready by moving Spark workloads onto Snowflake with fewer code changes.
These tools are becoming proactive teammates, with shared skills and memory, so teams build on each other’s work. The goal is not AI for its own sake. It is measurable business outcomes
Which verticals are showing the most traction for Snowflake in India and what are their use cases?
India’s strongest traction is coming from sectors where data is already central to how businesses operate and AI can directly improve decisions and outcomes. We are seeing momentum across financial services, retail and consumer businesses, manufacturing, travel and digital-native companies. Across these sectors, the journey is similar: build a trusted, governed data foundation first, then scale analytics, AI and increasingly agentic workloads.
In financial services, Mirae Asset is leveraging Snowflake as a unified data foundation to consolidate and govern data, supporting production AI use cases across compliance, HR and fund management. Meanwhile, Swiggy is using Snowflake across food delivery, Instamart and Dineout to bring data together and enable faster, self-service decision-making. Its data workflows have improved by 90 to 96%, with reporting moving from six hours to near real time, while creating an AI-ready foundation governed by the same security and access framework.
The opportunity for India is to move AI from pilot to production at scale. For Indian enterprises, the next phase will be about applying AI to high-value, industry-specific use cases and scaling them into measurable business outcomes.
How are Indian enterprises shifting AI pilot projects to production-level deployments, and how is AI-led consumption beginning to contribute alongside traditional data workloads?
India is already showing strong momentum. Snowflake’s ROI of Gen AI and Agents 2026 research found that 71% of Indian respondents are seeing measurable returns from generative AI investments, compared with 61% globally, while 42% of Indian organizations are already using GenAI across multiple use cases. Indian organizations are also ahead of global averages in areas such as code generation, code review and debugging, cybersecurity incident response and infrastructure optimization.
That early success is now pushing enterprises toward production, and that is the harder step. A model that works in a pilot is not enough on its own. Running AI at scale takes trusted data, validated business context, integration with operational systems, strong governance and security, and more and more, cost discipline. These requirements only grow as agents become more autonomous. So the question in boardrooms has changed from “What can this model do?” to “How do I run AI reliably, securely and economically across my business?”
The enterprises making this shift well are bringing AI to where their governed data already lives, rather than moving data out to AI. That approach is also changing how they use their data platforms. AI workloads, from document processing and conversational analytics to AI agents, now run alongside traditional analytics and data engineering. AI is not replacing core data workloads; it is adding to their value, because every AI application depends on high-quality, well-governed data underneath.
In India, the next phase of enterprise AI won’t be measured by how many pilots organizations run. It will be measured by how many AI workloads run in production on trusted data, and by the business value they deliver.
What are the key pillars of Snowflake’s GTM strategy in India and what initiatives support this strategy?
India is a strategic growth market for Snowflake, and our go-to-market strategy is increasingly focused on helping enterprises move from data modernization to production AI and agentic use cases that deliver measurable business outcomes. We are deepening engagement across enterprise, mid-market and digital-native businesses, with a strong focus on sectors including financial services, manufacturing, government, telecom, retail and CPG and digital natives. The opportunity is to take more industry-specific use cases to market rather than approach AI as a horizontal technology.
Partners are fundamental to how we scale this opportunity. Around half of Snowflake’s APJ partner ecosystem is rooted in India, and we are increasingly enabling partners to go beyond traditional migration work and co-create repeatable, industry-specific AI solutions. Through the Snowflake Partner Network, SPN Learn, Snowflake University and Partner Champions, we are investing in co-selling, technical enablement and deeper expertise across data engineering and AI/ML.
How has Snowflake’s India partner ecosystem evolved and how are you moving India partners away from basic data migration tasks toward high-impact, repeatable AI and business-outcome use cases?
Our partner ecosystem in India is evolving from being primarily implementation-led to becoming solution and outcome-led. With around 50% of Snowflake’s APJ partner ecosystem rooted in India, the opportunity is to leverage partners’ deep technology and domain expertise to create repeatable solutions around specific industry challenges. Increasingly, we want partners to build differentiated IP and AI-led offerings that can be taken across multiple customers, rather than focus primarily on one-time migration engagements.
Importantly, we are also improving the economics and business opportunities for partners. Recent enhancements to the Snowflake Partner Network provide Services partners with up to five times more demo and development credits, expanded co-marketing opportunities and CoCo Foundational Accreditation. Qualified partners can also resell Snowflake as part of integrated customer solutions, giving them greater opportunity to participate across the customer lifecycle and build sustainable businesses around their Snowflake expertise.
The next step is to turn that capability and commercial opportunity into repeatable AI use cases with measurable outcomes. As adoption of Snowflake CoCo and CoWork grows, partners can combine their industry expertise with Snowflake’s data and AI capabilities to develop production-grade solutions for specific business problems. That is the shift we want to accelerate in India with Snowflake Marketplace: from implementing technology to co-creating and monetising solutions that drive customer outcomes.
What are Snowflake’s unique differentiators over competitors operating in the AI-led enterprise consumption environments?
Snowflake’s difference is simple: AI runs where the data already lives. Customers do not copy governed data into a separate AI system, or rebuild security and access controls for every new application.
Three things set us apart.
Trust travels with the data. Agents on Snowflake inherit the governance, security, and access controls customers already use.
Choice without lock-in. One platform, across clouds. Teams pick the best model for each job and work in open formats, instead of one model, one cloud, or a closed stack.
Value follows use. We earn when customers run workloads on Snowflake, so spend follows usage on one governed platform.
For Indian enterprises scaling from pilots to production, Snowflake CoWork moves business users from insight to action, and CoCo moves builders from prompt to production. That is agentic AI at enterprise scale: trusted, flexible, and measured by real work.
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