AI Data Cloud company Snowflake aims to make it easy for enterprises to innovate faster and get more value from data. Times of India spoke to Christian Kleinerman who serves as Snowflake’s EVP of Product and has been with the company since 2018. He oversees the company’s global product strategy and vision. Christian is a database expert with over 20 years of experience working with various database technologies and has more than 15 years of management and leadership experience. Most recently, Christian worked at Google leading YouTube’s infrastructure and data systems. Prior to that, he served as General Manager of the Data Warehousing product unit at Microsoft where he was responsible for a broad portfolio of products. Christian holds a BS in Industrial Engineering from Los Andes University in Colombia, and he is a named inventor on numerous Snowflake patents. Kleinerman’s spoke about AI agents, Snowflake’s role in the AI era, company’s India market and more. Q. Snowflake has positioned itself as the platform where enterprise data and AI come together. As every major cloud and AI company is trying to become the orchestration layer for enterprise AI, what differentiates Snowflake’s vision of the “agentic control plane”, and why should enterprises trust you with that role?The industry is gradually realizing that powerful models alone aren’t enough. Models are improving at a remarkable pace, and increasingly interchangeable. The real challenge for enterprises is giving those models the context they need to make useful decisions. If you think about how work actually gets done inside a company, context comes from more than just data. It comes from business definitions, governance policies, security, operational systems, permissions, and the relationships between them. That’s where Snowflake fits. We’ve spent more than a decade helping enterprises solve problems like governance, access control, lineage, compliance, and interoperability. Our strategy isn’t to build the best foundation model or create another closed ecosystem. At Snowflake we are committed to give our customers access to the leading frontier and open source models even as the leaderboards evolve. This allows enterprises to maintain flexibility and maximize value of their AI initiatives.Q. Many companies are experimenting with AI agents, but governance, security and compliance remain major hurdles. Do you believe most enterprises are actually ready for autonomous agents today? What capabilities has Snowflake built to ensure AI agents remain auditable, secure and within policy boundaries?We have delivered capabilities to ensure that agents can be identified in Snowflake, so their activity and accessible permissions can be customized to the level of privilege desired for an agent. And not every company is ready for autonomous agents, but we see some companies at the forefront creating real business value. Agents summarize information, answer business questions, turn insights into action, and help people complete work much faster. Before an agent updates a CRM record, approves a payment, or triggers a business workflow, organisations need confidence that every action happens within the same governance capabilities and guarantees that applies to people modifying those same systems. They need auditability, clear permissions, and policy enforcement. That’s an area where Snowflake starts from a position of strength. Governance has always been built into the platform. Every AI interaction inherits the same role-based access controls, lineage, audit trails, and security policies that already protect enterprise data.We believe autonomy will happen incrementally based on customers’ comfort with AI. Some will begin with agents that recommend actions, introduce human approval where appropriate, and gradually automate more as confidence grows. The future belongs to organizations who can give AI the trusted enterprise context and guardrails needed to operate safely at scale.Q. Enterprise CIOs are under pressure to justify AI investments with measurable business outcomes. Can you share specific examples—globally and from India—where Snowflake customers have demonstrated tangible ROI from AI deployments, and what metrics matter most?Enterprise AI success is ultimately measured by business outcomes, not AI adoption or model benchmarks. CIOs should focus on tangible metrics such as faster deployment, lower costs, improved productivity, and better customer experiences. We’re already seeing customers achieve that in production. Providence Health is using Snowflake Cortex AI to extract insights from clinical notes more quickly, helping clinicians make faster decisions. Thomson Reuters is applying AI to legal and compliance workflows, accelerating how professionals work with complex regulatory information. And organisations like DTCC, Wakefit and Urban Company are using our solutions. We also believe it’s important to use these technologies ourselves. More than 6,000 Snowflake employees now use our GTM AI Assistant every week, handling over 30,000 questions across sales and marketing. Tasks like researching customers, writing SQL, and preparing for meetings used to require significant manual effort, and now they happen in minutes. Those are the kinds of productivity gains we want our customers to achieve as well. By powering internal operations and decision-making through Snowflake CoWork and CoCo, we rigorously test, refine, and prove our products before bringing them to customers, supporting over 8,000 employees worldwide in product delivery, development, and sales productivity.Q. The AI landscape is becoming increasingly fragmented, with enterprises choosing between proprietary and open-source models while traditional SaaS is also being reshaped. How does Snowflake plan to stay relevant in a world where foundation models are rapidly commoditising, and what advice do you give customers on selecting the right models for different workloads?We’re moving into a world where traditional software is being disrupted and AI is becoming the primary interface that people use to interact with data and applications. As that happens, foundation models will continue to improve rapidly, costs will come down, and customers will have more choice than ever before.Snowflake has built its strategy around that reality. Rather than asking customers to commit to a single model or AI provider, Snowflake is deliberately model-agnostic. We work across the leading proprietary and open-source models because we don’t want our customers to be locked into any one vendor. We want them to take advantage of whichever models are best as the technology evolves.In this new era, what doesn’t commoditise is enterprise data and enterprise context. In order to perform reliably in an enterprise, models will always need trusted data, business semantics, governance, security, and operational context fueling them. That’s where we see Snowflake’s role. We provide the trusted data foundation that allows customers to adopt new models without having to rethink their entire AI strategy every time the technology changes.My advice to customers is to optimize for business outcomes, not model preferences. Use the right model for the right workload. Smaller, more efficient models are often ideal for tasks like classification or information extraction, while frontier models are best suited for more complex reasoning and planning. The important thing is building an architecture that gives you the flexibility to evolve as the model landscape evolves. That’s exactly why we’ve taken an open, model-agnostic approach from the beginning.Q. India has emerged as one of the world’s fastest-growing AI and digital infrastructure markets. Where does India fit in Snowflake’s long-term global strategy—in terms of customers, engineering talent, partnerships and product innovation—and what new investments or priorities should we expect over the next few years?India is one of our most important strategic markets because it brings together four things that are critical for the AI era: a vibrant digital economy, robust public digital infrastructure, world-class engineering talent, and enterprises that are modernising rapidly, along with a growing number of our global customers who are setting up their Global Capability Centre (GCC) base. Over the past year, Snowflake has doubled its sales team in India and 50% of its APJ partners are based here, underscoring the country’s pivotal position in our global strategy and its vast, expanding data and AI market.
