From Implementers to Innovators: How Snowflake Is Empowering Partners to Co-Create the AI Future

As enterprises accelerate from AI experimentation to large-scale deployment, Snowflake is redefining the role of partners, transforming them from technology implementers into strategic co-creators of secure, governed, and business outcome-focused AI solutions powered by agentic intelligence.

As enterprises worldwide race to transform AI ambitions into measurable business outcomes, Snowflake is emerging as one of the industry's most influential forces, redefining how organisations harness data, AI, and agentic intelligence at scale. No longer just a cloud data platform, Snowflake is positioning itself as the trusted control plane for enterprise AI, enabling businesses to unify data, strengthen governance, accelerate innovation, and deploy AI securely within a single ecosystem. Through innovations such as Cortex AI, Snowflake CoWork, Snowflake CoCo, and the Snowflake Marketplace, the company is empowering customers and partners alike to move beyond experimentation and build production-grade AI solutions that deliver tangible business value.

In this exclusive interaction with SME Channels, Dhiraj Narang, Director & Head of Partnerships – India, Snowflake, shares how the company is reshaping the future of enterprise AI through a thriving partner ecosystem, robust governance frameworks, and a relentless focus on business outcomes. He explains why India has emerged as one of the world's fastest-growing AI markets, how agentic AI is creating unprecedented opportunities for startups, ISVs, and system integrators, and why trusted data will remain the foundation of every successful AI initiative.

In a thought-provoking conversation with SME Channels Editor Manash Ranjan Debata, Narang offers valuable insights into the evolution of the partner landscape, the growing importance of domain-led AI innovation, the rise of outcome-based engagement models, and Snowflake's vision of becoming the enterprise knowledge layer powering the next generation of intelligent business operations. The discussion provides a compelling roadmap for organisations seeking to navigate the rapidly evolving AI era while highlighting how partners can evolve from technology implementers into strategic co-creators of business transformation. Edited Excerpts…

Q. Snowflake is increasingly positioning itself as a control plane for enterprise AI, especially with innovations like Snowflake Intelligence and Cortex Code. How do these capabilities change the role of partners from traditional implementation support to active co-creators of agentic enterprise solutions?

Viewed through the lens of our evolution, Snowflake has transformed from a data warehouse and data lake company into a comprehensive AI Data Cloud platform. As generative AI enables organisations to engage with data in more meaningful ways, the role of data platforms—and the broader ecosystem surrounding them—has evolved significantly.

Snowflake eliminates data silos and prepares data for AI at scale in a secure, governed, and user-friendly manner. Our philosophy is straightforward: we bring AI to the data, rather than moving data to the AI model. This approach ensures that governance, security, and compliance remain embedded within the platform.

Innovations such as Snowflake CoCo and Snowflake CoWork have fundamentally expanded the role of partners. Today, it is no longer sufficient to implement a data warehouse or data lake and conclude the engagement. Partners are increasingly expected to deliver measurable business outcomes.

This requires partners to leverage Snowflake’s capabilities to accelerate value creation while continuously evolving with market demands. It involves developing industry-specific accelerators for areas such as capital risk management, supply chain optimisation, and demand forecasting. More importantly, it calls for a combination of deep engineering expertise and domain knowledge to create differentiated solutions that drive meaningful outcomes for customers.

"The future belongs to organisations that can combine trusted data, agentic AI, and domain expertise to deliver measurable business outcomes at scale."

— Dhiraj Narang, Director & Head of Partnerships–India, Snowflake

Q. The idea of “agentic enterprises” is gaining traction globally. From what you are seeing in India, how ready are enterprises to move from AI experimentation to deploying autonomous or semi-autonomous AI agents in real business workflows?

Indian enterprises are progressing from AI pilots to production deployments at an exceptionally rapid pace. In fact, India has emerged as one of the fastest-moving markets globally in terms of AI adoption and implementation.

Our research indicates that 92% of early adopters have realised positive returns on their generative AI investments, with organisations reporting an average return of 49% among those that have measured outcomes.

We are witnessing widespread adoption across diverse use cases, including supply chain management, demand forecasting, customer operations, analytics, and software development. Notably, 75% of respondents in our research indicated that their organisations use AI for code generation, with nearly half of all code now estimated to be AI-generated.

Agentic AI adoption is also gaining momentum, with 32% of organisations already deploying agentic AI solutions in production environments. More broadly, Snowflake serves over 13,000 customers globally, and more than 7,300 of them actively utilise Snowflake’s AI capabilities on a weekly basis. These figures clearly demonstrate that enterprises are moving beyond experimentation toward scalable, real-world AI adoption.

Q. How is the Snowflake Partner Network evolving to support this new AI-first phase, particularly for system integrators, ISVs, and SaaS startups building on the Snowflake platform?

Partners have consistently demonstrated an ability to adapt to major technology shifts, and the AI era is no exception. Across system integrators, ISVs, and SaaS providers, we are seeing partners build AI capabilities directly on Snowflake.

They are bringing proprietary tools, intellectual property, and domain expertise onto the platform to accelerate customer transformation. This enables them to develop proofs of concept, demonstrations, and production-ready solutions more efficiently while leveraging capabilities such as Cortex AI and Snowflake CoWork to deliver faster business outcomes.

At the same time, we recognise our responsibility to help both customers and partners navigate this transformation. Consequently, we continue to invest heavily in enablement and training initiatives that ensure partners remain aligned with Snowflake’s latest innovations.

One example is the Snowflake Partner Champions Programme, launched in India. This intensive five-month programme provides deep technical and business training across data engineering, AI, and machine learning specialisations. Participants gain hands-on experience with technologies such as OpenFlow, Polaris, Cortex AI, Horizon Studio, and Document AI, while working through real-world business scenarios. The objective is to equip partners with the skills required to build and deploy production-grade AI solutions that deliver measurable customer value.

We are also collaborating closely with ISVs and SaaS providers to help them unlock new revenue streams through data sharing and the Snowflake Marketplace, which remains a critical component of our value proposition globally and in India.

Q. Indian enterprises often operate in highly fragmented and regulated environments. How are partners helping customers ensure governance, security, and interoperability while deploying AI agents on sensitive enterprise data?

Snowflake is built upon three foundational principles: being easy, connected, and trusted. Ease of use, seamless connectivity, and trust in the underlying data are central to enabling successful business outcomes.

Trust becomes particularly important in highly regulated and fragmented environments such as India. Organisations require confidence that their data is secure, governed, compliant, and reliable.
Snowflake maintains rigorous standards around security, compliance, operational excellence, and secure-by-design architecture across all markets in which we operate. We enable organisations to define and control how data is protected, governed, and accessed, including the secure sharing of data across internal teams and external stakeholders.

Our latest innovations within Snowflake Horizon Catalog further strengthen enterprise governance, contextualisation, and security at scale. These capabilities ensure that every user, tool, and AI agent operates from a common, trusted business context, while new AI security features provide purpose-built controls for governing enterprise AI systems.

For AI workloads specifically, governance is embedded within the platform architecture. Models operate directly within Snowflake’s secure environment, minimising exposure risks and eliminating unnecessary data movement. This ensures that customers work from a single, governed source of truth while generating AI-driven insights and outcomes.

In India, we also work closely with customers and partners to align deployments with evolving regulatory and privacy requirements, including considerations related to the Digital Personal Data Protection framework. Ultimately, our approach enables organisations to achieve trusted, governed AI outcomes with complete visibility, security, and control.

Q. Could you share examples of how Indian partners are building domain-specific AI or agentic workflows across sectors like BFSI, retail, manufacturing, or mobility? Which use cases are showing the strongest business impact so far?

There are several compelling examples that demonstrate how our partner ecosystem is delivering tangible value through AI.

TVS Next, a leading digital and AI consulting firm, leverages Snowflake as the AI and data platform for NexOps, its operational and enterprise intelligence platform. Built on Snowflake’s AI Data Cloud, NexOps provides manufacturers with an integrated view of plant operations and enterprise data while modernising data pipelines, strengthening governance, and enabling scalable AI deployments globally.
In the banking and financial services sector, EY India has launched the EY India ART platform, an automated regulatory reporting solution built on a cloud-native data architecture. The platform simplifies and modernises regulatory reporting for financial institutions across India.

Within the mobility sector, Chalo is using AI and machine learning to analyse large volumes of real-time and historical data to improve public transport operations. Its platform predicts bus arrival times with high accuracy, enhancing both passenger experience and operational efficiency.

These examples illustrate how partners are combining Snowflake’s AI capabilities with deep industry expertise to create solutions that deliver measurable business outcomes. We are seeing particularly strong impact in manufacturing intelligence, regulatory compliance, operational forecasting, and customer experience enhancement.

Q. For startups and emerging ISVs, does the rise of agentic AI create a fresh opportunity to build monetisable products and solutions within the Snowflake ecosystem? What kinds of innovation areas are you most excited about from India?

This represents one of the most significant opportunities within the Snowflake ecosystem. A core element of our strategy is fostering a robust data product ecosystem because modern enterprises are increasingly creating, packaging, and monetising data as strategic assets.

While the industry has historically focused on storage, databases, and infrastructure, Snowflake enables organisations to operationalise and monetise data and AI at scale in a seamless and secure manner.
A major differentiator is the Snowflake Marketplace, which currently hosts more than 3,400 live data products, applications, and agentic tools. It enables organisations to collaborate, share, and monetise data without moving it or creating complex integrations.

The Marketplace also offers flexible procurement models, including the use of Snowflake credits, reducing friction and accelerating innovation.

For Indian startups, ISVs, and SaaS providers, this creates a substantial monetisation opportunity. The Marketplace effectively serves as a global distribution channel, enabling companies to monetise proprietary data assets, AI applications, and agentic solutions across Snowflake’s worldwide customer base.

We are already seeing examples of success, including FirstHive’s AI-powered solutions built on Snowflake’s AI Data Cloud. India plays a highly strategic role within our ecosystem, accounting for more than half of Snowflake’s APJ partner network. This includes global system integrators, advisory firms, hyperscalers, regional partners, and emerging ISVs.

Leading organisations such as TCS, HCLTech, Infosys, and Tech Mahindra have developed some of the largest pools of Snowflake-certified talent globally. At the same time, regional partners such as Cosmo Prolim, SHI | Locuz, Rapidi, and Quantiphi are not only driving implementations but also building software solutions and AI applications that can be monetised globally through the Snowflake Marketplace.

Agentic AI therefore represents a significant opportunity for Indian startups and ISVs to build scalable, monetisable AI products for global markets.

Q. As enterprises begin treating AI agents as operational collaborators rather than just productivity tools, what new skill sets or business models will partners need to succeed in this evolving ecosystem?

This is a topic we discuss extensively with both our internal teams and partners because the business landscape has changed dramatically over the past 12 to 18 months.

Customers today possess a far more sophisticated vision for AI and expect their partners to move at the same pace as their transformation initiatives. More importantly, they are no longer seeking standalone technology projects; they expect measurable business outcomes.

As a result, the market is shifting toward verticalised AI solutions, repeatable accelerators, and outcome-based engagement models rather than one-off implementations.

At Snowflake, our vision is to foster an integrated, insight-driven partner ecosystem centred on co-creation, innovation, and measurable outcomes. Delivering quantifiable business value has become the defining success metric.

Partners must therefore evolve from implementation providers into trusted advisors across the entire AI lifecycle—from identifying business opportunities to delivering and scaling outcomes through consumption-based models.

Success will depend on combining deep industry expertise with advanced AI, data, and engineering capabilities. The partners that thrive will be those capable of solving highly specific business challenges through a combination of domain knowledge and technology.

A strong example is ML AI, a Gurgaon-based partner that has developed specialised expertise in areas such as supply chain optimisation and demand forecasting. By combining domain knowledge with Snowflake’s platform capabilities, the company helps organisations improve forecasting accuracy, reduce inventory stockouts, and generate measurable business impact.

This is the direction in which we expect the ecosystem to evolve: stronger domain-led AI capabilities built on the Snowflake AI Data Cloud, delivering scalable and production-ready outcomes. With innovations such as Snowflake CoWork and Cortex AI, partners can now build and deploy these solutions far more efficiently than ever before.

Q. Looking ahead, do you see enterprises eventually managing fleets of AI agents in the same way they manage applications today? If so, what role does Snowflake aim to play in that future enterprise architecture?

The future is already taking shape. Organisations are rapidly transitioning from AI experimentation to deploying AI and agentic capabilities that drive meaningful business outcomes.

Through capabilities such as Cortex AI, organisations can build and deploy trusted AI agents directly within Snowflake’s secure environment. Furthermore, through open protocols such as MCP, these agents can interact and collaborate with other agents and enterprise systems.

This is where we see Snowflake evolving into the control plane for enterprise AI—enabling organisations to move from fragmented experimentation to scalable, production-grade deployments.

As enterprises begin managing fleets of AI agents and intelligent applications, the key challenge will be ensuring trusted data access, governance, interoperability, and coordination across these environments. This is precisely where Snowflake delivers value.

As a unified AI Data Cloud and enterprise knowledge layer, Snowflake enables organisations to power agentic AI experiences using governed, real-time, enterprise-wide data without unnecessary data movement.

This allows organisations to deploy AI agents that are secure, context-aware, interoperable, and capable of generating meaningful business outcomes across functions.

Looking ahead, we believe Snowflake will play a foundational role in enabling enterprises to orchestrate AI-driven operations at scale and support the next generation of enterprise AI architectures.