How Snowflake Intelligence Is Enabling Retail and Consumer Goods Companies to Scale AI at the Enterprise Level

For a global enterprise such as the Mark Anthony Group (MAG), managing data isn’t just a technical requirement — it’s a massive exercise in complexity. As a privately owned collection of beverage companies with high-profile brands such as White Claw under its belt and a suite of wineries in British Columbia, MAG operates across diverse markets, each with its own set of unique employees, operations and processes. But a large portfolio can also come with an array of operational challenges.

“Typically, a company has one marketing department, one finance, one operations and one HR,” explains Sam Wong, Senior Director of Data, Analytics and AI at the Mark Anthony Group. “In our case, we have multiple finance, marketing departments, sales organizations and so on to collaborate with, that all may have different CRMs, ERPs and market data they rely on.”

To successfully navigate these intricacies while remaining competitive, MAG has evolved its data ecosystem beyond traditional data warehousing. With Snowflake Intelligence, MAG is moving away from passive business intelligence to what Wong calls a “generative BI evolution.” It’s putting the power of conversational data directly into the hands of the business users at every company within MAG, fueling the organization’s shift toward becoming an agentic enterprise.

From data warehouse to innovation partner

MAG’s journey with Snowflake began with a focus on building the foundations of a strong data strategy, but it quickly shifted toward evangelizing data sharing standardization with Snowflake across its business relationships. For Wong, Snowflake isn’t just another vendor but rather a benchmark for innovation — and a nonnegotiable part of MAG’s data sharing process.

“When we do RFPs for various software requirements, one of the questions we ask is ‘Are you able to support Snowflake Secure Data Sharing?’” says Wong. “This is how strongly we view Snowflake as a partner. And companies that embrace Snowflake demonstrate a sense of innovation, capability and technical thought leadership that we want to work with.” Standardizing on Secure Data Sharing has allowed MAG to push third-party data providers and even some global vendors to move away from archaic flat-file transfers toward reliable, real-time data integration. According to MAG, this strategy reduces the total cost of ownership, increases reliability and helps data quality issues get identified and addressed at the source. No more unexpected file format changes or corruption, no more convoluted sharing processes — just a win-win for everyone.

In fact, MAG even uses Secure Data Sharing with its own partners for whom they handle sales and distribution, which MAG reports has massively improved its ease of access to that sales and distribution data. “While many peers in our space have very traditional SFTP and ETL processes, we do things differently,” says Wong. “With Secure Data Sharing, it’s simple for our partners to get access to the data they want and pull it at any time. They’re ecstatic with how easy it is.”

Empowering the enterprise with Snowflake Intelligence

Committed to finding new ways to grow and innovate, MAG decided to experiment with the Snowflake Intelligence engine to customize it for its enterprise-specific needs. MAG recently deployed a custom-built, global enterprise application that serves as a wrapper for the Snowflake Intelligence engine.

This application, currently in pilot with a production rollout underway, is designed to democratize data for every team, every business unit, every company across the group. By using the text-to-SQL capabilities of Snowflake Intelligence, MAG is enabling users to ask complex questions of their data in plain English — or even via voice commands — without needing to see the underlying SQL code.

Some key features of MAG’s Snowflake Intelligence implementation include:

• Explainability and observability: The tool includes a crowdsourced capability that describes data sets and provides context, providing users with the meaning behind the numbers (or not just the “what” but the “why”).

• Mobile and voice integration: Built to be web enabled and mobile friendly, the tool allows executives to query sales figures or operational metrics on the go.

• Collaboration through Teams: Rather than building a siloed app, MAG is integrating this intelligence directly into Microsoft Teams and exploring other integration methods to offer multiple paths of access.

Solving the semantic challenge

One of the primary hurdles of any AI implementation is ensuring the model understands the specific language of the business. For MAG, this means defining common terms that get used internally and across multiple disparate markets.

To solve this, MAG is integrating Snowflake with partners such as Ataccama to build a tool-agnostic semantic layer. By merging its business glossary and data catalog into Snowflake, it’s essentially tuning the data so the AI knows exactly what a user means when they use a specific acronym or term.

Driving tangible business outcomes

For MAG, AI isn’t a technology initiative — it’s a business enabler with the goal of achieving specific business outcomes: increased revenue, reduced costs and improved customer experience.

Though the implementation is still in its early stages, the projected impact is significant. By combining data in Snowflake, MAG has already built AI/ML engines to drive sales performance recommendations and enrich existing data with new attributes. The transition to Snowflake Intelligence is expected to help reduce both the time for decision-making and the time to take action on those decisions across the company. “I now have quicker access to all my data, which will help me across so many different initiatives,” says Wong. “What are some new revenue opportunities? What are some operational inefficiencies we can target? How do I improve product quality now that I have greater insights into it?”

“It’s going to trigger a new utilization of data that we haven’t had before,” says Wong. “That’s going to fundamentally change our business processes and workflows, bringing to life a vision of agentic enterprise.”

Expanding to unstructured data

As part of MAG’s data evolution, it’s pushing the envelope by applying Snowflake Intelligence to unstructured data in addition to structured data. The team is working on prototypes that allow users to ask questions of hundreds of standard operating procedure (SOP) documents and receive quoted, referenceable instructions in seconds. By leveraging technologies such as Document AI, MAG is working to make every piece of information — whether in a database or PDF — an actionable asset.

Advice for the AI journey: Use cases as the north star

As retail and consumer goods organizations look to follow MAG’s lead in starting their AI journey with Snowflake, Wong offers a critical piece of advice: Never lose sight of the problem you’re trying to solve.

“That should always be your starting point,” says Wong. “Make sure that’s your north star. Otherwise, it’s going to become nothing more than a technology initiative, something that feels forced rather than a solution that enables you to accomplish incredible business outcomes. Snowflake Intelligence has the potential to transform your operations and data strategy, but to achieve the most significant transformation, your application of the technology should always be in service of that north star.”

If you want to hear more from retailers and consumer goods companies that are building a data and AI strategy with Snowflake for an agentic future, register for our digital event Accelerate Retail & Consumer Goods 2026!