Snowflake NYSE: SNOW executives said artificial intelligence is changing the pace of data migrations, expanding the company’s potential customer base and accelerating adoption of its newer AI products, including its CoCo coding agent.
Speaking at the Goldman Sachs Communacopia Conference, Chief Executive Officer Sridhar Ramaswamy said customers are increasingly viewing AI as a way to modernize data environments faster and pursue business outcomes rather than simply complete technology projects.
“AI is having a pretty profound impact on how quickly you can get those done,” Ramaswamy said of data migrations. He cited a large manufacturing customer pursuing a Teradata migration that expects to complete the effort in less than three quarters, a timeline he said would have been unusual several years ago.
Ramaswamy said discussions with customers have shifted toward applications such as invoice-processing automation, supply-chain optimization and faster creation of custom customer data platforms. In one example, he said a large energy manufacturer estimated that a one-percentage-point improvement in efficiency on roughly $10 billion in annual payments would represent a significant opportunity.
AI Changes Migration Economics
Ramaswamy said coding agents could reshape the services industry by reducing the time and uncertainty associated with migrations. Rather than charging under traditional time-and-materials models, more system integrators may be able to provide fixed-price, outcome-based engagements, he said.
“The progressive system integrators are going, ‘I can guarantee outcomes,’” Ramaswamy said. He added that services are unlikely to disappear, but could become smaller and more closely tied to customer outcomes.
Chief Financial Officer Brian Robins said Snowflake bases guidance for its core platform and migrations on observed customer behavior, supported by years of historical data. For newer products, however, the company takes a more conservative approach because it has less adoption history to model.
Robins said Snowflake had two quarters of data for CoCo and was becoming more confident in what it could infer from customer usage. He also said customers are reaching consumption run rates faster than in the past as they deploy the platform more quickly using Snowflake, partners and AI agents.
To support faster implementation, Ramaswamy said Snowflake has created roles including activation engineers and activation solution engineers focused on helping new customers go live sooner.
CoCo Broadens Customer Conversations
Ramaswamy said Snowflake’s internal deployment of coding agents has helped the company identify ways to deepen CoCo adoption. The company can observe repeat workflows and recommend skills that customers could build or reuse, he said. Snowflake also offers hands-on labs led by technical personnel to help customers become more effective with the technology.
Robins said CoCo has expanded the range of executives Snowflake can address. He said that, after joining the company about a year ago, he initially had relatively few customer conversations but now meets with three to five CFOs weekly to discuss Snowflake’s internal use of CoCo and potential customer applications.
“Once you show them what you do internally, the art of the possible, and how quickly you can speed up things, they are extremely interested,” Robins said.
Application Layer and Model Choice
Ramaswamy described a future in which internally developed applications may be built from smaller “skills” operating on governed data already stored in Snowflake. As an example, he outlined an internal survey application that could use employee hierarchy data, survey tables, notifications and on-demand interfaces without requiring a conventional standalone software procurement.
He said Snowflake’s cross-cloud approach and support for multiple AI models could be an advantage as customers seek flexibility. Ramaswamy said competition among model providers, including proprietary and open-source offerings, is beneficial because it gives customers more choice and limits dependence on any one supplier.
Snowflake’s approach to inference depends on whether it creates customer value, Ramaswamy said. He said the company does not want to be merely a “blind reseller” of model capacity, but sees an opportunity to offer choice, optimize spending and integrate inference as part of a broader data-platform offering.
Robins said the company prioritizes launching products that customers adopt and find valuable, then pursuing efficiency as scale increases. He said Snowflake remains committed to operating leverage and has models to assess the gross-margin impact of AI-product adoption.
Latency, Open Formats and Pricing
Ramaswamy acknowledged that Snowflake has not historically addressed ultra-low-latency data requirements as well as it could. He said the company’s streaming offering has reduced data freshness to a two-to-three-second range and that teams are working toward approximately 500-millisecond freshness.
He also said faster migrations into Snowflake could mean faster migrations out, making it important for the company to deliver value beyond data storage. Snowflake supports open formats and offers Snowflake-managed Iceberg tables, which Ramaswamy said allow data stored with Snowflake to be queried by other engines.
Looking ahead, executives said Snowflake aims to compete through governance, disaster recovery, observability, agent-building capabilities and customer support. Robins said the company monitors customer consumption patterns and may alert customers when spending appears unusual, reflecting what he described as a customer-first approach.
On pricing, Robins said each new platform generation must improve price-performance for customers. While architectural enhancements can create pricing deflation, he said Snowflake expects volume growth and new workloads to help offset those effects.
About Snowflake (NYSE:SNOW)
Snowflake Inc NYSE: SNOW is a cloud-based data platform company that helps organizations store, process, analyze and share data. Its platform is designed to support data warehousing, data lakes, data engineering, data science, application development and business intelligence across public cloud environments.
Snowflake's Data Cloud enables customers to consolidate and access structured, semi-structured and unstructured data while supporting secure data sharing and collaboration. Its offerings include Snowflake Cortex, which provides artificial intelligence and machine-learning capabilities, as well as tools for developing data applications and using data from Snowflake's marketplace and partner ecosystem.
Founded in 2012, Snowflake serves businesses, government organizations and other institutions globally through cloud infrastructure provided by major public-cloud platforms.
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