One of the primary challenges enterprises face is managing large volumes of data while effectively utilising it. This concern has been repeatedly voiced by CEOs, founders, and engineers alike.
However, over time, this issue has become less prominent, largely due to companies like Snowflake, which have significantly helped tackle this problem. Their solutions have not only made the challenges less intimidating but, in many cases, have nearly eliminated it altogether.
Snowflake’s approach is rooted in the idea that AI should operate directly on data where it is stored, instead of requiring data to be moved to separate AI platforms.
Recent updates announced at the Snowflake Summit 2025 highlight the company’s continued commitment to this vision.
Snowflake’s new features focus on leveraging generative AI to develop a production-ready data infrastructure. These capabilities aim not only to improve performance efficiency but also to make them accessible to non-technical professionals within organisations, ensuring that a lack of programming skills does not hinder their problem-solving abilities.
For instance, the newly announced Snowflake Intelligence provides users with direct access to enterprise data through natural language queries, enabling them to retrieve actionable insights effortlessly. This means that one doesn’t need to rely on SQL or any dashboards to obtain information about their databases.
“Snowflake Intelligence breaks down these barriers by democratising the ability to extract meaningful intelligence from an organisation’s entire enterprise data estate—structured and unstructured data alike,” Baris Gultekin, head of AI at Snowflake, said.
While Snowflake Intelligence allows users to extract insights from data without writing SQL, the company recognises SQL’s enduring value and has enhanced its capabilities through AI integration. One of the more interesting announcements at this year’s summit was the Cortex AISQL framework.
Reimagining SQL
Snowflake’s Cortex AISQL framework enhances traditional SQL by expanding it to multimodal AI, allowing analysts to query unstructured data such as documents, images, and audio. The platform includes native AI operators for metadata extraction, sentiment analysis, and embedding search.
Owing to Cortex AI-SQL’s performance optimisation, organisations can achieve 30-70% improvements in performance depending on datasets, along with up to 60% cost savings when filtering or joining data across thousands of records.
“The goal is to bring the power of AI to analysts and personas that are typically comfortable with database technology,” said Christian Kleinerman, EVP of Product at Snowflake.
Along these lines, Snowflake made several announcements at the summit. While new features and offerings bring in a sense of excitement, it is also important to address how they can be deployed in production-ready environments.
A Cutting Edge, But a Production-Ready AI Infrastructure
While organisations are often tempted to make the big AI shift due to the promise the technology offers, moving AI applications from proof-of-concept to production requires reliable infrastructure and monitoring. Snowflake addresses this with AI Observability tools that measure accuracy and performance without requiring additional coding.
The platform now provides access to industry-leading models from OpenAI, Anthropic, Meta, and Mistral. These models run inside Snowflake’s security boundary, ensuring data never leaves the governed environment. Teams can match the best model to each use case without managing separate infrastructure.
Another problem that organisations face is that shared AI services create unpredictable performance, which can derail production deployments. Snowflake offers a provisioned throughput feature that offers dedicated inference capacity for applications that need consistent performance.
This addresses the reliability issues that often prevent AI applications from scaling beyond pilot projects. The feature is accessible via REST API across all Snowflake regions.
Besides, organisations also need to take into account the governance aspect, which becomes exponentially more complex when AI usage scales across an organisation. Snowflake also offers an AI Governance Gateway that provides centralised control over model access and usage.
Role-based access controls ensure that teams only access appropriate models, while granular usage tracking helps manage costs. Budget enforcement controls prevent runaway spending on AI services.
Furthermore, the company also announced various features that ease the burden on the engineers and developers in crafting a machine learning pipeline for their organisation.
For instance, the Data Science Agent from Snowflake automates ML pipeline creation by using natural language prompts, powered by reasoning models from Anthropic.
This can save hours of manual experimentation while producing production-ready code.
AI Has Come a Long Way, and So Has Snowflake
As Snowflake gears up to release the announced features for general availability, one cannot help but wonder how quickly the ecosystem has shifted with the advent of AI, despite only happening in the last three years.
In a recent conversation with AIM, Vijayant Rai, the company’s managing director for India, resonated a similar sentiment. He said that while 2022 brought in the initial excitement with LLMs, and 2023-24 brought a few proof of concepts, 2025 will see more value coming out of these systems.
“Companies have started looking deeper into this, and I think everybody has realised that this is a pivotal moment,” Rai said. “A lot of legacy enterprise companies have been scrambling around to get their data estates in place so that they can apply AI on top of it, and a lot of them have been experimenting with AI straightaway as well.”
Snowflake has also published several customer success stories detailing all the tangible results achieved using their offerings, and the list is only going to grow larger, given the company’s ambitions in helping enterprises realise the potential of AI.
The post Enterprises are Turning to Snowflake for Advanced AI That Actually Works appeared first on Analytics India Magazine.
