If you’ve ever felt trapped in a digital ecosystem, whether it be digital software, smartphones, or cloud storage, then you understand the issues with committing your data to one place. This is a common occurrence not only in the lives of individual people, but for large organizations as well.

In a world powered by the usage of AI, it is important for companies to be able to work through their large sets of data without having to deal with issues that may arise from exporting thousands of documents to external AI servers: like those of Open AI for analysis. Not only is this process of big data transfer slow and expensive for AI companies, it represents a sunk cost since it would not be easy to move to a different AI service if they have better-aligned features for your goals. In the end, your data ends up trapped in a server which you can’t control.

Recently, Snowflake Cortex has addressed this problem in a way that represents a major innovation for how an organization’s AI usage is handled (phData, n.d.). Snowflake handles AI usage by running “In-Database AI” that can run large language models (LLMs), like Llama 3 or Mistral, directly inside of an organization's data warehouse. A data warehouse is a centralized data collection based upon the focus of a company, similar to a library for a digital enterprise. The ability to run an AI within a company’s existing data infrastructure, whether on the cloud or local servers can make getting helpful insights much more efficient. In reality, it is so efficient and intuitive that a user can run an AI using just some simple SQL functions, which are functions like the ones used in Excel for data analysis. For example, the function “SNOWFLAKE.CORTEX.SUMMARIZE(<text>)” with <text> being replaced by an input can be used to summarize information within your database. This is a major win since the large amount of data doesn’t have to be moved to the servers of whichever AI is desired for AI data analysis. This makes the process a lot faster and much more secure, since the data is centralized in one place reducing the chances of leakage (Apptad, 2026; phData, n.d.).

An example of a Snowflake command used for getting an answer from AI. (phData, n.d.)

This innovation has had real-world tangible impact on many companies and organizations across various industries. The corporation “Siemens Energy”, which develops energy technology, had over eight hundred thousand pages of technical documents and manuals which needed structure for ease of access by employees (Hinc, 2025). Thus, they used Snowflake Cortex to build a chatbot that engineers could use to get answers with citations to actual documents in their database. This allowed Siemens Energy to possibly turn days of work for their employees into seconds. Most importantly, this solution was designed without having to move their 800k+ pages to an AI server from their existing servers.

A Snowflake Cortex chatbot implementation, which can reference and cite specifics from data. (phData, n.d.)

Siemens Energy is a poignant example of the impact that Snowflake Cortex and other implementations of this methodology can have on businesses (Hinc, 2025). Instead of having to move their data outside of their data warehouse for AI Analysis, these AI tools are integrated in their existing environment. For Digital Enterprises focused around computing, AI, and cloud computing, the benefits of this methodology allow for the frequent use of AI to become more affordable and efficient. Since digital enterprises are mostly digital, the ability to use AI tools on large data sets without external API keys or movement fees is a big step forward (phData, n.d.).

Snowflake Cortex is not only a big step for major enterprises, but also for smaller, upcoming businesses. This innovation contributes to the democratization of AI in a major way, since these improvements in cost and speed trickle down to benefit even smaller organizations, which may be more volatile in the face of major infrastructure costs, like that of AI data analysis. 

For students, this innovation provides a better way of learning to use AI in digital enterprise management. Instead of learning the functions and syntax for working with a specific AI model, students can focus more on how they want to use data to find meaningful results. The barrier for entry in learning and affording these tools is reduced more and more everyday, allowing for students from a diverse set of fields to learn how AI tools impact their career and industry. Snowflake Cortex represents one of these advancements.

By allowing for integration of AI into the existing servers and infrastructure of a company, Snowflake Cortex allows for a cheaper, faster, and more secure use of AI to analyze the data of a company for use in the implementation of chatbots, lookups, and other useful tools. Snowflake Cortex is pushing forward ease of use, and lowering the barrier to entry for use of AI for industry veterans and students alike.

References

Apptad. (2026, January 12). Snowflake Brings Google's Gemini 3 to Cortex AI. https://apptad.com/blogs/snowflake-brings-googles-gemini-3-to-cortex-ai/

Hinc, G. (2025). Snowflake in 2025: 5 real-world use cases that could transform your business. Snowstack. https://snowstack.ai/blog/snowflake-2025-5-real-world-use-cases-transform-business

phData. (n.d.). What is Snowflake Cortex? https://www.phdata.io/blog/what-is-snowflake-cortex/