If 2023 was the year of gen experimentation, 2024 is the year of gen AI implementation. As companies embark on their implementation journey, they need to deal with a host of challenges, like performance, GPU efficiency and LLM risks. These challenges are exacerbated in highly-regulated industries, such as financial services and telecommunication, adding further implementation complexities. Below, we discuss these challenges and present some best practices and solutions to take into consideration.

Generative AI doesn’t just work with unstructured data.

Testing tools in AI and data automation have evolved to offer sophisticated features that ensure the quality of the end product and reduce its time to market. With reports suggesting 50% of manual testing being replaced by automated, these tools can help with various aspects of software testing, including unit testing, performance testing, and security testing. They can be integrated practically anywhere across the CI/CD pipeline for continuous testing and shift left testing.

‍McKinsey’s latest research projects that generative AI could contribute between $2.6 trillion and $4.4 trillion annually across various sectors. Experts have also observed that integrating AI-driven automation, threat detection, and low-code platforms redefines next-gen software development. Whether it is code generation, bug fixing, or even designing a new digital component, generative AI is seeping into all product engineering processes.

The Snowflake AI Data Cloud is an end-to-end platform that supports all types of data, compute, use cases and personas across an entire organization. By delivering a single, unified platform for all users, it is no surprise that organizations continue to expand their use cases on Snowflake. And therefore, it is extremely important for us to reaffirm our commitment to price-performant queries for our customers on a consistent basis.