In today’s dynamic business landscape, numerous organizations are transitioning to the Snowflake Data Cloud, seeking more agile, secure and efficient solutions to manage and activate customer data. Yet, the timelines and engineering resources needed to support implementation haven’t always kept pace with the increased market demand, impeding innovation.

As an industry built on data, financial services has always been an early adopter of AI technologies. In a recent industry survey, 46% of respondents said AI has improved customer experience, 35% said it has created operational efficiencies, and 20% said it has reduced total cost of ownership. Now, generative AI (gen AI) has supercharged its importance and organizations have begun heavily investing in this technology.

Your company collects huge amounts of data about everything from customer transactions to supplier contracts to system performance. This valuable resource becomes even more valuable when you combine it with data about financial market and economic trends, consumer spending, regional demographics and other elements that provide broader context and insights for your business decisions.

ServiceNow is focused on making the world work better for everyone. More than 7,700 customers rely on ServiceNow’s platform and solutions to optimize processes, break down silos and drive business value. Achieving 20% year-over-year growth with a 98% renewal rate (as of Q1 2023) requires a data-driven understanding of the customer journey.

The rise of generative AI (gen AI) is inspiring organizations to envision a future in which AI is integrated into all aspects of their operations for a more human, personalized and efficient customer experience. However, getting the required compute infrastructure into place, particularly GPUs for large language models (LLMs), is a real challenge. Accessing the necessary resources from cloud providers demands careful planning and up to month-long wait times due to the high demand for GPUs.

The best marketing is truly data-driven, creating powerful product promotions and offers through an understanding of customer needs and preferences. But for many organizations, building this understanding is more akin to solving an ever-growing jigsaw puzzle (with no easy edge pieces!) than reading data insights from a beautiful dashboard.

When businesses share sensitive first-party data with outside partners or customers, they must do so in a way that meets strict governance requirements around security and privacy. Data clean rooms have emerged as the technology to meet this need, enabling interoperability where multiple parties can collaborate on and analyze sensitive data in a governed way without exposing direct access to the underlying data and business logic.

Historically, only a few AI experts within an organization could develop insights using machine learning (ML) and predictive analytics. Yet in this new wave of AI, democratizing ML to more data teams is crucial—and for Snowflake SQL users, it’s now a reality.

The excitement (and drama) around AI continues to escalate. Why? Because the stakes are high. The race for competitive advantage by applying AI to new use cases is on! The launch of generative AI last year added fuel to the fire, and for good reason. Whereas the existing portfolio of AI tools had targeted the more technically minded like data scientists and engineers, new tools like ChatGPT handed the keys to the kingdom to anyone who could type a question.