Secrets of Gen AI Success: Real-World Customer Stories

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For the past couple years, generative AI has been the hot-button topic across my conversations with customers, prospects, partners and everyone in between. People want to know how they can harness the power of AI to become more innovative, efficient and competitive — and they want to do it as soon as possible.

For many organizations, however, turning AI ideas into reality has proven elusive, with Harvard Business Review reporting that up to 80% of AI projects fail to make it into production.

Yet Snowflake customers are finding ways to overcome common roadblocks and unleash the potential of AI across their businesses. I have the privilege of hearing about these success stories often in my conversations with customers, and now, we’re spotlighting these wins on a larger scale in our new book, Secrets of Gen AI Success. Here, we feature how leading organizations across industries are turning to Snowflake’s unified AI and data platform to bring their generative AI goals to fruition — from BI platform Sigma increasing sales efficiency and accelerating sales cycles, to Terakeet pinpointing new market opportunities for its customers 98% faster. 

These real-world gen AI use cases are delivering real results for companies both big and small. Here’s just a small sample of how leaders are using Snowflake to capture tangible value from their AI use cases:  

TS Imagine adopts AI at scale to save 30% in costs and 4,000+ hours of effort

TS Imagine delivers a SaaS platform for integrated electronic front-office trading, portfolio management and financial risk management. Previously, its homegrown data systems required extensive manual monitoring of over 100,000 emails and more than 60,000 annual support and data tickets. Missing even a single email could lead to a downstream product outage — and very unhappy customers.  

By using Cortex AI to automate email monitoring and streamline management for support and data tickets, TS Imagine reduces manual tasks, optimizes data processing and improves efficiency to save time and cut costs.  

Benefits include: 

  • 30% cost savings — without sacrificing performance — by using Snowflake vs. other industry-leading, pretrained LLM APIs 

  • Ease of use for data teams to go from traditional natural language processing to generative AI at scale in only six months

  • Massive time savings for employees, from more than 4,000 hours spent on manual email monitoring tasks for just one use case to near-instant, AI-powered categorization 

Siemens Energy’s AI chatbot democratizes access to 700K+ pages of knowledge

Siemens Energy has a deep knowledge base contained in more than 700,000 pages of proprietary research and development documents. The company wanted to make the valuable information in these paper documents available to its globally distributed R&D team — but reading all this information would take someone a staggering four years. 

With Snowflake Cortex AI and Streamlit in Snowflake, Siemens Energy built an AI chatbot to quickly surface and summarize more than half a million pages of internal documents. This helps the R&D team maximize productivity and effectiveness while accelerating decision-making, speeding time to market and sharpening the company’s competitive edge.

Benefits include:

  • Information from 700K+ pages of dense documents summarized and available to teams for faster R&D, innovation and time to market

  • ~25 highly specialized R&D engineers now having near-instant access to data and insights

  • Hours of time saved — amounting to years — by eliminating the need to manually search for data

Bayer’s business teams make better, faster decisions with self-serve insights 

Businesses like Bayer move fast — and need near real-time, accurate insights to make quick, informed decisions as part of their go-to-market strategy. Yet most teams only had access to this data via dashboards, which often resulted in information overload, manual search for users and cumbersome, time-consuming development for data teams. 

Bayer’s team now uses Cortex Analyst (in public preview) to convert natural language queries into accurate and actionable SQL, empowering business users to easily self-serve insights through an intuitive Streamlit in Snowflake chatbot interface. This helps them find answers faster while unearthing richer, tangible insights, such as financial reports or product performance metrics, to streamline decision-making and data access — no technical expertise required.

Benefits include: 

  • More strategic decision-making by giving business teams the ability to ask questions of Bayer’s curated data sets in natural language 

  • Insights at scale for users across the enterprise — from sales to customer finance to demand planning

  • Better productivity for both technical and nontechnical departments by reducing reliance on data teams and enabling self-serve data access 

Your path to gen AI success

These stories are just the beginning of how Snowflake is helping our customers make the most of enterprise AI. 

Download the book, Secrets of Gen AI Success, to explore how these and other customers have streamlined development and reduced complexity to realize the potential of AI across their organizations. 

Ready to discover how Snowflake can help your organization be more innovative and competitive through generative AI? Visit Snowflake’s gen AI webpage for more information and hands-on resources to help you get started.