Unlocking the Future: Real-World Applications of Generative AI

The conversation surrounding artificial intelligence, particularly generative AI (Gen AI), tends to be overflowing with statistics and projections. While discussions about project numbers and anticipated cost savings are interesting, many benefits remain largely hypothetical. But what’s the real deal with AI in the business world? To form a clear picture, we listened to actual users in a recent New York dinner hosted by Snowflake, a notable data warehousing vendor.

Insights from the Frontlines of AI

Snowflake gathered prominent customers to share their experiences with rolling out AI applications. The overall sentiment? Generative AI is making waves with practical applications like document search that can yield results within six months or less from implementation. Admittedly, these anecdotes come with the caveat that Snowflake aims to showcase success stories to underscore the value of its cloud data warehouse services.

Simple Use Cases, Big Impact

One particularly compelling story was shared by Thomas Bodenski, COO and head of analytics at TS Imagine, which offers a cloud-based securities trading platform. Traditionally, his team spent an astounding 4,000 hours per year sifting through crucial emails for actionable insights. Bodenski described the critical nature of this task: “If I’m not ready for this, there are 500 clients that will be down.” This immense workload required a globally-minded team of highly skilled individuals.

But with the introduction of a generative AI solution, Bodenski reports that he now manages this task for just 3% of the previous labor cost. “Just do the math,” he said, highlighting how the costs of AI compared favorably against human labor. TS Imagine built their initial app with Snowflake’s help, utilizing Meta Platforms’ open-source Llama large language models alongside Snowflake’s Arctic, improving the process of managing incoming emails.

Swift Implementations Transforming Businesses

After initially tinkering with the AI application, Bodenski’s team transitioned the project to Snowflake’s Cortex AI—allowing them to classify emails by sensitivity and urgency within a matter of days. “I detect the brushfire before it even becomes a fire,” he explained, emphasizing improved customer mishap detection and response accuracy.

Bodenski’s situation isn’t unique. At S&P Global Market Intelligence, Daniel Sandberg shared a similar transformation story. Their in-house application, Spark Assist, integrates with Microsoft Office and automates email summaries. This tool intelligently prioritizes emails based on relevance and importance, streamlining Sandberg’s workflow among 14,000 employees. “I don’t think I could go back,” he remarked regarding the efficiency gains.

ROI and Future Perspectives

When discussing return on investment (ROI), both executives acknowledged the ongoing evaluation of their AI applications. Sandberg opined, while the individual applications show significant promise, the cumulative financial payoff remains somewhat uncertain. He drew a parallel to the early days of the internet, suggesting that advancements in Gen AI will lead to broader adoption and returns in the future.

Snowflake’s AI head, Baris Gultekin, also shed light on how Gen AI applications can reduce operational costs. For instance, their Cortex Analyst product allows businesses to automatically answer questions that would have traditionally required time-consuming analyst queries, greatly enhancing ROI.

Managing Costs as You Scale

Chris Child, Snowflake’s VP of worldwide sales engineering, encouraged enterprises to forecast their costs when scaling AI implementations. “Try it, run it once,” he advised, suggesting that businesses start small and expand based on initial findings. The flexible consumption-based model employed by Snowflake ensures that organizations can adjust and predict expenses effectively.

Conclusion: Embracing AI’s Potential

These insights paint a compelling picture of the ongoing AI revolution in the business sector. Whether it’s speeding up processes like email classification or streamlining employee tasks, the examples shared highlight just how transformative AI can be within modern enterprises. The key takeaway? Start small, evaluate, and scale thoughtfully as ROI becomes clearer.

The AI Buzz Hub team is excited to see where these breakthroughs take us. Want to stay in the loop on all things AI? Subscribe to our newsletter or share this article with your fellow enthusiasts.