As the AI/ML revolution pushes forward, many organizations face an increasingly common dilemma. On the one hand, they understand the rising need to innovate and grow revenues, as well as drive operational efficiency with AI/ML. On the other hand, they face limitations on scale, including a skills gap and insufficient infrastructure to handle increasingly compute-intensive AI/ML workloads.

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McKinsey estimates that AI and deep learning have the potential to deliver $1 Trillion in annual value to the banking industry alone.1 What does that mean for financial services institutions?

According to a recent survey released by SambaNova, 81% of banking, insurance, and financial services organizations plan to significantly increase their AI/ML investments. Is your organization among them? Don’t wait to hear about your competitor’s AI success story. Gain the insights you need to build the right AI strategy and approach to deliver key business benefits and competitive advantage, faster than you may have thought possible.

Evolving AI/ML requirements are presenting challenges beyond the capability of conventional GPU and CPU systems. The next generation of AI challenges will be met with solutions that utilize deep neural networks and software-defined hardware platforms.

Manufacturers are using AI to respond to competitive pressures and changing business requirements, delivering critical competitive advantages as industry and customer needs continuously become more dynamic.

Compliance is an ever-changing environment that’s costs can consume up to 10% of a financial institution’s revenue. With modern collaboration tools that use voice, chat, video, thousands of documents, and massive volumes of disparate data, analyzing and monitoring critical financial data in real-time is challenging.