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Dozens of GPT-driven products are already on the market, and hundreds more are in development. These tools collectively aim to revolutionize traditional roles and processes, transforming them into dynamic, parallelized workflows using variants of LLM models, such as AutoGPT. Tools like GitHub Copilot, powered by OpenAI's Codex, are demonstrating significant improvements in developer productivity, with some studies showing up to a 55% increase in task completion speed. 

Over the last decade, public introductions of advanced technologies from big tech firms, such as Google Glasses and Meta's (then-Facebook) Metaverse platform, were met with infamous disappointment. In stark contrast, initial iterations of generative AI (GenAI) in large language models and other tools deployed across the global economy by smaller players like OpenAI in the last few years are having a far more lasting impact. A recent report from McKinsey noted that the staying power of GenAI could add $2.6 trillion to 4.4 trillion USD annually to the global economy.

Where new, emerging capabilities in AI mean new opportunities for financial institutions – especially those that operate financial exchanges, where data and predictive analytics are paramount – the palpable risk of failure in AI adoption initiatives can prove a significant hurdle. 

This interview analysis is sponsored by Deloitte and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.

This interview analysis is sponsored by Uniphore and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.