JPMorganChase was the first big bank to roll out generative AI to almost all of its employees through a portal called LLM Suite. As of mid-May, it’s being used by 200,000 people. “We think that AI has the potential to really deliver amazing scale and efficiency as well as client benefit,” Teresa Heitsenrether, chief data and analytics officer, told. The bank, like many others, has used traditional AI and machine learning for years in areas like fraud detection, risk management and marketing. “But the big surprise really came with generative AI, which really opens up new possibilities for us,” Heitsenrether said. LLM Suite is an abstraction layer through which large language models like OpenAI’s GPT-4 are swapped in and out. The models are trained on proprietary JPMorganChase data. The bank’s lawyers use LLM Suite to analyze contracts. Bankers use it to prepare presentations for clients and to generate draft emails and reports. The project is “advanced in scope and ambition,” said Alex Jimenez, lead principal strategy consultant at Backbase. “Deploying a proprietary large language model at this scale is an industry-leading move. Unlike others, they aren’t just testing but embedding it deep into the daily workflows of bankers, compliance teams, technologists. The real advancement isn’t just the tech but the institutional integration.” This project is setting the tone for other banks, he said. “The rollout likely puts pressure on peer banks to accelerate or scale up their own gen AI initiatives. It is influencing vendor roadmaps and internal AI governance discussions across the industry.” The bank tests and vets new models for safety and security, as well as their applicability to different use cases, before bringing them into its LLM Suite. Some large language models are good at synthesis and reasoning, while others are good at coding or complex document analysis, Waldron said. Small models can be fine-tuned for specific tasks. Generative AI models generally have per-seat licensing costs, which can add up for a bank the size of JPMorganChase. “That’s been one of the roadblocks to widespread adoption, because business leaders naturally are asking the question up front, what’s the ROI for that particular person?” Waldron said. But because JPMorganChase built an internal platform, the only variable cost is compute, he said. If an employee doesn’t use it, the bank does not pay for it. “That value proposition turned out to be very desirable to business leaders,” Waldron said. For its overall AI adoption and use of AI, JPMorganChase has been at the top of Evident’s AI Index from the scorecard’s launch in 2023.Real-time, accurate data is important for these models to generate useful answers. The bank is gradually connecting its datasets to LLM Suite, including all of its news subscriptions and earnings transcript libraries. “When these get connected and distributed to the whole population, all of a sudden, employees can do things in an automated way that they could never do before,” Waldron said. (The bank will still pay for its news subscriptions, but for firmwide access rather than individual accounts.)