The ‘Letter from the Editor’ series features exclusive insight and opinion-driven analysis from Tearsheet editor Sara Khairi. The focus is on linking ideas, questioning assumptions, and tracking shifts across both mature and emerging trends in financial services.

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Issue # 11

Financial services is an industry built around knowledge, language, and decision-making. If any industry was ready for AI and generative AI, surely it was banking. So, companies moved quickly. They announced partnerships with OpenAI. They rolled out ChatGPT Enterprise. They built ChatGPT-like AI assistants. And gave employees access to large language models.

But two years into this experiment, the industry is now asking: What needs to be in place around AI before we can actually trust it?

The beginning of it all: The ChatGPT timeline

2023: “We have ChatGPT”

In 2023, the focus was access. Financial institutions rushed to experiment with the tech after ChatGPT demonstrated something that felt almost impossible at the time: a machine capable of understanding and generating human-like language.

The first wave was about discovery. The excitement was understandable, but it also created unrealistic expectations. Because the assumption was that if AI could understand language, it could eventually understand finance, which isn’t exactly true.

2024: “We have AI copilots”

In 2024, the industry’s language started changing. The idea of AI replacing workers became less prominent. The idea of AI assisting workers became more realistic.

Morgan Stanley’s OpenAI partnership became one of the most visible examples. The firm integrated GPT-4 into tools designed to help financial advisors search through its research and access information more efficiently.

The interesting part was what the model was not asked to do. It was not managing client relationships. It was not making investment decisions. It was not replacing financial judgment. It was just helping advisors spend less time searching and more time advising. The technology became valuable when it supported expertise, not when it attempted to replace it.

Around the same time, banks and fintechs across the industry began launching their own ChatGPT-like assistants, copilots, and enterprise LLM deployments, first for employees, then for customers, to search information, draft content, answer questions, and streamline everyday work.

Again, the biggest opportunity was removing friction from everyday work. Finding information, drafting documents, helping employees move faster. In general, the most successful AI implementations were asking LLMs to make the bank work better.

2025/2026: “We need governance, data and ROI”

By late 2025 and into 2026, the conversation had shifted again. Institutions began building AI agents designed to execute specific financial tasks like underwriting, compliance, servicing, and other financial workflows on top of proprietary data.

As the AI novelty disappeared, the industry then moved from experimentation to accountability. Investors became less interested in hearing that companies had AI initiatives; they wanted evidence around productivity gains and revenue opportunities.

Those points exposed a reality that was easy to overlook during the excitement. The model was never the hardest part. 

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