The New Face of Financial AI
In the high-stakes world of financial data, where a single wrong digit can wipe out millions of dollars, FactSet leader Kate Stepp just claimed a major crown. WatersTechnology named her the Technology leader of the year (vendor) at the Women in Technology & Data Awards 2026. This is a big win for practical builders.
As the Chief Artificial Intelligence Officer of her firm, Stepp is steering a massive aircraft carrier of financial data through a fast-moving machine learning revolution.
In a world of talkers, she builds.
To understand her daily work, look at the tool called FactSet Mercury. This generative artificial intelligence assistant acts like an ultra-smart, tireless helper that never sleeps. Under Stepp's watch, the company integrated this conversational assistant to help clients search through mountains of complex financial documents in seconds.
For years, junior analysts wasted hours copy-pasting numbers from PDF files into spreadsheets.
Now, they simply ask the computer for the answer.
It is a massive shift in how Wall Street processes information.
But the path to this tech crown is paved with fierce resistance. Traditional computer programmers are terrified of large language models because these tools sometimes make up facts. In global finance, a mistake is not a funny quirk; it is a lawsuit waiting to happen.
Stepp faces the hard task of keeping the tools smart enough to be useful, yet restricted enough to remain perfectly accurate.
Many critics argue that creating a chief AI role is just a cheap public relations stunt to please tech-hungry investors.
Stepp's work proves those critics wrong by putting up strong guardrails that keep the software on a very tight leash.
The Great Wall Street Bot Brawl
Let us be honest about the panic in investment banking offices today. Old-school managers are sweating through their expensive suits because they do not trust a machine to write their market reports. At the same time, they force these tools on their teams because human labor is expensive.
It is a funny contradiction.
We demand absolute perfection from machines while human analysts regularly make typos after their third cup of coffee.
Stepp is caught right in the middle of this corporate tug-of-war, dragging stubborn bankers into the future.
The Hard Truth of the AI Pivot
The actual value of great technology leadership is not about writing clever code. It is about changing minds. For all the talk about algorithms, the hardest part of the job is convincing nervous human beings to trust the machine. Leaders who survive this era will be those who treat technology as a partner, not a replacement.
The Ghost in the Financial Machine
Consider the sheer volume of data flowing through global networks every single second. We are talking about billions of prices, trades, and corporate filings. If you feed bad data into an artificial intelligence model, you get expensive garbage out. By focusing on cleaning the data before training any models, Stepp solved the main problem that makes other corporate tech projects fail spectacularly.
Unseen Details of the Tech Shift
- Before stepping into the chief AI role, Stepp spent years as the company's Chief Technology Officer, giving her deep knowledge of legacy computer systems.
- The company relies heavily on the Microsoft Azure OpenAI Service to run its models safely behind secure corporate firewalls.
- This technology transition involves updating a database foundation that has been growing since the company started back in 1978.
- Since early June 2026, the firm has expanded its conversational tools to cover complex environmental, social, and governance data, cutting research times in half.
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