A quiet revolution is happening inside the world’s financial institutions. OpenAI’s Project Mercury is bringing together more than 100 former Wall Street bankers to train large language models capable of building financial models on their own.
This isn’t about replacing bankers overnight. It’s about redefining what entry-level work means for AI in finance and what skills the next generation of analysts will need.
TL;DR
AI in finance is shifting entry-level roles, not eliminating them.
- Automation will handle repetitive modeling and data prep.
- Analysts will start higher up the learning curve with more strategic work.
- Firms that retrain early will gain a competitive edge.
AI in Finance and the Future of Entry-Level Roles
As OpenAI develops Project Mercury, the first wave of automation is expected to target structured, repeatable tasks. Think: updating assumptions, reconciling datasets, and formatting models – the kind of work new analysts spend much of their time on today.
Speaking with Fortune’s Nino Paoli, I said:
“I’m not convinced that we get rid of entry-level workers anytime soon, but I could imagine a world where the skillset we need those entry-level workers to have is different.”
In other words, AI in finance will lift analysts higher up the value chain. The entry level isn’t disappearing, it’s moving up.
AI in Finance and Automation of Repetitive Work
Within the next year, firms are expected to automate 60%–70% of the time analysts currently spend on lower-level tasks.
Harvard Business Review notes that early AI adopters in financial services are freeing staff from manual work and redeploying them into more strategic functions (Harvard Business Review).
The result? Analysts start closer to the decision-making layer: interpreting outcomes, validating assumptions, and guiding AI-generated models instead of manually building them.
AI in Finance Requires Conceptual Thinkers, Not Just Technicians
For finance leaders, this shift changes what “entry-level talent” looks like. Technical precision, once the most valued skill in junior hires, will matter less than conceptual understanding and the ability to guide AI systems toward the right outcomes.
AI will demand a new class of finance professionals fluent in judgment, ethics, and scenario analysis.
This creates an inflection point for talent development: early-career training must now teach analysts how to think with AI, not just how to model with Excel.
AI in Finance and the Competitive Edge for Firms
Companies that embrace retraining will move faster and compete harder. Financial firms with integrated AI training programs have seen productivity gains of up to 35% among analysts and associates.
The most agile organizations are redesigning entry-level hiring. By embedding AI tools into workflows, they’re producing analysts who contribute strategic insights within months, not years.
FAQs
What is Project Mercury?
An OpenAI initiative developing AI systems that can autonomously build financial models, reportedly involving more than 100 former Wall Street bankers.
Will AI replace analysts in finance?
Not immediately. AI automates repetitive work, but human oversight and conceptual guidance remain essential.
How should firms prepare?
By retraining early-career analysts to work with AI systems, focusing on interpretation, validation, and scenario thinking.
What skills will matter most?
Judgment, communication, and ethical reasoning — the skills that guide AI models toward accurate, responsible decisions.
Conclusion
AI in finance isn’t erasing the entry-level—it’s redefining it. Project Mercury signals a future where analysts spend less time formatting and more time thinking. The firms that invest in retraining now will shape not just productivity, but the entire culture of modern finance.
Automation is coming. But the future still belongs to people who can ask better questions — and teach AI to do the same.
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