AI and I
By: Kennedy Lewallen
Dec. 5, 2025
AI in Finance: The New Skill Every Student Analyst Needs to Learn
If you think learning AI can wait until after graduation, it cannot. Artificial intelligence is not a far-off buzzword anymore. It is already reshaping entry-level roles many students are preparing to enter. In investment banking, asset management, corporate finance, and more, AI is quickly becoming a baseline expectation rather than an optional advantage. The analysts who know how to use it effectively will rise faster, and those who do not will spend valuable time catching up.
How It is Being Used Today
Across the industry, the most powerful use cases are not flashy robots or futuristic automation. The most impactful use cases focus on efficiency and speed. Firms are now using AI systems to query internal databases like SharePoint or Salesforce to surface relevant materials, precedent deals, and historical data. Work that used to take hours of filtering, scanning, and searching is now produced in a fraction of the time.
Market research is another area where AI is extremely helpful. AI tools can summarize 10-Ks, investor presentations, and regulatory filings, helping analysts identify where to focus first. This does not remove the need to read and interpret these documents independently. Instead, AI helps analysts begin deeper work faster, with a more precise starting point. The same applies to comparable company screens, where AI can provide an initial peer set before analysts refine multiples and valuation comps manually.
Prompting is the New Excel Skill
Prompting is becoming just as essential as Excel shortcuts and modeling techniques. AI performs best when it is given structured context, clear instructions, and a defined tone. A well-written prompt increases output accuracy, reduces revisions, and allows analysts to extract value quicker. This should include the role do you need your AI to play, the goal, context, expectations, tone, and any sources or examples you can give. Students who learn how to prompt effectively will position themselves better for success in the roles they will enter tomorrow.
The Analyst Role is Not Disappearing, It Is Shifting
There is growing discussion about whether AI will eliminate entry-level roles in finance. In reality, it is shifting what analysts spend their time doing. Instead of manually gathering data and preparing base structure documents, analysts can now spend more time applying judgment, forming insights, and communicating with deal teams and clients. AI is making analysts more available to focus on strategic thinking and relationship building, which remain essential in this industry.
What This Means for Students
AI fluency is becoming a baseline skill expected from new analysts, interns, and junior hires. Financial firms are increasingly expecting familiarity with AI-supported research workflows, document summarization, basic modeling support, and using AI to accelerate repetitive work. The students who practice integrating AI into their assignments, research, sector analysis, and project work will be significantly more prepared when they enter the workforce.
5 Steps to Build AI Fluency Before Graduation
Learn prompting fundamentals and practice clearly structured instructions.
Test AI on real finance workflows such as summarizing filings or outlining valuation models.
Use AI to support research, identify market signals, and test explanations of complex concepts.
Stay critical and validate AI outputs against primary sources and filings.
Document how you use AI to improve efficiency and problem solving, so it becomes a personal differentiator.
AI is not replacing analysts. It is replacing analysts who refuse to adapt. Time is the most expensive resource in finance. The ability to automate repetitive tasks without losing creativity or analytical precision is becoming one of the most powerful competitive edges. The earlier students begin practicing, the stronger their career trajectory becomes.
