Why AI Makes Financial Modeling Skills More Valuable Than Ever

Written by Kelly Bailey

Reviewed by Jeff Schmidt

Read Time 5 minutes

Do Traditional Modeling Skills Still Matter?

Today’s finance professionals face a critical question. How do traditional financial modeling skills fit into workflows that are increasingly powered by AI?

CFI recently hosted a webinar, “Becoming a World-Class Financial Modeler in the Age of AI,” featuring three modeling experts who shared insights from decades of building models for equity research and finance teams. Here’s what this expert panel revealed about thriving as a financial modeler in the AI era.

AI Financial Modeling Skills

Key Highlights

Building financial models the traditional way teaches you how businesses actually work with deeper expertise than what AI-generated models provide.
Financial modeling skills are becoming MORE valuable, not less, because AI cannot replicate human critical thinking and experience.
Alongside traditional Excel modeling and AI capabilities, develop strong communication and presentation skills to showcase your expertise to employers or clients.

A Balanced Approach to AI in Financial Analysis

Brian Egger, Global Head of Financial Modeling and Senior Analyst at Bloomberg Intelligence, explained how AI enhances financial modeling with two examples:

Example 1: Enhanced Document Processing

AI transforms how analysts extract insights from documents. Brian demonstrated this with Norwegian Cruise Lines, one of his coverage companies. Instead of manually combing through earnings call transcripts, he uses natural language queries like “How has occupancy been trending?” with Bloomberg’s proprietary AI tool to instantly extract insights.

The results revealed why Norwegian’s occupancy was declining. The company was shifting its route mix toward Asian markets, which typically have lower occupancy rates than Caribbean routes. AI made it easier — and quicker — to identify this important context.

Example 2: Access to Advanced Data Tools for Non-Coders

AI breaks down technical barriers by translating plain English requests into complex code. Analysts no longer need to memorize syntax to access sophisticated data tools. They simply describe what they need, and AI handles the technical execution.

However, Brian emphasized that AI outputs require human oversight.

“Treat any AI-generated table or chart as the first draft subject to the same rigorous quality control checks and balances as any non-dynamic model.” – Brian Egger, Bloomberg Intelligence

No Substitute For Experience

True modeling instincts develop only through years of hands-on experience. Professional financial modelers roll up their sleeves and learn what works through trial and error. This can’t be outsourced to AI or coding.

Jeff Schmidt, VP of Financial Modeling at CFI, shared why experience matters. While AI excels at improving efficiency and can help flag anomalies, recognizing when something is off requires experience with modeling different companies and assets to understand the patterns. Unlike in The Matrix, where expertise is instantly downloaded, AI isn’t capable of that yet.

Traditional Excel modeling also forces you to examine what drives a business. As Jeff explained, building models yourself sharpens this analytical process over time in ways that AI cannot replicate. Models are instruments for understanding business drivers and valuation impacts. AI can support this work, but shouldn’t replace it.

Jeff noted, “As a financial modeler, you are ultimately responsible for the work, even when AI makes mistakes. Building a strong foundation in traditional Excel modeling gives you the expertise to catch AI errors.”

“Building a strong foundation in traditional Excel modeling gives you the expertise to catch AI errors.” – Jeff Schmidt, CFI

AI Financial Modeling Skills - The Role of Human Oversight in AI-Driven Decisions — Source: CFI’s **Introduction to AI in Finance** course

Modeling Boosts Your Business Acumen

Duncan McKeen, EVP of Financial Modeling at CFI, reinforced a fundamental truth from his equity research experience: reading company reports only gives you surface knowledge. The real learning happens when you build the model.

When initiating coverage of a company, Duncan would read all available information from investor presentations, earnings reports, and call transcripts. He’d feel knowledgeable about the company. Then came the modeling phase, and everything changed.

“Until you’ve built a model of a company, you don’t really know that company,” Duncan emphasized.

“Building a financial model forces you to create an electronic replica of the entire business. You learn way more than you knew before. It’s how you build instincts.” – Duncan McKeen, CFI

Duncan views AI as an assistant in gathering data, rather than a replacement for human modelers. While AI excels at automating data collection and processing, humans must remain in control of what information makes it into the final model.

This balanced approach leverages AI’s efficiency while preserving the human judgment essential for quality financial analysis.

Ready to dive deeper into these insights? Watch a full replay of this webinar on thriving as a financial modeler in the AI era.

Advice for New Graduates Seeking Financial Modeling Roles

Brian advised viewing AI as a valuable first-draft generator that still requires human expertise for interpretation and analysis. He recommended gaining experience with generative AI tools like ChatGPT since they’re transforming workflows in unexpected ways. The key is learning AI best practices without becoming dependent on AI.

The panel also stressed that strong communication and presentation skills distinguish successful financial analysts. Directors at top investment banks succeed because they can present complex model insights effectively.

So, what should new graduates focus on? Developing the skills AI struggles to replicate, such as:

Analyzing data with context and nuance.
Questioning assumptions and thinking critically about what numbers really mean.
Forming well-supported opinions that go beyond surface-level observations.

When you combine these capabilities with AI fluency and strong presentation skills, you’ll discover that AI technology increases the value of your modeling skills.

Traditional Financial Modeling Skills Remain Essential in the AI Era

AI transforms how we work, but traditional financial modeling skills have become more valuable than ever. Building models the traditional way teaches you to connect financial statement relationships, understand business drivers, and gain deep knowledge of how companies actually operate. AI alone cannot replicate these capabilities.

The future belongs to professionals who combine AI’s efficiency with traditional modeling expertise. This powerful blend enables you to deliver insights enriched by context and experience, making you invaluable when strategic decisions need real understanding.

Want to develop the skills these experts recommend? From building world-class financial models to enhancing your analysis with AI, CFI offers job-ready skills training tailored to the needs of finance professionals. Master financial modeling and learn practical AI applications with CFI’s flexible online programs and all-access memberships.