Every technological revolution is met with hesitancy and suspicion, which simply translates into one question: Will machines replace people? In finance, that question has become sharper than ever as artificial intelligence (AI) begins to automate tasks that once defined the work of junior analysts.
No. AI will not replace financial analysts. But it is rapidly replacing the way many junior analysts used to work. For decades, the first few years of a finance career followed a familiar path. Young professionals spent hours updating financial models, building comparable company analyses, reading annual reports, summarising earnings calls, reconciling data, and preparing the first draft of research notes. These repetitive yet essential tasks formed the apprenticeship through which analytical thinking was developed.
Can India become a global hub for future-ready management talent?
Today, AI can perform much of this work within minutes. However, that does not signal the end of finance careers. It signals the end of the mundane as the primary source of value. The World Economic Forum’s Future of Jobs Report 2025 predicts continued growth in AI-driven and data-centric roles, even as administrative and transactional functions decline. Therefore, the career ladder is changing. While routine entry-level roles may shrink, opportunities will grow for professionals who combine financial expertise, technology, and independent thinking.
This means the entry barrier into a finance career has moved higher. Accounting principles, Excel, financial statement analysis, and valuation are no longer competitive advantages. They have become baseline expectations. Employers increasingly seek professionals who combine financial knowledge with data literacy, using tools such as SQL, Power BI, Python, and AI to analyse information rather than merely process it. The most valuable junior analyst of the future will not be the one who produces the fastest spreadsheet. The real value of a junior analyst will lie in tasks that AI cannot perform reliably on its own. Some instances are:
Kushal Yuva Program extended till 2031: Check eligibility, benefits and other details
Identifying whether an AI-generated conclusion is confidently wrong
Challenging the assumptions built into a valuation model
Draw a connection between accounting, industry dynamics, corporate strategy and macroeconomic trends instead of treating each subject as a separate chapter
Distinguishing information from insight
Finance has always rewarded professionals who can separate signal from noise, recognise risks hidden beneath impressive numbers, and understand the story behind the financial statements. Those capabilities remain innately human.
For students, the preparation strategy is simple. Pick a listed company. Read its annual report. Build your own investment thesis. Then instruct AI to assess it independently. Do not focus only on what AI gets right. Analyse what it overlooks. Ultimately, professionals who thrive in the coming decade will not compete against AI. They will know how to question it, verify it, and use it as a force multiplier. The sky will be the limit for those analysts who decide to co-author with AI rather than being co-dependent on it.
(This article is written by Sanjay Saraf, Founder & CEO, Sanjay Saraf Educational Institute)
