Spreadsheets still dominate the first year of most finance jobs, yet the work surrounding them is changing faster than students realise. Automation tools now handle large portions of routine accounting and audit tasks, and teams expect new hires to engage with context, documentation quality, and analytical judgment from the beginning. While colleges still teach core ideas, the realities inside finance teams now extend beyond the training most graduates receive.
The uncertainty is visible in the choices graduates make. Some hesitate to commit to finance because they cannot see a path beyond repetitive execution. Others enter the field with enthusiasm, then slow to a hesitant halt when the role feels narrower than expected. The questions that matter today revolve around movement: how early-career professionals grow, what responsibilities open up, and which skills lead to meaningful progression.
To understand these changes from inside the industry, we reached out to Sripal Jain, co-founder of Simandhar Education. His work with learners and companies allows him to observe how finance roles are evolving, how hiring filters are changing, and how early-career professionals can move from task-heavy execution to roles that involve deeper thinking.
We asked him to outline the realities young professionals encounter in their early years, the skills that guide advancement, and the practices that support long-term growth in the field.
Many young finance professionals worry their first job will be repetitive and limiting. What creates this pattern, and why do so many graduates feel stuck early on?
Many young professionals experience early repetition because the entry layer of finance was historically designed around manual processes, reconciliations, documentation, basic checks. These tasks are foundational, but when organisations fail to connect them to the bigger picture, juniors assume repetition equals stagnation. The real challenge is that onboarding in many firms has not evolved at the same pace as workflows. Freshers often work on fragments of a process without understanding the insight that their work ultimately supports. When context is missing, even meaningful tasks feel mechanical. But once juniors see how a simple variance ties into controls, risk, or business performance, the same role becomes an apprenticeship for judgement. Repetition becomes limiting only when it is detached from interpretation, not when it is part of learning.
As automation expands across accounting and audit workflows, which entry-level tasks are becoming obsolete, and which areas now demand more human judgement?
AI is accelerating past the stage where it only assists; it now performs many entry-level responsibilities with high reliability. Tasks like invoice reading, reconciliations, data extraction, sampling, and template-driven reporting no longer depend on human effort. But the work that defines a finance professional remains distinctly human. Decisions around materiality, control evaluation, interpreting unusual transactions, assessing compliance implications, and forming audit positions require context and defence, which something no algorithm can own. Organisations rely on AI for speed and accuracy, but they rely on people for accountability and reasoning. The emerging workplace operates on a simple division: AI produces patterns, but people decide whether those patterns matter, and why. Automation eliminates tasks. Judgement remains.
What kinds of new or hybrid finance roles are emerging for young professionals across GCCs, fintech companies, Big Four firms, and mid-tier audit teams?
Finance roles are evolving into hybrid profiles that blend accounting principles with analytics and automation oversight. Across GCCs, we see large demand for audit analytics associates, exception-management specialists, and FP&A modellers who can interpret AI-generated insights at scale. Fintechs are creating product-informed finance roles where professionals understand both accounting logic and digital behaviour. Big Four firms now expect auditors who can work with analytics engines rather than only checklists. Mid-tier firms increasingly prefer associates who can validate automated outputs and document their reasoning clearly. The common thread is that every new role requires the ability to translate data into decisions. Companies are no longer hiring for manual execution; they are hiring for judgment, adaptability, and the ability to work confidently with technology.
What signals do employers look for when deciding whether a fresher is ready for responsibility beyond basic documentation and spreadsheet tasks?
The strongest signal is whether the junior reduces managerial rework. Employers look for clarity in documentation, precision in questions, and the ability to summarise trends or anomalies instead of merely extracting them. They notice when a fresher takes ownership of a small analysis, automates a recurring step, or proactively closes loops with cross-functional teams. Tool readiness also matters: comfort with analytics platforms, AI assistants, advanced Excel or BI tools shows the candidate can handle redesigned workflows. Credentials add credibility, but behaviour establishes trust. Managers promote the junior who converts information into insight, who anticipates what reviewers need, and who communicates findings confidently. These attributes show readiness for higher-value work long before seniority does.
Students often underestimate the behavioural and communication skills needed in finance. Which non-technical abilities matter most in the first year?
In the first year, professionals succeed not because they know everything, but because they communicate clearly and reason well. The most critical skills are concise writing and the ability to produce a one-page note that answers the actual question and verbal clarity, especially when explaining exceptions or proposed actions. Stakeholder empathy matters: juniors must understand what information their reviewers need and structure it accordingly. Strong time management and calmness under deadlines are essential, especially in month-end or audit cycles. Above all, professional scepticism and ethical judgement define early career credibility. Managers trust juniors who raise issues thoughtfully, ask purposeful questions, and show the discipline to validate their own work. These behavioural strengths often accelerate careers more than technical skills.
Many graduates feel unsure how to self-learn effectively. What learning path would you recommend for someone who wants to stay relevant over the next decade?
A future-proof finance career demands a structured blend of domain depth and AI fluency. Graduates should anchor their journey with a global credential such as CPA, CMA, or ACCA, which builds conceptual discipline and signals credibility to employers. Parallel to this, they must learn actual workflows, audit cycles, close processes, FP&A models, control testing because understanding context is what builds judgement. Digital fluency is equally non-negotiable: tools like Power BI, Copilot, and advanced Excel are becoming standard expectations. Graduates should cultivate a weekly practice of analysing small datasets, writing short insights, and presenting them aloud. The goal is to pair technical mastery with clear articulation. Professionals who can interpret, validate and communicate AI-assisted outputs will remain indispensable in every finance environment.
In your experience, what separates young professionals who stagnate from those who progress rapidly into analytical or decision-support roles?
The difference is behaviour. Fast-growing professionals take ownership of outcomes rather than tasks. They consistently turn data into insight, even on small assignments, and they communicate their thinking proactively. They treat AI tools as extensions of their capability, not as replacements. Meanwhile, those who stagnate often remain excellent executors but weak interpreters; they complete steps but rarely explain what the numbers imply. Growth hinges on one habit: adding insight. The junior who reviews an anomaly and writes a short note on its impact becomes visible to decision-makers. Over time, these habits compound into trust, and trust accelerates career movement. In an AI-enabled workplace, interpretation gets promoted.
What should colleges and training institutes change so their graduates can contribute meaningfully in modern finance teams from day one?
Institutes must move from theoretical teaching to workflow-based readiness. Students should learn using real audit workpapers, FP&A models, close calendars and exception logs the materials they will see from their first day at work. Courses must include tool fluency: Excel, BI tools, automation basics and AI-assisted analysis. Assessments should move beyond MCQs to short memos, oral defences and case-based reasoning. Industry micro-internships should expose students to tasks like variance analysis, exception resolution and stakeholder communication. When students practice judgement before they enter the workforce, organisations no longer need to spend six months retraining them. The goal is simple: produce graduates who can think, question, and interpret, while also completing steps.