Its 8 a.m. on a Monday. You have barely settled at your desk when requests start pouring in. The CEO wants revised projections after a customer delays an order, the bank needs an updated cashflow forecast, and the board pack is due before lunch.
Not long ago, that meant hours rebuilding Excel models and rewriting reports. Today, an AI assistant can produce a solid first draft in minutes.
The bigger question is not productivity. It is this: if AI can perform much of the technical work, where does the real value of a finance professional lie?
The answer is higher up the value chain. For years, finance careers began with collecting data, reconciling accounts, updating spreadsheets and producing routine reports before progressing to interpretation, commercial judgment and strategic decision-making.
AI is rapidly compressing those lower-level tasks, freeing professionals to spend less time producing information and more time interpreting what it means for the business.
That shift makes human judgment more valuable, not less. AI can generate convincing answers that are inaccurate, based on flawed assumptions or unsupported conclusions. In finance, a wrong figure can influence lending, investment or board decisions. AI should accelerate analysis, but accountability must remain with people.
There is also a paradox to using AI effectively. It requires context. Professionals must explain the business, define assumptions and clarify objectives before the technology produces useful results.
That initial effort pays dividends as future analyses become faster and more relevant. This is particularly significant for Africa, where many finance teams operate with limited staff. Rather than reducing headcount, AI offers lean teams greater capacity.
Time saved on reporting and documentation can be redirected to scenario planning, working-capital management and providing better insights to leadership.
The profession will also need to rethink how young finance professionals are trained. Routine modelling and reporting have traditionally been part of learning the fundamentals. Those skills remain essential because professionals must understand the mechanics well enough to question AI-generated output.
The finance leaders of 2030 will not be valued for building spreadsheets faster.
They will be valued for asking better questions, challenging assumptions and turning numbers into sound business decisions. AI changes the tools, but judgment, context and accountability remain the profession's greatest assets.
