African finance functions have a unique opportunity to leapfrog traditional development stages by leveraging artificial intelligence (AI). This is not a new phenomenon, as millions of people in Africa skipped traditional banking and went straight to mobile money. According to local reports, the finance function can now follow a similar path, with AI adoption not dependent on the newest enterprise resource planning (ERP) system.
The most common objection to AI adoption from finance leaders is that their systems are not ready. However, this assumption may not be entirely accurate. Modern AI can read and process existing data, such as exports, statements, invoices, and spreadsheets, making it a potential bridge across old systems.
Starting Points for AI Adoption
There are several areas where AI can be applied to improve finance functions. One such area is the month-end close process, where AI can take on tasks such as collecting, matching, and chasing data. AI agents can pull trial balance exports, read bank statements, propose matches, flag items that do not reconcile, and draft variance commentary for review.
Another area is cash forecasting, where AI can help improve accuracy by learning from historical data. Most firms already hold the raw material for a better forecast, such as receivables ageing, supplier terms, and historical receipts by customer. A practical first step is to create a model that learns how each major customer actually pays and refreshes the thirteen-week forecast every week.
AI can also help with foreign exchange (FX) exposure by finding and organizing the exposure. Reading contracts and purchase orders to identify which commitments are priced in which currency and when they fall due is a task well-suited for modern AI. This can provide the CFO with a live picture of what the business owes and is owed, by currency and by date.
Initiatives such as the Pan-African Payment and Settlement System are changing the way money moves across African borders, giving finance teams more options. A clear exposure map can make these options visible, allowing for more informed hedging decisions.
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Before starting AI adoption, it is essential to get a few things right. Data protection is critical, as finance data includes sensitive information such as salaries, customer details, and bank information. Regulators across the continent are increasingly active, and finance teams must ensure they comply with relevant regulations.
Ownership is another important aspect, as every AI process in finance needs a named owner who understands what it does, reviews what it produces, and can switch it off. Honest measurement is also vital, with a clear measure per use case, such as days to close or forecast accuracy against actuals.
The finance teams that do well with AI are rarely the ones with the most advanced technology. Instead, they are the ones who start with a specific, unglamorous problem, keep a person accountable for every number, and build from there. By putting AI to work on existing documents and exports, finance functions can modernize core systems on their own timetable, rather than treating it as a precondition.
Adam Riccoboni, founder and CEO of Critical Future, notes that the businesses that skipped landlines did not wait for a perfect copper network. African finance functions can do the same, and the key consideration is how they will utilize AI to improve their functions.
One question is whether teams will continue retyping system output during month-end.
