Traditionally, planning and forecasting were confined to the finance department − an annual budgeting exercise focused on operating expenditure and capital expenditure allocations. That mindset no longer serves the modern enterprise.
Today, planning is a cross-functional activity, requiring close collaboration between finance, operations, sales, HR and IT. Every function contributes to shaping a unified organisational view of the future, grounded in shared data and strategic priorities.
Integrating data from multiple systems − operations, supply chain, customer behaviour and macro-economic indicators − has become essential. The result is a new era of advanced forecasting and predictive finance, powered by machine learning and automation.
The future of finance belongs to those who combine human judgement with intelligent automation.
In South Africa, we have seen strong growth in demand for advanced predictive finance capabilities − particularly after the COVID pandemic, which illustrated the operating environment can change rapidly.
Traditional what-if scenario analyses did not help organisations pivot when markets changed rapidly, and businesses realised they had to be more agile to survive sudden change. The focus has shifted to agile, continuous planning − with rolling forecasts updated monthly or even weekly to reflect the latest realities.
Added to that, there has been exponential growth in the amount of data organisations have at their disposal, and there is growing need for more advanced tools to analyse it all and help organisations become more predictive.
Data at the heart of predictive finance
Achieving predictive finance capabilities starts with data. This is not just general ledger data, but internal and external data relating to market forces, organisational strategy, sales, planning and more.
High-quality, well-managed data determines the accuracy of every model, forecast and insight. As planning, budgeting and forecasting evolve into organisation-wide disciplines, data management and governance must become core responsibilities of the modern finance office.
Budgeting and forecasting were once seen as primarily a finance-led activity, but now, it should be an enterprise-wide endeavour. Whether it's IT, marketing and planning, or workforce management, every department must have a plan, with their operational and capital needs, which informs forecasting. It's all interlinked. For example, marketing budgets may impact sales, so preparing the data involves understanding the various data sources, interlinking them, and confirming all this data is quality, trusted data.
Because external factors like geopolitical change impact day-to-day business, provision must also be made for integrating external data sources into predictive finance. There are a number of reputable sources one can get this data from, bringing it in with APIs.
However, integrating and cleaning this data should not just be the role of IT alone − teams need to get closer together to ensure external data influencing predictive finance serves the business's needs.
Taking flight
The finance team is much like the flight crew on an airplane. You may have vital instrumentation, dashboards and tools like autopilot to help you navigate, but ultimately, the pilot has to remain in control of the aircraft.
On any flight, pre-flight checks are crucial. In the world of predictive finance, these pre-flight checks are things like ensuring data quality and management are in place. The data assures the accuracy of those advanced onboard systems the pilot depends on.
Without accurate data, the fuel gauge might − for example − indicate the airplane has sufficient fuel when in fact it does not. Clearly, inaccurate data could lead to disaster.
One of the biggest fundamentals of the pre-flight checks is therefore the data layer − where the data is coming from, whether the data quality is acceptable, and how it is being integrated to offer a full and accurate picture of the situation.
South African businesses are generally very interested in harnessing AI to deliver advanced forecasting and predictive finance abilities. However, they have to get the basics in place first − before investing in the AI component.
AI depends on data, and if an organisation has poor quality data, they need to address this issue first before building advanced analytics on top of it. What we see in the market is a lot of organisations invest in AI without getting the data layer right, which results in limited ROI, or even failed projects.
Equally important is the dashboard view that gives the team visibility of the data and everything taking place underneath it. Data literacy is also crucial − the organisation must strive to have a good data strategy that aligns with the business strategy, so everyone in the enterprise fully understands what the data is telling them.
The future of finance belongs to those who combine human judgement with intelligent automation, and who recognise that agility, collaboration and data trust are the true engines of performance in an unpredictable world.
To make the most of predictive finance opportunities, organisations need trusted advisors to help unpack the complexity of achieving embedded AI and analytics. It should be noted this is not just an IT process − business and finance have to be involved in an integrated plan and process to ensure AI, and predictive finance, deliver a good ROI.
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