Finance directors have never been under as much pressure to deliver more accurate cash forecasts. More precise forecasting is essential not only to driving profitability, but has now become even more important as companies try to navigate the continuing difficulties created by COVID-19.
The challenges are clear, making it opportune to incorporate artificial intelligence and machine learning strategies.
By using AI and machine learning, FDs and their teams can effectively automate modelling across multiple scenarios. Freeing their finance and treasury teams from manual forecasting work also allows the FD to lead the team’s efforts on initiatives that deliver more value for the organisation. AI transformations achieve analysis with a depth and precision exceeding the capabilities of manual processes.
Problems with manual forecasting
When relying on manual processes, financial forecasts often struggle to align with the real results. For example, forecasts produced manually are often poorly integrated with sales and operations forecasting. This leads to findings that are out-of-date by the time they’re prepared.
Given these and other challenges of producing and updating forecasts manually, FDs and their teams are less able to investigate multiple scenarios and can struggle to deliver timely and accurate insights when it matters most.
Incorporating AI effectively eliminates the challenges of manually compiling forecast data, as well as the limitations of spreadsheet-based processes. Whereas manual data input and imports can produce errors and inconsistencies.
AI automation enables larger data sets to be collected and put to use. At the same time, AI algorithms learn and improve the accuracy of forecasts far more quickly than can be achieved manually.
FDs who champion their internal AI transformations to be used for forecasting processes have the opportunity to introduce new levels of speed, while enhancing their treasury and finance teams’ value to the organisation.
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