AI dominates most of the talk in today’s enterprises when it comes to technology. We’re always finding new ways of using AI to generate better business outcomes. But the jury is still out on how AI will affect our work and life in the future.
In the financial context, though, one question often comes up in conversations with our clients: How to arrive at the right short- and long-range revenue forecasts. Revenue projections have far-reaching implications because they drive annual budgeting and planning cycles. When forecasts are inaccurate, firms budget and invest inefficiently, making it harder to meet targets and leading to poor financial performance.
That’s why, for effective decision making, any AI forecast methodology must be intelligent, agile, and able to accurately reflect market dynamics. For agility, it should decode relevant signals from the structured and unstructured data it gathers from internal and external sources — and factor them into projections. To improve accuracy, it should analyze what causes the variance between forecast and actual numbers, continually learning on the job.