Predictive Analytics (PA) revolutionises decision-making for Finance leaders by delivering forward-looking insights. Experience from early adopters and innovators shows a clear benefit from using PA in both efficiency gains and better decision-making. First use cases are typically around sales and revenue and then extend further across the entire Finance function.

The maturity of PA tools is already advanced and ready to be scaled. The use of PA is advancing rapidly, with 22% of companies already using it and 62% planning to implement it soon. Timely implementation is crucial as the performance gap between proactive and reactive companies is likely to widen as proactive companies benefit from the experience their users gain.

Most Finance leaders do not see implementation costs as a concern. There is an asymmetry present between the concerns of aspiring users and current users. The primary concern for the former is the cost of implementing PA tools. The latter do not mention this concern but rather face challenges in integrating tools within existing systems and building necessary skills. Organisations need to demonstrate the added value of PA to overcome scepticism.

There are clear signs of technological transformation within the Finance function, with PA transitioning from being exotic to a vital tool within the department. A wide range of solutions is readily available on the market, most of them fit-for-purpose, including standalone predictive tools and custom-built in-house applications. This variety reflects the differing needs and strategic priorities of each organisation. Many organisations start with straightforward models like time-series forecasting before transitioning to more sophisticated solutions, applying an evolutionary approach.

Most companies leverage internal historical data for their PA tools. Organisations increasingly incorporate external data like macroeconomic indicators and ESG data for enhanced insights. Perfect data is not a prerequisite for utilising PA tools; correct and clear data ownership is. Fostering data ownership within the organisation and making continuous progress in data improvement are other crucial steps.

The tone set by leadership is crucial for the organisational transformation towards integration and acceptance of PA. Integrating PA tools into talent attraction strategies ensures new hires possess the necessary skill set. Demonstrated performance over time can persuade late adopters within the organisation.