Finance leaders use AI and ML to drive business success

It’s tough being a finance leader right now. You’re facing waves of rising inflation, economic disruption, mounting cybersecurity threats and continued supply chain issues.  

With economic shock continuing to reverberate from Russia’s war in Ukraine, core inflation is at an all-time high, making forecasting all the more difficult to achieve with speed and accuracy. 

And to make matters worse, finance teams also face widening skills gap in the sector, as demand increases for expertise in digital and data analysis skills.  

To ease these pressures, and help to navigate economic uncertainty, progressive finance leaders are turning to AI and machine learning (ML) to streamline their financial planning and analysis (FP&A) processes. 

AI is getting a very bad press in some contexts, but its ability to deliver tangible benefits is taking centre stage for finance teams.  

A recent survey in North America revealed three quarters of finance leaders are beginning to integrate AI and ML to analyse their data, with almost half of them increasing their investments in these technologies compared to last year.  

As volumes of data increase more than we ever thought conceivable, being able to accurately forecast is getting more and more complex for CFOs and finance teams.  

By making use of ML, these vast datasets can be processed more efficiently and accurately. Eliminating the need to build custom solutions from scratch, the output from ML-powered FP&A applications, provides more streamlined results. This is enabling teams to produce thousands of forecasts at speed, at scale, and at any level of granularity. 

Making use of AI not only allows financial teams to maximise the value of their data, it also helps plug the skills gaps within their teams. As businesses use the right tools, they can stay ahead of the curve and remain competitive.  

Traditional methods of forecasting have been done by taking last year’s numbers and making a few manual adjustments. In other words, making high-level estimations without factoring in critical business drivers and factors. We know holidays, prior year sales and unemployment trends are time-consuming variables to accurately process using manual techniques.  

To alleviate this, AI and ML-powered FP&A solutions automate these processes, allowing indirect relationships between variables to be accounted for, which would be left unobserved with human-led methods. 

A survey report by OneStream and Hanover Research found that just under half of respondents believe sales/revenue forecasting, planning sales, marketing and customer service could benefit most from the introduction of AI and ML this year. Embracing ML will give finance teams the time back that they can put towards laser-focusing their strategic advantage.  

Today, ML is becoming a necessity for competitive advantage, especially in unpredictable environments. AI-powered FP&A software provides a unified customer solution, allowing the full data pipeline to be confined inside one platform. These aspects include data ingestion and quality, feature generation and model building, all the way through to dashboard analytics.  

Looking ahead, as the advantages of AI become clearer, we can anticipate how AI and ML will continue to expand its influence within the finance function. When ML is accessible to everyone, it allows predictive forecasting to be undertaken by various roles – rather than just data science teams. 

Alongside this, the ability to run simulations will not only aid financial planning but also strategic initiatives. These have become a business-critical activity for making smart decisions as finance teams can quickly generate multiple scenarios while flexing various drivers. This quick, iterative process allows finance teams to identify risks and opportunities, evaluate how different decisions impact financial performance and then identify the best course of action. 

These innovations in AI and ML hold huge potential for enhancing efficiency and decision-making in the finance function, with the increasing demand for real-time insights and quick, informed decision-making. Leaders will leverage these technologies to drive efficiency, productivity, and strategic value within their organisations, ultimately positioning them for success. 

Businesses can now create, consume and maintain these models without relying on data scientists who are less available today. As a result, finance teams can now perform more of a business analyst role, looking beyond the balance sheet and applying themselves more within core business operations.

So, while ML is a mighty new sidekick for automation, the real hero remains human expertise, able to disseminate these data patterns and take smart decisions that forge business success.