7 insights from The Rise of AI in the Finance Function report and debate

Brady Cale, CTO, considers the findings of Taulia’s research into AI with peers

The rise of artificial intelligence (AI) in the finance function is not a future state; it is here and now. Taulia’s research into AI adoption by the finance function and discussions with finance leaders reveal high levels of trust, experimentation, and a core belief that AI will empower finance teams to deliver much more to the organization.

AI is like Detroit in the early days of the automotive sector; we understand the power of the technology, but we have no way of knowing the speed with which it will transform our lives – just as those early pioneers of the automobile had no concept of how fast the car would revolutionize the world of transport and everything impacted by it.

The finance teams and their leaders are essentially the pioneers of enterprise AI adoption. The Taulia survey into The Rise of AI in the Finance Function finds that over half (53%) of global finance functions rely on AI for decision-making, 59% of senior management being the main AI users. An early indication of the impact can be seen in the results, with 45% of the surveyed leaders planning on hiring AI specialists into their teams.

Taulia surveyed 600 global finance leaders and found that 97% are using AI or plan to use AI over the next two years to improve process automation and efficiency, and a further 96% for cash forecasting.

Respondents believe inventory and supply chain management are the functions that have the most to gain from the deployment of AI. Here are some further key takeaways from the survey and our recent webinar on AI.

Takeaway 1: Empowered teams

AI empowers teams to analyze data rather than spend time collating data. Eli Soffer, Head of Corporate Finance at Amdocs, said of the survey findings and this trend: “I don’t see machines making decisions. I see AI co-piloting, and the technology is helping people with the knowledge to get to better decisions faster, through validation and assurance. So, they are spending more time on understanding the data.”

This has the power to empower all areas of the finance function. For example, risk teams could have direct access to data about credit availability, the business regions, market size insights, and funds available.

This data currently relies on a series of manual processes; AI can surface all that data and guide decision-making. As the survey finds: “AI’s true value lies not just in presenting figures, but in offering actionable insights. A mere notification of a change in turnover is ineffective unless it provides the underlying reasons and actionable strategies for improvement.”

Takeaway 2: More models, and better ones

Finance functions that use AI to do the data-heavy lifting will improve the quality of the models they use for decision-making. Finance teams have been using models to inform decision-making for a long time, and you cannot run a global finance function without some reliance on models, as there are just too many factors to consider.

With AI enablement, finance functions can use deeper, more complex, and more detailed models to improve decision-making, as the report states, organizations can generate more precise forecasts by analyzing a broader array of inputs – 50 instead of just five. Such extensive analysis is beyond the capabilities of even the most seasoned statisticians.

Taulia’s models draw from dozens of data sources, encompassing billions of data points. In a landscape where commercial success hinges on making timely and accurate decisions, no organization can afford the luxury of weeks spent manually sifting through vast datasets, only to arrive at outdated conclusions.

Takeaway 3: Trust the AI

Finance functions will only benefit from AI if they can trust it. When AI is trusted, then it is used. Our own experience revealed that when AI users were presented with two different styles of result, one that looked like an answer from ChatGPT, and another that was a Google search result, complete with citations of where it found the information for its answer; the finance professionals preferred the Google-like result so they could validate the answer.

This ensures that the finance function knows they have control and can double-check the workings of the AI, in much the same way as they would an intern.

The report finds that trust in AI for process automation, inventory, supply, cash, and planning management is high. Eli Soffer from Amdocs rightly points out: “You cannot expect the technology to get you the right answer if your data is misleading or your documentation is not up to date.” Data quality is vital to trust in AI.

Takeaway 4: A new era of decision making

With over half of finance leaders using AI-generated insights for commercial decision-making, it is clear we are entering a new era. In some cases, AI is helping inform decision-making; for example, by matching credit details with the questions in application forms, this improves insight and can accelerate the decision-making process, which drives business growth. For other corporate decisions, finance leaders can rely on real-time data; Eli Soffer states: “In meetings, we are moving from prepared slides to live analytics, which pushes forward decision making, so we are making decisions at a much faster pace.”

Takeaway 5: Process improvements

With improvements in decision-making, business processes right across the organization will improve. In our own organization, an AI chatbot has broken down the walls between organizations within the company to help our colleagues with corporate processes such as invoicing, pay slips, and procurement practices. Soffer says similar systems at Amdocs are saving staff time.

Takeaway 6: Getting started

Finance functions may wonder where to start the adoption of AI. Eli Soffer realized the best use cases for AI would come from his team members. “Give teams the tools, and they will begin to pull rather than be pushed. We are doing basic training on AI for all finance employees, and we did a “Shark Tank” event and took smart analytics on the General Ledger idea into development. There is a limit to what organizations can do top down, but if you have ideas coming up, that multiplies.”

Takeaway 7: Invest in your people

To get the right ideas from your people, invest in them so they develop confidence in AI and don’t block the technology because they fear it will replace their positions. Eli Soffer has done this: “We are doing basic training on AI for our finance employees. Each session is 30 interesting minutes.”

The rise of artificial intelligence (AI) in the finance function is not a future state; it is here and now.

Getting real value from it requires the right combination of man and machine and a commitment to educating and upskilling. Get that right and the benefit will reach far beyond the function.