While finance AI has generated significant interest from CFOs looking to maximise resources and improve efficiency and decision-making, considerable hype in the marketplace is likely to lead to a period of disillusionment with various technologies in this space, according to Gartner, Inc.

Gartner has created what it calls a ‘Hype Cycle for Finance AI and Advanced Analytics’, showcasing the leading innovations revolutionising finance. It maps various AI and analytics innovations to multiple stages of development, starting at Innovation Trigger followed by the Peak of Inflated Opinions, to the Trough of Disappointment and the Slope of Enlightenment, finishing at the Plateau of Productivity. While many of the innovations featured are readily available and widely used today, others are forward-looking and present the greatest promise for the future. Gartner says CFOs can use this Hype Cycle to build a finance transformation roadmap that delivers short-term value while simultaneously preparing for the future.

“At the very Peak of Inflated Expectations in finance is generative AI,” said Mark D. McDonald, senior director analyst in the Gartner finance practice. “A range of publicly available generative AI (GenAI) tools have generated enormous publicity for the technology in the last two years, but as finance functions adopt this technology, they may not find it as transformative as expected.”

Temper expectations around GenAI in finance

Although Gartner experts forecast disillusionment with GenAI tools in finance in the future, this isn’t to say the technology won’t be useful in finance. On the contrary, GenAI has several applications for finance professionals, they just might not be as transformative as many finance leaders think right now. GenAI uses text as its source, so for tasks that require text analysis, such as contract analysis, it excels.

“Finance functions could also use GenAI to do things they currently don’t,” said McDonald. “For example. comparing an inbound vendor invoice with the negotiated pricing to make sure charges align with the agreed prices. The main strengths of GenAI in finance are its ease of access and simplicity of use. With many vendors offering private in-house GenAI solutions, harnessing such tools is largely a case of teaching employees how to use it and under what circumstances it is a reliable solution.”

However, when it comes to tasks based on numerical data, finance functions will need to rely on other AI techniques, most notably various applications of machine learning. Machine learning can help finance professionals with tasks like forecasting revenue or finding errors in large volumes of data.

“Machine learning can also help with new more sophisticated methods of analysing our financial results, detecting trends that otherwise could be missed,” said McDonald. “One of the main benefits of machine learning is that finance leaders can quantify the quality of the algorithm's output which can serve as evidence for auditable transactions.”

However, using machine learning will require some new skills. Finance organisations are beginning to employ the citizen data science model, which teaches finance professionals a subset of data science capability and the skills to employ fundamental data science techniques.

Composite AI potential

Composite AI is at the Innovation Trigger stage of the Hype Cycle. It refers to the combined application of different AI techniques to improve the efficiency of learning and broaden the level of knowledge representations. 

“As AI adoption matures in finance functions, it will become clear that no single AI technique is a panacea,” said McDonald. “Combining AI techniques is much more effective than relying only on heuristics or a fully data-driven approach.”

The growing reliance on AI for decision-making is driving organisations toward composite AI because the most appropriate actions can be better determined by combining rule-based and optimisation models - a combination often referred to as prescriptive analytics. Small datasets or the limited availability of data have also pushed organisations to combine multiple AI techniques.

Agent-based modelling is the next wave of composite AI. A composite AI solution is composed of multiple agents, each representing an actor in the ecosystem. Combining these agents into a “swarm” enables the creation of common situation awareness, more global planning optimisation, responsive scheduling and process resilience.