Gartner Hype Cycle Shows Generative AI in Finance is at the Peak of Inflated Expectation

Gartner experts will be discussing finance AI at the Gartner CFO & Finance Executive Conference 2024 in London in September.

The Gartner Hype Cycle for Finance AI and Advanced Analytics showcases the leading innovations revolutionizing finance. This Hype Cycle provides CFOs with the current state of key AI and advanced analytics techniques relevant to finance, helping them align technology roadmaps with business strategy. 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. 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 (see Figure 1),” 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.”

“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.”

When it comes to tasks that are based in numerical data, however, 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 analyzing 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.”

Using machine learning will require some new skills, however. Finance organizations are beginning to employ the citizen data science model that teaches finance professionals a subset of the data science capability and the skills to employ fundamental data science techniques.

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

Agent-based modeling 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 optimization, responsive scheduling and process resilience.