Financial planning & analysis (FP&A) and strategic planning activities are especially ripe for AI applications that spot new patterns and draw fresh insights from massive data flows. Generative AI and other AI applications can boost advanced analytics and identify process improvements. Furthermore, AI governance now represents a pivotal component of data security and privacy, which is the top overall finance priority globally for CFOs and finance leaders.

On this count, AI and finance have something in common. As with AI, the purview of the CFO’s finance function is extending more broadly throughout the enterprise. This is good news because CFOs’ broadening responsibilities throughout the enterprise position them to help business partners measure the returns on AI investments while applying proper governance to their AI usage. At the same time, finance groups have potentially game-changing AI deployments of their own to design, deploy and refine. More progress is needed on that count, as nearly two-thirds of finance organisations are not yet employing generative AI (although we expect this to change soon).

Adopting and optimising AI applications, in finance and throughout the enterprise, requires finance leaders to become involved in a number of key activities, in partnership with the CIO, CISO and other C-suite leaders. These activities include the following:

• Developing and refining governance over AI usage: Effective AI governance requires a number of important steps, including the creation of an AI advisory board; alignment among the AI governance framework, policies, standards and controls; and user education. Many CFOs are also involved with developing and monitoring an organisational inventory of existing AI technologies and use cases – while also ensuring that each use case is risk-rated and subject to appropriate controls.
• Measuring ROI: While the short-term payoffs on generative AI pilots may be negligible, longer-term returns can be enormous. Finance leaders are helping other organisational leaders develop business cases for pilots that address how the investments support longer-term technology investment plans, as well as identify key metrics – and data – to track and assess periodically.
• Differentiating between AI’s cost-avoidance and value-generation benefits: In the past two years or so, finance groups that have deployed generative AI solutions have often used the tools to complete compliance forms or to strengthen fraud detection and protection. These types of applications, which center on increasing the speed and precision of anomaly detection, help reduce and/or avoid costs. Other finance applications that leverage AI’s predictive detection can pinpoint valuable opportunities to increase revenue, enhance the customer experience, increase customer value and improve profitability. While leading finance groups pursue both sets of benefits, they recognise that the latter type offers better odds for delivering greater long-term value. So, it is encouraging to see that among current finance organisations that are employing generative AI, a majority are using it for financial forecasting.
• Prioritising AI investments in finance that drive long-term value: In addition to improving forecasting, AI and generative AI tools can support FP&A, cash flow management, expense management, cost optimisation and portfolio management. These types of uses help finance groups generate longer-term value for the organisation. In the coming year, CFOs plan to use AI to improve cash flow management throughout the order-to-cash cycle, transform order-to-cash capabilities, generate long-term revenue projections that lead to proactive revenue assurance adjustments, and help sales and marketing groups gain deeper understandings of customer behaviors.