AI-Powered Productivity: Finance

Finance enterprise operations, traditionally known for meticulous recordkeeping and complex calculations, is now poised at the precipice of a significant transformation driven by agentic AI and automation.

This Data Story delves into the untapped potential of agentic AI in finance ops, highlighting the current proof-of-concept discoveries and the promising future realizations of agentic autonomous operations.

It explores the transformative capabilities of agentic AI, including its potential to optimize financial modeling, intelligent reconciliation, and the role of AI-powered virtual assistants in streamlining day-to-day transactional financial operational tasks. It also underscores the anticipated benefits of AI automation, such as improved forecast accuracy and shorter cycle times, and provides actionable recommendations for CFOs embracing their agentic AI enterprise transformation journey.
 

AI at work: From finance assistants to autonomous operations

AI automation journeys often begin with rules-based systems that handle repetitive tasks. Then, these systems evolve to AI assistants that support financial professionals through query-based natural language interactions that analyze transactions, take actions, and make recommendations.
 

The latest development? AI agents that both carry out tasks and make decisions autonomously.

Infusion of AI agents throughout both finance and enterprise operations enables people and AI to coexecute tasks—and agents to engage with other agents across processes and even systems.

According to recent IBM Institute for Business Value (IBM IBV) research, 68% of executives report experimental use of AI automation with digital assistants advancing to autonomous agents in finance operations for self-service.  
 

By 2027, 37% of executives expect to implement touchless automation in predictive insights, and 29% in financial analysis and reporting.

The advent of agentic AI offers unprecedented innovation for finance ops

Through simulation and virtualization, financial modeling AI agents digest historical data to build predictive models, enabling in-the-moment, accurate forecasting of outcomes such as cash flow projections or budget variances. This not only enhances forecast precision but frees up finance professionals to concentrate on assessing prediction uncertainties and formulating risk mitigation strategies.

Moreover, multiagent systems can automate journal entries, match transactions across diverse environments, and flag anomalies. This capability not only speeds up error detection and resolution but also fortifies the enterprise against fraudulent activities.

AI-powered virtual assistants and agents work together in collaboration and in tandem with finance professionals to further streamline transactions and personalize responses, such as inquiries about payment processing, expense tracking, and compliance reporting.
 

By handling these routine queries, agents empower finance professionals to dedicate more time to intricate corporate and analyst reporting and the establishment of robust financial practices across the organization.

Finance executives project substantial benefits from AI automation

Agentic AI automation is poised to transform key aspects of financial operations, propelling unprecedented forecast precision, streamlined closing processes, and optimized cash flow management.

CFOs anticipate significant benefits of agentic AI automation in finance operations. Expected improvements span across various critical areas, including forecast accuracy, continuous close processes, days sales outstanding (DSO), and cycle times minimization for accounts payable and receivable.
 

CFOs project a 24% improvement in forecast accuracy by 2027, a 23% enhancement in touchless continuous close processes, and a 29% reduction in days sales outstanding (DSO). 

For more details and key recommendations, download the Data Story.