APQC surveyed over 600 finance leaders across industries and regions to find out how their organizations are using AI and supporting its adoption in financial processes. Our research, conducted late last year, revealed that AI and generative AI are transforming the finance function, with nearly all of the organizations surveyed at least exploring or piloting AI.
The extent of adoption varies, with some firms fully optimizing AI while others remain in the exploratory phase. Challenges around labor skills shortages and integration with systems and processes are obstacles organizations must overcome before fully realizing the potential of AI.
Key Areas for AI
APQC’s research shows that for the finance function, AI has made notable strides in these key process areas: financial planning and analysis; order-to-cash; record-to-report; and procure-to-pay.
FP&A
FP&A is benefiting from a high level of AI adoption: 37 percent of organizations are actively using AI and another 35 percent report being in the early stages of an evaluation or pilot.
The data on specific FP&A use cases show that AI is often employed for forecasting and modeling, with 46 percent of organizations reporting this use. And 39 percent have found broad application of AI across the entire end-to-end spectrum of FP&A processes.
Order-to-Cash
Order-to-cash (O2C) processes are impacted by AI for 41 percent of organizations who report actively using the technology. Another 34 percent are in the early stages of adoption. The most common uses of AI in O2C include customer invoicing (46 percent) and processing accounts receivable (44%).
Record-to-Report
Thirty-one percent of organizations actively use AI for their record-to-report (R2R) processes, while another 39 percent are in the early adoption stage. The most popular applications include reconciling intercompany transactions (44 percent) and general ledger reconciliation (38 percent).
Procure-to-Pay
Finally, 25 percent of organizations actively leverage AI in procure-to-pay (P2P), with another 39 percent in the early stages of AI adoption. Of the four key areas, P2P had the highest level of undecided respondents, with over one-third of organizations not yet ready to evaluate the use of AI in their P2P processes.
But of those utilizing AI in P2P, nearly half (47 percent) apply AI across the entire workflow, suggesting a trend toward full automation.
Challenges and Opportunities
The adoption of AI and generative AI by the finance function reflects an increasing optimism about the ability of technology to streamline financial forecasting, reporting and data-driven decision-making. To benefit from these advantages, organizations are investing significant resources in readying their data and workforce for future changes, but significant challenges remain.
To support the adoption of AI within the finance function, the organizations we surveyed reported undertaking several initiatives. The most popular were:
- Process mining: Evaluating how finance processes currently operate and identifying any gaps and opportunities for AI to optimize these processes.
- Data lakes: Investing in secure, well-managed data repositories.
- Data management and acquisition: Automating acquisition of external and internal data, identifying and standardizing structured and unstructured data and implementing data management solutions.
After data-based initiatives, the next highest-ranking support activities center on people: They include building a data-culture and educating employees on AI technology and tools and how to collaborate with them.
When asked which skills their organization has invested in to support AI initiatives in finance, respondents reported:
- 65% of organizations investing in skills needed for machine learning and algorithms to train cognitive systems.
- 62% focusing on agile methodology approaches to system implementations.
Top Challenges
For many organizations, several barriers exist in the drive to fully implement AI and generative AI. APQC’s 2025 Financial Management Priorities and Challenges Report noted that the lack of available talent with the requisite skills stands out as the top challenge in adopting digital solutions. Other significant obstacles include employee resistance to change and technical challenges involving AI integration with existing software and process workflows.
To speed up AI readiness, organizations should:
- Expand external recruitment efforts to find talent with the requisite skill set.
- Upskill current talent (due to the shortage of technical skills in the labor market).
- Implement change management best practices to overcome employee resistance to new processes and technology and build a data-driven culture.
- Focus on IT planning and business process management (BPM) strategies that integrate systems and processes.
