APQC surveyed over 600 financial leaders in the industry and region to see how organizations use AI and support recruitment in the financial process. Our research conducted later last year revealed that AI and generator AI are transforming financial functions.
The degree of recruitment varies, with some companies fully optimizing AI, while others remain in the exploratory stage. The challenges regarding lack of labor skills and integration with systems and processes are obstacles that organizations must overcome before fully realising the possibilities of AI.
Important areas of AI
APQC research shows that AI has made significant advances in these key process areas when it comes to financial functions. Financial planning and analysis. Cash from orders; Report from records. And raise your salary.
FP&A
FP&A benefits from the high level of AI adoption. 37% of organizations are actively using AI, while another 35% report is in the early stages of evaluation or maneuvering.
Data on specific FP and A use cases show that AI is most frequently adopted for prediction and modeling, with 46% of organizations reporting this use. Also, 39% found widespread application of AI across the end-to-end spectrum of FP and A-processes.
Cash from order
The order-to-cash (O2C) process is affected by AI for 41% of organizations that actively report technology. Another 34% are in the early stages of adoption. The most common uses of AI in O2C include handling customer invoices (46%) and accounts receivable (44%).
Report from Record
Thirty-one percent of organizations actively use AI in their reporting from record (R2R) processes, while another 39% are in the early adoption phase. The most popular applications include intercompany transaction adjustments (44%) and general ledger adjustments (38%).
Procurement
Finally, 25% of organizations are actively using AI through procurement to payment (P2P), with an additional 39% in the early stages of AI adoption. Of the four key areas, P2P was an undecided respondent at an undecided level, with over a third of the organization not yet ready to assess the use of AI in the P2P process.
However, almost half (47%) of those using AI in P2P apply AI across their entire workflow, suggesting a trend towards full automation.
Challenges and opportunities
The adoption of AI and generation AI through financial functions reflects an increasingly optimistic view of the technology's ability to streamline financial forecasting, reporting and data-driven decision-making. To benefit from these benefits, organizations are investing a great deal of resources in preparing their data and workforce for future changes, but important challenges remain.
Support and investment
To support the adoption of AI within the financial function, the organisations surveyed reported that they were implementing several initiatives. The most popular are:
- Process Mining: Assess how financial processes are currently operating and identify gaps and opportunities for AI to optimize these processes.
- Lake Data: Investing in secure, 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.
Following the database initiative, the highest-ranking support activities are then centered around people. It involves building a data culture and educating employees about AI technologies and tools, and how they work together.
When asked what skills their organizations invested in supporting financial AI initiatives, respondents reported:
- 65% of organizations invest in the skills needed for machine learning and algorithms to train cognitive systems.
Main challenges
In many organizations, there are several barriers in the drive to fully implement AI and generate AI. APQC's 2025 Financial Management Priorities and Issues Report noted that the lack of available talent with the required skills stands out as the biggest challenge in adopting digital solutions. Other important obstacles include employee resistance to change and technical challenges, including AI integration with existing software and process workflows.
To accelerate AI preparation, organizations need to:
- Expand external recruitment efforts and find talent with the skillset you need.
- Upskill's current talent (due to a current lack of technical skills in the labor market).
- Implement change management best practices to overcome employee resistance to new processes and technologies and build a data-driven culture.
- It focuses on IT planning and business process management (BPM) strategies that integrate systems and processes.
