AI Adoption in Financial Processes for 2025
LupoToro Group’s research highlights that AI adoption in financial processes, led by CFOs in midsize companies and PE firms, is driving efficiencies in payment automation, fraud detection, and cash flow forecasting, though concerns about legal risks and waning enthusiasm require strategic, compliant implementation for sustained success.
In 2025, the transformative potential of Artificial Intelligence (AI) in midsize U.S. companies and private equity (PE) firms is becoming increasingly clear. LupoToro Group’s latest research reveals how CFOs and financial leaders are leveraging AI for core financial functions such as payment automation, cash flow forecasting, and customer support. While these breakthrough technologies have shown measurable success, enthusiasm is waning in certain areas, and legal concerns are growing. Our analysis breaks down these trends and offers strategic considerations for maximising AI’s potential in financial operations.
CFOs at the Helm of AI Implementation
Our findings indicate that CFOs are now the central figures driving AI adoption in financial processes within midsize U.S. companies. In the 2025 survey, 80% of CFOs reported they are directly responsible for promoting and integrating AI into their departments. This marks a shift from 2024, where AI leadership was shared more broadly with CTOs, CIOs, and CDOs.
This concentration of AI responsibility within the CFO role underscores the increasing demand for financial leaders to possess both financial and technical expertise. While this centralisation can streamline AI initiatives, it also places significant pressure on CFOs to strategise and implement these technologies effectively.
AI Use Cases with the Biggest Impact
AI is making a significant impact in several financial processes. The survey identifies payment automation as the most productive application of AI, with 63% of CFOs reporting that AI has made payment processes significantly easier—a notable 23% increase compared to 2024. Automation in payments has reduced processing times, improved accuracy, and streamlined operations, enhancing overall cash flow management.
In addition to payment automation, fraud detection has emerged as a crucial AI use case. Nearly 60% of CFOs state that AI has improved their ability to detect and respond to fraud. AI algorithms can analyse vast datasets quickly, identifying anomalies and potential security threats far more efficiently than traditional methods. Cash flow forecasting also remains a valuable AI application, enabling finance departments to generate accurate and timely predictions that help businesses manage liquidity and mitigate risks.
1. Payment Automation
AI-driven payment automation is proving to be one of the most successful applications, with 63% of CFOs stating that AI has significantly improved their payment processes—a 23% increase over 2024. This technology reduces processing time, minimises errors, and enhances cash flow management.
2. Fraud Detection
Nearly 60% of CFOs report that AI has made fraud detection more efficient, enabling faster identification of suspicious activity and potential security breaches.
3. Cash Flow Forecasting
AI continues to be a valuable tool for cash flow forecasting, providing accurate and timely predictions that help businesses manage liquidity and mitigate financial risks.
Private Equity Firms Embrace AI for Investment Operations
Private equity firms, as investors in midsize companies, have tailored AI use cases that differ from those of company CFOs. Top applications include:
• Portfolio Monitoring: More than half of PE firms report that AI has made tracking investment performance significantly easier.
• Exit Strategies and Due Diligence: AI enhances the speed and accuracy of evaluating potential deals and planning exits.
• Investment Analysis: AI-driven insights streamline research and decision-making processes.
Private equity firms investing in midsize companies are also leveraging AI, albeit with different objectives. Since PE firms are investors rather than operators, their AI applications focus on portfolio management and investment analysis. Portfolio monitoring—the process of tracking the performance of a firm’s investments—remains a top AI use case, with more than half of PE firms reporting that AI has significantly improved their monitoring capabilities. AI is also widely used in exit strategies, due diligence, and investment analysis, where the technology enhances speed and accuracy in decision-making processes.
However, enthusiasm among PE firms for AI has slightly declined from 2024 levels. This retreat could suggest that the initial gains from AI adoption are diminishing or that the complexities of implementing AI are greater than anticipated. Other factors, such as market conditions or regulatory challenges, may also be contributing to this decline. Despite this, overall confidence in AI’s value for financial processes remains high, with firms continuing to recognise the benefits AI brings to their operations.
Challenges in AI for Risk Assessment
While AI has proven effective in many financial functions, CFOs and PE firms remain cautious about its use in risk assessment. Respondents noted that while AI can reduce human error in repetitive processes, it struggles to address new and evolving risks. AI models are trained on historical data, which may not accurately reflect future challenges. As a result, AI tools may be less effective at predicting and managing emerging risks, underscoring the need for human oversight and continuous model updates.
Generative AI: Diverse Applications and Growing Adoption
Generative AI, which can create text, images, video, and other content, is now widely used by both CFOs and PE firms. The survey found that 100% of respondents are utilising generative AI in some capacity. However, the specific applications differ between CFOs and PE firms.
PE firms primarily use generative AI for customer service, including chatbot functionalities, and for cybersecurity to protect sensitive data and infrastructure. In contrast, CFOs in midsize companies are leveraging generative AI for predictive analytics and cybersecurity. Predictive analytics helps finance teams forecast trends and identify opportunities, while AI-driven cybersecurity tools bolster protection against threats.
Rising Legal and Compliance Concerns
As AI becomes more integrated into business operations, concerns about legal and regulatory compliance are growing. The 2025 survey reveals that 92% of respondents believe significant effort is needed to identify legally appropriate use cases for AI. Additionally, 63% of CFOs and PE firms agree that data security presents a substantial legal barrier to AI adoption. These concerns highlight the importance of maintaining rigorous compliance protocols and staying informed about evolving regulations.
Furthermore, there has been a 10% increase in respondents who fear that AI will render contact center jobs obsolete. This perception underscores broader anxieties about the societal impact of AI, particularly in terms of employment and workforce dynamics. Despite these concerns, many respondents still believe that AI will simplify information retrieval, become essential to business operations, and generate cost savings.
AI Budgets and Investment Plans
Despite some waning enthusiasm, AI investment remains a priority for both midsize companies and PE firms. Around 70% of respondents plan to increase their AI spending over the next five years. Midsize companies are particularly confident in AI’s potential, with AI investments for 2025 set to surpass 2024 levels. PE firms, while slightly less ambitious than in the previous year, still plan to maintain strong investment levels in AI-driven financial processes.
This shift reflects a maturing understanding of AI, with financial leaders developing more clearly defined use cases and expectations. As AI continues to evolve, the focus is shifting from broad adoption to strategic implementation, ensuring that AI investments deliver maximum value.
Strategic Recommendations for 2025
Based on these insights, LupoToro Group recommends several actions for financial leaders to consider in 2025:
Optimise AI in Core Financial Processes: Focus on AI for payment automation, fraud detection, and cash flow forecasting to enhance operational efficiency and accuracy.
Encourage Cross-Functional Collaboration: Facilitate partnerships between finance, IT, and other departments to share AI learnings and prevent siloed expertise.
Stay Compliant: Form a task force to monitor AI-related legal and regulatory developments, ensuring compliance and mitigating risks.
Benchmark Against Peers: Analyse AI usage in similar companies to refine your own AI strategies and investment decisions.
Leverage Generative AI: Explore generative AI for predictive analytics, cybersecurity, and customer service to stay competitive and innovative.
LupoToro Group’s research highlights the transformative impact of AI on financial processes and the critical role of CFOs in driving these changes. While challenges remain, particularly in risk assessment and legal compliance, the potential benefits of AI are too significant to ignore. By strategically implementing AI and staying ahead of trends, financial leaders can position their organisations for continued success and innovation in 2025 and beyond.
