Behind the scenes, the finance function — and all of its spreadsheets, manual processes, and legacy systems — is undergoing a seismic shift.
Artificial intelligence (AI) is emerging as a force within business process enhancement. Enterprises are turning to AI to automate not just repetitive tasks but also more complex processes like compliance monitoring, fraud detection and supply chain optimization, using tactics such as combining robotic process automation (RPA) with AI to streamline workflows.
The timing couldn’t be better. The back office has long been overlooked in conversations about innovation, but its transformation is no longer optional. With rising uncertainty, regulatory complexities and competitive pressures, companies are seeking ways to streamline operations, improve decision-making and unlock efficiencies.
The back office is ready for its glow-up — and the appropriate, effective and responsible use of AI could be the key.
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For decades, the finance function has operated as the company’s operational backbone, but not without significant challenges. Manual data entry, reconciliation and reporting dominate workloads, with employees often spending hours navigating disjointed systems.
“At any time, when you have paper, you introduce manual processes,” Duncan Lodge, global head of supply chain finance and EMEA head of trade at Bank of America, told PYMNTS. “That means someone has to extract information, process it and ensure its accuracy — introducing delays, inefficiencies and the potential for error.”
These inefficiencies are amplified in small and medium-sized businesses (SMBs), which often lack the resources for large-scale finance teams or advanced tools. For these companies, the pressure to “do more with less” has reached critical levels.
AI offers a compelling solution by automating repetitive tasks, enhancing accuracy, and delivering real-time insights. While automation tools have existed for years, the addition of AI transforms them into dynamic systems capable of learning, adapting and uncovering patterns that humans might miss.
The PYMNTS Intelligence report “CFOs Eye Accounts Receivable as New Direction for AI Investments” found that 55% of chief financial officers (CFOs) representing middle-market businesses would be willing to pay 3% of the invoice amount to accept payments using a solution that automates invoice approval and payment.
Compared to the risks of paper checks, adding AI to payments systems can result in a fraud defense that excels at anomaly detection, identifying potential fraud in real time by recognizing unusual patterns in transactional data. Moreover, these systems can simplify compliance by continuously monitoring regulatory changes and updating processes to reflect new requirements.
See also: AI’s Growing Role Across B2B Payments Will Be Impossible to Ignore in 2025
Traditional accounts payable (AP) and accounts receivable (AR) processes are laden with inefficiencies, from invoice approvals to payment collections. AI-powered solutions can automate invoice processing, flag discrepancies, and predict payment behaviors. Tools like machine learning algorithms can also help analyze payment patterns to improve cash flow forecasting, giving CFOs and treasurers a clear view of working capital.
With AI, financial reporting is no longer a static, backward-looking process. Machine learning algorithms can synthesize data from multiple sources — ERP systems, bank feeds, and even external economic indicators — to provide predictive insights. This empowers finance teams to move from reactive reporting to proactive strategy.
According to a PYMNTS Intelligence report, “Outlook 2025: CFOs Envision Growing Role for Generative AI in Finance,” CFOs are also adopting generative AI in finance for strategic and financial tasks. More than 60% of CFOs reported using GenAI for creating data visualizations and reports to help improve the clarity and accessibility of complex financial data.
“Incorporating data into the money flow will provide significant improvements for businesses,” Seamus Smith, executive vice president and group president at FIS, told PYMNTS. “Organizations that are early adopters and larger-scale consumers of new technology will accelerate ahead.”
Despite its promise, the adoption of AI in the back office is not without challenges. Resistance often stems from two primary sources: cultural inertia and perceived complexity.
Finance teams have traditionally been cautious about adopting new technologies, often prioritizing reliability over innovation. Convincing stakeholders to invest in AI requires a clear articulation of the return on the investment. Still, per the data in the PYMNTS Intelligence report, “Most CFOs See Limited ROI From GenAI, but Boost Its Investment,” 75% of CFOs plan to increase their AI investment.
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