“More than 75% percent of AP teams reported processing more invoices in the last quarter, according to “Demystifying AI’s Capabilities for Use in Payments”, a Working Capital Tracker® Series report by PYMNTS in collaboration with Billtrust.

This figure marked an increase from the previous two quarters, where only 66% of accounts payable (AP) teams reported doing so, necessitating AP employees to work longer hours to manage the increased workload.

Nearly half of the respondents in the report observed that this added burden is leading to “Rapid burnout, up four percentage points from last year. Much of this owes to a change in workplace culture during the pandemic, with increased productivity expectations placed on fewer workers. As a result, nearly one in four AP professionals are considering resignation. Many of them said they view automation, ironically, as the solution to retaining staff and improving morale.”

Assessing the current scenario, the report stated that accounts receivable (AR) and AP processes continue to be impeded by legacy systems that are primarily reliant on manual procedures to manage the rising volume of invoices, causing AP staff to endure longer hours of work.

Faced with the challenge of handling increasing invoice volume, companies are seeking out solutions driven by artificial intelligence (AI) and machine learning (ML), as per the PYMNTS report.

PYMNTS Intelligence found that embedding AI and ML into payment processes can boost employee productivity by freeing them from manual processes, allowing them to be more thoughtful and innovative.

Additionally, “AI can also circumvent human error by reading both paper-based and scanned invoices, allowing companies to handle more invoices than financial professionals would be able to process”, the report mentioned.  

AI-powered systems are known to process high volumes of payments in less time than traditional methods, while improving the customer payment experience, however, there are a variety of challenges that hinder its widespread deployment.

The report suggests that incorporating AI into payment processes requires building technical infrastructure and high-caliber training data to facilitate algorithmic learning. Even so, the preparation of this data is both time-consuming and costly, posing significant challenges for businesses.

“AI’s use also elevates the importance of maintaining data privacy, as data sharing, user consent and unlawful surveillance are key issues in regulatory compliance and security. Similarly, data transparency and the avoidance of bias are critical matters in financial decision-making, running counter to AI systems’ ‘black box’ inner workings and opaque, often unknowable processes”, the report added.

While many companies may find the adoption of AI systems to be a daunting prospect, what may help these organizations is building a team of AI experts that blend the hybrid skills of data science, ML engineering and software development.

In conclusion, companies are facing myriad challenges over the past three years, with elevated inflation and borrowing costs prompting finance and treasury professionals to reduce operating expenses and improve cash flow.

An important way to achieve this is by streamlining and upgrading costly legacy and manual methods that continue to constitute the majority of business-to-business (B2B) payment processes today, the PYMNTS report recommends.

“Against this backdrop, AI technology has emerged as a virtual silver bullet, with potential payments applications ranging from enhancing transaction efficiency to improving fraud detection to personalizing the customer experience — and that’s only the beginning”, the report further explains.

Great strides can be made by improving the work culture and the experience of AP employees through automation, which could be the key to increased efficiency and less burnout. For now, automating as many steps as possible should be a top priority before AP staffers become disengaged and resign.