Procurement AI Tackles ePayments Adoption

The procure-to-pay function is no exception, with businesses continuing to show demand for technologies that can analyze spend and purchasing behavior. According to Xeeva SVP of Business Development, Product and Growth Vikas Shah, this disruption is among the largest he’s seen in the industry as of late, he recently told PYMNTS.

“The market is moving [toward] prescriptive analytics,” he explained, adding that spend analytics technologies must be designed to address organizations’ unique and specific rules and requirements to promote compliance and growth. “The trend is shifting from just getting information about what’s happening in the company to giving specific direction [and] guidance on what you should be doing with that information.”

Collaboration Through Data

Data integrations with procurement and spend analytics technologies, like those offered by Xeeva, are finding empowerment through data integrations with a range of other back-office platforms in middle-market and large enterprises. The ability to take advantage of the electronic data generated by digital platforms, from accounting to inventory management, means a greater volume of quality information — from which analytics technologies like artificial intelligence (AI) and machine learning can develop those valuable insights.

This data integration trend is also driving another critical trend in procurement and spend analytics, according to Shah: enterprise-wide collaboration.

“We’re seeing the analytics space being disrupted dramatically by making sure there is a bridge between all business units within the function,” he said. “Traditionally, companies have not done a good job in making sure all business stakeholders within an organization have a consistent and streamlined view of their business spend.”

That means ensuring that procurement, finance, accounts payable (AP) and other departments are not only on the same page, but can interconnect their data with each other, as well as into a unified platform for the most holistic view of procurement and spend activity.

Sluggish ePayments Adoption

One of the greatest barriers to achieving data integration and producing actionable insights through analytics is a lack of quality information at the disposal of organizations and their service providers, said Shah. Despite the lofty promises and objectives of the spend analytics industry, technology is still limited in providing the kind of valuable insights that corporates demand when a high volume of line-level, granular information is missing — a problem that Shah said leads to spend leakage and a lack of visibility.

Exacerbating this challenge is the sluggish pace of electronic payments adoption, preventing systems from obtaining valuable, robust transaction data.

“We’re seeing slow progress right now. We continue to see the proliferation of paper checks. We’re seeing purchase card transactions, and a slight uptick in the use of virtual cards — but it’s only slight,” Shah said. “By and large, the pace of innovation and electronic payments adoption — at least in terms of the information management of that data around purchase orders and invoices — is not as rapid as we anticipated.”

AI Steps In

The lack of data integrity and availability, resulting in lackluster digital payments adoption, as well as an array of other factors, is not an easy challenge to address. Couple that with a lack of expertise within the enterprise on how to make use of the data that does exist, and the challenge seems insurmountable.

“There are two constraints in the industry today,” Shah said. “One is quality of data. It continues to be very poor, or imperfect. Another constraint is the lack of subject matter and domain expertise to make any of this data relevant for business action.”

However, he noted that AI — a technology traditionally viewed as a tool to support analytics of this information — can operate as a facilitator of filling in data gaps, so long as the technology is developed specifically for procurement and spend analytics applications. Xeeva’s strategic investments in AI are part of the company’s efforts to address issues like a lack of data, a lack of quality data and a lack of analytics expertise for its corporate customers.

As corporates advance toward real-time spend analytics (Shah said he has seen a rapid shift from the enterprise being satisfied with quarterly analytics only a few years ago to demanding monthly and weekly analytics today), the need to generate and maintain high-quality data will only grow. While AI can help to fill the gaps, Shah emphasized the role that pan-enterprise collaboration plays in not only supporting data integration, but in driving the generation of that data in the first place.

“Businesses are still struggling with a lot of fundamental challenges within finance, AP and procurement departments,” he said. “There’s a lack of collaboration that continues to persist between AP, finance and procurement. To dramatically move the needle in terms of electronic adoption, there has to be cross-functional stakeholder alignment within large organizations — and that’s really hard  to achieve.”