Esker Launches Automated Tool to Streamline Order Processing
Cloud-based automation software platform Esker released a tool to streamline order processing.
Synergy Transformer provides the service by optimizing data extraction, according to a Tuesday (Sept. 10) press release.
“The model harnesses advanced Transformer technology, while training data is specifically tailored to the nuances of order language, ensuring precise and efficient data extraction,” the release said.
The product showcases Esker’s commitment to environments where artificial intelligence automation lets employees — especially customer service reps (CSRs) — spend more time on higher-value job duties, per the release.
“This new product feature further liberates CSRs by automating error-prone order data entry, freeing them to focus on strategic priorities,” Esker Product Manager Aurélien Coq said in the release. “At Esker, we are constantly listening to users and seeking out new opportunities for improvement to deliver even greater value through AI. Introducing Synergy Transformer to our solutions marks the latest step forward in that ongoing mission.”
PYMNTS spoke last week with Aaron LeHew, director of invoice-to-cash at Esker, about the need for companies to embrace automation.
Many enterprise firms still use paper-based processes for their accounts receivable (AR) tasks. When faced with being bogged down by paper invoices or going paperless, many firms choose to do nothing.
“Organizations with well-oiled AR processes can rely on their own liquidity, reducing the need to tap into external financing,” LeHew said. “This allows them to invest in growth initiatives and other strategic priorities.”
Business customers increasingly demand transparency, flexibility and ease of doing business, while their employees want meaningful work and opportunities for growth — not just manual and repetitive tasks, he said.
In other automation news, PYMNTS wrote earlier this week about the technology’s ability to combat fraud.
“Automated systems can be programmed to detect anomalies in real time, flagging unusual patterns such as unauthorized access to sensitive information, unusual financial transactions, or deviations from typical employee behavior,” that report noted.
Over time, these systems become smarter by refining their algorithms to improve detection accuracy.
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