Artificial intelligence has become a near-constant topic in finance leadership discussions, yet accounts receivable teams continue to ask a more grounded question: where does AI genuinely improve performance, and where does it simply introduce another layer of complexity?
That practical distinction framed the recent Controllers Council and Billtrust roundtable, AI in AR: Real Talk on What’s Working, What’s Not, and What’s Just Hype, featuring Gregg D’Eon, VP/Controller at LTK, Philip Peck, VP Finance Transformation at Peloton Consulting Group, and Ahsan Shah, SVP AI and Analytics at Billtrust. Moderated by Controllers Council Executive Director Neil Brown, the discussion examined how finance organizations are applying AI across invoicing, collections, reconciliation, forecasting, and cash application while also confronting governance, process, and adoption challenges.
Throughout the conversation, panelists returned to a common theme: organizations that approach AI as a practical operational tool rather than a sweeping transformation initiative are seeing the strongest results.
AI Adoption Is Advancing, But Most Teams Remain in Evaluation Mode
Audience polling during the webcast reflected both growing interest and ongoing caution. While many attendees indicated that automation and AI initiatives have become a priority inside finance and accounting, most organizations are still evaluating where those technologies fit into existing workflows.
Philip Peck explained that the strongest implementations typically begin with focused operational problems rather than broad experimentation.
“People who’ve been most successful in leveraging AI in accounts receivable first focus on a relatively small number of high-value, high volume use cases first,” Peck said. “Go after that which adds the most value immediately, establishes credibility in the organization.”
That measured approach resonated throughout the session. Rather than attempting large-scale automation projects all at once, panelists encouraged finance leaders to identify recurring bottlenecks, repetitive processes, and areas where teams spend disproportionate time reviewing transactions manually.
AR Teams Are Using AI to Reduce Manual Review Work
Gregg D’Eon shared how LTK approached AI adoption by concentrating on operational friction points inside the accounting organization.
“The approach that I took personally was I took a look at, okay, where are the biggest pain points in my team and where are the biggest volumes of data that we’re sorting through?” D’Eon explained.
LTK’s finance team focused early efforts on cash management and receivables monitoring, particularly around identifying overdue invoices, tracking payment activity, and prioritizing collections outreach. D’Eon described how the organization built internal tools that help surface the highest-priority collection items each day.
“We have an agent that runs through each of those invoices, kind of pulls out the highest value ones that will help us chop down that 120 plus bucket as much as possible,” he said.
Instead of forcing AR specialists to manually sift through large invoice populations, the system highlights the accounts most likely to improve collection performance quickly. D’Eon noted that the technology also helps summarize patterns and status updates across receivables activity, making communication with executives substantially easier.
“AI does a really good job of aggregating themes so that you can communicate to executives,” he said.
Panelists repeatedly emphasized that one of AI’s strongest immediate contributions inside finance is not necessarily complete automation, but rather the reduction of time spent sorting, reconciling, reviewing, and organizing information.
Data Quality Remains the Foundation
Despite the enthusiasm surrounding AI capabilities, every panelist stressed that unreliable data and weak operational discipline remain major barriers to success.
Shah cautioned that organizations expecting AI to compensate for disorganized systems are likely to encounter disappointing outcomes.
“There’s a lot of research that says AI is just going to amplify your existing current state,” Shah said. “If your coding is not a good process, if your master data is not a good process, if your data is all over the place, AI is not going to solve that for you.”
Peck echoed that concern, calling data quality one of the most common obstacles encountered during implementation projects.
“Poor master data quality,” Peck said, is often the primary issue organizations face. He added that many companies attempt to automate processes before resolving deeper operational inconsistencies.
“We fundamentally believe you need to address the data and the process-centric realm before you’re going to achieve the value of going through the AI and the automation.”
The discussion reinforced a reality many finance leaders already recognize automation maturity depends heavily on disciplined accounting operations, standardized workflows, and clean financial data.
Controllers Are Looking Beyond Traditional Metrics
The panel also explored how organizations are measuring AR performance as automation expands.
D’Eon outlined several metrics LTK tracks beyond traditional DSO and aging measures, including invoice cycle speed, payment application timing, and payout efficiency.
“One of the metrics we look at is when a deal goes to closed won, how long does it take you to invoice?” D’Eon said. “We track the percentage of that that goes out within the first two days.”
Panelists also discussed newer operational indicators emerging alongside AI adoption, including touchless processing rates, dispute trends, customer self-service utilization, and predictive cash forecasting accuracy.
Shah highlighted the growing importance of benchmarking and trend analysis across industries.
“We are actually taking it to the step where, how did you do versus yourself? Are you on track?” Shaw said. “And even at the buyer segmentation.”
The conversation suggested that finance organizations are beginning to view AR performance through a broader operational lens, one that incorporates customer behavior, process efficiency, and forecasting reliability alongside traditional collection metrics.
AI is Reshaping Tasks More Than Eliminating Roles
One of the webinar’s most direct exchanges centered on a question many finance professionals continue to ask: will AI eliminate accounting jobs?
D’Eon responded quickly.
“My response to that is over my dead body,” he said. “My commitment to my team was, look, I’m not replacing anyone’s job with AI. I’m enhancing your ability to do more.”
Shah added that finance leaders should distinguish between job replacement and task automation.
“AI doesn’t replace jobs, it replaces tasks,” Shah said. “People that know accounting, that know finance, that know their domain are actually going to be very well amplified and positioned.”
Peck expanded on that idea, arguing that reducing repetitive administrative work allows finance professionals to operate more strategically across the business.
“If they’re stuck in the drudgery of the task or the things that are lower value added, they don’t have the capacity, the time, the bandwidth to focus on being an indispensable business partner,” Peck said.
Rather than presenting AI as a workforce reduction initiative, the discussion framed automation as an opportunity to elevate finance teams toward more analytical and advisory responsibilities.
Finance Leaders Are Encouraged to Start Small
As the session concluded, panelists offered consistent guidance for controllers and CFOs beginning their own AI initiatives.
D’Eon advised finance leaders to focus on operational pain points first.
“Find out how to use it to solve friction within your teams,” he said.
Shah encouraged finance professionals to become personally familiar with AI tools instead of viewing them as highly technical systems reserved for specialists.
“The big misconception is you have to be super technical,” Shaw said. “It’s opened the door not just for controllers and financial people, but for a lot of different domains.”
Peck emphasized the importance of momentum.
“Get started,” he said. “Start piloting, start using AI, see the value, get credibility in the organization.”
Across the discussion, the message remained practical and restrained. The strongest AI initiatives inside accounts receivable are not necessarily the most ambitious. They are the ones solving tangible operational problems, improving visibility, reducing repetitive work, and allowing finance teams to spend more time on judgment, analysis, and decision-making.
Watch the full webinar here.
ABOUT THE SPONSOR:
Accounts receivable needs people with vision, talent, and the technological genius to streamline B2B transactions from both sides. And it needs champions of the accounts receivable (AR) department who know their teams’ time could be better spent beyond the monotony of manual keying and paper processes. Billtrust began questioning the status quo in 2001, just as a new millennium was taking off. Perfect timing for the merger of inspiration and innovation to propel real change in AR automation. Today, the company continues its mission to modernize processes for a better balance throughout the order-to-cash cycle. Learn more at www.billtrust.com.
