Why AR teams are the biggest untapped opportunity for AI
Moveo AI Team
in
🏆 Leadership Insights
In October 2025, Wakefield Research published a survey of 500 finance decision-makers at North American companies with revenues above $250 million.
Among organizations already using artificial intelligence in accounts receivable (AR) management, 99% had successfully reduced their DSO (Days Sales Outstanding), and 75% cut that figure by six days or more.
For a company with $500 million in annual revenue, six fewer days in the receivables cycle translates to tens of millions of dollars freed from working capital.
What is equally striking, though, is where the gap actually lives. Most enterprise AR teams have some form of automation in place. Invoices go out, reminders fire, payments get applied. What is missing is the intelligence layer that connects AR to everything else happening with the same customer.
When support handles a billing dispute on Monday and AR sends an automated collection notice on Thursday without knowing any of it, the problem is not automation; it is context.
According to BillingPlatform, only 14% of organizations have deployed AI in AR so far, even as 80% rate it a high or critical priority. The gap is not awareness. It is execution.
That gap is precisely why accounts receivable represents the single largest untapped opportunity for AI in finance. The function combines high-volume repeatable tasks, transaction data rich enough to power predictive models, and direct, measurable impact on cash flow. Few areas of a finance organization offer that combination at the same time.
Accounts Receivable: the function that drives cash flow, and the intelligence gap holding it back
Few indicators have as immediate an impact on a company’s financial health as the performance of its AR department.
It determines how quickly billed revenue becomes available cash, and how efficiently the business funds its own operations without external capital.
The strategic relevance of the function is now widely recognized: in 2025, working capital optimization became the top priority for CFOs, after years in seventh place, according to The Hackett Group. Yet the intelligence infrastructure connecting AR to the rest of the customer journey has not kept pace with that ambition.
Accounts receivable accounts for the largest share of excess working capital, an opportunity valued at $600 billion, according to The Hackett Group’s 2025 U.S. Working Capital Survey, driven by an 18-day DSO gap between top-quartile and median performers.
AR now represents the largest single component of excess working capital across the 1,000 largest U.S. public companies.
The root of that accumulation is execution. There is an 18-day DSO gap between top-quartile companies and median performers. Top-quartile companies close their receivables cycle in 28 days. Median companies take 46. That difference is not the result of worse customers or harder markets. It is the result of more mature processes, better automation, and more effectively used data.
Only 14% of organizations have deployed AI in AR so far, even though 67% are currently evaluating it. The challenge is not that AR teams lack tools. It is that the tools they have operate in isolation: the invoicing system does not know what support was resolved last week, and the collections workflow does not know what AR was agreed to last month.
Context gets lost at every handoff, and that is where revenue leaks.
Why managing Accounts Receivable with AI delivers faster results than any other area of finance
When finance leaders assess where to apply AI first, the conversation often centers on FP&A, fraud detection, or report automation.
Those are sound bets, but the Accounts Receivable department presents a combination of structural characteristics rarely found together in other finance functions: predictable volumes of structured tasks, a naturally rich behavioral data environment, and a KPI with direct impact on cash that the CFO tracks daily.
Add to that the fact that AR sits at the intersection of customer service, billing, and collections, and you have a function where the absence of shared context creates compounding losses. These conditions make AR the most fertile ground for AI to deliver measurable returns at speed.
High-volume repeatable tasks as the foundation for AR automation
The AR cycle generates a constant flow of tasks with predictable logic: invoice generation and delivery, channel-specific reminders, cash application, payment reconciliation, and delinquency escalation.
Each step follows defined rules and processes to structure data, the exact type of work where automation systems deliver the greatest efficiency gains with the lowest risk of error.
When automation absorbs that volume, Accounts Receivable teams stop chasing tasks and start managing exceptions.
Complex disputes, strategic account negotiations, and high-value customer relationships, activities that require genuine human judgment, finally receive the time and attention they warrant.
Critically, when those repetitive workflows are handled by intelligent agents rather than disconnected point tools, the data they generate feeds back into a shared context layer that improves every subsequent interaction.
Rich transactional data as fuel for predictive models in AR management
The AR department accumulates, over time, one of the most valuable data assets in the entire finance operation.
Every customer leaves a behavioral trail: payment dates consistently met or missed, communication channels that generate responses, seasonal delay patterns, and dispute history. That data exists in AR by definition and does not need to be built from scratch.
When structured and fed into propensity to pay models, that data enables the AR team to segment the portfolio with real objectivity: who pays with a simple reminder, who needs proactive outreach, and who represents genuine delinquency risk.
That segmentation shifts AR logic from volume to precision, and it is what separates high-performance teams from those still running one-size-fits-all collection cadences across the entire customer base.
Direct working capital impact as a measurable KPI for the accounts receivable team
DSO is not an abstract metric. Every day removed from the receivables cycle has a calculable financial value.
For a company with $500 million in annual revenue and a current DSO of 46 days, closing the gap to the top-quartile benchmark of 28 days, per The Hackett Group, releases approximately $25 million in working capital.
That is no longer a credit with a cost. It is operational liquidity available for investment, supplier payments, or debt reduction.
The practical consequence is that AI's impact on AR is traceable in a way many finance technology investments are not. DSO falls. The aging report improves. The percentage of disputes resolved on first contact rises. The CFO does not need to wait for an annual review to know whether the investment delivered.
What happens when the Accounts Receivable department adopts AI
Adoption data already allows us to move beyond projections and hypothetical cases.
Companies that have implemented AI in accounts receivable management are reporting concrete, consistent results across multiple studies and verticals.
The Billtrust and Wakefield Research survey of 500 finance leaders at large North American companies is clear:
99% of organizations using AI in AR reduced DSO
75% cut the figure by six days or more
and separately, 82% improved productivity and scalability without adding headcount
These are not results from companies with exceptional analytical maturity. They represent the average across 500 large organizations spanning multiple industries.
On operational efficiency, McKinsey documents that finance teams with robust AI adoption spend 20 to 30% less time on manual data processing tasks.
The time recovered is redirected toward analysis, customer relationships, and decision-making, precisely the shift AR needs to move from back-office processing to its rightful role as a direct driver of cash flow.
On cash flow predictability, organizations with AI in AR report a 43% improvement in cash flow predictability and a 67% reduction in cost per collection contact. Together, these results reflect what happens when AR stops reacting to delinquency and starts anticipating it.
In practice, what changes when AI agents with contextual memory enter the receivables cycle is the ability to treat each customer individually at scale, with full awareness of everything that has already happened.
A customer who has always paid on time and missed a payment for the first time receives a different approach than one with a recurring delinquency history. A customer who opened a support dispute is not automatically sent a collection notice three days later without any context.
The agent knows what happened across every touchpoint and acts accordingly, closing the intelligence gap that isolated systems leave open.
Report: The $7.5B Recovery Opportunity
Access the data showing where the highest return potential lies for AR teams adopting AI with contextual intelligence.
How to manage Accounts Receivable with AI: From isolated AR to connected receivables intelligence
The transition from isolated AR tools to connected receivables intelligence does not have to be a disruption.
For most organizations, the most effective path is progressive: each stage delivers independent value and prepares the data infrastructure and shared context that the next level requires.
The risk of trying to connect everything at once, without that progression, is real. The risk of not starting is higher.
Stage 1: building the foundation for connected AR management
The most efficient entry point is automating high-volume, low-variability tasks: invoice generation and delivery at the moment of service, programmatic reminder cadences by channel (email, SMS, WhatsApp), and machine learning-powered cash application for automatic payment reconciliation.
These functions consume the largest share of the AR team’s operational time and represent the clearest path to building a structured, high-quality data layer that more advanced intelligence can act on later.
Organizations that automate omnichannel collections at this stage lay the groundwork for a faster receivables cycle. When the right reminder reaches the right customer on the right channel at the right time, a significant share of simple delays resolves before they escalate, and every interaction is logged in a way that informs the next one.
Stage 2: adding predictive intelligence to the AR management process
With core processes automated and data flowing in a structured way, the second stage introduces predictive models into the prioritization logic.
The delinquent portfolio is segmented by payment propensity, not just by days past due. The team receives a daily list prioritized by recovery potential and engagement complexity, informed by behavioral signals rather than static aging buckets.
This stage is also where receivables forecasting gains reliability. With models trained on each customer's payment history, the AR team can project with greater confidence when each invoice will be collected, giving the CFO cash planning data grounded in actual behavioral signals rather than broad averages.
The accounts receivable collection strategies that support this evolution are covered in depth in the Moveo.AI blog.
Stage 3: memory-driven agents as the differentiator for the accounts receivable team
The third stage is where accounts receivable management stops being an isolated function and starts operating as part of a shared intelligence layer. This is where AI agents with contextual memory change the nature of customer interaction.
At Moveo.AI, TrueThread is the persistent memory layer that retains the context of every customer interaction across the full lifecycle: what was promised in support, which channel they prefer, the history of prior negotiations, why they were late last time.
When an invoice comes due and the AR agent reaches out, it does not start from scratch. It knows who this customer is, what is happening on the account, and which approach is most likely to produce a resolution.
This is what differentiates compounding intelligence from siloed systems.
An AR agent that does not know the customer opened a support dispute three days earlier will send an automated collection notice and create unnecessary friction.
With Moveo.AI, AR, Customer Service, and collections share context through TrueThread and execute within the governance guardrails of TruePath, ensuring every action respects business rules and regulatory requirements.
What still holds Accounts Receivable teams back from making the leap
If the data is this clear and the roadmap is mapped out, why have only 14% of AR teams deployed AI so far? The barriers are real, but none of them is structural. Understanding them is the first step to moving past them.
Budget and IT prioritization
Budget constraints and IT backlog top the list of obstacles organizations report, according to BillingPlatform.
AR has historically competed for resources against higher-visibility projects and lost.
The answer lies in the business case: with the DSO reductions that 75% of companies with AI in AR have already documented, return on investment materializes in months, not years.
Data fragmentation
For predictive models to work, payment data, customer interaction history, and dispute records need to be accessible in a unified system. In most organizations, that information is distributed across ERP platforms, CRM systems, and siloed collection tools.
Consolidation is preparation work, but it is the foundation without which any AI investment in AR produces limited results.
Internal positioning of the function
As long as AR is treated as an operational support function, investment will remain proportionally insufficient.
The shift begins when finance leadership recognizes that collections and receivables management is a strategic liquidity lever, not a cost center to be minimized.
The clearest signal of that shift came from the market itself: in 2025, working capital optimization became the number one CFO priority, according to The Hackett Group, after years in seventh place.
The working capital you need is already on your balance sheet
$600 billion in excess working capital is trapped in accounts receivable. An 18-day DSO gap between top-quartile companies and the median. And 99% of organizations that adopted AI in AR reporting DSO reductions.
The data is consistent: the largest return available to finance leaders today is not a new revenue source. It is the speed and intelligence with which revenue already billed converts into cash, across a customer journey where every department finally shares the same context.
Specialized AI for financial services is no longer a future bet. It is the operational differentiator separating AR teams that process collections in isolation from AR teams that deliver liquidity intelligently, with full context on every customer at every stage.
The path starts with structured automation, evolves into predictive intelligence, and culminates in agents that coordinate AR, customer service, and collections through a shared memory layer, so that nothing gets lost between departments.
Moveo.AI works with finance teams that want to make that transition with precision, governance, and measurable results. If your accounts receivable department is still operating with disconnected systems and no shared context across the customer journey, the time to change that is now. Talk to a specialist →