{"id":9156,"date":"2026-05-15T11:58:55","date_gmt":"2026-05-15T11:58:55","guid":{"rendered":"https:\/\/www.zintego.com\/blog\/?p=9156"},"modified":"2026-05-15T12:17:40","modified_gmt":"2026-05-15T12:17:40","slug":"accounts-receivable-automation-statistics","status":"publish","type":"post","link":"https:\/\/www.zintego.com\/blog\/accounts-receivable-automation-statistics\/","title":{"rendered":"Accounts Receivable Automation Statistics"},"content":{"rendered":"<p>Key insights on AR automation adoption, market growth, DSO, cash application, collections, AI, reporting, and finance-team productivity.<\/p>\n<p><strong>Accounts Receivable Automation Statistics<\/strong><\/p>\n<p>Accounts receivable automation has become a serious finance priority because cash collection is no longer viewed as a narrow back-office routine. It affects liquidity, forecasting, customer experience, borrowing needs, sales operations, and the amount of time finance teams spend chasing information that should already be visible inside the invoice-to-cash workflow.<\/p>\n<p>The statistics around AR automation show a market that is growing quickly, but they also show a workflow that remains deeply manual inside many organizations. Companies may use digital invoices, online payment processors, customer portals, or ERP systems and still rely on manual follow-up, spreadsheet-based aging reviews, email reminders, delayed cash application, and disconnected reporting.<\/p>\n<p>That gap is what makes accounts receivable automation statistics useful. Market forecasts show where software spending is moving, while operating benchmarks show where finance teams still lose time. A company with short payment terms and a few recurring customers will see the problem differently from a global enterprise managing thousands of invoices, deductions, credit memos, partial payments, and disputes. The underlying goal is similar: reduce manual work, collect cash faster, and make receivables easier to manage before balances become overdue.<\/p>\n<p>AR automation now covers a wide range of capabilities. It may include digital invoice delivery, automated reminders, <a href=\"https:\/\/www.zintego.com\/receipt-template\/cash-receipt-template\">customer payment portals<\/a>, collector work queues, cash application, remittance matching, deduction and dispute management, credit-risk scoring, payment links, forecast dashboards, and AI-assisted prioritization. Because vendors and research firms draw the boundaries differently, market-size estimates should be read directionally rather than as one perfectly comparable number. The stronger signal is that finance leaders are investing in more structured, visible, and data-driven invoice-to-cash processes.<\/p>\n<h2><strong>Key accounts receivable automation statistics<\/strong><\/h2>\n<ul>\n<li>Grand View Research estimated the global accounts receivable automation market at $4.79 billion in 2025 and projected it to reach $12.86 billion by 2033.<\/li>\n<li>Mordor Intelligence estimated the market at $3.44 billion in 2025, $3.84 billion in 2026, and $6.66 billion by 2031 under its forecast model.<\/li>\n<li>IMARC Group estimated the market at $3.0 billion in 2025 and projected it to reach $6.9 billion by 2034.<\/li>\n<li>A 2025 AR automation survey found that only 3 percent of respondents had fully automated AR, while 49 percent were considering AR automation solutions and 39 percent were implementing them.<\/li>\n<li>The same survey found that 78 percent cited poor cash flow or high DSO as the most significant consequence of inefficient AR operations.<\/li>\n<li>Most organizations in the survey reported average DSO between 30 and 60 days, with 65 percent between 31 and 60 days and only 17 percent below 30 days.<\/li>\n<li>Manual workflows were cited as top challenges in invoicing, collections, payments, and reporting, showing that receivables automation is a full lifecycle issue rather than one isolated task.<\/li>\n<li>AI is gaining attention in AR: 67 percent were evaluating AI use, but only 14 percent had deployed it in AR operations.<\/li>\n<\/ul>\n<h2><strong>How to Read AR Automation Statistics Correctly<\/strong><\/h2>\n<p>Accounts receivable automation numbers can be misleading when every percentage is treated as though it measures the same thing. A market forecast measures vendor revenue. A DSO statistic measures collection speed. A cash application statistic measures how payments are matched to open invoices. A survey about AI adoption may include anything from early experimentation to production use inside a governed workflow.<\/p>\n<p>The cleanest way to interpret the data is to separate market statistics from workflow statistics. Market statistics show how fast spending is growing. Workflow statistics show where teams still rely on manual work. A market can expand rapidly even while many companies remain at early stages because large enterprises are modernizing existing AR systems, mid-market companies are adding portals and collections tools, and vendors are bundling AR features into broader order-to-cash platforms.<\/p>\n<p>Definitions also matter. One company may call its process automated because invoices are emailed from the billing system. Another may reserve the term for a workflow that includes automatic invoice delivery, dynamic reminders, customer self-service, payment capture, remittance matching, dispute routing, cash application, and collections analytics. Both can appear in the same automation category even though their operating maturity is very different.<\/p>\n<p>DSO statistics need similar care. A high DSO may reflect weak collections, but it can also reflect industry payment terms, customer mix, contract structure, disputes, seasonality, or intentional credit policy. Automation does not magically make every customer pay faster. It helps finance teams act earlier, remove avoidable friction, track promises, surface disputes, and apply cash more quickly once money arrives.<\/p>\n<p>The strongest AR automation statistics answer a specific operating question. Market forecasts answer whether the category is growing. Manual-workflow statistics answer whether teams still need process improvement. DSO statistics answer how long cash remains tied up. <a href=\"https:\/\/www.zintego.com\/blog\/cash-app-basics-learn-how-to-send-and-receive-money-safely-and-fast\/\">Cash application statistics<\/a> answer whether payments become usable information quickly. AI statistics answer whether teams are ready to let automation assist judgment-based decisions such as prioritizing collections or spotting risky accounts.<\/p>\n<h2><strong>What to separate before comparing numbers<\/strong><\/h2>\n<ul>\n<li>Market-size estimates should be used for category direction, not as proof of one company&#8217;s internal ROI.<\/li>\n<li>Workflow statistics are more useful for diagnosing daily friction, such as manual invoicing, collections, payment processing, reporting, and cash application.<\/li>\n<li>DSO should be compared with payment terms, customer mix, industry norms, and dispute rates before it is treated as a pure collections-performance score.<\/li>\n<li>AI adoption statistics should distinguish deployed tools from tools under evaluation, because planned use and governed production use are very different maturity levels.<\/li>\n<\/ul>\n<table>\n<tbody>\n<tr>\n<td width=\"284\">\n<p><strong>Statistic type<\/strong><\/p>\n<\/td>\n<td width=\"284\">\n<p><strong>What it measures<\/strong><\/p>\n<\/td>\n<td width=\"284\">\n<p><strong>How to use it<\/strong><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td width=\"284\">\n<p>Market forecast<\/p>\n<\/td>\n<td width=\"284\">\n<p>Vendor revenue and category growth<\/p>\n<\/td>\n<td width=\"284\">\n<p>Useful for investment context, not daily AR efficiency.<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td width=\"284\">\n<p>DSO benchmark<\/p>\n<\/td>\n<td width=\"284\">\n<p>Time receivables stay outstanding<\/p>\n<\/td>\n<td width=\"284\">\n<p>Useful for cash-conversion and liquidity analysis.<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td width=\"284\">\n<p>Cash application maturity<\/p>\n<\/td>\n<td width=\"284\">\n<p>How payments are matched to invoices<\/p>\n<\/td>\n<td width=\"284\">\n<p>Useful for diagnosing unapplied cash and reconciliation work.<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td width=\"284\">\n<p>AI adoption<\/p>\n<\/td>\n<td width=\"284\">\n<p>Use or evaluation of AI-enabled AR features<\/p>\n<\/td>\n<td width=\"284\">\n<p>Useful for understanding technology direction and governance needs.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><strong>Market Size and Growth Outlook<\/strong><\/h2>\n<p>Public market estimates consistently point to growth in AR automation, although the totals differ. Grand View Research estimated the market at $4.79 billion in 2025 and projected $12.86 billion by 2033. Mordor Intelligence estimated $3.44 billion in 2025 and $6.66 billion by 2031. IMARC Group estimated $3.0 billion in 2025 and $6.9 billion by 2034. The spread is normal for a category that can include software, implementation services, cloud subscriptions, payment features, analytics, customer portals, and adjacent order-to-cash capabilities.<\/p>\n<p>The practical takeaway is not that one estimate should replace all others. The useful takeaway is that multiple independent models show continued expansion. AR automation sits at the intersection of cash flow, customer communication, ERP modernization, payment digitization, and AI-enabled finance operations. Those forces make receivables automation more strategic than a simple reminder-email tool.<\/p>\n<p>Growth is also being driven by the visibility problem. Finance leaders want earlier insight into which invoices are at risk, which customers need follow-up, which payments are unmatched, which disputes are delaying cash, and how current receivables affect short-term liquidity. Manual aging reports often show the problem after it has already developed. Automation gives teams a chance to act earlier.<\/p>\n<p>Cloud deployment is another growth driver. Cloud AR systems make it easier to add customer portals, payment links, configurable workflows, analytics dashboards, and AI-assisted work queues without waiting for large ERP upgrade cycles. That does not remove integration work, but it gives finance teams a more flexible path to improve the invoice-to-cash process.<\/p>\n<p>The market outlook also reflects convergence. Collections automation, cash application, payment processing, billing, credit management, and receivables analytics are becoming more connected. Vendors that used to focus on one step are expanding toward broader platforms. Buyers are also asking broader questions: not only whether invoices can be sent automatically, but whether cash can be forecast, collected, applied, and explained with less manual effort.<\/p>\n<p><strong>Market-growth statistics to know<\/strong><\/p>\n<ul>\n<li>Grand View Research projected 13.2 percent CAGR for the AR automation market from 2026 to 2033.<\/li>\n<li>Mordor Intelligence projected 11.64 percent CAGR from 2026 to 2031.<\/li>\n<li>IMARC Group projected 9.34 percent CAGR from 2026 to 2034.<\/li>\n<li>Grand View Research identified North America as the largest region in 2025 and Asia Pacific as the fastest-growing region.<\/li>\n<li>Mordor Intelligence estimated North America at 38.40 percent of market share in 2025 and Asia-Pacific as the fastest-growing region through 2031.<\/li>\n<li>Mordor Intelligence estimated solutions at 67.33 percent of market share in 2025 and cloud offerings at 79.21 percent of market size in 2025.<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-9158 alignnone\" src=\"https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-1.jpg\" alt=\"\" width=\"2296\" height=\"1227\" srcset=\"https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-1.jpg 2296w, https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-1-300x160.jpg 300w, https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-1-1024x547.jpg 1024w, https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-1-768x410.jpg 768w, https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-1-1536x821.jpg 1536w, https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-1-2048x1094.jpg 2048w\" sizes=\"auto, (max-width: 2296px) 100vw, 2296px\" \/><\/p>\n<p><em>Figure 1. Public AR automation market estimates differ by methodology, but each points toward continued category growth.<\/em><\/p>\n<h2><strong>Why Manual AR Work Still Matters<\/strong><\/h2>\n<p>Manual AR work remains important because receivables are not finished when an invoice leaves the billing system. A customer may not receive the invoice, may dispute a line item, may need a different payment method, may promise to pay later, may pay without remittance details, or may send a partial payment that has to be matched against several open items. Each of those moments creates manual work if the process is not structured.<\/p>\n<p>A 2025 AR automation survey found that manual workflows were the leading challenge across multiple parts of the lifecycle: invoicing, collections, <a href=\"https:\/\/www.zintego.com\/blog\/what-is-a-payment-stack-key-components-of-online-payment-processing-explained\/\">payments<\/a>, and reporting. That matters because a company can improve one step and still experience friction somewhere else. Faster invoice delivery does not solve cash application if remittance data remains incomplete. Better payment links do not solve collections if follow-up remains inconsistent. Stronger reporting does not solve disputes if ownership is unclear.<\/p>\n<p>Manual work also hides in communication. Collectors may send reminders from their inboxes. Sales may receive customer complaints without updating finance. Customer service may answer billing questions without visibility into payment status. Finance may track promises to pay in spreadsheets. These activities do not always appear in system metrics, but they consume capacity and make cash forecasting less reliable.<\/p>\n<p>Manual receivables work becomes especially painful during growth. More customers, more invoices, more usage-based billing events, more payment methods, and more international accounts can all increase complexity. If the workflow depends on people remembering what to chase, the process becomes fragile. Automation helps by turning AR activity into trackable work instead of scattered messages.<\/p>\n<p>The goal is not to remove every human decision. Collections, disputes, credit, and customer communication still require judgment. The goal is to reduce repetitive handling, make priorities clear, and give AR teams enough context to act on the right accounts at the right time.<\/p>\n<p><strong>Manual-process statistics to know<\/strong><\/p>\n<ul>\n<li>63 percent of respondents cited manual workflows as the top challenge in invoicing.<\/li>\n<li>57 percent cited manual and time-consuming workflows as a top collections challenge.<\/li>\n<li>60 percent cited manual workflows as a leading challenge in payments.<\/li>\n<li>67 percent said AR reports require manual effort, creating delays and inefficiencies.<\/li>\n<li>86 percent described cash application as fully manual, mostly manual, or partially automated.<\/li>\n<li>88 percent reported collections processes that were fully, mostly, or partially manual.<\/li>\n<li>Only 23 percent reported mostly or fully automated payment processing.<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-9159\" src=\"https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-2.jpg\" alt=\"\" width=\"2363\" height=\"1224\" srcset=\"https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-2.jpg 2363w, https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-2-300x155.jpg 300w, https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-2-1024x530.jpg 1024w, https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-2-768x398.jpg 768w, https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-2-1536x796.jpg 1536w, https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-2-2048x1061.jpg 2048w\" sizes=\"auto, (max-width: 2363px) 100vw, 2363px\" \/><\/p>\n<p><em>Figure 2. Manual workflows remain common across cash application, collections, payments, invoicing, and reporting.<\/em><\/p>\n<h2><strong>DSO, Cash Flow, and the Receivables Pressure Point<\/strong><\/h2>\n<p>Days Sales Outstanding is one of the most visible AR metrics because it translates receivables performance into time. When DSO rises, cash is tied up longer. The business may still be profitable on paper, but liquidity becomes less predictable. That can affect borrowing needs, supplier payments, hiring, investment timing, and the confidence finance leaders have in cash forecasts.<\/p>\n<p>The 2025 AR automation survey found that 78 percent of respondents cited poor cash flow or high DSO as the most significant consequence of inefficient AR operations. That result is important because it shows finance leaders are not viewing receivables inefficiency only as an administrative issue. They connect it directly to liquidity and working capital.<\/p>\n<p>DSO reduction is not only about asking customers to pay faster. In many companies, the delay begins before the customer ever decides to pay. Invoices may be delivered late, sent to the wrong contact, lack supporting detail, contain errors, or require a dispute before payment can proceed. Collection follow-up may be inconsistent. Payment status may be difficult for customers to check. Cash may arrive but remain unapplied because remittance information is missing.<\/p>\n<p>Automation helps by reducing those internal sources of delay. Digital invoice delivery can shorten the time between billing and receipt. Automated reminders can make follow-up consistent. Customer portals can give customers access to invoices, balances, statements, and payment options. Cash application automation can reduce the time between payment receipt and account update. Analytics can help collectors focus on accounts that matter most.<\/p>\n<p>The strongest DSO programs combine automation with policy. Payment terms, customer onboarding, credit review, invoice accuracy, dispute ownership, and collection escalation rules all affect outcomes. Automation gives the team better execution, but leadership still has to define the rules of the receivables process.<\/p>\n<p><strong>DSO and cash-flow statistics to know<\/strong><\/p>\n<ul>\n<li>78 percent cited poor cash flow or high DSO as the top consequence of inefficient AR operations.<\/li>\n<li>65 percent of surveyed organizations reported DSO between 31 and 60 days.<\/li>\n<li>35 percent fell in the 46 to 60 day DSO range.<\/li>\n<li>Only 17 percent reported DSO below 30 days.<\/li>\n<li>Automating invoice delivery and follow-up was the top planned action to reduce DSO, selected by 56 percent.<\/li>\n<li>Enhancing reporting and visibility into AR metrics was selected by 55 percent as a planned DSO-reduction action.<\/li>\n<li>Prioritizing collections based on customer risk or invoice aging was the top current action to reduce DSO, selected by 45 percent.<\/li>\n<\/ul>\n<h2><strong>Cash Application Is a Core Automation Opportunity<\/strong><\/h2>\n<p>Cash application is one of the most operationally important parts of receivables because cash is not fully useful until it is matched to the right customer and invoice. A payment can arrive in the bank account while the customer&#8217;s account still shows an open balance if remittance details are missing or matching rules are weak. That creates confusion for collectors, customers, controllers, and cash forecasters.<\/p>\n<p>Manual cash application is common because B2B payments rarely arrive in one perfect format. Customers may pay several invoices in one transfer, short-pay because of a dispute, deduct credits, send remittance data separately, or use payment references that do not match invoice numbers. A human analyst may need to interpret the payment, check customer history, and clear exceptions manually.<\/p>\n<p>Automation improves this step by using structured remittance data, bank information, customer records, invoice history, and matching logic to connect payments to open items. AI can help when the relationship between payment and invoice is not exact, but finance teams still need exception queues and review thresholds. The value is not simply speed. Clean cash application gives sales, collections, and customer service a more accurate view of what is actually outstanding.<\/p>\n<p>Poor cash application can also distort collection behavior. If a customer has already paid but the payment is not applied, collectors may chase the wrong account. If unapplied cash sits too long, finance may misread liquidity and aging. If disputes and deductions are not classified properly, recurring customer or billing problems may remain invisible.<\/p>\n<p>The strongest cash application programs measure both automation rate and exception quality. A high auto-match rate is useful, but it must be paired with accuracy. Teams should know which customers, payment methods, deduction types, and remittance formats create the most manual work. That information helps improve the process upstream rather than treating every unmatched payment as an isolated problem.<\/p>\n<p><strong>Cash application statistics to know<\/strong><\/p>\n<ul>\n<li>86 percent of survey respondents described cash application as fully manual, mostly manual, or partially automated.<\/li>\n<li>Only 2 percent reported fully automated cash application.<\/li>\n<li>Partial automation was the most common cash application stage at 38 percent.<\/li>\n<li>Only 14 percent reported mostly or fully automated cash application.<\/li>\n<li>AI adoption plans were strong for cash application, with 53 percent reporting planned use.<\/li>\n<li>Cash application automation market subsegments are forecast to grow faster than some broader AR automation estimates, reflecting the importance of matching and reconciliation.<\/li>\n<\/ul>\n<h2><strong>Collections and Dunning Are Still Highly Manual<\/strong><\/h2>\n<p>Collections automation is often where the business case becomes most visible. When invoices age, the problem appears in cash forecasts, management dashboards, and customer conversations. Yet collections remains heavily manual in many organizations. The work may depend on collectors building their own account lists, sending reminders from email, and following up based on aging buckets rather than risk, payment behavior, or predicted cash impact.<\/p>\n<p>Manual collections can be inconsistent even when the team is experienced. One collector may follow up quickly, another may wait until the account crosses a threshold, and another may prioritize the largest balance even if a smaller account is at higher risk. Without automation, managers may struggle to see which accounts were contacted, what was promised, and which follow-ups are due next.<\/p>\n<p>Dunning automation improves the repeatable parts of collections. It can send reminders before and after due dates, adjust tone by customer segment, escalate accounts based on aging or amount, and avoid reminders when a dispute is already open. The best workflows are not robotic. They use automation to keep routine communication consistent while giving collectors time to handle complex conversations.<\/p>\n<p>Collections prioritization is also changing. Instead of working the oldest invoices first, teams can use risk indicators, <a href=\"https:\/\/www.zintego.com\/blog\/the-top-9-payment-methods-chosen-by-freelancers\/\">customer payment<\/a> history, invoice value, dispute status, relationship importance, and probability of payment to decide where effort should go. AI and analytics are attractive here because they can help identify which accounts deserve human attention sooner.<\/p>\n<p>The risk is over-automation. Customers may react poorly to reminders that ignore context, repeat after payment, or escalate while a dispute is unresolved. Strong collections automation must connect with cash application, dispute management, account notes, and customer communication history so outreach is timely and accurate.<\/p>\n<p><strong>Collections and dunning statistics to know<\/strong><\/p>\n<ul>\n<li>88 percent of respondents reported collections that were fully manual, mostly manual, or partially automated.<\/li>\n<li>Only 1 percent reported fully automated collections.<\/li>\n<li>50 percent described collections as fully or mostly manual.<\/li>\n<li>Manual emails or templates were the most common collections notification method at 49 percent.<\/li>\n<li>Only 31 percent used automated reminder workflows.<\/li>\n<li>Very few respondents used customer portals for collections notifications, with portal use at 11 percent.<\/li>\n<li>52 percent still relied on mostly or fully manual dunning processes.<\/li>\n<li>Only 12 percent had mostly or fully automated dunning.<\/li>\n<\/ul>\n<h2><strong>AI Is Moving From Interest to Practical Use Cases<\/strong><\/h2>\n<p>Artificial intelligence is becoming part of the AR automation conversation, but the data shows a clear difference between interest and deployment. Many finance leaders are evaluating AI, while far fewer have put it into production across receivables operations. That gap is normal for a finance function that deals with customer balances, payment promises, credit risk, disputes, and audit trails.<\/p>\n<p>The most practical AI use cases in AR are not abstract. AI can help prioritize collection follow-up, recommend dunning actions, detect invoice anomalies, suggest cash application matches, support cash-flow forecasting, and identify customers whose payment behavior is changing. These use cases are valuable because they help teams focus attention and reduce the manual review burden.<\/p>\n<p>Collections prioritization is especially important. Aging buckets are useful, but they are blunt. Two customers can both be 45 days past due and still require different actions. One may always pay after a reminder, another may be disputing a line item, and another may be showing signs of financial stress. AI can help spot patterns that a static report misses.<\/p>\n<p>Cash forecasting is another promising use case. AR data contains signals about when cash may arrive, but those signals are often fragmented across invoices, payment behavior, disputes, promises, customer segments, and collection notes. AI-assisted forecasts can help finance teams estimate cash inflows more realistically, as long as the model is monitored and grounded in reliable data.<\/p>\n<p>Governance remains essential. AI should support finance judgment, not replace accountability. High-risk accounts, material balances, disputed invoices, credit decisions, write-offs, and customer-sensitive communications still require human review. The strongest AR AI programs will combine clear rules, audit trails, explainability, and exception handling with automation that reduces routine work.<\/p>\n<p><strong>AI and intelligent-automation statistics to know<\/strong><\/p>\n<ul>\n<li>67 percent of respondents were evaluating AI use in AR.<\/li>\n<li>Only 14 percent had deployed AI in AR operations.<\/li>\n<li>60 percent planned to use AI for collections prioritization.<\/li>\n<li>59 percent planned to use AI for dunning optimization.<\/li>\n<li>57 percent planned to use AI for anomaly detection in invoice errors.<\/li>\n<li>53 percent planned AI use for cash-flow forecasting.<\/li>\n<li>53 percent planned AI use for cash application.<\/li>\n<li>Current usage of AI tools across major AR use cases remained low, generally in the 12 to 16 percent range.<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-9160\" src=\"https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-3-scaled.jpg\" alt=\"\" width=\"2560\" height=\"1129\" srcset=\"https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-3-scaled.jpg 2560w, https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-3-300x132.jpg 300w, https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-3-1024x452.jpg 1024w, https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-3-768x339.jpg 768w, https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-3-1536x678.jpg 1536w, https:\/\/www.zintego.com\/blog\/wp-content\/uploads\/2026\/05\/Article2-Chart-3-2048x903.jpg 2048w\" sizes=\"auto, (max-width: 2560px) 100vw, 2560px\" \/><\/p>\n<p><em>Figure 3. AR teams are evaluating AI most actively for collections prioritization, dunning optimization, anomaly detection, forecasting, and cash application.<\/em><\/p>\n<h2><strong>Customer Self-Service Portals Can Reduce AR Friction<\/strong><\/h2>\n<p>Customer self-service portals are important because many receivables delays are communication delays. Customers may need copies of invoices, statements, payment links, tax forms, purchase-order references, or dispute status before they can pay. When those requests are handled manually, AR teams spend time answering basic questions instead of resolving higher-value issues.<\/p>\n<p>A useful customer portal does more than display invoices. It should let customers view balances, download documents, make payments, see payment status, update contacts through controlled workflows, and communicate about disputes. It should also integrate with the core billing and AR system so the information customers see is current.<\/p>\n<p>Portals can reduce aging when they remove friction from the payment process. A customer who can find the right invoice, confirm the amount, select a payment method, and receive confirmation is less likely to delay because of administrative confusion. The portal does not force payment, but it reduces avoidable reasons for non-payment.<\/p>\n<p>Customer experience matters here. A poorly designed portal can frustrate customers if login is difficult, invoice data is incomplete, or payment options are limited. A strong portal should reduce effort for both sides. It should not simply shift internal AR complexity onto the customer.<\/p>\n<p>Portals also improve visibility. When customers view invoices, download statements, submit disputes, or make payments, the AR team gains signals about account activity. Those signals can inform collections prioritization and reduce unnecessary follow-up.<\/p>\n<p><strong>Customer portal statistics to know<\/strong><\/p>\n<ul>\n<li>Improved DSO was identified as the top benefit of customer payment portals, selected by 51 percent of respondents.<\/li>\n<li>Faster and more flexible customer payments were cited as a portal benefit by 45 percent.<\/li>\n<li>Fewer support and collection calls were cited by 36 percent.<\/li>\n<li>Only 18 percent reported no measurable portal benefits yet, suggesting most adopters expect or experience value.<\/li>\n<li>Portal use in collections notifications remained underutilized, with only 11 percent using portals for that purpose.<\/li>\n<li>Customer portals are most valuable when they connect invoice visibility, payment options, dispute communication, and status updates in one place.<\/li>\n<\/ul>\n<h2><strong>Reporting and Analytics Are Part of the Automation Problem<\/strong><\/h2>\n<p>Receivables reporting is often treated as an after-the-fact activity, but it is central to automation maturity. If finance leaders cannot see aging, disputes, promised payments, collector activity, customer behavior, and cash forecasts in a timely way, they cannot manage AR proactively. They can only explain what happened after cash was already delayed.<\/p>\n<p>The 2025 AR automation survey reported that manual reporting remains a major issue. That matters because manual reports are often slow, inconsistent, and difficult to trust. A collector may maintain one view, the controller another, and leadership a third. When reports do not agree, teams spend time reconciling the report instead of acting on the receivables problem.<\/p>\n<p>Analytics also affects prioritization. A static aging report shows overdue invoices, but it may not show which invoices are most likely to be recovered, which customers need intervention, which disputes are blocking cash, or which collection action is most effective. Automation can bring these signals together so AR teams manage exceptions rather than simply sorting balances by age.<\/p>\n<p>Cash forecasting depends on reporting quality. If expected collections are based only on due dates, the forecast will miss customer behavior, payment patterns, disputes, and promised dates. Better AR analytics can help finance teams produce forecasts that reflect what is likely to happen rather than what the invoice terms say should happen.<\/p>\n<p>The reporting goal is not more dashboards for their own sake. The goal is decision-ready visibility. A useful AR dashboard should help teams decide which invoices to chase, which customers to contact, which disputes to escalate, which cash inflows to expect, and where the process is breaking down.<\/p>\n<p><strong>Reporting and analytics statistics to know<\/strong><\/p>\n<ul>\n<li>67 percent of respondents said AR reports require manual effort.<\/li>\n<li>48 percent cited lack of predictive insights as a reporting and analytics challenge.<\/li>\n<li>40 percent cited fragmented data as a reporting and analytics challenge.<\/li>\n<li>Nearly half of respondents had limited or no visibility into key AR metrics.<\/li>\n<li>77 percent had limited or moderate visibility into collector performance and collection effectiveness.<\/li>\n<li>Only 15 percent reported high or real-time visibility into collector performance and collection effectiveness.<\/li>\n<li>9 percent had no visibility at all into collector performance and collection effectiveness.<\/li>\n<\/ul>\n<h2><strong>Payment Processing and Digital Payment Options<\/strong><\/h2>\n<p>Payment processing is part of AR automation because the collection process is only complete when the customer can pay in a convenient, controlled, and trackable way. If invoice delivery is digital but payment remains difficult, the customer experience is still fragmented. If payment is accepted but remittance data does not connect cleanly to the invoice, cash application remains manual.<\/p>\n<p>Many organizations already use payment processors, but that does not mean the AR process is automated. A processor may handle the transaction while finance still manually tracks payment status, reconciles deposits, follows up on failures, and applies cash. The value comes from connecting payment acceptance with invoice, customer, bank, and accounting data.<\/p>\n<p>Payment method flexibility also matters. Customers may prefer ACH, card, bank transfer, digital wallet, direct debit, or local payment methods depending on region and customer type. Offering more options can reduce friction, but it also increases the importance of controls, reconciliation, fees, and fraud monitoring.<\/p>\n<p>Transaction cost is a recurring concern. Some businesses switch payment providers to reduce fees; others prioritize integration with ERP and billing systems. The right trade-off depends on payment volume, average invoice value, customer expectations, and how much manual reconciliation the current process creates.<\/p>\n<p>Payment automation should not weaken control. Failed payments, refunds, chargebacks, suspicious changes, and unusual payment patterns need clear workflows. The best AR systems connect payment events to customer records and collector activity so finance has a single view of what happened.<\/p>\n<p><strong>Payment processing statistics to know<\/strong><\/p>\n<ul>\n<li>Only 23 percent of surveyed organizations reported mostly or fully automated payment processing.<\/li>\n<li>Lower transaction costs were the leading reason for switching payment processors, selected by 68 percent.<\/li>\n<li>Better integration with ERP or billing systems was selected by 52 percent as a reason to switch providers.<\/li>\n<li>Support for digital wallets remained limited, with only 17 percent currently offering that option.<\/li>\n<li>Local and global payment method support was already used by 30 percent and planned by 39 percent.<\/li>\n<li>Fraud and compliance capabilities were described as only moderately effective by 53 percent of respondents.<\/li>\n<\/ul>\n<h2><strong>Disputes, Deductions, and Revenue Leakage<\/strong><\/h2>\n<p>Disputes are one of the most important reasons AR automation cannot stop at invoice reminders. A customer may refuse payment because pricing is wrong, service was incomplete, tax was disputed, a purchase order is missing, a credit memo is expected, or delivery details do not match the invoice. If disputes are not routed quickly, DSO rises even when collectors are active.<\/p>\n<p>Manual dispute handling is difficult because it crosses departments. Finance may need sales, customer success, operations, logistics, tax, or billing teams to resolve the issue. If ownership is unclear, the invoice remains open while everyone assumes someone else is responding. Automation helps by assigning owners, tracking status, and keeping the financial impact visible.<\/p>\n<p>Deductions and short payments create similar problems. A customer may pay most of an invoice and deduct a credit, claim, discount, or penalty. Without structured deduction management, cash application teams may leave balances open or clear them without proper investigation. Over time, recurring deductions can become hidden revenue leakage.<\/p>\n<p>Dispute automation should classify the issue, link evidence, identify the owner, and track resolution time. The goal is not just to close one invoice. It is to find patterns that can be prevented upstream through better contracts, billing accuracy, delivery documentation, tax setup, or customer communication.<\/p>\n<p>A strong AR automation program treats disputes as a cash-flow issue and a process-improvement signal. Every dispute that delays cash is also a clue about where the order-to-cash process is failing.<\/p>\n<p><strong>Dispute and revenue-leakage statistics to know<\/strong><\/p>\n<ul>\n<li>Revenue leakage was cited by 56 percent as a major consequence of inefficient AR operations.<\/li>\n<li>Increased operational costs were cited by 49 percent as a major consequence.<\/li>\n<li>Invoice disputes were cited as a common collections issue by 41 percent.<\/li>\n<li>Cross-department collaboration was the biggest dispute-resolution pain point, cited by 46 percent.<\/li>\n<li>Lack of standardized workflows was cited by 43 percent as a dispute-resolution barrier.<\/li>\n<li>Slow internal response times were cited by 39 percent.<\/li>\n<li>Limited visibility into dispute status also affected efficiency, making timely resolution harder.<\/li>\n<\/ul>\n<h2><strong>Enterprise, SME, and Industry Differences<\/strong><\/h2>\n<p>AR automation needs differ by company size. Large enterprises often have higher invoice volumes, more business units, more customer segments, more payment methods, and more complex approval or dispute structures. They may also need tighter controls, global process standardization, and integration across ERP, CRM, billing, payments, and treasury systems.<\/p>\n<p>Small and midsize companies may have fewer invoices, but they often feel receivables pain more directly. One late-paying customer can affect cash planning. A small finance team may not have dedicated collectors, cash application analysts, and reporting specialists. Automation can help by making reminders, payment links, invoice visibility, and account follow-up more consistent without building a large AR department.<\/p>\n<p>Industry differences are also important. Subscription and SaaS businesses often care about billing accuracy, failed payments, renewals, and usage-based invoices. Manufacturing and distribution teams may deal with deductions, shipping documents, and large customer accounts. Healthcare and life sciences may face compliance and payer complexity. Financial services buyers may emphasize controls, auditability, and customer data security.<\/p>\n<p>Retail, ecommerce, and consumer businesses may process high transaction volumes and need tight payment reconciliation. Professional services firms may focus on milestone billing, project disputes, and partner approvals. Construction and field-service companies may need AR workflows that connect invoices to contracts, job status, change orders, and customer acceptance.<\/p>\n<p>Because workflows vary, AR automation should not be selected only by generic feature lists. The useful question is how the tool handles the company&#8217;s actual invoice types, customer behavior, payment methods, disputes, reporting requirements, and system landscape.<\/p>\n<p><strong>Buyer-segment and industry statistics to know<\/strong><\/p>\n<ul>\n<li>Mordor Intelligence estimated large enterprises at 58.71 percent of AR automation spending in 2025.<\/li>\n<li>Mordor Intelligence projected SMEs to record a 12.07 percent CAGR between 2026 and 2031.<\/li>\n<li>Mordor Intelligence estimated BFSI at 23.46 percent of the market in 2025.<\/li>\n<li>Healthcare and life sciences were projected by Mordor Intelligence to post a 13.24 percent CAGR through 2031.<\/li>\n<li>Grand View Research identified large enterprises and accounting\/BFSI as dominant 2025 segments in its market summary.<\/li>\n<li>SMEs are more likely to value quick setup, payment links, simple reminders, and accounting integrations than heavy customization.<\/li>\n<\/ul>\n<h2><strong>Cloud Deployment and System Integration<\/strong><\/h2>\n<p>Cloud deployment has become central to AR automation because receivables work is distributed across finance, sales, customer success, banking, payments, and customers themselves. A cloud workflow can support remote approvals, customer portals, automated reminders, and shared dashboards more easily than a process built around local files and manual reports.<\/p>\n<p>Integration determines whether automation removes work or simply creates another system to manage. AR tools need to connect with ERP, billing, CRM, payment processors, banks, customer portals, tax systems, and reporting tools. If data does not flow reliably, finance may still rekey information, reconcile systems manually, or delay action because the dashboard is not trusted.<\/p>\n<p>ERP-native AR functionality may be attractive because receivables data stays near the system of record. Specialist AR platforms may offer deeper cash application, collections, payment, portal, or analytics features. Many companies evaluate a trade-off between platform depth and integration simplicity.<\/p>\n<p>Data quality is part of integration. Customer master records, billing contacts, payment terms, tax setup, credit status, and dispute categories need to be clean enough for automation to work. If customer records are duplicated or payment terms are inconsistent, the workflow will produce exceptions that still require human cleanup.<\/p>\n<p>The strongest integration strategy starts with process design. Finance leaders should map where invoice data comes from, where payment data arrives, where disputes are recorded, who owns customer communication, and how cash forecasts are built. Only then can the technology design support the actual operating model.<\/p>\n<p><strong>Integration and deployment statistics to know<\/strong><\/p>\n<ul>\n<li>Mordor Intelligence estimated cloud offerings at 79.21 percent of AR automation market size in 2025.<\/li>\n<li>Cloud offerings were projected by Mordor Intelligence to advance at a 12.11 percent CAGR through 2031.<\/li>\n<li>Better integration with ERP or billing systems was the second-leading reason for switching payment processors, cited by 52 percent.<\/li>\n<li>Fragmented data was cited by 40 percent as a reporting and analytics challenge.<\/li>\n<li>Limited system integration was cited by 36 percent as a collections challenge.<\/li>\n<li>Integration quality determines whether automation reduces handoffs or simply moves manual work between systems.<\/li>\n<\/ul>\n<h2><strong>Metrics AR Leaders Should Track<\/strong><\/h2>\n<p>AR automation should be measured with a balanced scorecard. DSO is important, but it is not enough. A team can reduce DSO temporarily through aggressive collections while damaging customer relationships. A team can improve payment acceptance while leaving cash application manual. A team can automate reminders while ignoring disputes. The right metrics show speed, quality, customer experience, and control together.<\/p>\n<p>Invoice delivery metrics show whether customers receive accurate bills quickly. Collection metrics show whether follow-up is timely and prioritized. Cash application metrics show whether payments are matched accurately and quickly. Dispute metrics show whether issues are routed and resolved. Reporting metrics show whether leadership has reliable visibility. Customer metrics show whether the process is easier or more frustrating for the people who pay.<\/p>\n<p>Teams should also track capacity. Invoices managed per collector, accounts per collector, cash applied per analyst, manual touches per payment, and backlog levels all show whether automation is helping finance scale. These measures are especially useful when the company is growing and wants to avoid adding headcount at the same rate as revenue or invoice volume.<\/p>\n<p>Quality metrics matter because speed can hide errors. Auto-reminder rates should be paired with dispute flags. Auto-cash-application rates should be paired with match accuracy. Portal adoption should be paired with payment success and customer support volume. AI prioritization should be paired with outcomes, not just usage.<\/p>\n<p>The best AR dashboards connect metrics to action. A report should not only say DSO is rising. It should show whether the rise comes from disputes, customer segments, payment failures, delayed invoicing, missing remittance data, weak follow-up, or a few large overdue accounts.<\/p>\n<p><strong>Additional metrics worth adding to the AR scorecard<\/strong><\/p>\n<ul>\n<li>DSO measures the average time receivables remain outstanding, but it should be read with payment terms and customer mix.<\/li>\n<li>Collection effectiveness index can show how much collectible receivables are actually being collected over a period.<\/li>\n<li>Cash application auto-match rate shows how much incoming cash can be applied without manual intervention.<\/li>\n<li>Unapplied cash aging shows how long payments sit before they become usable account information.<\/li>\n<li>Dispute aging shows whether customer issues are delaying cash beyond normal terms.<\/li>\n<li>Promise-to-pay kept rate shows whether customer commitments translate into actual receipts.<\/li>\n<li>Portal adoption, payment link usage, and support-call volume show whether customer self-service is reducing friction.<\/li>\n<li>Collector productivity should be measured by cash impact and risk reduction, not only number of emails sent.<\/li>\n<\/ul>\n<h2><strong>Common Barriers to AR Automation<\/strong><\/h2>\n<p>The barriers to AR automation are often operational rather than purely technical. A company can buy software and still struggle if customer records are poor, invoices contain errors, payment terms are inconsistent, collectors do not trust the work queues, and customers keep contacting individual employees instead of using the official channel.<\/p>\n<p>Budget and IT backlog are common constraints, but they are not the only blockers. Receivables modernization touches billing, sales, customer service, payments, treasury, and accounting. If those teams do not agree on the workflow, automation can expose disagreements that were previously hidden inside manual work.<\/p>\n<p>Customer behavior also matters. Some customers require purchase orders, portals, <a href=\"https:\/\/www.zintego.com\/blog\/the-best-free-invoice-templates-you-didnt-know-you-needed\/\">special invoice formats<\/a>, local payment methods, or internal approval cycles. AR automation needs enough flexibility to support these customer-specific rules without turning every account into a custom process.<\/p>\n<p>Data quality can derail automation. Incorrect billing contacts, duplicate accounts, missing tax details, unclear payment terms, stale customer records, and inconsistent dispute codes all create downstream work. Automation performs best when the data foundation is clean enough for rules and AI models to operate reliably.<\/p>\n<p>Change management is the final barrier. Collectors may be used to building their own lists. Sales teams may worry about customer communication. Customers may resist portals. Controllers may worry about automated cash matching. These concerns are manageable, but they require training, governance, and a phased rollout that builds trust.<\/p>\n<p><strong>Implementation barriers to watch<\/strong><\/p>\n<ul>\n<li>Budget constraints and IT backlog were reported as barriers that delay progress toward AR automation.<\/li>\n<li>Lack of internal expertise was the leading barrier to AI adoption at 32 percent.<\/li>\n<li>Manual reporting, fragmented data, and lack of predictive insight remain major analytics barriers.<\/li>\n<li>Low portal adoption can limit customer self-service benefits even when portal technology exists.<\/li>\n<li>Poor customer master data weakens invoice delivery, collections routing, cash application, and reporting.<\/li>\n<li>Weak cross-department ownership can slow dispute resolution even when collectors are active.<\/li>\n<\/ul>\n<h2><strong>Future Outlook for Accounts Receivable Automation<\/strong><\/h2>\n<p>The future of AR automation will likely be more predictive, more connected, and more customer-facing. Basic reminder workflows and invoice delivery will remain useful, but the larger opportunity is to connect receivables activity with cash forecasts, customer behavior, credit risk, payment choices, dispute signals, and business planning.<\/p>\n<p>AI will increasingly support prioritization. Instead of asking collectors to work a static aging list, systems can recommend which accounts are most likely to affect cash, which customers need a different message, which invoices are blocked by disputes, and which payment promises are unlikely to be kept. That can make collections more strategic, as long as finance teams keep human oversight for sensitive accounts.<\/p>\n<p>Cash application is also likely to become more intelligent. Better remittance capture, bank-data integration, matching models, and exception learning can reduce unapplied cash and improve account visibility. This is especially important as payment methods diversify and companies accept more digital payments across regions.<\/p>\n<p>Customer self-service will continue to matter. Customers increasingly expect easy access to invoices, statements, payment status, and payment options. Businesses that make payment easier may improve both collections and customer experience, while businesses that make customers chase documents may create unnecessary payment delays.<\/p>\n<p>The strongest AR teams will not treat automation as a one-time project. They will keep improving invoice accuracy, payment options, dispute workflows, collector prioritization, analytics, and cash forecasting. The goal is a receivables process that is faster, more transparent, and easier to manage as the business grows.<\/p>\n<p><strong>Outlook statistics and watch points<\/strong><\/p>\n<ul>\n<li>Double-digit growth forecasts from several market researchers suggest continued investment in receivables automation.<\/li>\n<li>AI use cases are likely to expand first in collections prioritization, dunning optimization, invoice anomaly detection, cash-flow forecasting, and cash application.<\/li>\n<li>Customer portals and payment links will become more important as buyers expect simpler digital payment experiences.<\/li>\n<li>Cash application automation will remain a high-value area because payment matching affects collections visibility and financial reporting.<\/li>\n<li>AR analytics will move from backward-looking aging reports toward predictive cash and risk visibility.<\/li>\n<li>Finance teams that combine automation with clean data and clear ownership will gain more value than teams that only digitize existing manual habits.<\/li>\n<\/ul>\n<h2><strong>Frequently Asked Questions<\/strong><\/h2>\n<p><strong>What is accounts receivable automation?<\/strong><\/p>\n<p>Accounts receivable automation uses software to streamline invoice delivery, collections follow-up, customer payment options, cash application, dispute routing, receivables reporting, and customer account visibility. The goal is to reduce manual work while improving cash flow, DSO, customer experience, and auditability.<\/p>\n<p><strong>Why do AR automation market estimates differ?<\/strong><\/p>\n<p>Research firms define the category differently. Some count only core AR automation software, while others include services, cloud subscriptions, payment functionality, customer portals, analytics, order-to-cash suites, or adjacent finance automation tools. The exact totals differ, but the shared pattern is continued growth.<\/p>\n<p><strong>What is the most important AR automation metric?<\/strong><\/p>\n<p>DSO is the most visible metric, but it should not stand alone. Finance teams should also track cash application auto-match rate, unapplied cash aging, dispute aging, promise-to-pay kept rate, portal adoption, collection effectiveness, manual touches, and forecast accuracy.<\/p>\n<p><strong>Does AR automation reduce DSO?<\/strong><\/p>\n<p>AR automation can reduce DSO when it removes avoidable delays in invoice delivery, reminders, payment acceptance, disputes, and cash application. It works best when automation is paired with clear payment terms, clean customer records, accurate invoices, and consistent collection policies.<\/p>\n<p><strong>How does AI fit into accounts receivable automation?<\/strong><\/p>\n<p>AI can help prioritize collections, recommend dunning actions, detect invoice anomalies, improve cash-flow forecasts, and suggest cash application matches. It should support finance judgment rather than replace controls, especially for material balances, disputes, credit decisions, and sensitive customer relationships.<\/p>\n<p><strong>Can small businesses benefit from AR automation?<\/strong><\/p>\n<p>Yes. Small businesses may not need complex enterprise workflows, but they can benefit from digital invoice delivery, reminders, payment links, customer portals, simple aging visibility, and cleaner accounting integration. The main gain is often consistency and faster visibility rather than large-scale headcount reduction.<\/p>\n<p><strong>What is the biggest implementation mistake?<\/strong><\/p>\n<p>The biggest mistake is treating AR automation as only a software purchase. Companies need clean customer data, clear ownership of disputes, payment terms discipline, integration with billing and accounting systems, and training for collectors, sales teams, and customer-facing staff.<\/p>\n<p><strong>Which AR process should be automated first?<\/strong><\/p>\n<p>The best starting point depends on the pain. High-volume billing teams may begin with invoice delivery. Teams with slow collections may start with reminders and work queues. Teams with heavy unapplied cash may start with cash application. Teams with poor leadership visibility may begin with AR reporting and analytics.<\/p>\n<p><strong>Final Takeaway<\/strong><\/p>\n<p>Accounts receivable automation statistics show a function under pressure to become faster, more visible, and more predictive. Market forecasts point to sustained software investment, but operating data shows that many finance teams still rely on manual collections, manual reporting, partly automated cash application, and limited real-time visibility into customer payment behavior.<\/p>\n<p>The best AR automation programs do not chase technology for its own sake. They target the points where cash slows down: invoice delivery, customer communication, payment friction, disputes, cash application, aging visibility, and collection prioritization. When those steps become more structured, AR moves from a reactive follow-up function to a strategic cash-flow discipline that helps the business forecast, collect, reconcile, and grow with more confidence.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Key insights on AR automation adoption, market growth, DSO, cash application, collections, AI, reporting, and finance-team productivity. 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