Regardless of how many goods a company sells or services they perform, if customers are consistently missing invoice payments — or worse, not making them at all — the business may end up in a cash crunch and unable to pay its own bills. Traditionally, the accounts receivable (AR) teams at businesses extending credit have had to balance two competing ideas: Spend long hours developing credit terms based on each customer’s specific financial profile, or save time by creating broader, one-size-fits-all policies that increase the risk of unpaid invoices and time-consuming follow-ups. But modern businesses have another option that can save time without sacrificing that personalized touch — artificial intelligence (AI).

This article explores how AI is transforming AR and offers a comprehensive guide for businesses that want to leverage this powerful technology.

What Is AI in Accounts Receivable?

Businesses leverage AI technology to enhance their AR processes by automating routine and error-prone tasks like entering data, linking invoices to purchase orders and sending payment reminders to customers to reduce errors while gaining valuable insights into customer payment behaviors. When used properly, AI acts as a constant assistant, meticulously handling invoicing, payment tracking and customer follow-ups. These tools let accounting teams and business owners focus on more complex problems, as well as on growth and strategic planning.

By integrating AI into AR, businesses can automate and improve the entire AR process, from the generation and sending of invoices to tracking customer payments in real time. According to Strategic Treasurer’s and Corcentric’s 2023 Modernizing Accounts Receivable Processing survey, forecasting has become the No. 1 pain point in the AR process, cited by 39% of respondents, up from 13% in 2021. With AI, businesses can predict when and whether customers are likely to pay, which, in turn, informs credit decisions and payment terms. This predictive capability helps businesses forecast cash flow more accurately and time incoming and outgoing payments appropriately, ultimately allowing business leaders to free up cash for other important initiatives.

Key Takeaways

  • When purposefully deployed, AI can significantly improve AR processes by automating routine tasks, increasing accuracy and providing valuable predictive insights. These improvements enhance cash flow management and create a more efficient AR operation.
  • AI systems in AR rely on sophisticated technologies with a broad range of applications, including machine learning, natural language processing, robotic process automation and predictive analytics.
  • Successfully implementing AI in AR requires careful and deliberate planning to ensure that the AI system is fueled by accurate data and that users of all ability levels can understand and benefit from the technology.

AI in Accounts Receivable Explained

AI can be integrated into various aspects of the AR process by leveraging four key technologies, which will be explored in greater detail in the next section. The resulting tools work together, alongside accounts payable, to help businesses proactively manage cash flow. With more cash control, businesses reduce the risk of incurring deficits when their bills come due and free up resources that they can strategically invest when opportunities arise.

Even though many of AI’s capabilities are still evolving, businesses across industries are already finding ways to implement it. According to Accenture’s analysis of U.S. Bureau of Labor statistics in its report titled “A New Era of Generative AI for Everyone,” 59% of tasks in the business and financial operations category have high potential for automation and augmentation through generative AI, placing it in the top five of all job categories included in the report. With AI, accounts receivable teams can achieve faster invoice processing, up-to-date and accurate cash flow measures and improved customer relationships and transparency. However, AI tools carry some degree of risk. Businesses must implement them carefully and deliberately — along with appropriate safeguards and governance policies — to ensure accuracy and avoid potential financial and reputational harm if errors are left undiscovered.

AI Technologies Applied in AR

Businesses can introduce AI into AR in several ways, with each relying on different advanced technologies to enrich processes. This section examines four key AI technologies — machine learning, natural language processing, robotic process automation and predictive analytics — and their AR applications.

  • Machine Learning (ML)

    ML involves algorithms that continuously learn from historical data to make predictions that become more reliable over time without the need for explicit programming changes or updates. In AR, ML analyzes customers’ past payment behaviors to predict their future payment patterns, and it identifies high-risk accounts and creates more reliable collection strategies. For example, if a customer always pays its invoices five days late, a business might use ML-powered AI to identify this pattern and send reminders a week before the due date to increase the likelihood of receiving an on-time payment. By implementing pattern-based strategies like this throughout AR, ML cuts manual effort, boosts accuracy and helps the business develop targeted, proactive policies that reduce delinquent payments and improve key financial metrics, such as days sales outstanding.

  • Natural Language Processing (NLP)

    NLP is a subsection of AI that enables computers to understand and interpret human language. In an AR context, NLP can be used in chatbots to automate customer interactions by processing and responding to queries and generating personalized communication better than traditional — and often frustrating — chatbots can. These sophisticated chatbots are more likely to correctly answer customer questions, which frees up staff to work on more complex issues. If human interaction becomes necessary to satisfy a customer’s request, AI with NLP capabilities can extract relevant information from calls, chats and other unstructured data sources, such as emails and invoices, to arm representatives with the knowledge they’ll need to resolve the issue before the call even begins. These capabilities improve service quality, reduce the time needed to solve problems, facilitate timely follow-ups on outstanding invoices, and streamline data extraction processes, ultimately honing efficiency.

  • Robotic Process Automation (RPA)

    RPA relies on software to automate repetitive tasks that follow set rules, such as data entry, invoice matching and payment reconciliation. Manually completing these menial tasks can be a time-consuming and error-prone process. By integrating RPA into AR, businesses can significantly minimize the time and effort spent on these routine tasks. And by freeing up AR teams to focus on other activities, such as assisting customers or setting up new accounts that grow revenue, businesses can improve operations while reducing errors and processing times and streamlining the overall AR workflow.

  • Predictive Analytics

    Predictive analytics uses statistical algorithms to analyze historical data and make predictions about future events. Many business platforms, such as enterprise resource planning systems, use predictive analytics to model scenarios, forecast future outcomes and provide business leaders with actionable strategies that can help their businesses maintain or improve performance, even in the face of uncertainty. In AR, predictive analytics can forecast payment delays and cash flow trends, as well as identify risky credit terms or customer actions. By deploying predictive analytics, businesses gain insights into customer payment behaviors, improve credit risk assessments and make informed decisions on how to mitigate risks. But companies must remember that past performance is not always a perfect indicator of future outcomes, and they should take steps to make sure appropriate oversight and accountability is in place when leveraging AI to make predictions, especially when it comes to customer behavior and other potential high-volatility factors.

Benefits of Integrating AI in AR

Integrating AI into AR processes can benefit businesses in several ways, all of which create a more predictable cash flow. Below are four key benefits of integrating AI into AR and a look at how businesses can monitor the impact of AI in each category.

  • Increased efficiency: Businesses can use AI to automate repetitive and time-consuming tasks, such as invoice generation, payment tracking and data entry, as well as to help staff quickly search accounting records to find financial and customer information when it’s needed. This means staff can process invoices faster, spend less time on collections and improve overall operational efficiency. Businesses can monitor AI’s impact on efficiency by tracking key performance indicators like order processing time and the number of invoices handled per employee in a given period.
  • Accuracy and precision: AI technologies, including ML and NLP, create a more accurate and precise AR process by analyzing large datasets to identify patterns, predict payment behaviors and accurately match payments to invoices and purchase orders. This diminishes the risk of errors and discrepancies and helps businesses quickly generate accurate financial documents, both at financial close and for ad hoc reporting. Using quality control metrics, including error rates and forecast accuracy, businesses can compare AI results against traditional methods to better inform oversight decisions and policies.
  • Scalability: Traditional AR processes weren’t able to scale without a proportional increase in resources, including bringing in more staff to create, send and process invoices and collect payments. But AI systems can process large datasets quickly and accurately, giving businesses a scalable AR solution. For example, AI-driven systems can process customer payments and automatically update account status much faster than a human AR team could. As with many other systems, businesses should monitor how the AI system performs under varying workloads, especially during peak periods, to be sure the system is capable of maintaining accuracy as the business expands.
  • Enhanced customer experience: AI can provide timely and personalized communication through AI-powered chatbots and virtual assistants. These automated customer service features handle customer inquiries, send payment reminders and offer 24/7 support, even outside of traditional business hours. This leads to faster response times and a more satisfying customer experience, especially for quick and/or minor customer inquiries that don’t require human intervention, such as those related to order status or business hours. Additionally, AI can analyze customer payment behaviors to provide insights into personalized payment plans and early payment incentives. Businesses can track customer satisfaction through direct feedback, reviews and surveys to catch any unexpected complications, incorrect information or inefficiencies and fix them before they become widespread issues that cause reputational harm or drive customers to the competition.

Challenges With AI in AR

Due to the complexity of both AI and AR, businesses must inevitably navigate challenges when integrating the two. By understanding these challenges, businesses can create risk management strategies and contingency plans to mitigate their impact. Here are four major challenges businesses should anticipate and tips on how to address them.

  • Integration complexity: Before integration, businesses must first test compatibility between new AI technologies and their legacy systems, including both accounts receivable and payable. This testing is often conducted by experienced AI vendors that can run thorough and detailed technical evaluations on both existing and new frameworks. To mitigate the risk of system rejection, many businesses use a phased implementation plan to identify data gaps or integration failures before they affect the entire system. This gradual approach helps minimize disruptions and allows the business to maintain operational continuity during much, if not all, of the implementation process.
  • Data privacy and security: AI solutions are only as good as the data they’re powered by, and the vast amounts of data required to maintain helpful AI tools must be kept private and secure. This is especially true for highly sensitive financial functions, such as AR, where mishandled data can lead to financial or legal penalties. To protect this data, businesses using AI for AR should implement robust data governance frameworks, such as those with sophisticated encryption and access controls. And because of ever-changing cybersecurity threats, companies must regularly audit and test their data security practices and always prioritize the safety and security of their customers’ financial and personal information.
  • Cost: Depending on the scope of the planned integration, AI tools can be costly, especially for small and medium-sized businesses with limited resources. Expenses typically include purchasing or licensing AI software, upgrading infrastructure and onboarding and training staff. To minimize costs, businesses can start with a targeted AR pilot to demonstrate return on investment to stakeholders and investors before scaling up. Additionally, companies can opt to choose an AI-as-a-service vendor that handles system maintenance and upgrades for the length of the contract, often as part of a larger business platform that includes AR. These systems typically come with flexible pricing and customizable features, ensuring that a business pays only for what it needs rather than a large suite of sophisticated technology that may not directly benefit its profitability.
  • Change management: Like other applications of AI, implementing AI in AR can significantly impact employees and the larger company culture. Employees may resist changes due to perceived disruptions to their typical workflows, unfamiliarity with the tools or fears of job displacement. To ease their worries and smooth the transition, businesses must be transparent, including informing staff of clear timelines, benefits and expectations for both the implementation project and the employees themselves. They should also include comprehensive training programs to prepare workers for the future, as well as provision of support policies to help them adjust to the new technology. These change management policies can significantly increase the likelihood of a smooth AI adoption.

Implementing AI in Accounts Receivable

To successfully implement AI in AR, businesses need careful planning and deliberate strategies. While specific steps may vary based on a business’s legacy systems, customer preferences, accepted payment methods and other unique operational factors, decision-makers can follow these 11 steps as a road map for developing their own implementation plan.

  1. Evaluate current AR processes: As a first step, businesses should evaluate existing AR processes to identify inefficiencies and bottlenecks that are slowing down operations. This assessment will provide a clear understanding of current workflows and pinpoint specific tasks that AI can remedy, as well as any internal problems or data gaps that should be addressed before AI is implemented. If left unaddressed, these issues may affect AI’s ability to reliably improve the AR process.
  2. Define specific goals for AI implementation in AR: By clearly defining objectives, such as reducing invoice processing time and enhancing customer interactions, businesses can set specific, measurable goals to guide what, when and how they deploy AI technologies. Overly broad or unrealistic goals can lead to scope creep and other challenges that can reduce AI’s effectiveness and skyrocket costs.
  3. Research and select appropriate AI technologies: Some AI applications may not be a good fit for certain businesses, so leaders should research available options that fall within their budget and choose which ones best fit with their AR goals, rather than just choosing the most popular or trendy option. Consider the potential costs, capabilities and scalability of each technology, as well as how well each tool will integrate with existing systems.
  4. Choose suitable AI vendors or develop in-house solutions: Some companies may have the in-house resources and expertise required to create and implement their own AI solutions. But for businesses that don’t already have AI specialists on staff, it might be better to work with a vendor to develop a system that will satisfy their needs. Before choosing a vendor, businesses should evaluate all potential candidates with regard to their track record, available support services and compatibility with existing systems. This due diligence minimizes the chances of encountering suboptimal performance or integration delays and can lead to a long-term relationship with a valuable business partner.
  5. Prepare and clean data for AI training and use: Inaccurate and poorly organized data can skew AI’s output and lead to poor performance and inaccurate insights. Because AI systems rely on high-quality data for training and operation, businesses should implement data governance practices and conduct regular audits to maintain data integrity and ensure that the business is continually feeding the system reliable and useful data.
  6. Pilot test AI solutions in a controlled segment of AR operations: Pilot tests allow businesses to evaluate the impact of AI in AR in controlled and focused environments. This approach lets analysts, decision-makers and stakeholders identify any issues and make adjustments before full-scale implementation. By carefully testing AI, businesses trade short delays now for long-term reliability and greater profitability and efficiency gains later.
  7. Train AR staff and stakeholders: Comprehensive training gives both staff and stakeholders an understanding of how they’ll use AI tools, while also ensuring that everyone is comfortable with the new processes. Collaborative and open training sessions also encourage participants to speak up and address any concerns before AI is fully implemented in their work, minimizing the risk of redundant processes or overlooked complications when the system goes live.
  8. Integrate AI systems fully into AR workflows: After the system has been tested and users have been trained, it’s time for targeted AR deployment. Collaborations among IT and AR teams are essential to address any technical challenges that arise, especially in these early stages, where unexpected bugs and user errors are the most common setbacks. Once pilot AI tools have been integrated into some AR workflows, businesses can start to see productivity improvements and other investment returns from the new system.
  9. Monitor AI system performance and adjust as needed: During these early phases, end users and IT teams can identify hiccups before they become widespread and costly headaches. Then, businesses — or their external AI vendors — can use this feedback, alongside performance data, to inform adjustment strategies before full deployment. For example, if the system is struggling to read the numbers on purchase orders, the system can be recalibrated or POs can be formatted differently to fix the issue now, rather than wait until the system has already incorrectly processed hundreds of POs.
  10. Scale AI integration: Once AI systems have proven effective in the pilot phase, businesses can scale them across all AR operations. During this stage, IT teams should monitor infrastructure to make sure it can support the increased load, as well as remain responsive to any issues that staff report. Scaling should be deliberate and phased to prevent issues from spreading too quickly and causing business disruptions.
  11. Continuously update and refine AI models: AI systems are continuously learning as new data is added, so businesses must monitor their performance to guarantee that they’re delivering their expected benefits in line with current objectives. As with any new technology, especially one as quickly evolving as AI, these systems will require ongoing updates to prevent them from becoming outdated.

Choosing the Right Accounts Receivable AI Software

Modern businesses are implementing AI into nearly every inch of their operations, but choosing where, when and what to implement can be a major challenge. With Artificial Intelligence in NetSuite, businesses can embed AI functionality across their organization, creating a centralized information hub to power and support their AI tools, including those for AR. By analyzing historical AR data, NetSuite AI gives businesses insights that inform future strategies, such as credit decisions and proactive customer account management. And thanks to continuous model refinement, NetSuite AI remains accurate and relevant, even as economic conditions and financial challenges evolve.

NetSuite’s cloud-based AI solution leverages technologies , such as optical character recognition and document object detection, to automate AR tasks, including generating and processing invoices. Additionally, NetSuite’s AI-driven content generation tools can automatically send personalized payment reminders and customer communications, ensuring timely follow-ups on outstanding invoices to improve cash flow. With NetSuite, businesses spend less time focusing on chasing late-paying customers and more time investing in growth and maintaining a competitive advantage.

AI technology can transform AR processes, from automating routine tasks to providing predictive insights that bolster decision-making — but only when implemented and managed carefully. With deliberate and targeted strategies, AR teams can leverage AI-driven tools to achieve greater efficiency, accuracy and scalability, while also improving customer satisfaction and financial performance. And as AI continues to evolve, businesses must continually monitor and adjust their AI systems to maintain up-to-date standards and best practices, ensuring that both their staff and their customers are benefiting from this new and evolving technology.

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AI in Accounts Receivable FAQs

Will accounts receivable be replaced by AI?

While it’s impossible to fully predict the future, it’s unlikely that artificial intelligence (AI) will entirely replace accounts receivable (AR). Instead, humans will leverage AI to augment AR processes by automating repetitive tasks, improving accuracy and providing predictive insights. Human oversight is still a necessary part of strategic decision-making, solving complex issues and maintaining personalized customer relationships.

What is generative AI in accounts receivable?

Generative AI in accounts receivable refers to the use of advanced artificial intelligence (AI) models that can create meaningful content to perform certain tasks, such as generating personalized payment reminders or responding to customer queries with more detailed information than traditional chatbots can provide. This technology is powered by machine learning and natural language processing to enhance accounts receivable workflows.

Can accounts receivable be automated?

Many, but not all, aspects of accounts receivable (AR) can be automated using artificial intelligence. The parts of AR best suited for automation include menial or routine tasks, such as invoice generation, payment tracking and sending payment reminders. By automating error-prone manual tasks, businesses can reduce mistakes, speed up invoice processes and free up AR teams to focus on more strategic activities and complex problem-solving.

How is AI used in accounting?

Accountants can leverage artificial intelligence (AI) to quickly complete data-intensive tasks, such as generating accurate financial statements, analyzing large amounts of information and uncovering trends and insights into customer behavior. Additionally, AI can save significant time by automating repetitive tasks, including invoice generation and matching payments, invoices and purchase orders. And, beyond analyzing the past, many AI tools also have predictive scenario modeling capabilities to inform future cash flow and customer payment strategies.