Artificial intelligence (AI) is helping to solve some of the biggest pain points in accounts payables (AP) today. Many AP departments still struggle with manual processes that slow down payments to suppliers, introduce needless errors and drive up costs in the form of both labor and lost opportunities. AI can automate the processes that have resisted automation till now and can improve the results from existing automated processes. Furthermore, AI enables AP departments to deliver strategic value to the business by not only managing cash flow and payments, but also providing insight into trends in those activities that can improve financial planning and forecasting.
This guide delves into how AP teams are using AI to address these issues. It explains how companies are using AI models and algorithms to speed up payment processing, reduce costs and manage risks while maximizing visibility into what’s contributing to expenses on the balance sheet — and gleaning insights from that data.
What Is AI in Accounts Payable?
AP departments use AI in AP automation and reporting to improve the efficiency of payment processing and provide more robust reporting to the business to minimize human intervention in areas such as general ledger (GL) account coding, extracting and verifying information from paper invoices, approving payments, confirming three-way matching and ensuring regulatory compliance.
The addition of AI and machine learning (ML) algorithms to automated accounting systems enables these platforms to perform complex data capture and analysis that previously eluded accounting technology. This means organizations that use AI in AP can ditch antiquated manual data entry processes. As a result, they can slash their costs per invoice and speed up resolution cycles, reducing errors and fraud in the process. Most importantly, AI in AP spurs a reduction in late payments, bolstering procurement practices and improving supplier relationships over time.
In addition, AI analytics fed by all the data generated during invoice and payment processing can improve reporting to the business on cash flow and spend management trends. AI-backed analytics can also help AP teams more quickly detect fraud and errors in the billing and payment ecosystem.
Key Takeaways
- Artificial intelligence in accounts payable can speed up processing time and reduce errors in AP documentation.
- AI in AP workflows can reduce labor costs by automating traditionally manual tasks, such as invoice processing and three-way matching.
- AI in AP analytics can help payment professionals identify trends in spend management and produce more accurate financial forecasting for the business.
- AI analysis can provide insight into when to take advantage of early payment discounts based on predicted cash flow.
- AI algorithms can spot AP fraud and compliance issues before they grow very costly to the business.
AI in Accounts Payable Explained
From invoice automation to payment automation and dynamic discounting to expense audits, AI can play a vital role in improving automated tasks and optimizing work across the full cycle of AP processes. AI also can help AP professionals glean better insights from invoice and payment data for more accurate reporting and risk management.
The Current State of AP Automation
Some of the biggest challenges AP teams face today stem from manual payables processes, many of them still mired in paper-driven documentation.
According to the Institute of Financial Operations and Leadership (IFOL), 75% of accounts payable departments either use no automation or only partially automate their tasks. And, when they do introduce some automation, AP processes are still slowed at various choke points that require human intervention. Recent studies show that fewer than a third of invoices submitted to organizations are automated with straight-through or touchless processing. One study found that 69.8% of payments need manual handling from AP staff somewhere in the progression from receiving invoices to processing them, making GL entries, handling approvals and exceptions and scheduling for payment. Another study found that 82% of organizations still manually key at least some of their invoices into their accounting systems.
That translates into poor AP process efficiency and lackluster performance in paying bills on time and economically:
- 47% of AP professionals say their payment approvals take too long.
- 45% say they face a high number of exceptions.
- 23% say they regularly deal with consequences from delivering late supplier payments.
- 22% say they wrestle with high invoice processing costs.
How AI Transforms AP Automation
AI stands to transform manual AP processes and improve previously automated processes because the power of AL/ML algorithms makes AP automation software practical in far more complex data processing and workflow situations.
The main reason why so much AP work remains manual is that many of the tasks required are either too complicated or interact with too many varied data sources for earlier accounting automation software to execute without higher-order analysis or decision-making. Traditional automation deals best with predictable and structured data sources, highly repetitive, bite-sized steps and simple action triggers.
But businesses often must deal with invoices submitted on paper, in nonstandard formats and containing undetected errors that have to be rectified. Standard AP automation needs significant human intervention to overcome those issues and deliver accurate results. Similarly, searching for noncompliance or fraud across a wide swath of payment activity can’t be easily automated without advanced algorithms trained to look for complex behavior patterns.
This is where AI/ML comes in. AI algorithms learn from historical and ongoing data streams, derived from both an organization’s own activity and public data sets. Pairing that capability with AP automation helps create self-learning systems that can capture and analyze unstructured data and accurately automate multistep processes with very little human involvement. For example, by pairing optical character recognition technology with natural language processing (NLP) algorithms, an organization can scan invoice images and automatically extract details, such as dates, descriptions and amounts due with high accuracy. AI pattern matching can then automate GL coding and map that data for entry into accounting software. In another example, by pairing automated search functions with advanced AI pattern-matching algorithms, AP teams can detect evidence of AP fraud with greater accuracy and speed.
This is why AI in AP opens up so many opportunities to digitize AP and increase automation to streamline AP processes.
AI’s Role in AP Reporting and Analytics
In addition to enhancing automated AP workflows, AI can play a valuable role in helping payment professionals make more data-driven decisions and provide more accurate reporting to the business. Improving AP reporting and data analytics was the number-one priority in a recent survey of AP and finance leaders, chosen by 48% of respondents.
The advanced capabilities afforded by AI data analysis can help payables departments bolster their spend management and improve cash flow forecasting for the business. For example, AI analytics can produce more accurate cash flow forecasts by analyzing historical payment data and supplier behavior, enabling organizations to optimize their working capital and make better-informed financial decisions. Or, AI can evaluate supplier performance by analyzing the same historical data, helping organizations to identify the most reliable vendors or negotiate better terms, which can raise their supply chains’ resiliency or lower its costs, respectively. These insights can be delivered through both accounts payable dashboards and reports generated by AP staff. They can be used to help with financial planning and coordination with stakeholders in other departments, such as procurement and accounts receivable.
According to the IFOL, 18% of AP departments currently use AI technologies in spend management processes for tasks like analyzing spending trends and identifying expense compliance issues or fraud. Another 33% say they’re considering adopting AI technology for spend management in the next year.
Benefits of AI in AP
The use of AI in AP can generate tremendous value for the accounting department and the business as a whole. AI drives smarter automation of more complex AP processes, eliminating time-intensive manual labor. Additionally, AI makes it easier to analyze large and varied AP data sets. This enables AP teams to uncover trends in spending, quickly detect instances of payment fraud and speed up audits of payment activity for compliance. Some of the most common business benefits of AI in AP are:
Increased processing speed and accuracy: AI-backed automation in invoice processing, payment scheduling and accounts reconciliation speeds up the processing cycle and boosts accuracy along the way. Industry estimates say that best-in-class organizations can process payments 81% faster with AI support.
Enhanced fraud detection: Typical AP fraud, such as billing fraud, can cost a median of $5,600 per month for each incident, and typically lingers for 12-14 months before detection, according to the Association of Certified Fraud Examiners. AI pattern matching can enhance fraud detection mechanisms to help AP teams more quickly find evidence of fraudulent behavior.
Cost savings: AI-enabled AP automation can slash the amount of manual labor needed across payment workflows. Industry estimates show that this level of automation can reduce payment processing costs by as much as 76%. Additionally, AI can optimize the use of early payment discounts by dynamically deciding when to pay early based on cash flow and other business considerations.
Optimized cash flow management: AI analytics can help AP departments look at real-time trends in the context of both AP and accounts receivable (AR) historical data. This helps to optimize cash flow management by predicting potential cash flow issues and planning payment schedules in a way that balances timely payments with working capital.
Strategic insights into spending patterns: More than one in three AP professionals say they’re investing in technology to improve data analytics in the coming year. With AI analysis, spending patterns can not only contribute to more accurate cash flow analysis, forecasting and financial planning, but also reveal supply chain insights that inform collaborative discussions between finance and procurement.
Strengthened supplier relationships: By streamlining exceptions handling and reducing the number of late payments, AI in AP can help organizations strengthen relationships with suppliers who value timely payment and frictionless interaction with their customers.
Challenges and Considerations of AI in AP
As organizations bake AI into AP systems and processes, they need to watch for stumbling blocks that could get in the way of successful adoption. The following are some of the most common challenges and considerations that AP departments should keep in mind as they implement AI solutions to meet their AP goals:
High initial costs: Cost management is one of the top priorities for AP leaders in 2024, so it’s understandable that they’d be concerned about the initial investment required to adopt AI in their automation and analytical stack. Companies can reduce their initial outlay by choosing cloud services that charge on a more pay-as-you-go basis.
Resistance to change from employees: While 94% of AP staff are enthusiastic about the prospect of tools to automate the most repetitive parts of their job, some 64% of payables professionals are concerned about the lack of human oversight when AI is embedded into automation. Organizations may need to use change management strategies, such as building human checkpoints into AI-enabled AP processes, to help overcome cultural resistance.
Dependency on high-quality, consistent data: AI requires high-quality and consistent training data to fuel accurate predictions and pattern matching. Organizations should understand that early AI performance may not be indicative of what a system can do in the long run. For example, an AI invoice processing system will get better at accurate data extraction as it is trained on a greater volume and variety of invoices over time.
Need for specific technical expertise: AI/ML technology can require very specific technical expertise for successful adoption, depending on the implementation model. Organizations will have to look carefully at how functional a vendor’s AI capabilities are “out of the box,” and how much training or tuning a vendor will require of customers before its products or services can work well within the company’s AP processes.
Integration challenges: The real benefits of AI-enabled AP automation come when the technology is tightly embedded into accounting software or the enterprise resource planning (ERP) system that is shared by finance and the rest of the business. This is required for so-called “touchless” AP processes, in which the entire invoice processing workflow is automated and requires no manual intervention. AP leaders considering point AI solutions that tackle limited tasks in the payables workflow should look closely at the integration challenges they may face in getting them to coordinate with the rest of their tech stack.
Applications of AI in AP
From the tactical to the strategic, there are many different use cases in which AI can be applied to AP processes. The lowest-hanging fruit tends to involve invoice processing and smarter automation. However, even highly automated AP departments can benefit from looking more deeply into AI capabilities to bolster their ability to run robust reporting and analytics. Here are eight top ways that AI is being applied in AP.
Automated Invoice Processing
AI adds the language recognition and advanced decision-making needed to automate invoice processing tasks that have remained stubbornly manual for years. NLP and advanced pattern-matching algorithms are used with optical character recognition for data capture that turns invoice images into machine-readable data that can be automatically input into accounting platforms. In the meantime, AI decision algorithms can automatically route invoices through approvals and exception-handling.
Three-Way Matching
AI tools are extremely powerful for validating and verifying that invoice data is free of errors and complies with payment rules. One way that AI can speed up payment approvals and help companies achieve AP best practices is by powering the automation of two- or three-way matching of invoice details against purchase orders and receipts. Not only can AI be used in the matching analysis, but it also can help with automated exceptions-handling when discrepancies are found. This is a crucial capability for touchless AP automation.
Fraud Detection
The verification processes and behavioral analytics of AI systems can automatically flag AP issues that represent potential fraud. AI is very good at spotting anomalous invoicing and payment behavior. This could include a combination of abnormal invoice amounts, duplicate invoices, strange payment activity or inconsistencies in vendor details. Additionally, AI can be used to detect expense report abuse if employees try to exploit expense policies.
Dynamic Discounting
AI analysis can be used to look for early payment discount opportunities in invoices and automatically evaluate the benefit of expediting payment based on a range of variables set by AP departments. AI and the automation of AP processes make it possible for companies to be more proactive about monitoring and managing cash flow and triggering eligible early payments when cash flow is healthy or predicted to be so. Many AP departments stuck in manual processes struggle to identify these opportunities quickly enough to reap discounts across a broad range of vendors, instead sticking to discounted payments for just a few preferred suppliers. Some studies say companies can recoup 2% of their annual spend when they manage early discounts well.
Vendor Management
A big chunk of the vendor management process is tied to AP process execution. The more that AP teams process invoices and pay vendors according to defined terms, the happier those suppliers will be — and the less time AP staff will spend on the phone or in email resolving vendor questions. AI facilitates payment automation and can automate other key tasks in vendor management, such as onboarding new vendors into payment systems, managing vendor documentation and automatically facilitating query and dispute resolution. One study showed that one in five companies reports that this kind of automation is worth the investment just to cut down on time spent on the phone with suppliers.
Predictive Analytics
AI/ML technology excels in the kind of predictive analytics that AP departments can use to improve the accuracy of their financial forecasting and planning for the rest of the business. AP uses AI-driven analytics in spend management and cash flow management. Together with analytics of AR functions, this can fuel more accurate balance sheet forecasts for the business.
Regulatory Compliance
AI pattern matching and automation tuned for government regulations and internal policies can help AP staff speed up auditing of invoice and payment activity. In addition to running proactive, real-time checks for compliance violations as invoices are processed, AI can be used to quickly generate reports for auditors and company decision-makers on past activity, easing the entire regulatory compliance process.
Customer Service Automation
While AP may not traditionally be viewed as a customer-facing function, AI-enabled customer service automation can do a lot to reduce AP workloads over time. Many AP employees burn a lot of time answering queries and resolving disputes from vendors and internal employees who submit expense reports. Customer service automation technology like AI chatbots and self-service portals can reduce workloads for AP staff who have to field these calls.
Take Advantage of AI in AP With NetSuite
Featuring a full complement of cloud-based ERP applications that span financial and line-of-business operations, NetSuite embeds AI functionality across its entire platform to support automated workflows and insights for AP and beyond. The use of AI in NetSuite AI puts smart automation technology in the hands of AP staff, helping them cut down on manual tasks in invoice processing and speed up workflows for approvals, three-way matching and exceptions handling. Most critically, NetSuite’s AI capabilities provide simplicity and assurance in the integration of AP documentation and data analysis within the context of broader financial data sets. As a business unit within Oracle, NetSuite is set apart from many other accounting software solutions in that it has access to leading AI technology, partnerships and infrastructure capabilities. This means that AP departments can rest assured that NetSuite’s AI capabilities come ready with the best-performing prebuilt machine models needed to power AI-driven strategies.
The impact of AI in AP is far-ranging for organizations willing to make the right investments in their accounting and ERP technology stack. Effective use of AI in AP can help payment teams bring end-to-end automation to the entire procure-to-pay process. AI technology, such as pattern matching and NLP algorithms, makes it possible to eliminate manual data entry by extracting information accurately from paper or electronic invoices. Plus, AI-powered decision-making can streamline approvals and exception-handling workflows once invoice data has been automatically entered into the accounting platform. When well implemented, these AI enhancements to the tactical execution of AP tasks speed up payment processing times and reduce the cost of labor. Furthermore, AI analytics can also help AP leaders improve reporting and analysis of spending trends, helping to fuel the business with data for better financial decision-making.
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AI in Accounts Payable FAQs
How is AI used in accounts payable?
Artificial intelligence (AI) is used in accounts payable to enable more consistent, end-to-end automation of invoice processing and payments. AI can also be used to power complex data analysis of payment trends. This can help AP teams bolster spend management. It also speeds detection of fraud and errors that could result in overpayment or underpayment.
Can accounts payable be automated?
Yes, through the use of artificial intelligence (AI) and machine learning (ML) algorithms, many manual accounts payable workflows can now be automated. Many accounts payable departments have successfully set up touchless payments that automate invoice processing, payment approval and payment scheduling with no human intervention.
How is AI used in the payment industry?
Artificial intelligence (AI) is used in the payment industry to facilitate end-to-end automation of the entire accounts payable cycle, from invoice processing, to payment, to reconciliation. AI-backed data analytics is also used to examine spending trends and detect fraud.
How is AI used in accounting?
Artificial intelligence (AI) is used in a broad range of accounting processes within accounts payable, accounts receivable and other finance functions to create smarter automated workflows and glean insights from accounting data.