How to Best Utilize AI Within Accounts Payable

Accounts payable (AP) departments face many challenges, from time-consuming processes to frequent errors. Companies continue searching for automated solutions to streamline these processes, and artificial intelligence (AI) has emerged as a powerful tool to address these challenges. However, not all AI is the right solution. Understanding how this technology is best utilized for accounts payable and what to be cautious about when companies sell you on AI is crucial.

Quick Facts: What to Know About the Growth of AI

It’s no question that AI is transforming industries across the globe with unprecedented growth. Here are some quick facts to note about the rise of AI:

AI is showing up in more places every day, sometimes unexpectedly. We’re seeing its generated results in artwork, stock trading, crime solving, sustainable city building, supply chain management, video gaming, dating apps, and many other popular software tools.
The AI market is projected to increase by 368 percent from $86.9 billion in 2022 to $407 billion in 2027.
AI is expected to contribute a 21 percent net increase to the US GDP by 2030.
64 percent of businesses expect AI to aid in increasing their productivity.
25 percent of companies are turning toward AI adoption due to labor shortages.
Forecasts predict that the manufacturing sector will reap the greatest financial benefit from AI adoption – a potential $3.8 trillion gain by 2035!

And, perhaps most importantly:

Over 75 percent of consumers are concerned about misinformation from AI.
43 percent of business owners are concerned about technology dependence.

Don’t Get Caught in the Hype: The Truth of AI

While AI offers tremendous potential, it’s essential to distinguish between hype and reality. Many organizations have unrealistic expectations about AI:

Hype: The newest AI technology equals the BEST AI technology!
Reality: Unlike most other software applications, new AI programs are not as adept or accurate as those that have been around for several years. Mature AI has had time to learn, and since its core function and value is learning and improving over time, the older it is, the more informed and functional it will be.

Many DataServ competitors make it seem like their invoice processing platform is ready-to-go “right out of the box”. They either don’t know enough to talk about or intentionally leave out that a new AI program has to learn how to produce accurate, meaningful results over time. Most of their AI-based platforms are about five years old, while ours is 15, meaning it’s had much more time to learn.

Hype: AI works exactly like a human brain.
Reality: Some AI areas, such as language and relevance judgment, remain extremely challenging. The systems are designed to build on the information fed into them to generate new ideas. Some systems, like Chat GPT, for instance, work from information that is several years old, meaning not all the information it generates is as accurate as possible. AI fulfills the saying, “What you put in is what you’ll get out.” To function effectively, it requires high-quality data.

Hype: Intelligence machines can do the learning independently after you set it up once.
Reality: Human users must input the necessary data for AI learning and improvement. Machines can’t implement key intelligence components, like problem-solving and planning, at this point in history. They’re like babies, requiring your input and guidance to grow and learn over time. This also means they can learn your bad habits and errors, so data input should be done carefully.

The truth is AI is an oversaturated marketplace. It’s so pervasive, popular, and talked about that there is a lot of social pressure to understand AI and its workings. When clients go into a conversation with a potential AI solution and don’t understand how it works, it’s easy for them to be fooled or misled. Even worse, if the sales rep trying to sell you on the solution doesn’t fully understand what they’re selling, they can mislead you with or without intention.

Most non-IT people don’t fully understand how AI works, but odds are they don’t care to know all of the ins and outs. They just want promised and delivered results, which, unfortunately, makes it easy for a “salesman” to sell them on their platform. No one should be overpromising you about what to expect. Even if the complexities of how the AI functions are outside of your interest, it’s important to have a decent understanding of how it works to make an informed decision in finding the right solution for your business.

Hype: AI is going to take away all the jobs!
Reality: Currently, AI takes on boring, repetitive tasks, allowing your teams to concentrate on more creative, challenging work. Be careful, though – make sure your purchase solution is designed to eliminate these repetitive tasks, not simply facilitate work. It’s better suited for specific tasks, like data verification, rather than creative processes.

The Role of AI: Implementing it Into AP Automation Processes

In the context of accounts payable, AI can revolutionize processes by:

Checking and validating data more quickly than manual methods
Flagging potential issues and discrepancies before they become problematic
Enhancing efficiency by automating routine tasks such as invoice processing

That said, it’s important to remember that AI learns from you, not for you. It can replicate good and bad practices, making it critical to provide clean, accurate data for AI to learn from.

Don’t Run From AI, But Don’t Jump In Too Quickly

The allure of AI’s capabilities can be tempting, but it’s important to approach its implementation with careful planning. Taking a thoughtful, deliberate approach to AI implementation allows you to maximize its benefits while minimizing potential pitfalls. Rushing into AI adoption without proper planning exposes your company to unnecessary risks, such as data breaches, operational disruptions, and financial losses.

Here’s how to take a measured approach that will help you get the most out of AI for AP:

Gradual Implementation:

Step-by-Step Integration: Begin by integrating AI into smaller, manageable parts of your AP processes. For instance, start with automating data entry before moving on to more complex tasks like data validation and error flagging.

Pilot Programs: Launch pilot programs to test AI applications in a controlled environment. This allows your team to understand its functionality, identify potential issues, and refine processes before a full-scale rollout.

Understanding Data Governance:

Data Quality and Management: AI systems rely heavily on data quality. Ensure your data governance policies are robust, focusing on data accuracy, completeness, and consistency. Poor data quality can lead to inaccurate AI outputs, harming your AP processes.

Compliance and Security: Implementing AI necessitates a thorough understanding of data compliance requirements and security measures. Ensure your AI solutions adhere to industry standards and regulations to protect sensitive financial information.

Preparing Your Team:

Training and Education: Equip your team with the necessary skills to work alongside AI systems. Provide training sessions that cover the basics of AI, its applications in AP, and how to interpret AI-generated insights.

Change Management: Foster a culture that embraces change. Communicate the benefits of AI and involve employees in the implementation process to reduce resistance and increase acceptance.

Risk Management

Identify and Mitigate Risks: Assess potential risks associated with AI implementation, such as data breaches or system failures. Develop contingency plans to address these risks promptly and effectively.

Vendor Assessment: Carefully evaluate AI vendors to ensure they offer reliable, secure, and scalable solutions. Look for vendors with a proven track record and robust support systems.

Iterative Improvement

Continuous Monitoring and Optimization: AI systems require ongoing monitoring and optimization. Regularly review AI performance, gather feedback, and make necessary adjustments to improve accuracy and efficiency.

Feedback Loops: Establish feedback loops between your team and the AI system. This helps identify areas for improvement and refine AI models based on real-world usage.

Data Accuracy: When the Wrong AI Ends Up Costing You Time

Accurate data is paramount in accounts payable. Inaccurate data entry can lead to significantly wasted time and resources. For example, a medium-sized company with 100 employees might process 10,000 monthly invoices. Many AP automation companies boast a 60 percent accuracy rate for invoice data capture, which may sound great in conversation, but once you evaluate the numbers? Not so much. If even a quarter of these invoices contain errors, it can result in a full-time employee’s worth of weekly work hours spent only on remedying inaccuracies. If you have to check at least some invoices and verify the fields are accurate, that is still tedious and takes time away from the more valuable tasks. There’s nothing that would say which invoices are incorrect, so 60 percent accuracy actually results in manually verifying 100 percent of the invoices that come through.

Best Practices for Using AI in AP

Implementing AI in accounts payable can transform your operations, but to truly reap the benefits, following best practices is essential. Here are key strategies to ensure effective AI integration into your AP processes:

Train and Educate Staff: Develop training programs that educate your AP team on how AI works, its benefits, and how to interact with AI systems. This will help demystify AI and reduce resistance to new technology. Be sure to keep staff updated on AI advancements, current limits, and best practices. Regular workshops, webinars, and training sessions can ensure continuous learning and adaptation.

Continuous Improvement and Refinement: AI models need regular reviews and updates to ensure optimal performance. Schedule periodic assessments to evaluate AI performance and make necessary adjustments. Establish feedback mechanisms where employees can report issues and suggest improvements. Use this feedback to refine AI algorithms and enhance accuracy and efficiency.

Integrate with Existing Systems: Ensure your AI solutions integrate seamlessly with your existing ERP and accounting systems to reduce friction and enhance the overall efficiency of your AP processes. Maintain a unified data management approach for consistency and accuracy across all systems. This helps leverage AI’s full potential for data analysis and decision-making.

Data Quality Management: AI relies on high-quality data to deliver accurate results. Implement stringent data quality management practices to ensure the data fed into AI systems is clean, accurate, and up-to-date. Establish robust data governance policies that define how data is collected, stored, and used to maintain data integrity and compliance with regulatory standards.

Select the Right AI Tools: Carefully evaluate AI vendors to choose the right tools for your AP needs. Look for solutions with proven expertise, strong customer support, and scalable solutions. Opt for an AI solution that can be customized to fit your specific AP processes and workflows. The ability to customize ensures the AI tools align with your business needs and objectives.

Risk Management: Assess potential risks associated with AI implementation, such as data breaches or inaccurate outputs. Develop risk mitigation strategies and ensure strong cybersecurity measures are in place. Your AI use should comply with industry regulations and ethical standards, including safeguarding sensitive financial information and maintaining transparency in decision-making processes.

Build a Collaborative Environment: Foster a collaborative environment where humans and AI systems work together. Encourage your staff to leverage AI insights while applying their expertise in complex decision-making. Implement effective change management practices to help your team adapt to AI integration. Clear communication, involvement in the implementation process, and addressing concerns can ease the transition.

Monitor and Establish Performance Metrics: Establish key performance indicators to monitor the AI’s effectiveness in your AP processes, like error rates, processing times, and cost savings. Continuously monitor AI performance to identify areas for improvement. Use analytics and reporting tools to gain insights into AI operations and make data-driven decisions.

Following these best practices will help your organization effectively leverage AI to transform AP processes, drive operational excellence, and achieve long-term success.

Be Selective About the Processes You Automate

When trying to make your AP processes more efficient, it’s tempting to automate as many of them as possible. However, not every process is suitable for automation, and automating a flawed process can lead to more problems than solutions. Your approach must be selective and strategic to truly benefit from AI and automation.

Identify the Tasks Ideal for Automation

First, identify processes that are well-suited for automation. These are typically repetitive, time-consuming tasks involving large volumes of data and are prone to human error. Processes like data entry, invoice matching, and payment processing are prime candidates for automation because of their repetitive nature and the high potential for errors when handled manually.

That being said, before automating any process, you must scrutinize its current state. If a process is inefficient or problematic, automation will not magically fix these issues but merely increase inefficiencies and accelerate errors. Take the time to analyze and optimize your AP processes. Look for bottlenecks, redundancies, and areas where errors frequently occur. By improving these processes first, you lay a solid foundation for successful automation.

Consider Process Complexities

Consider the complexity of the processes you aim to automate. Some processes involve nuanced decision-making and exceptions that are challenging for AI to handle without human intervention. For example, handling complex vendor disputes or managing exceptions in invoice approvals might require human judgment and cannot be fully automated. In such cases, a hybrid approach can be more effective where AI handles straightforward tasks while humans manage the exceptions.

Many DataServ competitors seem to have a basic misunderstanding of optical character recognition (OCR) vs. AI. We recently heard one competitor say, “We don’t use OCR; we just use AI to do this,” which shows that this individual doesn’t quite understand how AI works. OCR reads the characters while AI works to understand what is being said. AI can’t read independently, and OCR is needed to feed that information into the AI.

Explore Data Quality

Another critical factor is the quality of your data. AI relies on data to learn and make decisions. AI will not perform optimally if your data is inaccurate, incomplete, or inconsistent. Ensure that your data governance practices are strong, and your data is clean and reliable before implementing an AI solution. High-quality data improves AI accuracy and enhances the overall efficiency of automated processes.

Consider Scalability

As your business grows, your AP processes will become more complex. Ensure the AI tools you implement can scale with your business and continue to deliver value as your needs evolve. Scalable solutions provide long-term benefits and prevent the need for frequent overhauls of your automation systems.

Improve AP Processes with DataServ’s AI

DataServ’s AI capabilities offer solutions that enhance your accounts payable processes. Leveraging decades of experience and continuous learning, our AI helps you significantly improve across various stages of AP. Here’s how we can help you transform your AP operations:

High Data Accuracy: The DataServ AI extracts invoice data with exceptional accuracy (over 99 percent!), reducing manual data entry errors to ensure your financial records are precise and reliable.

Reduced Manual Effort: By automating the data extraction process, your team can focus on more strategic tasks rather than repetitive data entry.

Consistency Checks: Our AI verifies the extracted data against predefined rules and checks for consistency so you know all necessary information is present and accurate.

Error Detection: It quickly identifies discrepancies and errors in invoices, such as mismatched totals or missing fields, allowing for prompt resolution.

Automated Matching: The DataServ AI automatically matches invoices to purchase orders and receipts, streamlining the three-way matching process and reducing the likelihood of errors.

Anomaly Detection: Our AI algorithms detect unusual patterns and flag potential errors or fraudulent activities, helping mitigate risks before they impact your finances.

Automated Approvals: DataServ automates routine approval workflows based on predefined criteria, reducing the time taken for invoice approvals and ensuring timely payments.

Continuous Improvement: Our AI learns from historical invoice data and user interactions to continuously improve accuracy and efficiency.

Adaptability: We adapt to your specific business processes and requirements, providing more relevant and effective automation solutions over time.

Scalable Solutions: As your business grows, DataServ scales with you, accommodating increasing volumes of invoices and complexity without compromising performance.

DataServ is designed to be reliable and resilient, ensuring it always works alongside your AP team’s processes. Our AI-driven AP solutions, such as Intelligent Vendor Capture and our Invoice Processing Machine, provide a comprehensive approach to improving efficiency, accuracy, and control over your accounts payable processes.

Looking Ahead: The Future of AI in Accounts Payable

AI’s role in AP will only continue to evolve. Some of the future trends that will become even more seamless and impactful include:

Greater integration with other business systems:

Future AI solutions will offer greater integration capabilities with ERPs and other critical accounting systems to create a unified ecosystem where data flows seamlessly across different departments and functions for enhanced overall operational efficiency. Enhanced integration will also ensure the data is synchronized real-time, meaning any updates in one system will instantly reflect across all connected platforms, such as vendor information changes or payment status updates. Real-time data synchronization reduces the risk of errors and ensures all departments can access the most up-to-date information.

Organizations can leverage comprehensive data analytics by integrating AP and other business systems. For instance, integrating with the ERP system allows for better cash flow management and financial planning, as AP data can be analyzed alongside sales, inventory, and other financial data. This holistic view supports more informed and strategic decision-making. Seamless integration enhances workflow automation by connecting the AP process with procurement, finance, and supply chain management.

Enhanced predictive analytics for better decision-making

The future of AI in AP will be characterized by advanced predictive analytics capabilities, enabling organizations to make more proactive and informed decisions. Predictive analytics will enable more accurate cash flow forecasting by analyzing historical payment data, current financial trends, and external economic indicators. This helps organizations anticipate cash flow needs, optimize working capital, and avoid liquidity issues.

AI-driven predictive analytics will provide deeper insights into spending patterns, helping organizations identify opportunities for cost savings and supplier negotiations. By analyzing past expenditures and predicting future spending, businesses can make more strategic sourcing decisions and negotiate better terms with suppliers.

AI-powered predictive analytics will improve budgeting and planning processes by forecasting future financial needs and trends. Organizations can use this information to create more accurate budgets and allocate resources more effectively. This ensures that financial planning is aligned with business goals and market realities.

Increased use of AI to identify and prevent fraud

AI will continue significantly enhancing fraud detection and prevention capabilities in AP processes. Advanced AI algorithms will excel in detecting anomalies in transaction data. By continuously monitoring patterns and comparing them to historical data, AI can identify unusual activities that may indicate fraud, such as duplicate invoices, abnormal payment amounts, or changes in vendor details.

These systems will analyze the behavior of users and vendors to identify deviations from normal patterns. For example, if a vendor suddenly changes their banking information or there are multiple high-value transactions in a short period of time, AI can flag these as potential red flags for further investigation. Real-time alerts for suspected fraudulent activities are sent to AP teams for swift action, whether halting the payment, conducting further investigations, or implementing corrective measures.

AI will be better equipped to cross-reference data from multiple sources, such as financial records, vendor databases, and external data feeds, to identify inconsistencies and potential fraud. This comprehensive approach ensures no suspicious activity goes unnoticed. Additionally, AI systems will continuously learn from each detected fraud instance for improved accuracy and effectiveness. Machine learning algorithms will adapt to new fraud tactics and evolving patterns, ensuring that fraud detection capabilities remain up-to-date.

AI will help organizations comply with regulatory requirements related to fraud prevention and financial reporting. By maintaining detailed audit trails and ensuring transparency in AP processes, AI supports adherence to legal and regulatory standards.

Embrace the Benefits of AI with DataServ

Selecting the right AI solution for AP is crucial. The wrong solution can be detrimental, costing you more time and money than anticipated. Using the right solution, like DataServ, can transform your AP processes in ways that truly work to enhance and elevate your company’s productivity.

Contact DataServ today to learn more, or check out a demo of our AP automation solution.

For more information, download and view our AI presentation from the 2024 IOFM spring show.