ERP vs AI AP automation why OCR isn't enough for touchless invoicing

Touchless invoicing was meant to be the endpoint. Invoice captured, matched against the PO, approved, and then payments processed, all without having to touch a single thing manually. It seemed like an achievable goal for those business leaders who spent their money and time implementing ERPs and AP automation technologies. It is not there yet for most businesses. Though much work and effort have gone into digitizing processes, the reality is that the vast majority of accounts payable teams still experience difficulties with handling invoice exceptions, correcting mistakes manually, and approving invoices. The problem is not one of automation itself, the problem is like that automation.

Most of the AP processes that use ERP technology depend on OCR, which is a technology used to convert a scanned invoice into digital, machine-readable text. It's definitely an important step towards automation, but it is not an intelligent one. will neither be able to adapt when faced with a new supplier format, nor resolve a three-way match issue, nor anticipate possible issues that can arise out of certain invoices. OCR simply stops at an invoice not meeting expectations, and then comes a human employee.

This blog will tell you how OCR technology fails in its mission to achieve touchless invoicing, what limits ERP technology for AP automation, and what is different about AI-powered processing.

The reality of modern accounts payable

When you ask an AP professional about their workdays, the description seldom correlates with what was said in the automation presentation. Even though there are now digital workflow systems and integrations to ERP software, there is still a lot of manual effort that goes into invoice processing, which is getting worse.

⇒ An increased number of invoices represents the beginning of the issue. Due to the expansion of suppliers and more frequent transactions, AP departments handle larger amounts of invoices than ever before. At the same time, there is no proportional increase in the number of employees, meaning that all of them have to do even more and that any process inefficiencies become magnified.

⇒  Different formats of the supplier invoices demonstrate the next structural vulnerability of the standard AP process. It needs to be noted that all suppliers submit their invoices in their own way. While some use structured PDFs, others send their invoices as scans and through online portals, whereas others submit their bills via email in different formats each time.

⇒  Delayed approvals exacerbate the problem even further down the line. Invoices requiring manual signature are caught in the inboxes of unavailable managers, routed to the wrong addresses, and lost in messy email chains without clear resolution. Hours turn to days, payments are getting closer to deadlines, and the pressure is mounting on suppliers.

⇒  It's in manual exceptions where accounts payable productivity becomes hidden. Invoice exceptions are a natural part of the accounts payable workflow, as there will always be invoices that do not match POs, lack proper information, or exceed certain approval thresholds. However, in many cases, any invoice exception means an absolute halt, and each one must be looked into manually by someone and then corrected.

⇒  The added pressure from management is what ties everything together. Today, accounts payable is more than just a department for processing financial transactions. Compliance standards have tightened, timely payments affect compliance, and the CFO demands real-time tracking of liabilities. At the same time, accounts payable processes have been unable to keep up with such expectations.

What finance leaders mean by touchless invoicing

Touchless invoicing is arguably the most commonly referred to and most misinterpreted term in accounts payable automation. It is either used to describe fewer manual activities or to refer to an entirely digital process. However, neither of these definitions describes the vision of finance executives who set a touchless invoicing goal.

⇒  Touchless invoice processing entails the movement of the invoice from the point of receipt to the point where it is approved for payment without requiring any human interference at all. This means that no human involvement will be required at any stage, whether during data entry, during exception handling, or while chasing approvals. The keyword here is autonomously. Any invoice processing that requires human interaction at any point just once, cannot be said to be a touchless process.

⇒  Straight through processing is what determines an organization’s actual progress towards being completely touchless. The figure represents the proportion of invoices that flow through the entire AP cycle without requiring any kind of human intervention. If the STP rate is 80%, then it means that 8 out of every 10 invoices are processed in an end-to-end manner.

Organizations using ERP-based or OCR-based AP cycles have very poor STP figures relative to the potential of the system. This is attributed to high exception levels, variable supplier invoices, and strict match requirements. To have a touchless AP, an organization must have a system capable of handling variable invoices, not merely automatic processes.

⇒  Touchless AP has become a key consideration in finance management for reasons that extend beyond efficiency. Quicker invoice processing translates into timely recognition of the payments that need to be made, thus making cash flow planning more precise. An increased STP ratio implies that there will be fewer expenses per invoice processed and that the need to allocate more manpower in order to cope with volume growth will be minimized. With the increasing complexity of regulatory compliance concerning timely payments and auditing,

The touchless process represents an advantage from the perspective of risk management as well.

How invoice automation has evolved over time

The system of invoice automation did not happen in one fell swoop but came through a series of steps. Each step tackled the immediate issues facing invoices at the time and revealed flaws that needed addressing in future steps.

Stage 1: Manual invoice processing

Without any sort of automation system for invoice handling, all processes were purely manual. Invoices would come through via post or fax, get manually sorted out, and then be passed to accounts payable specialists who had to manually enter the information into ledger books or basic enterprise resource planning software solutions. Anything and everything, the name of the vendor, invoice number, itemized details, amounts of taxes involved, as well as other important elements, would have been entered manually. Approval would have occurred either via email or physical signatures, with physical transfer of the invoice through departments. As expected, errors happened regularly, delays became an issue, and, unless a person created an audit log, there was no way to track progress and ensure accuracy.

Stage 2: OCR-based invoice capture

OCR is short for optical character recognition, and it is one of the first major milestones on the road to invoice automation. This technology scans texts and numbers written by hand or using printers and converts them into data. There is no need anymore to input every detail manually. OCR seemed to be a true miracle when it was incorporated into the accounts payable workflow. Instead of spending minutes, you spend seconds capturing the invoices digitally. The processing rate increases without hiring new people. And for businesses with large volumes of invoices to deal with, it was salvation. Indeed, this technology was called revolutionary. And not without reason. However, OCR also comes with its limitations. This technology is able to read what is present on the document. It cannot interpret what it means. Modify the font style, move the fields, rearrange the design, and all your information will be extracted incorrectly by the tool. There is no ability for it to understand whether the line item description is different from the payment terms if it has been presented differently from expected. This tool lacks context, learning, and even handling of ambiguities.

The OCR technology has initiated the path towards invoice automation, but it could not finish this task.

Step 3: Automated accounts payable using ERP systems

With further development in ERP systems came more advanced AP modules. Data gathered using OCR processes could be automatically imported, triggering matching processes, proper routing, and centralized invoice tracking. These changes improved the efficiency of accounts payable significantly. Process automation took care of routing tasks. Approval workflows were strictly followed. The centralization of invoices helped finance departments know where each invoice is in the AP process. A clear audit trail was established. While accounts payable processes had been done using a combination of various disconnected software tools and emails, accounts payable automation using ERP systems proved to be a step up. This approach offered structure to processes that had been previously quite chaotic.

But there was one drawback to these systems. Their main purpose is process control, ensuring that invoices go through the correct process and not intelligence, such as understanding the meaning of an invoice, dealing with variations, and acting on data that is incomplete. Thus, if an invoice did not meet predefined requirements, the system stopped, and human intervention was needed.

Stage 4: AI-driven AP automation

AP automation powered by AI technology presents a paradigm change in what the automated invoice processing process can achieve. Not only can it be significantly faster, but it can also become highly intelligent.

⇒  Intelligent invoice understanding involves the process where the system recognizes and extracts invoices in the same way as a well-trained AP specialist does. Contextual analysis, field detection based on semantics rather than location, and automatic data extraction are all performed without templates.

⇒  Smart decisions include making the decision about whether an invoice should be considered valid or needs to go through the approval process. The AP automation system makes the decision based on comparing the invoice to existing information, such as purchase orders or goods receipt records.

⇒  Continuous learning differentiates the current version of AI-powered invoice automation technology from previous solutions. It keeps getting better because every invoice it processes provides another learning opportunity vendor-specific invoicing logic, common exceptions, more accurate extraction, and more precise matching without having to make any changes manually.

⇒  The result of such developments is touchless execution. Thanks to intelligent capture, automatic matching, intelligent approvals, and exception handling, a vast amount of invoices goes from being received to being approved without requiring any human assistance. This is what invoice automation should be understood as and this is why invoice automation can only occur at this stage of its development.

Why OCR is no longer enough for modern AP teams

Certainly, OCR was quite an innovative development at the time. However, today’s AP environment has evolved beyond the capacity that OCR could possibly cope with. It’s simply too much data, from too many different suppliers, and with very high standards in terms of speed and accuracy.

1. OCR reads text but does not know its meaning

OCR executes only one task it identifies characters on a document and turns them into a computer-friendly format. This is called data extraction, and this process is quite different from data analysis. An AP specialist familiar with invoices knows what data he/she needs to extract, can tell when there is something abnormal about this document, and has to make decisions in case of ambiguity. OCR cannot do any of those things; it simply finds all characters in predetermined spots. Matching and data is extracted, non-matching, and nothing happens. It is the result of missing the context of business transactions. OCR has no idea how much a regular invoice should cost, if the items listed are appropriate, or whether there is something wrong with vendor billing behavior.

2. OCR’s challenges due to invoice variation

There will always be a need to customise the OCR procedure for every new vendor that joins the company because they all utilise various template formats. Any variation in layout leads to immediate failure of extraction. The moment a vendor updates their template design, the template used by the OCR software becomes invalid, and such invoices will have to be corrected manually. Scans cause yet another form of inconsistency in the OCR process. Issues like poor scanning, misalignment of scans, or even handwritten notes affect the accuracy of the data extracted without necessarily pointing out the correct data. An empty field yields just that: an empty field with no ability to determine the information to be captured from it.

3. OCR cannot process exceptions

Mismatch between PO and extracted value is the primary form of exception that OCR is not able to process. There is no consideration of the fact that the difference is within a tolerable range. Duplicate documents are ignored by the system when there is any slight variation in the document that has been submitted again. Even if the number or date is changed, it is considered a new document altogether. The process bottlenecks occur because of the exceptions that OCR is not able to process. Each one of these exceptions becomes a task for some other individual, and these tasks grow more quickly than

4. The hidden cost of OCR reliance

Manual verification remains the most consistent hidden cost. As OCR technology cannot guarantee data extraction accuracy in non-standard invoice formats, AP teams must perform manual checks to ensure extracted data is accurate because they cannot rely solely on the system. The next hidden cost involves rework. Any mistakes that slip through the initial manual verification process can emerge during the matching or approval phases, necessitating reprocessing. In addition, delays in the processing stage become apparent. Invoice processing is delayed due to the failure of OCR technology, and ends up in either an extraction error queue or an exception queue. Finally, higher operation costs can be seen as an accumulation of these costs. While OCR saves money from manual invoice processing, there are still costs left that, when multiplied by the volume, remain significant.

5. Why ERP-based AP automation still requires human intervention

The introduction of ERP solutions helped structure the process of managing accounts payable. However, structure does not mean intelligence, and this is actually the point that makes the difference and results in the necessity for manual handling of automated AP through ERP tools.

6. ERP automates workflow, not decision-making

This is the main drawback of AP using ERP systems. The software is able to push an invoice through the process from capture, matching, routing to approval, provided that it matches pre-set criteria. Otherwise, the process gets stuck waiting for someone to make a decision. The process of automation performed by an ERP system is deterministic, which means that with a given input, it will result in a certain output. Such processes are suitable for invoices that follow pre-set criteria. They are not fit for the majority of other types of invoices that represent a great percentage of the flow.

7. Exception queues keep increasing

Exception management becomes an essential part of ERP-based AP since any transaction that does not meet the matching criteria, contains incomplete information, or violates any rules will be classified as an exception. At this point, the work of the software comes to an end, while the human factor starts playing an important role. However, the major drawback of exception queues is that they do not go down automatically. The more invoices are processed, the more exceptions occur. This leads to a situation where more time is spent on exception handling rather than on invoice processing.

8. Changing suppliers causes processing interference

The setup for the accounting system’s accounts payable module depends on knowing certain suppliers, having certain formats, and being aware of the manner of billing. When any of these things change, for example, the supplier changes their billing format, the software they use, or how items are billed per invoice, the configuration fails. Their invoices no longer extract or match. Someone needs to determine why, configure the module, and then process their invoices again. If you operate within an environment where there are many suppliers and/or this number tends to change frequently, you have an ongoing problem.

9. Approval process blockages persist

Consistency in the approval chain process is assured by ERP applications, though they do not guarantee faster approvals. Approval requests sent to managers who might be out of office, away on business, or handling conflicting tasks will have to wait for the manager to take action. There is no provision for escalation, intelligent distribution, or recognition of unnecessary delays within the application. The effect of such issues is that the approval process remains lengthy, regardless of the ERP system being fully integrated into the workflow. The process requirements are fulfilled on time by finance departments, though payments end up getting delayed.

10. Manual invoice approvals reduce scalability possibilities

Each invoice needing a person's involvement in some manner, for reasons such as exception handling, error correction, or follow-up, means there is an upper limit on how much scaling can occur without the additional hiring of personnel. Scaling with ERP-based AP automation has its limits and does not eliminate them. The more invoices that must be processed, the more manual approvals that will need to be carried out. Businesses that expand their supplier base, move into different locations, or make more purchases find that their processes of AP are scaled both in terms of cost and volume with the help of ERP.

ERP vs AI AP automation understanding the difference

ERP systems and artificial intelligence AP automation systems are not rivals they perform different functions in the finance stack. This knowledge helps finance managers make decisions on when to invest in which system.

The core capabilities that make AI AP automation different

The difference between AI AP automation and traditional AP tools lies in intelligence, not speed. Every feature listed below describes an issue that cannot be solved using rule-based automation without human involvement, but can be solved using AI AP automation.

1. Invoice processing without templates

Conventional AP solutions need a template for each supplier format. The technology renders this approach obsolete. Context-driven logic is used to process the invoices. Fields are recognized through meaning rather than location. Onboarding of new suppliers takes place without configuration, and any change in formats does not impact processing.

2. Intelligent data extraction

While OCR scans characters, AI makes sense of the content. Intelligent data extraction recognizes what each field means regardless of document layout, font variations, or poor scanning quality. This leads to much improved accuracy levels in extracting data from a wide variety of invoices, and minimal need for manual validation on the other side.

3. Contextualized three-way matching

Traditional matching considers every mismatch as an anomaly. AI analyzes variances based on the bigger picture, considering the variance against past trends, behavior by specific vendors, and tolerance levels. Invoices that would normally raise an exception flag through strict rules processing will be automatically validated without requiring any manual intervention.

4. Duplicate invoice identification using AI

While conventional duplicate identification systems focus on finding invoices that are identical in number and amount, AI-based identification can recognize the submission of resubmitted invoices where there is only slight variation in terms of the invoice number and date. This helps to minimize the chances of duplicate payments.

5. Approval process suggestions by AI

An AI system can study the process of approvals in previous years and offer suggestions on how an invoice should be processed and who should sign off on it. The more standardized invoices will have little or no delay because they will not need approval, while those that require approval are sent to the right person.

6. Self-learning exception management

The process of AI AP automation continuously changes based on lessons learned from each exception that is resolved, in contrast to traditional systems that handle exceptions using a continuous procedure. Gradually, it learns recurring exception categories, predicts failure points for invoices, and becomes more adept at resolving exceptions automatically. As the system grows older, the size of the manual exception queue decreases. This is the compounding benefit that truly distinguishes AI.

The CFO's business case for AI-Driven AP automation

When one is a CFO, any investments in technology must prove their worth financially. With AP automation through AI, the business case goes far beyond efficiencies because it affects costs, cash flows, regulatory risks, and suppliers.

♦  Decreased cost per invoice

While it might seem obvious, the cost of processing an individual invoice manually, considering labor costs, corrections, and exceptions, is considerably higher than what most finance departments measure officially. By introducing automation to the process through AI-powered AP automation software, this cost decreases through eliminating the need for any human intervention in the majority of cases. As straight-through-processing improves, existing AP systems can process more invoices at no extra cost.

♦  Improved invoice processing speeds

In manual processes, invoice cycle times expand to many days simply due to the nature of the invoice waiting at every step of the process. With AI technology, however, these cycle times become extremely short, with invoices being captured, matched, and automatically routed to their proper destinations within hours rather than days.

♦  Improved visibility of working capital

With invoices piling up in queues and awaiting approval through emails, the finance department has no real-time insight into outstanding invoices. AP automation using artificial intelligence provides a structural solution here; since processing takes place inside the system, CFOs gain real-time insight into the status of the invoices and payment requirements, as well as cash flow projections. This makes it easier for the organization to make effective working capital management decisions.

♦  More effective early payment discounts

For an early payment discount to apply, the invoice must be processed and paid before the specified period lapses. For organizations running inefficient systems that take too long to process invoices, early payment discounts are rarely achievable, as the discount period elapses before the finance department has had the chance to process them. Artificial intelligence can significantly reduce this problem.

♦  Decreased compliance risk

There is no doubt that AP is a very high-risk compliance area. Invoice fraud, duplication of payments, unauthorised approvals, and late payment can only happen because of the invoice process. The entire audit trail is created consistently by AI for all invoices. It monitors compliance with regulations such as GST reconciliation and timely payment to MSMEs as required by Section 43B(h).

♦  Improved supplier satisfaction

The most important thing for suppliers is that they are paid on time and correctly. If AP processes take a long time or have problems, suppliers will contact the company, initiate disputes, or even change terms as a way to mitigate their risks. AP automation reduces delays, giving more predictability regarding the payment date. It identifies discrepancies ahead of time and prevents disputes. Fewer follow-ups from the supplier strengthen the business relationship.

♦  Increased efficiency of AP teams

The AP teams working in an environment where processes depend on manual and OCR processing will be engaged most of the time in activities having little value. These include data validation, exception handling, and pursuit of approvals. All this work can be done automatically with the help of AI technology. AP staff will be able to use their time to reconcile vendor invoices and conduct spend analysis.

Conclusion

OCR scanned invoices. ERP optimized process flow. Both were steps forward, but neither solved the same problem: neither system could make any decision, hence human interference was a common feature in all AP activities, irrespective of automation. This problem is solved by AI. By incorporating intelligent data capture, contextual match, and self-learning-based exception management, the possibility of implementing touchless invoicing stops being wishful thinking and becomes a practical reality.

Our ZeroTouch AP Automation suite of TYASuite products was designed with this end result in mind, combining AI-based invoice scanning throughout the entire AP cycle with maximum efficiency and minimum human involvement. With ZeroTouch AP Automation, you can finally implement touchless invoicing.