Is your business treating invoice processing as an unavoidable cost of doing business (pun intended!)? You don’t have to when you use accounting process automation!
When seemingly mundane activities are not optimized using intelligent invoice processing automation, you could be leaking significant dollars without even realizing it. Interestingly, it is such tasks that can be transformed to be not only effortless but also provide deep insights into your fund management.
Any opportunity to improve business processes and create bandwidth for other critical activities – especially during the current economic conditions – is valuable and worth pursuing. Manual effort spent on repetitive administrative tasks like managing invoices results in a significant waste of time, effort and money. Studies show that Accounts Payable automation software can reduce invoice processing costs by 90 percent. 1
It is common for large enterprises to interact with thousands of vendors and pay thousands of invoices per year. Most organizations still require a great deal of manual intervention to manage various aspects of invoice processing even after implementing RPA to automate the process.
How Do Businesses Currently Capture Invoice Data?
Large enterprises receive thousands of invoices every month from numerous vendors, each in a different format. Many suppliers still present their invoices via paper or as a PDF attachment in an email. These delivery methods require a great deal of manual processing, including scanning, data verification, and exception management. The goal of every organization is to get to a point where their Accounts Payable teams can perform end-to-end touchless invoice processing.
An invoice goes through several stages of scrutiny before it gets paid by the account payables department. The dedicated teams that handle invoice processing manually feed the data into enterprise workflow tools like SAP and similar ERP applications to ease the orchestration of invoice processing, including routing the data for multiple scrutiny checks, approval and validation for payment.
Potential Invoice Processing Concerns in Today’s Scenario
A majority of the invoices today are electronically processed where a vendor uploads the invoice on a supplier portal of the organization. Internal systems like SAP have integrated automated invoice processing solutions to manage the invoice data electronically. However, many vendors don’t adhere to the submission process through a supplier portal and end up sending the invoices directly via email. Even invoices submitted electronically are not standard and the same vendor may regularly change formats and fields.
Larger enterprises have teams to manually sort all the invoices, create the invoices in the system, validate the details across the ERP and GRN (Goods Receipt Number) systems and transfer the entries into the SAP system for further processing and approval.
The to-and-fro communication between the business and the finance team for validation and the overhead efforts to perform a match between a number of invoices and associated business units included in the delivery of goods makes it difficult in many cases to validate a one-one match between the purchase order to a goods receipt.
For delivery of services, there is no concept of a GRN. In such cases, the invoice process goes through additional approval. It requires human intervention to route the invoices for the required approval, which further increases manual input.
Manual processing can result in high processing costs, increased risk, process and accounting errors, duplicate and late payments. According to AQPCs 2018 survey2 of 1,480 organizations reporting data on the cost to process an invoice, the bottom 25% are spending $10 or more per invoice processed. The median cost to process an invoice was $5.83.
The Challenges of using RPA for Invoice Processing in Accounts Payable Automation
With the advent of RPA, automation of invoice processing was done based on fixed invoice templates or rules. With the number of vendors increasing, it was difficult to train the automation tasks to handle frequent changes in invoice templates.
As organizations added new vendors and invoice formats, the RPA system failed to handle the process due to a lack of cognitive ability or flexibility to understand different formats, and an inability to adapt to new features.
RPA solutions were also weak in ensuring the accuracy of data validation owing to the inability to perform 3-way match in the invoice validation and business rule enforcement throughout the invoice process.
RPA solutions were not able to handle exception cues or provide many analytic insights of invoice status, improving cashflow or reducing processing costs.
Every touch to an invoice increases costs associated with processing the invoice and accuracy in handling and payments.
Accounting Process Automation for Touchless Invoice Processing
Our Automated Invoice Processing HyperApp with integrated Artificial Intelligence and Machine Learning capabilities can exercise full control over Supplier Portal customizations to ease supplier onboarding and invoice management. It has the built-in cognitive ability to handle complex invoice formats and create templates for new invoices.
Equipped with a powerful validation engine, the validation process is dealt with ahead of ERP processes to avoid downstream updates in the ERP system minimizing errors. The accounting process automation platform allows faster implementation of configurable workflows for new suppliers, product lines or invoices. The potential to provide powerful data analytic features gives the competitive edge for Accounts Payable automation.
cfo.jiffy.ai/’s Automated Invoice Processing HyperApp is truly touchless in every sense. Our customers reported significant improvement in straight-through invoice processing, 90% improvement in invoice processing turnaround times and more than 85% efficiency improvement after implementation.
Accelerate your automation journey with cfo.jiffy.ai/'s low-code platform.
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Written by Babu Sivadasan, Chairman & CEO | Updated on February 2, 2021
Have you ever said, “Let’s start small and then build it up based on how it goes,”? You sure have. So have most of us. In our world, this is typically how all automation begins.
During the initial days of robotic process automation (RPA), organizations were mostly skeptical. They saw potential but were unsure of real impact.
So, they tried it out for small non-critical functions — they wanted to minimize risks. Understandably. Say, the finance department would automate one task in the Accounts Payable first such as reading data from a file and transferring that to the ERP system. However, other aspects of the Accounts Payable process would continue to remain manual. Also, understandable.
This is what is called partial automation — quite literally, automating just a part of something much bigger.
But why would anyone do that?
In fact, there are plenty of reasons for handling automation this way.
For one, the earliest automation systems could only automate basic screen capture – in other words, anything that couldn’t be seen on a screen would break the process and need manual intervention.
Some of them are financial — end-to-end automation is more expensive and incurs higher opportunity costs to run business-as-usual in the interim because every sub-task would need investment in a bot. Partial automation, on the other hand, was cheaper. Organizations could pick a few bots for shorter processes and pay-as-they-go. This also helped them understand the effectiveness of automating and calculate ROI in the longer term.
Some industries worried about security. A bank would use RPA tools to move data from a front-end system to a legacy back-end system but wouldn’t let bots analyze their customer data. Even to this day, security remains an important reason companies choose partial automation. Why risk exposing critical data while their mandate – bolstered by regulatory requirements – is to protect it and keep it confidential?
Some others just weren’t ready for end-to-end RPA — automating a process end-to-end would necessitate standardization of formats, fields and rights, and that requires an investment of finances, as well as time and energy from their internal teams.
It also didn’t help that monitoring each automated process or bot was not easy. So, there was greater risk of broken automation if the scope was end-to end.
The initial RPA landscape had its limitations, lacking seamless integration with the human input when the time came for decision-making and without a human-in-the loop concept.
Most also feared that they might not have the people trained and equipped to intervene and improve the end-to-end RPA, making it a bigger risk. Partial automation is less demanding.
To be clear, in all these cases organizations certainly understood the value of RPA, invested in partial automation and derived value from it. Most of them are “somewhat happy” with the results their RPA systems are delivering.
Partial automation only provides partial success. Why?
Process measurement issues: Partial automation meant that a major part of the processes still had to be done manually, so there was no way to measure the ROI per process or per team/department. In other words, there was no way to make a strong case for automation because the results couldn’t be measured objectively.
Efficiency deficit: The improvement in overall process efficiency, while automating only a part of it, can often be so minimal it doesn’t seem worth the effort.
Savings deficit: As efficiency is only marginally improved, cost savings also end up being marginal.
Stagnation: Partial automation can be a dead investment without the bot’s ability to learn, adapt or grow with the needs of the organization. Likewise, it can be a dead investment if the organization doesn’t have the ability to see and manage how automation is being applied across the enterprise.
Resource blocking: Without the ability to improve intelligently, partial automation still needs people to fill its gaps. This means that people continue to work on mundane tasks, leading to low productivity, fatigue and dissatisfaction.
Right, so is Intelligent Automation a possible end-to-end solution?
Intelligent or Cognitive Automation in its simplest form, is an intelligent version of RPA — one that can learn from the data and apply it to present needs. Automation can become limiting when not supported by the learning capabilities of AI, which is where intelligent automation comes into the picture. It is flexible enough to understand and adapt to non-templatized data inputs. It can process structured, semi-structured and unstructured information with ease.
Take cfo.jiffy.ai/’s cognitive automation tool, for instance. It is able to read and extract non-templatized information. Even in cases where cfo.jiffy.ai/ doesn’t understand or cannot read certain parts of the document, it will extract all the other parts and reduce manual intervention to a bare minimum. This way, with cognitive RPA, you can automate the entire process, not just a part of it.
With its ability to learn, cognitive RPA is also scalable. As a business becomes more complex and processes more intricate, cognitive RPA can learn and grow along, making the ROI significant in the long term. For instance, intelligent automation systems that trigger alerts to floor supervisors in a manufacturing unit can learn to spot newer anomalies over time, making all aspects of productivity, quality and capacity predictable. Enterprises are addressing their requirement for end-to end automation using a combination of RPA tools (for repetitive tasks), BPM tools (for process management), OCR , IDP tools (for document extraction), Data platforms for data streaming and beyond.
Instead, a platform that makes all of these features available in a single stack can help save costs and time, and also translate to easily calculable returns over a period of time. This way, they can adopt cognitive RPA for all processes, interconnect them and enable them to work in tandem.
Cognitive RPA also comes with basic skills. Pre-built RPA systems, customized for industries and functions, are now available with the ability to hit the ground running immediately. Once installed, they are in auto-pilot mode needing very little help from people, even for setup, training or maintenance.
With prior knowledge, pre-built cognitive RPA solutions can automate end-to-end with a more meaningful understanding of the process landscape.
With cognitive RPA, the solution is no longer piecemeal. Unlike partial automation, cognitive automation impacts the entire value chain.
Today’s context
The global situation businesses face today is a reason for organizations to take seriously how end-to-end automation can help them to be more resilient in the face of crisis.
As an example, a large automaker based out of Europe has worked with cfo.jiffy.ai/ in automating their financial processes. This truly helped them recently when there was no business shutdown in their country, and they continued to send in their documentation to cfo.jiffy.ai/’s offices where physical offices were shut down. Thanks to automation, backend support continued seamlessly while production continued as planned.
It is completely understandable if you have a partially automated system now. It made sense in its day. But today, to see the real value of automation, end-to-end cognitive automation is the way to go. With a clear view of the entire system, end-to-end RPA will be able to bring together various processes into a smoother journey, be it for your customers, vendors or employees. It will also future-proof you as the system understands your existing processes and can expand to accommodate newer ones.
If you have adopted partial automation and aren’t fully realizing its potential, speak to one of our consultants to explore newer avenues. We understand where you are and we’re happy to help.
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