Benefits of Order Processing Automation

Many businesses have already put a lot of effort into automating their order-to-cash activities. This makes sense, as the cycle is complex. Orders may come in from multiple sources—including disparate point-of-sale systems, fax, email, the company website, and/or electronic data interchange (EDI) connections to business partners. When processes such as creating invoices, ensuring payments come in on time, and resolving disputes involve extensive manual work, problems can range from slow order fulfillment or incorrect shipments to data-entry errors and poor customer service. Staff who should be working closely with customers and partners instead spend their time doing the grunt work of receiving orders, shipping products, and processing payments.

Order processing software currently on the market can address a lot of these challenges. By automating many of the manual steps in the order-to-cash cycle, these solutions can save companies money through streamlined order processing, improved employee productivity, and reduced equipment and personnel spending. They may also enable customer service representatives to focus more time on ensuring customers' needs are met. In addition, orders may be processed and fulfilled more quickly and with fewer errors.

Order-to-cash automation has been a boon for many companies, but that doesn't mean there isn't room for additional improvement. Businesses that automate certain processes, such as order documentation, collections, or dispute resolution, sometimes leave other activities in human hands. For example, staff may continue to manually compare invoice numbers against open orders or match remittances to payments to ensure that what comes out of the automated workflow has been done correctly.

AI and Machine Learning Within Order-to-Cash

Artificial intelligence and machine learning can improve the order-to-cash cycle in myriad ways. In business, data is the coin of the realm, and these technologies are designed to leverage data to improve business operations and decision-making. They do that by analyzing vast volumes of information, looking for patterns that humans could not be expected to detect. When the AI lens is focused on order-to-cash data, businesses can leverage detected patterns to streamline processes and save time.

Here are a few examples of areas in which AI technologies might be able to further extend the benefits of a more traditional order-to-cash automation solution:

Order processing. Many companies still send in their orders via email. Traditionally, vendors have paid somebody to sort through the emails and pull out details such as order numbers and customer identifiers, then route the emails to the appropriate destination. Much of that work can be done automatically through an AI engine.

The engine can automatically identify the data in the email; recognize which items the customer is ordering; determine whether multiple orders in a single email need to be processed separately; ascertain whether there are duplicates; and, if so, wait for a human to pick the right order and process it. The AI engine can also resolve issues in how data is presented. Are dates from the customer's enterprise resource planning (ERP) system written with dots rather than dashes? Are zeros kept in? The software can automatically format data discrepancies and move orders forward through the process. Thanks to machine learning, the AI engine will always remember changes that are made and can apply them to future customers as well.

Dispute resolution is an important component of any company's order-to-cash process. Resolving payment disputes quickly and efficiently is critical in ensuring that affected customers remain satisfied with the organization. However, when the process depends on one-off, manual consideration of each customer dispute, it is time-consuming for staff, which can place a drag on resolution for customers and add to costs for the business.

Most disputes don't require any correction. If customers are calling a customer service representative (CSR), they may have been billed for something they didn't order, or they may want to report that the order is incorrect. The CSR may have some information about the sale—the product, amount, cost, and other data—and records of prior complaints, but he or she doesn't necessarily have the authority to make a final decision. There's typically an approval process that kicks in after the complaint is recorded. Resolution may require further discussions with the customer, gathering of more information, and documentation of steps the CSR is taking. The process may take three days to two weeks, during which time the customer may be placing additional orders.

AI technologies could greatly accelerate dispute-resolution processes. If software specifically designed to recognize patterns in data were unleashed, it might be able to rapidly identify which customer concerns are most likely to be valid. A business that can better prioritize disputes, based on which are most likely to need human review, will speed up the time to resolution for legitimate customer complaints.

Invoicing.  In accounts receivable (A/R), machine learning ensures that invoices reach the recipients more quickly and error-free. Invoicing traditionally has been a manual and painfully slow process, with a paper invoice moving from hand to hand internally before being sent out to the customer, who then turns it around and sends it back in with payment. At the least, automating the process of delivering invoices means customers get them more quickly and can turn around payment faster.

But AI also streamlines compliance with disparate regulations. Laws around invoicing and payments vary from one region to the next—special stamps are needed in some, digital signatures or particular layouts for others—and in many jurisdictions, they change frequently. Manually keeping up is almost impossible. Automated cash collections also are quicker and easier, and AI engines will remember who at a particular customer company is best to contact, or if they prefer to be contacted via phone or email.

AI also can play a crucial role in detecting fraud or deceit in invoicing, because detecting patterns and anomalies is a key capability of machine learning. Typically, when an invoice is sent, it's printed and then put into the mail. No one is looking at the data on the invoice. By incorporating AI into the process, the engine is analyzing all the data as it's going through and is detecting invoices on which order frequency or amount, for example, fails to fit the established pattern for a certain customer. When it detects a potential issue, the invoice can be flagged for staff to examine. For those receiving invoices, AI can be used to ensure that nothing is out of the ordinary—that they are being billed the same amount for the same services they usually use. The AI engine also can verify customer and invoice data and banking details before allowing a payment to be made.

These are certainly not the only possibilities. AI and machine learning technologies are designed to take in information and learn from it. Thus, they can enable individual processes to learn from one another and automatically improve their performance over time. Essentially, any process that relies on human investigation of data is ripe for AI-driven efficiency improvements. The potential for AI improvements is limited only by the company's development resources and analysts' imagination.

Considerations in Planning

AI technologies have been around for decades, but they've exploded in recent years as new techniques like deep learning and neural networks have been improved and applied. Machine learning essentially uses algorithms to analyze and learn from data and then make decisions based on what it's learned. Deep learning structures data in layers, helping to create neural networks, which are systems designed to reflect neuron patterns of the human brain.

In many ways, deep learning is about using images and data to identify information—maybe identifying and remembering a customer—and then using that information in the order-to-cash process. This may include on-boarding a new customer and accurately extracting the data to automate and improve the process. Deep learning can also help avoid downstream problems by ensuring that the data used in invoicing is correct so that there are no problems with billing. The advantage of deep learning is that it can use algorithms and vast amounts of data to teach itself. It doesn't learn simply through one or two orders, but by seeing millions of orders, images, and bits of data.

The potential benefits are significant, but businesses shouldn't be in a rush to adopt the first AI-based technology they can get their hands on. Treasury and finance managers should carefully consider their options. Four key considerations should help guide the selection process.

1. Should you look to the cloud?  Cloud providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform are on the cutting edge of AI and machine learning development. These vendors offer a broad range of services that companies can leverage to run many of their order-to-cash processes—think order receiving, invoice creation and sending, and payment collection—on a public cloud infrastructure. Customers using such solutions can host their databases in the cloud or link these processes back to their own data centers, and they can leverage the cloud provider's deep AI capabilities to automate the order-to-cash cycle.

Using a cloud-based AI provider enables a company to leverage the convenience, scalability, and subscription-price models that the cloud is known for, and the burden of expertise in areas such as infrastructure, AI, high availability, and data protection and retention lies with the provider. However, some companies are uncomfortable letting such mission-critical processes and data, including customers' personal information, run outside of their on-premises environments.

A growing number of vendors of order-management software are incorporating more and more AI into their products. Companies considering how to leverage AI to improve their order-to-cash process should weigh their priorities and then answer the questions that will arise: What functionality is most critical? Which cloud providers—or on-premises software vendors—do the best job of providing those capabilities? To what degree should we be worried about security, and does each vendor satisfy our concerns? What services and support come with each option?

2. How can you ensure your approach is appropriately holistic?  AI enables companies to take a holistic view of their business processes. The technology can drive data analytics, to show where the system is performing well and where it needs improvement, and can then automatically make changes based on the data to drive those improvements. AI solutions can address the entire order-to-cash cycle, from the moment an order is sent through to when it is fulfilled, invoices are sent, billing is completed, and payment is taken. At each point, the process is automated, which accelerates activities and reduces errors.

In addition, the automation available through AI brings consistency to the data and procedures in each step of the order-to-cash process. The data that is gathered from the initial order is the same as when the order is fulfilled, the invoice is sent, and payment is made. Up and down the process, each step is operating from the same data, rendered in the same way and taken or sent out through consistent procedures. Moreover, each invoice is sent to a particular customer in the same way.

The data connected to each customer is also collected and remembered by the AI engine. For example, if a customer that normally buys 1,000 widgets puts an order in for 100,000 widgets, the system automatically detects and flags that order. At the other end, if an invoice has anomalies, the system will detect a change in patterns and alert the company.

When AI and machine learning algorithms span multiple systems and departments, the order-to-cash cycle improves further, as the system learns and algorithms enable it to complete processes faster and more accurately. This capability to learn creates an environment where, once the system is in place, it's pretty much a hands-off operation.

As your organization works on developing an AI-driven solution for the order-to-cash cycle, think about taking a holistic approach; look for solutions that could tie together all processes from customer orders to final payments. AI-based solutions learn better and faster when they take in more data. That data may include information that lets the solution better know the customer—not only identifying numbers but buying histories, money spent, and how they like their invoices sent. With this type of information, the solution can pick out patterns that might indicate when something changes with the customer, if there's payment fraud, or whether an order's correct.

AI systems are as good as the data that goes into them, and the more data points they get, the more complete the view is of the whole operation. Also, being able to see information from multiple other systems will enable the solution to determine how processes can best work together. This may be something as simple as order numbers. Suppose that when an order comes in, an order number is generated. Through AI-based automation, that number is automatically put onto the shipping paperwork, the invoice, and eventually the payment receipt. For companies that are processing thousands of orders every day, automation of even a small step like this can lead to significant efficiencies. It's a key benefit of AI and machine learning.

3. Don't rush the decision, or the implementation.  Businesses may not want to rush into adopting AI in one fell swoop. These solutions present a lot of options and require decision-makers to make many discrete choices. They should take their time to assess their situation and then evaluate which options best suit their needs.

Once a company has selected an AI-enabled order-to-cash solution, it can take an incremental approach to deployment. Individually rolling out different parts of the overall system could save time and headaches by minimizing the impact of missteps and mistaken decisions taken early on. It also allows the company to develop expertise in AI and machine learning as the solution's scope and sophistication grow.

For many organizations, the first step is to understand their current processes, to get a better idea of how they can be improved. Companies then need to set goals. Why are you automating? Why do you want to bring AI, machine learning, and deep learning into your processes? What problems are you trying to solve? Companies need to understand what the data will show them and how they can get that data. After that, it's a matter of implementing the new process and bringing in the solutions that will enable them to leverage the AI capabilities.

Key to all this is getting end users involved at the beginning of the process. Some staff may worry that AI technology will result in the elimination of their jobs. For most people, implementing these solutions usually just means redirecting their efforts to other tasks. However, end users need to feel comfortable with the AI technologies and buy into the program, to ensure it runs smoothly from the start.

4. How will you measure the system's success?  Before you begin deployment, ensure that you will be able to track what data the solution is analyzing, what types of trends it's looking for, and—just as important—what it is achieving. Make sure you have clearly expressed expectations of how the performance of order-to-cash processes should improve as a result of the AI solution. Key performance indicators (KPIs) are crucial to any business operation, and AI technologies can help customers gain greater insights into system performance.

With AI, the world of KPIs changes in tune with what becomes important. In the manual world, if a piece of data is extracted at one point and changed downstream because it's incorrect, performance metrics may not reflect that situation. With AI, not only are those downstream issues eliminated, but the company's KPIs highlight steps that may improve the business or ways to move people around to better serve customers.

The metrics shift from measuring the number of orders coming in or the number of lines in the order to describing how orders are coming in, how many touchpoints there are, and who's touching the order at each point in the process. From there, given the amount of data in the AI solution, companies can see if there are problems with customers, a single employee, or the process. Correcting something downstream is no longer a problem.

Then the metrics can change again, to measure how much faster the company can get the product out the door. In other words: Is the AI system saving or making us money? Is it really enabling us to better serve customers? The answers give businesses a better view of the entire operation. They can educate their up-front people, their CSRs, and show them how the company's activities at one point in the order-to-cash process affect other points.

AI Living up to the Hype

Businesses are catching on to what AI can do for them. According to Statistica, 84 percent of enterprises say investing in AI will lead to competitive advantages, and 63 percent say AI will be needed in the future to reduce costs. Gartner analysts are forecasting that by 2020, AI technologies will be in almost every new software product and service.

Companies wanting to get a jump on the competition by reducing order processing time, costs, and errors, while improving efficiency and customer service, should give some thought to the ways in which AI technologies could support these goals. In today's increasingly fast-moving and constantly changing business climate, automating order-to-cash processes usually translates to happier customers who are less likely to look elsewhere for suppliers. Learning to better leverage data to improve every step in the order-to-cash cycle enables a business to reap the much-discussed benefits of AI.

Eric Bussy is worldwide corporate marketing and product management director at Esker, a producer of AI-driven software that automates accounts receivable and collections processes. In this role, Bussy is responsible for the development of strategic products, services, and solutions. He joined Esker in 2002 as director of marketing communications, and in 2005 extended his responsibilities to include product management.