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Does your organization find itself overburdened with manual tasks? Machine learning applications can supercharge your process automation initiatives by helping them get smarter over time. With machine learning powering your workflows, you can eliminate repetitive tasks and free up team members to focus on unique challenges that can’t be solved with advanced algorithms.
IntelliChief uses machine learning in highly inventive ways to help large, complex organizations consolidate workflows, mitigate errors, and cut costs. Through the IntelliChief Enterprise Content Management (ECM) platform and several integrated modules, our machine learning applications continuously identify, execute, and improve processes that can be automated.
Machine learning applications are software-based applications that utilize machine learning principles to enhance business processes while making them “smarter” over time.
Machine learning is considered a subfield of artificial intelligence focusing on the development of machine learning models that can make predictions. Machine learning applications are the result of algorithms, code, forethought, and, ultimately, measurable results.
Many organizations have turned to machine learning applications to help streamline and standardize business processes, like matching invoices against a purchase order or capturing data from an invoice for automatic processing and archival. These processes (and others like them) are considered repetitive, tedious, time-consuming, and potentially costly when mishandled. Process automation that leverages machine learning helps organizations get the most out of their solution by equipping it with intelligence to get smarter over time.
In the textbook, Machine Learning by Tom M. Mitchell, Mitchell notes:
The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.
Machine learning applications apply this principle to your business processes and the technology that powers them. For example, IntelliChief uses a combination of Robotic Process Automation (RPA) and machine learning to support one of the most complete Accounts Payable automation solutions available.
When it comes to using machine learning applications, the only limit is your imagination. Machine learning has been implemented in every industry across the globe, and it certainly has its uses in various departments in your organization, including Accounts Payable, Accounts Receivable, Human Resources, Customer Service, and more.
In 7 Machine Learning Uses for the Back Office, we discuss several of the tasks that can be streamlined or improved with machine learning, including:
Machine learning algorithms can identify part numbers, prices, and vendor information. Once this information has been captured, it can be matched against the information from the original purchase order. Machine learning applications can even cross reference part numbers to help filter out exceptions, reducing or altogether eliminating the need for a manual two- or three-way match when processing invoices.
With machine learning, your team doesn’t need to manually calculate tolerances. Instead, invoices and sales orders are read and automatically compared against information that has been saved in your company’s ERP system, resulting in fewer “touches” by users and higher straight-through processing (STP) rates.
Machine learning applications can read invoice due dates and determine which invoices need to be paid by a specific date to capture vendor rebates or discounts. This information can be extracted and presented during the voucher creation process. If your company isn’t capitalizing on vendor rebates or discounts, it could be costing your organization tens or even hundreds of thousands of dollars per year.
Most business documents are “unstructured,” meaning they don’t arrive in a standardized format. If they were, all documents would basically look the same, which is problematic for very different reasons. With machine learning, the format of an invoice or sales order that has been received from outside the organization is inconsequential. Once received, all data is captured and archived in a structured format, making processing much simpler for your team. High-quality machine learning applications can recognize data on unstructured documents, even those that have been filled out by hand.
As documents are captured, real-time machine learning applications can trigger automated workflows to keep them moving through your organization. For instance, a program that recognizes a vendor’s name could send that document to the appropriate user based on the vendor’s email address. Talk to one of our experts about your unique business processes to see if machine learning can be utilized to help your work smarter, not harder.
Have you ever noticed how people become more stressed out whenever an auditor is on-site? You’re not alone. Auditors play an important role in verifying that businesses are compliant, but they have a tendency to show up when you least expect it, such as in the middle of a crisis. Machine learning software can alleviate this stress but regularly check for issues. If a discrepancy is detected, it can be dealt with in short order by routing the issue directly to an employee for review, resulting in fewer audit-related interruptions and stress-free compliance.
Customer service representatives often spend time on mundane tasks like keying orders into an ERP system or collaborating with other departments like engineering or quality, resulting in increased lead times. Lead time can be difficult to track, but this is less of a cause for concern with machine learning technologies creating and routing sales orders automatically. This results in faster Sales Order Processing that allows CSRs to focus on better customer service.
Does your organization lack control over the manner in which tasks are completed? Over time, business processes have a tendency to become less consistent as on-the-spot decisions are normalized and integrated into your workflows. Non-standardized workflows become problematic when one of your employees takes the day off or retires. They can also hurt your business by slowing down your processing queue or increasing your cost-to-file.
Organizations can avoid these common pitfalls by utilizing automation technology with real-time machine learning capabilities. Process automation certainly minimizes time- and cost-related concerns, but with real-time machine learning, you can also ensure that your automated processes get smarter over time. It doesn’t take a long time, either. For example, the first time an invoice is received from a new vendor, it may require a small amount of manual processing; however, the next time one is received, the system will already know what to do. Machine learning software helps to mitigate the “learning curve” necessary when new vendors have been introduced or volume increases. In fact, our advanced process automation software is designed to scale with your business, allowing you to continue growing without worrying about your team’s capacity to get the job done.