7 Machine Learning Uses for the Back Office

Machine learning is transforming the way that companies do business. But there’s no “standard” approach that’s right for every organization. Some companies are using AI to improve their manufacturing processes, while others are focusing on back-office administration. If you’re trying to decide how – or where – these technologies make the most sense for your business, there’s a wide spectrum of applications to consider. Below, we discuss seven machine learning uses that can enhance various departments across the enterprise.

Real-Time Machine Learning Applications for the Enterprise

At their core, machine learning tools (also called “deep learning” tools) are designed to help you identify patterns, collect data, and create predictive models that become more accurate over time. While humans can do the same work, computers can do it more quickly, for a higher volume of data. This makes real-time machine learning incredibly useful for a variety of projects. Some of the most common business-driven use cases include:

1. Invoice Processing

Machine learning uses algorithms that can identify part numbers, prices, and vendor information, then reconcile that information with the information from the original purchase order. Computers can even cross reference part numbers to help sort out discrepancies. This eliminates the need for a manual two- or three-way match when processing invoices.

2. Managing Tolerances

Instead of a user needing to manually calculate tolerances, machine learning applications can read invoice or sales order values and automatically compare them against information that’s saved in the user’s core ERP. This helps create fewer “touches” for the user and increases the straight-through processing rate for back-office documents.

3. Managing Vendor Discounts

Machine learning applications can read invoice due dates, then determine which invoices to pay (at what times) to take advantage of vendor rebates or discounts. This information can be extracted and presented during the voucher creation process.

4. Collecting and Organizing Unstructured Content

Most business documents are considered “unstructured”. An invoice or sales order that’s received from outside the organization can come in any format. Today’s machine learning applications can recognize data on unstructured documents so users don’t have to manually key it into the ERP.

5. Automating Workflows

When business documents are captured, real-time machine learning applications can trigger automated workflows based on the information it collected from the file. For example, a program that recognizes a vendor’s name could route that document to an appropriate person based on the sender’s email address.

6. Streamlining Financial Audits

Auditors can periodically review sample transactions to make sure they are correct, but machine learning software can regularly check for issues. If a discrepancy is detected, it can be passed along to a user for review. This results in fewer audit-related interruptions and stress-free compliance.

7. Processing Customer Orders

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. This increases the company’s lead time and is difficult to track. Machine learning technologies can create sales orders automatically and route order types for review automatically.  This results in faster Sales Order Processing that allows CSRs to focus on better customer service.

Of course, there are many other machine learning uses, too. Siemens notes that the global market for smart machines is growing by almost 20 percent every year, while Dataversity notes that every single industry sector – from manufacturing and healthcare to financial services and law – has the potential to benefit from advances in automation.

What Can’t Machine Learning Do?

Although machine learning can handle a number of redundant tasks, there are certain areas where it’s not applicable – at least, with the technologies that are currently available.

An example: while computers can understand data, they cannot understand context. They can point out an anomaly, but they can’t always determine the underlying cause, and they don’t always know how to solve it. People are still needed to draw conclusions from the insight that predictive analytics provides — providing essential depth to your deep learning initiatives.

Of course, tasks that require in-depth analysis still need to be completed by humans. Computers can only handle processes that they’ve been specifically taught to automate, which means anything that requires critical thinking can’t be solved with an algorithm. That’s why most experts recommend viewing AI as a co-pilot – not an auto-pilot.

Machine Learning Projects Are on the Rise

In 2017, more than a quarter of companies had already set aside funds for machine learning projects – and that number is only projected to rise. If you’re looking to apply AI to your back office, IntelliChief can help.

Our software becomes more advanced the more that you use it. In many cases, it can complete processes that are currently handled by your employees, without any manual effort. For instance, it can help your Accounts Payable department process vendor invoices more effectively and even eliminate the data entry in sales orders. This can make your processes much more efficient – and much less costly.

And, as an enterprise-class solution, our machine learning software has countless potential applications throughout your business. From HR and order processing to accounting and finance, there are a number of ways that we can help you transform your business – and we’re here whenever you’d like to get started.

For more information about IntelliChief’s machine learning functionality, contact us today. Or, to see practical examples of how other companies have streamlined their workflows with our automation software, visit our Resource Library to download one of our peer-to-peer case studies.