As newer technologies evolve, differentiating one from another becomes challenging. The difficulty increases when you are new to the industry. While the technologies may have similarities, there are indeed differences between them. Machine learning is a subset of AI that uses statistical algorithms to give computers the ability to learn without being explicitly programmed. RPA technology on the other hand enables non-technical staff or machines to complete high-volume tasks, similar to human actions. Capacity provides customers with the best of both worlds, as ML and RPA technology is integrated into their various solutions which allows you to automate routine business processes and gather big data insights.
First, look for a feature called Robotic Process Automation (RPA). RPA allows companies to increase their output by automating repetitive tasks—ultimately reducing employee turnover and increasing job satisfaction.
Next, look for machine learning (ML). Machine learning, which relies on large data sets to understand the probable outcomes. ML then teaches computer systems to make decisions based on that information and is a subset of AI.
After comparing the features that manage data and automation, look for features that work together. True workflow empowerment brings the power of data, AI, and automation together. For example, Capacity captures knowledge, mines documents, and spreadsheets, and connects to over 50 popular apps—making everything instantly accessible through a comprehensive interface.
Robotic process automation (RPA) is programmable software that performs routine business processes. It is designed to automate tasks across an enterprise. RPA tools are programmed to replicate manual processes that were completed by employees. Some solutions capture the employee process, while others use documented procedures as the basis of the automated process.
Machine learning (ML) uses artificial intelligence (AI) to learn how to determine possible outcomes without explicitly programming them. The technology relies on large data sets to understand the probable outcomes. From that data, ML teaches computer systems how to make decisions. Although ML and AI are used interchangeably, they are not the same. ML is a subset of AI.
ML and RPA were developed for different purposes. RPA was designed to automate predefined business processes or workflows. ML was created to make quantitatively sound decisions in real-time. Perhaps, the best way to explain how the two technologies are different is by example.
All businesses have to request payments and pay bills. Most of these accounting processes follow the same steps. For example, sales teams generate an order for every item that is sold. When the sale is complete, the order is sent via email to the accounting department. Accounting downloads the attached order saves it electronically and creates an invoice to be sent to the customer.
Since the sales department always sends orders as an email attachment, RPA could automate the process. When an email from a sales rep is received, RPA can check the subject line for the words “closed sales order” and look for an attachment. If the attachment is present, it is downloaded and saved into a designated folder. RPA then notifies the accounting team that a sales order has been filed.
If the sales and accounting departments share the same format for orders and invoicing, RPA could be programmed to use copy and paste commands to generate the invoice. Alternatively, ML could be deployed to ingest the sales order, extract the information, and create the invoice. The invoice would then be sent to the customer for payment.
RPA was designed to complete discrete tasks such as receiving emails and storing files. Reading a sales order is outside its design scope because sales orders are unstructured or semi-structured data, meaning the data doesn’t always appear in the same place.
For example, the number of line items in a sales order changes from customer to customer. As another example, service sales orders differ from product orders. Since RPA requires explicit instructions, programming the software to adjust the copy and paste function to ensure it can process the variable number of line items from different order formats is almost impossible.
Instead of RPA notifying the accounting team, the software tells the ML application that an order has arrived in the designated directory. Because ML was designed for processing unstructured data, it reads the sales order, extracts the information, and places it in an invoice template. ML can forward the invoice to the customer or an employee for data validation.
Unlike RPA, ML solutions have the ability to adjust to deviations in a process. They understand that the number of line items changes and different products may have other invoicing formats. When ML encounters a new situation, it uses its knowledge base to make a decision on how to move forward. RPA could fail to function until the software is modified to accept the change.
Robotic process automation is all about the process. RPA was intended to help businesses automate repetitive procedures. It is especially effective in performing tasks that are rule-based and cut across department or system boundaries. In siloed organizations, a workflow can be stalled every time a different department is involved. For example, processing a vacation request can be delayed as it moves from the employee to a manager to human resources back to the employee. In our example, the sales order could be delayed as it moves from a sales rep to the accounting team to the customer.
ML, but specifically AI, is focused on data. It needs lots of quality data to do its job. When turning sales orders into invoices, ML requires examples of both sales orders and invoices. The more examples, the better the results. After learning about sales orders and invoices, an appropriate ML algorithm is trained to perform the process faster and with more accuracy than a human.
If a static business process needs to be completed as quickly and accurately as possible, RPA will probably meet the need. However, ML may be needed if the procedure requires on-the-spot decisions for what would be considered out-of-scope steps for an RPA solution.
As shown in the invoicing example, combining RPA and ML can result in a fully automated end-to-end solution. However, the cost for automated solutions increases as one moves from RPA to AI deductive analysis. What is needed is an analysis of which solutions are the best fit for your organization.
For example, RPA requires end-user participation to document a step-by-step process. Any changes in the process require a program change. ML also requires end-user participation to provide sufficient data to train an ML algorithm. With ML, programmatic changes should not be required.
The key to either an RPA or ML deployment is understanding what the solutions were designed to do and how they do it. For organizations wanting to move forward with digital automation, RPA can be an excellent entry-point. It can be deployed quickly and at a lower cost than AI-based solutions. However, expecting RPA to provide the same results as ML will only lead to frustration and failure.