Robotic Process AutomationRPA and Machine Learning: The Promise

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Robotic process automation has caused a great sensation in many different industries. When companies focus on the digital revolution, repetitive task automation can increase effectiveness while reducing human error, which is an attractive outcome. RPA will not feel tired, will not feel bored, and will accurately perform tasks to help its human counterparts and increase productivity. RPA allows others more time to focus on higher-level work. In addition to simple RPA, intelligent automation can also be achieved by combining RPA and machine learning, thereby realizing the automation of repetitive everyday jobs and human-like decision making.

Thanks to RPA, the human workforce no longer has to perform massive amounts of manual work. As a result, employees can spend extra time on essential tasks, improve their work results, and add more value to other important business tasks.

RPA has already proven to be a significant driver of digital transformation for companies by increasing throughput, reducing expenses, and ultimately fostering profits and business growth. RPA is more than just an idea; its market size is expected to reach $ 3.97 billion by 2025. By incorporating machine learning with RPA, we bring together functions of automation processes, but we go further. We can create high levels of RPA bot that can identify, understand, and draw conclusions from unprocessed data. This creates new intelligent RPA data analysis before working on it, continuously learning from data attributes, makes it smarter over time, and drives smarter decisions based on historical results.

Companies looking for the next innovation should consider using RPA and ML to smartly automate process automation. Machine learning encompasses learning and thinking but is managed by the RPA. Machine learning functions used in conjunction with RPA are technologies such as image and voice recognition or document information.

Linking machine learning to RPA makes sense when business equipment is installed in an integrated way with their system. The goal is to drive business processes near perfection.

Practical Case: Automated Payment Accounts

Let’s look at a prevalent business case. Many accountants worldwide get a message from their vendors every day, telling them how much to pay.

Thanks to digital innovation today, invoices arrive at the desktop as PDF files attached to an email.

The never-ending updates the accountant has to update the information is the same process over and over.

Here is what we can see is inherent in this process:

  • Processing statements is very time consuming and tedious for accountants.
  • These employees will have to spend considerable time reading invoices and manually selecting and extracting relevant information (e.g., supplier name, invoice number, the total amount to be paid, payment date, etc.)
  • After manually taking out the data, the employee opens the accounting model in the organization’s ERP request, enters the details into the form, and confirms the item by clicking a button.
RPA and Machine Learning: There Is A Difference

By design, RPA is not intended to mimic human intelligence. It is usually designed to mimic necessary human accomplishments. That is, it does not imitate human behavior but mimics human behavior. The RPA process is primarily driven by precise business rules that can be intently described, so the RPA has controlled capabilities to deal with unclear or complex environments. On the other hand, artificial intelligence replaces human judgment to select tasks with machines. More importantly, machine learning expects more data inputs and a more comprehensive range of results than RPA.

Artificial intelligence is a mechanism for making intelligent decisions and simulating human actions. On the other hand, machine learning is a necessary starting point for artificial intelligence because it contributes to inference analysis and predictive decisions that are closest to what humans can expect.

In June 2017, the IEEE Standards Association issued IEEE guidelines on terms and perceptions for intelligent automation processes.

In it, “Robotics Process Automation” uses predefined business guidelines and choreographed activities to comprehensively accomplish a permutation of procedures, actions, transactions, and everyday tasks on one or additional not linked software structures. It is defined as an example of software with close results. In other words, RPA is a system programmed explicitly for the job, ensuring that a specified set of everyday jobs can be performed repeatedly. However, with RPA, you cannot implement learning features to improve or adapt its skills to different situations. There, artificial intelligence and machine learning are increasingly playing a part in building smarter systems.

Businesses who want to stay competitive and efficient should consider adding artificial intelligence and machine learning to their existing RPA.

Relying On Smart Machines

With artificial intelligence, information is an integral part of which everything depends. In businesses such as self-driving cars and health care, where evaluations made by ML/AL can have severe consequences, the accurateness of training data informing these types of decisions is crucial. As with the precision of modern AI and machine learning types that use neural networks, these systems work more independently than ever before. Truly make decisions without human intervention.

Small changes or errors in educational information for machine learning can have surprising and unforeseen consequences. In this way, information’s reliability and accuracy become even more critical as people rely on intelligent machine decisions.

Profitable Machine Learning Requires Accurate Training Data

Data integration includes generating information from a proxy source and accurately identifying information before testing, training, and model deployments.

Data experts will tell you that the accessibility of accurate training data is probably the utmost important tool when working with AI. Examples of “dirty” information include missing, unfair, and bizarre data or data sets that do not represent information to be treated in a production environment. An essential phase in the engineering and machine learning process is to select the utmost important information that will inform you of the accuracy of a particular type of model. A successful modeling process needs to identify key components in each iteration in an online network where phases overlap. Incorrect training data can lead to selecting or weighting features incorrectly, leading to problems.

The Problem of Superstition

Modern AI and machine learning models fluctuate due to their dependence on data provided to them by the business. When deviations in result occur, individuals get nervous, but often they don’t know how to diagnose the issue. Working with machine learning experts can aid in overcoming any mistrust of an AI output. Just like with other superstitions, education is the answer.

Have Your Organization Soar

 Learning from accurate data is what will bring the most success for an organization considering RPA and machine learning as a solution. Many organizations may have RPA and AI in their companies today but are siloed and have never thought to bring them together. Others, whose companies are being crushed by manual tasks, expensive overhead, and high error rates, consider new ways to compete in the marketplace. In either case, your company has the potential for vast improvements, all of which create bottom-line results.

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