IPA

AI, RPA, and Machine Learning – How are they Similar & Different?

Here’s a guide to understand the unique applications and choose the right technology for your business.

 

AI, RPA, and machine learning, you must have heard these words echoing in the tech industry. Be it blogs, websites, videos, or even product descriptions, disruptive technologies have made their presence bold. The fact that we all have AI-powered devices in our homes is a sign that the technology has come so far. If you are under the impression that AI, robotic process automation, and machine learning have nothing in common, then here’s what you need to know, they are all related concepts. Oftentimes, people use these names interchangeably and incorrectly which causes confusion among businesses that are looking for the latest technological solutions. Understanding the differences between AI, ML, and RPA tools will help you identify and understand where the best opportunities are for your business to make the right technological investment.  

 

The Big Difference – RPA

 

According to IBM, “Robotic process automation (RPA), also known as software robotics, uses automation technologies to mimic back-office tasks of human workers, such as extracting data, filling in forms, moving files, etc. It combines APIs and user interface (UI) interactions to integrate and perform repetitive tasks between enterprise and productivity applications. By deploying scripts which emulate human processes, RPA tools complete autonomous execution of various activities and transactions across unrelated software systems.” In that sense, RPA tools enable highly logical tasks that don’t require human understanding or human interference. For example, if your work revolves around inputting account numbers on a spreadsheet to run a report with a filter category, you can use RPA to fill the numbers on the sheet. Automation will mimic your actions of setting up the filter and generate the report on its own. With a clear set of instructions, RPA can perform any task. But there’s one thing to remember, RPA systems don’t have the capabilities to learn as they go. If there is a change in your task, (for example if the filter has changed in the spreadsheet report), you will have to manually input the new set of instructions.  

 

Industrial Applications

 

The highest adopters of this technology are banking firms, financial services, insurance, and telecom industries. Federal agencies like NASA have also started using RPA to automate repetitive tasks.  

 

The Big Difference – AI

 

According to Microsoft, “Artificial Intelligence is the ability of a computer system to deal with ambiguity, by making predictions using previously gathered data, and learning from errors in those predictions in order to generate newer, more accurate predictions about how to behave in the future”. In that sense, the major difference between RPA and AI is intelligence. While these technologies efficiently perform tasks, only AI can do it with similar capabilities to human intelligence.  

 

Industrial Applications

 

Chatbots and virtual assistants are two popular uses of AI in the business world. In the tax industry, AI is making tax forecasting increasingly accurate with its predictive analytics capabilities. AI can also perform thorough data analysis which makes identifying tax deductions and tax credits easier than before.  

 

The Big Difference – Machine Learning

 

According to Gartner, “Advanced machine learning algorithms are composed of many technologies (such as deep learning, neural networks, and natural language processing), used in unsupervised and supervised learning, that operate guided by lessons from existing information.” Machine learning is a part of AI, so the two terms cannot be used interchangeably. And that’s the difference between RPA and ML, machine learning’s intelligence comes from AI but RPA lacks all intelligence. To understand better, let us apply these technologies in a property tax scenario. First, you can create an ML model based on a hundred tax bills. The more bills you feed the model, the more accurately it will make predictions for the future bills. But if you want to use the same machine learning model to address an assessment notice, the model will be of no use. You would then have to build a new machine learning model that knows how to work with assessment notices. This is where machine learning’s intelligence capabilities draw a line. Where ML fails to recognize the similarities of the document, an AI application would recognize it, thanks to its human-like interpretation skills.  

 

Industrial Applications

 

The healthcare industry uses ML to accurately diagnose and treat patients, retailers use ML to make the right products available at the right stores at the right time, and pharmaceutical companies use machine learning to develop new medications. These are just a few use cases of this technology.  

 

Is RPA Part Of AI?

 

No, but they can work together. The combination of AI and RPA is called smart process automation, or SPA. Also known as intelligent process automation or IPA, this duo facilitates an automated workflow with advanced capabilities than RPA using machine learning. The RPA part of the system works on doing the tasks while the machine learning part focuses on learning. In short, SPA solutions can learn to perform a specific task with the help of patterns. The three technologies, AI, RPA, and ML, and the duet, SPA hold exciting possibilities for the future. But only when companies make the right choice, the rewards can be reaped. Now that you have an understanding of the various capabilities of these technologies, adapt and innovate.

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IPA vs RPA: Similarities and Differences

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Here are the similarities and differences between IPA and RPA

 

Intelligent Process Automation (IPA) and Robotic Process Automation (RPA) relieve employees of the burden of mundane and repetitive duties, allowing them to focus on more creative and inventive tasks. Today, we’ll look at the similarities and differences between IPA and RPA. We will start by defining each term before moving on to the similarities and differences.  

 

What is IPA?

 

Intelligent process automation (IPA) is a digital solution for operations and maintenance business processes that use technologies like natural language processing (NLP), artificial intelligence (AI), robotic process automation (RPA), machine learning (ML) and perceptive document understanding to make it possible. Robotic process automation (RPA) is a technology for automating normal and repetitive customer care operations. IPA uses artificial intelligence technology to imitate human intellect, giving the tools and techniques necessary to accomplish high-functioning activities requiring thinking, judgement, decision-making, and analysis. This technology solution is essential because it allows personnel, such as customer service representatives, to spend more time conversing with consumers and building relationships.  

 

IPA Examples

 

Companies can concentrate their attention on more essential company activities thanks to intelligent automation. In the end, IPA saves time, and we all know that time is money. Let’s look at some instances of IPA in action in various sectors.  

 

Healthcare

 

Intelligent automation software can sift through reams of structured data and recommend therapy or diagnosis based on criteria like medical history or symptoms. What a doctor would spend hours researching takes a computer merely seconds. This frees up doctors and other healthcare workers to spend more time with patients rather than combing through medical research resources.  

 

Intelligent Virtual Assistant Market

 

Businesses are increasingly turning to sophisticated virtual assistants in place of chatbots (IVAs). IVAs employ IPA to begin human-like dialogues, whereas typical chatbots use scripts to simulate human conversations and interactions. IVAs can use natural language processing to accurately answer queries for which they have not been trained or programmed. They utilise deep learning and machine learning to grasp colloquial formulations, expand their vocabulary, and respond to client questions accurately. With informed and genuine interactions, IVAs provide a good client experience.  

 

Employee Onboarding and Offboarding

 

Onboarding and offboarding are operations that can take a long time and involve a lot of staff effort. While paperwork, certification, payment systems, and getting resignation letters are all relatively easy activities, they may be time-consuming and tiresome. These procedures, on the other hand, maybe simplified and executed in a timely and error-free way using IPA. Employees may focus their energies elsewhere, leaving the hard job to the machines.  

Inventory Control

 

Traditional inventory control frequently necessitates time-consuming and labour-intensive manual processes. Companies no longer depend on inventory workers to accomplish technological activities like writing invoices and issuing work orders thanks to clever automation. Automated inventory control systems, on the other hand, employ IPA to handle back-office activities including inventory monitoring, shipping and fulfilment, supply chains, and more.  

What Is RPA?

 

Robotic process automation (RPA) is a term that refers to programmes, scripts, or software that automates simple, repetitive, rule-based operations that are time-consuming to complete manually. RPA not only saves labour expenses but also eliminates human error. These “robots” are designed to carry out certain duties in a precise and self-contained manner. They are capable of retrieving data, analysing unstructured data, processing transactions, and even communicating with other digital systems. Manufacturing, commerce, healthcare, supplier management, and HR services were among the first sectors to use RPA technology, but now organisations from many industries utilise it.  

 

RPA Examples

 

Many RPA use cases exist across several sectors that might benefit from automation to free some workers’ time for creative tasks. The RPA examples below are some of the most frequent ways robotic process automation is used.  

Payroll Processing

 

Throughout the year, payroll processing necessitates numerous phases of human work. Fortunately, RPA systems may automate tasks like generating pay stubs, calculating costs and deductions, organising and storing critical data, and generating yearly reports. Payroll processing automation relieves the stress of understanding complicated tax regulations while also lowering expenses and increasing productivity and accuracy.  

 

Web Analytics

 

To better understand their consumers, all businesses rely on the capacity to analyse massive volumes of behavioural data online. Web analytics software that is fully automated can correctly anticipate customer behaviour, allowing businesses to sell products and services solely on this new data. This not only leads to more revenue, but also to a better user experience.  

 

Credit Card Applications

 

RPA technology is used to process the majority of credit card applications in financial organisations. The software is set up to gather data, evaluate documents, perform credit and security checks, and then decide whether or not to give a credit card to an individual.

 

Patient Registration

 

Every day, hospitals visit a large number of patients, and regular monitoring of all of their data manually may be time-consuming and tiresome. Patient registration, on the other hand, maybe sped up with the use of automation tools. IPA robots are capable of guiding patients through the registration procedure and providing them with all necessary information. Inpatient registration automation reduces the risk of human mistakes, improves quality, and saves time.  

 

IPA vs RPA: Major Difference

 

IPA is frequently confused with RPA, although the two are not the same. RPA can be implemented on IPA systems, although it is not required for RPA to work. RPA refers to technology tools and procedures that automate and finish time-consuming operations considerably more quickly than people. These activities are frequently rule-based, repetitious, and straightforward. Because the systems are built to carefully obey a set of rules, RPA can be troublesome at times. For example, if a client enters inaccurate information, the system will be unable to perform the operation. This is where clever automation enters the picture. When RPA is no longer enough, IPA is used to finish complicated procedures utilising AI reasoning and decision-making approaches.  

 

Conclusion

 

With the boiling rivalry in mind, the development of advanced job automation technologies has risen, and it is anticipated to climb much more in the future years. Beyond the confines of standard business process management. IPA software is intended to help processes with more than just operations management. Starting with locating and eliminating performance bottlenecks. Advanced analytics are used by the smart process automation software. This aids in the analysis of overall performance, the comprehension of ever-changing market structure, and the formulation of appropriate plans.

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