Beginner’s guide to intelligent automation tools

How to Level Up Your Understanding of Intelligent Automation Tools

Hyperautomation isn’t just a buzzword. It’s a game-changer for organizations, no matter what your line of business. Basic robotic process automation (RPA) entails developing or customizing automated programs to perform time-consuming, repetitive, and error-prone workflows. Hyperautomation goes further, incorporating advanced tools such as artificial intelligence (AI), natural language processing (NLP), optical character recognition (OCR), and advanced machine learning. With Intelligent Automation Tools, the machines and algorithms do most of the “thinking” and learning.

Your Intelligent Automation Strategy

As a business leader, you must understand how technology could help achieve your goals. These projects tend to be known as digital transformation opportunities. Hyperautomation is one component of a transformation initiative. To succeed with your hyperautomation program, you must first identify opportunities for automation in your organization, then create automations for these opportunities.

Determining which processes would be most meaningful to your team, and which would have the greatest ROI to your company is achieved through Automation Discovery.  Traditionally this would have been done by business analysts. Now in the age of hyperautomation, AI-powered process discovery tools exist to identify these tasks and compile the steps for automating in a fraction of the time.

Many of the processes ripe for automation are found in contact centers, shared service centers and finance and accounting. Basically, you can find them where lots of people are working on repetitive tasks.

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Understanding the Terminology for
Intelligent Automation Tools

NATURAL LANGUAGE PROCESSING (NLP)

Natural Language Processing is the key to understanding the sentiment of the spoken word when using Robotic Process Automation (RPA) to interact with human customers. NLP uses artificial intelligence to analyze unstructured data into written or verbal communication with human customers in several different languages.

Experts in NLP know how to get the bots to understand human language and verbal cues and interpret this data to understand customer needs. The skills required for NLP experts include deep learning models, sequential modeling, and an in-depth understanding of chatbots and autoprocessing. When done correctly, Natural Language Processing increases customer satisfaction while making the jobs of customer service-facing employees easier.

INTELLIGENT OPTICAL CHARACTER RECOGNITION (OCR)

While Natural Language Processing is focused primarily on voice, Intelligent Optical Character Recognition deals with text. OCR enables bots to recognize type fonts and ICR captures. Using Intelligent OCR/ICR, the textual content can be extracted from a scanned image and digitized to copy, share, and edit.

OCR/ICR are the basis for Intelligent Document Processing (IDP), making processing documents and forms faster and easier than manually. OCR and IDP streamline various business applications that include invoicing, documentation management, receipts, insurance policy processing, registration, medical recordkeeping, banking, and more. The typical file types for Intelligent OCR include .png, .gif, jpe, .jpg, .jpeg, .tiff, .tif, .bmp, and .pdf files.

INTELLIGENT/AI CHAT

Intelligent chat, or AI chat, uses artificial intelligence-powered digital workers – chatbots – to interact with an organization’s customers and users. AI chatbots use machine learning to get smarter over time, along with Natural Language Processing to extract the information needed so the chatbot can understand customer intent and sentiment.

From the beginning, standard chatbot software can understand language outside of pre-programmed commands. However, AI chatbots are different because the more input an AI chatbot receives, the more it can adapt further into conversations and “learn.” Generative AI chatbots can  come up with creative answers to questions posed by users. When trained on your own company data they can provide accurate responses for customer service, marketing, social media, human resources, and messaging apps. The skills required to implement intelligent/AI chat include understanding NLP and rules-based systems.

ROBOTIC PROCESS AUTOMATION (RPA)

Robotic Process Automation uses technology to perform recurring tasks instead of manual labor – it takes the humans out of the equation. These digital workers are trained to follow the steps carried out by a human employee, taking over repetitive and time-consuming tasks. Common examples of process automation in business include journal entry, invoicing, consolidated reporting, employee onboarding, and claims processing.

Developing process automations typically requires an understanding of how to configure a digital worker to follow steps in a workflow. An in house Automation CoE is set up with RPA developers, RPA controllers, solutions architects and IT professionals to manage RPA infrastructure.  However, as a leader in Robotic Process Automation (RPA) technology, WonderBotz has created RPA as a Service. Let our team handle the IT heavy lifting and the robot licensing, orchestrating single or mulit-platform Intelligent Automation strategies.

Your company should run at least one intelligent automation platform. However, many organizations run dual platforms. For example, WonderBotz often has clients with both Blue Prism and UiPath platforms. A dual-platform strategy can reduce conflicts and expand the number of use cases which can be automated. To do this companies need a combined orchestrator that can manage both platforms and integrate new AI Technologies. ARIA Cloud provides the secure cloud based platform to bring all of your intelligent automation tools together in one place.

RPA MIGRATION TO CLOUD COMPUTING

Cloud computing can be the backbone for hyperautomation and a critical path to AI readiness. When process automation is carried out in the cloud, it is scalable and reliable. The cloud makes it easy, fast, and economical to spin up a local testing environment for automation development.

Running your automations in a cloud environment can also lower your total cost of ownership by bypassing the costs of maintaining your hardware which can otherwise be quite high. Another benefit is the increased resources for cybersecurity in the cloud. To best take advantage of cloud computing for hyperautomation, you’ll need people on staff or a dedicated partner who has in-depth knowledge of cloud platforms and services.

DIGITAL WORKER/BOT

A digital worker, or bot, is the action unit in RPA automation. It’s an automated team member trained to carry out back-office workflows. As it is, automation digital workers perform the job faster than humans and make no “human” mistakes.

MACHINE LEARNING (ML)

Machine Learning is the process of getting computers to “think.” By using algorithms and statistical models, computer systems can now learn and adapt to new situations and tasks on their own without following explicit instructions or programs. Machine Learning models are the basis for all AI decision making.