Understanding how to implement and manage a successful intelligent automation program takes a keen strategy, careful planning and the right tools and technology to realize your business and operational goals. Having the right words to better describe what your organization needs also makes for a great start. As we look to help you pave your path to success with this ultimate guide to Robotic Process Automation (RPA) and intelligent automation, we also want to equip you with a glossary of the terms that will help you as you navigate your digital transformation journey.
Whether you're responsible for establishing an RPA center of excellence or you're a business user using automation software for the first time, it's important to understand the various automation technologies that will help you build a robust digital workforce.
Robotic process automation, or RPA, refers to the software that enables digital workers to carry out step-by-step tasks or business processes within your existing systems and applications, much like a human would. A critical building block for intelligent automation, RPA coupled with AI technology is no longer restricted to common back-office processes and now enables digital workers to use advanced cognitive automation capabilities to handle exceptions, variations and problem solving with little human intervention.
The term “Artificial Intelligence” (AI) describes the capability of a machine to imitate intelligent human behavior, or the simulation of intelligent behavior in computers. Matched with intelligent robotic process automation, artificial intelligence enables businesses to analyze, categorize and extract unstructured data, not only making it functional but also improving the output of complex automated business processes.
Intelligent Automation (IA), sometimes referred to as intelligent systems, links artificial intelligence, the simulation of human intelligence, with the capabilities of RPA, the simulation of human actions, to enable the expansion of automation capabilities. Backed by intelligent automation, digital workers can easily integrate with other cognitive technologies like computer vision, natural language processing, and machine learning, to expand the number of processes that can be automated, from the semi-structured such as financial services invoice processing, to the unstructured such as email triage.
Made up of a group of critical thinkers from across the business, a Center of Excellence (CoE) plays a pivotal role in the deployment and delivery of any successful enterprise-grade intelligent automation program. CoEs encourage buy-in and adoption, instill best practices, serve as a source of knowledge and resources, and demonstrate tangible benefits back to the business.
Computer vision attempts to emulate or exceed the capabilities of the human eye and visual cortex, which gives us the ability to see patterns, shapes, recognize faces and more. To achieve this, computer vision is made up of a range of algorithms and machine learning principles to recognize, interpret and understand images.
In intelligent automation, computer vision opens a world of new possibilities and can be employed in a range of ways, from simple use cases like working with systems to recognize where a button is on a screen and where it needs to click, to complex use cases like recognizing when a car is committing a parking violation.
Deep Learning (DL) is a subset of machine learning inspired by the structure of the human brain. However, where machine learning uses parameters based on descriptions of the input, deep learning uses data associated with an object or piece of data, as well as how it differs from another object or piece of data. In a real-world application, deep learning might help a digital worker easily decipher and understand handwriting by learning a variety of writing patterns and comparing it with data about how letters should look. A more complex application might be recognizing patient medical conditions.
Digital workers are super organized, multitasking software robots that work alongside people to automate and transform business processes. Like humans, digital workers can develop new skills over time, getting smarter and more capable. With AI, digital workers can be trained to take on increasingly complex tasks, manage vast workloads, and make critical decisions to tackle work with greater speed and productivity, thereby becoming a force multiplier in our customers’ businesses.
A graphical user interface enables computer users to interact easily with the computer, typically by making choices from displayed menus or groups of icons.
Machine Learning (ML) is an application of AI that enables a computer, system, or technology to learn and improve its own performance by continuously incorporating new data into an existing statistical model, or developing new behaviors based on experience. Machine learning has three key approach types, including supervised, unsupervised and reinforcement learning, each of which helps machines make decisions either autonomously, or semi-autonomously, to help them adapt to changes and deliver the best results in the shortest time without the need to constantly refer to a person for direct instruction.
Natural Language Processing (NLP) refers to the ability of computers to process human verbal and written language. Much like children learn to speak, computers may start by learning simple sentence structures and advance to understanding the irony in a sentence. In intelligent automation, NLP is used to enable chatbots and virtual agents to engage in human conversation, ultimately communicating with the same efficacy as a person.
Natural Language Classification (NLC) enables machines to learn domain-specific language so that they ultimately understand context much in the same way that a human would. With NLC, digital workers can understand, classify and respond to words depending on their placement or meaning in a particular sentence structure.
Natural Language Generation (NLG) enables digital workers to translate machine language which is difficult to compute into language that people can easily understand. Today, for instance, NLG might be used in a financial advising scenario such as monitoring stock fluctuations and advising whether to sell or invest.
Much like neurons that connect in the human brain, a neural network is a computer architecture where several computer processors are interconnected. These connections enable computers to learn by a process of trial and error. Conventional neural networks are used to recognize objects in videos or images, while recurrent neural networks form loops of networks, enabling information to remain within the computer architecture over time.
Optical Character Recognition (OCR), or intelligent OCR (iOCR) for more accurate processing, refers to the technology that enables written or typed data to be turned into digital text. OCR can be used effectively to read, understand, and digitize the information or flag variations/exceptions and return them to a human for manual processing. iOCR can learn from peoples’ actions, or through pattern recognition, and if the document doesn’t vary wildly, success rates can significantly improve.
Orchestration is one of three main approaches to managing automation, with the other two being manual and scheduling. Unlike manual, which is simply a person physically triggering a job for a specific process or task; or scheduling, in which a person instructs the digital workers to perform a task every two minutes between specified times, for example, orchestration uses data and algorithms to gain an understanding of when the best time would be to perform tasks or assign themselves to other tasks instead of sitting on the bench. This approach delivers peak efficiency and means digital workers aren’t slacking off or being ‘part-timers’ even for two minutes.
A Process Design Document (PDD) is typically a paper-based document that includes all information about the automated business process’s current state, its expected future state, and any constraints or dependencies. Although this is a significant step in the right direction, it can be hard to show and understand the varying levels of complexity within the process in a paper document, as PDDs can become complex, convoluted, and difficult to manage. As a result, it is recommended to replace paper based PDDs with a digital tool that makes it easy for teams to model, design and optimize process flows in a centralized and collaborative location.
Much like a Proof of Concept (POC), which is used across software products to prove that a concept, or technology has worked as was claimed, a Proof of Value (POV) is intended to demonstrate that the business case for automation can be delivered at scale for all specified business needs. While a POC will look at simple things such as ‘does the technology work as expected?’ and ‘how has it been deployed?’ a POV will scope the business case and the transformation and map, measure, design and forecast the potential outcome with leadership sponsorship.
Developed and offered exclusively by Blue Prism, the Robotic Operating Model (ROM™) is the strategic method for RPA software implementation and delivery that helps organizations launch, maintain, and scale their digital workforce. The ROM outlines standards, principles, and templates that reinforce the implementation of Robotic Process Automation in an organization. This proven strategy is built on seven foundations that we’ve identified as essential to a successful RPA rollout.
Software robots are super organized, multitasking digital workers that work alongside people to automate and transform business processes. Like humans, digital workers carry out specific tasks as assigned and can develop new skills over time, getting smarter and more capable. With AI, digital workers can be trained to take on increasingly complex tasks, manage vast workloads, and make critical decisions to tackle work with greater speed and productivity, thereby becoming a force multiplier in our customers’ businesses.
Next: Chapter 3
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