Glossary of Automation Terms

Robotic process automation (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. As a critical building block for intelligent automation, RPA couples with AI technology so it’s no longer restricted to common back-office processes. RPA now enables digital workers to use advanced cognitive automation capabilities to handle exceptions, variations and problem-solving with little human intervention required.

Artificial intelligence (AI) describes the ability of a machine to imitate intelligent human behavior or simulate intelligent behavior in computers. Matched with intelligent robotic process automation, AI 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 (NLP), and machine learning (ML) 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 and shapes, recognize faces and more. To achieve this, computer vision contains 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 various writing patterns and comparing them with data about how letters should look. A more complex application might be recognizing a patient’s 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 artificial intelligence, 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 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 artificial intelligence that enables a computer, system or technology to learn and improve its 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.

SS&C | Blue Prism® Decision creates machine learning-based processes to automate decision-making with minimal effort and expertise. Decision incorporates active learning to determine how you make decisions, then lets you train the model with sample inputs using autoML to build statistical models in mere minutes without coding or data science expertise.

Natural language processing (NLP) refers to computers’ ability to process human verbal and written language. Much like children learn to speak, computers 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 a domain-specific language so 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.

Chat GPT performs competitively on NLG tasks with commercial translation products such as Google Translator, balancing informativeness and conciseness. Chat GPT’s NLG capabilities are what makes it so good at summarization, story generation and data-to-text tasks.1

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 through 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 people’s actions, or through pattern recognition, and if the document doesn’t vary wildly, success rates can significantly improve.

By combining OCR with artificial intelligence, you can accurately extract information from documents quickly in a process known as intelligent document processing (IDP). SS&C | Blue Prism® Decipher performs IDP.

Process orchestration is one of three main approaches to managing automation; the other two are manual and scheduling. A manual approach is simply when a person physically triggers a job for a specific process or task. Scheduling is where a person instructs the digital workers to perform a task every two minutes between specified times, for example. But orchestration uses data and algorithms to gain an understanding of the best time 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’ – not 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; PDDs can become complex, convoluted and difficult to manage. As a result, it’s 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 to map, measure, design and forecast the potential outcome with leadership sponsorship.

Developed and offered exclusively by SS&C Blue Prism, the SS&C | Blue Prism® ROM (Robotic Operating Model) has now been upgraded to the SS&C | Blue Prism® Robotic Operating Model 2 (ROM™2). ROM 2 is the strategic method for RPA software implementation and delivery that helps organizations launch, maintain and scale their digital workforce. The ROM2 outlines standards, principles and templates that reinforce the implementation of RPA in an organization. This proven strategy is built on five foundations that we’ve identified as essential for 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 for enterprises.

Intelligent document processing (IDP) converts unstructured or semi-structured data into structured, usable information using artificial intelligence. SS&C | Blue Prism® Decipher IDP is our way of combining artificial intelligence (AI) and optical character recognition (OCR) to extract complex data quickly and accurately into a readable format. It has an easy-to-use UI with low code and no setup required, taking hours rather than days to be up and running.

Document automation is the process of turning handwritten documents into decision-ready information immediately. It digitizes, classifies and extracts information from paper automatically using the latest machine learning technology. SS&C | Blue Prism® Document Automation is one such program which extracts data to produce accurate, automation-ready data.

RPA director is a program that enables you to automatically assign work to your digital workforce. SS&C | Blue Prism® Director lets you prioritize tasks for your digital workers by entering your business parameters and dynamically prioritizes your work based on that configuration. It organizes work across multiple queues and Director’s dashboard gives an overview of the effectiveness of your digital workforce, whether SLAs are met, cost savings, the number of hours saved and so on.

Business process management (BPM) is a structured way of improving business processes by analyzing, modeling, implementing, monitoring and automating them. SS&C | Blue Prism® Chorus is our BPM suite made to help you rapidly build and automate processes, optimize workflows, create digital customer experiences and accelerate straight-through processing. Chorus’s suite includes digital process automation, case management, omnichannel interactions and operational analytics.

Business process analysis (BPA) is the method of reviewing the processes governing your business operations. It’s a multi-step analysis of your process to identify what’s working well, what needs improvement and how such improvements can be made.

Process discovery is the method of manually or automatically gathering data via BPM and process mining to create a representation of an organization’s current business processes. It allows you to objectively identify, analyze, qualify, prioritize and schedule business processes for automation to form a demand pipeline.

Business process optimization (BPO) is a way of improving an organization’s efficiency by optimizing its core processes. BPO is part of the process of business process management (BPM).

Process intelligence brings together the best aspects of process mining, task mining and business intelligence. By using process intelligence, you can transform how your processes work through process discovery, analysis and optimization. This lets you scale your automation faster by identifying the right processes and running them at peak performance.

Process mining uses data from information systems to create an accurate model of how a process is executed. The data for process mining is collected from the log data of applications and databases and fed into an analytical engine that mines that data and produces a map of how the process operates in production, then compares it with a model of how the process is designed to operate.

Task automation uses technology such as robotics process automation (RPA) to complete tasks rather than completing them manually with human effort. Task automation saves an organization the time and money usually spent on repetitive manual tasks.

Task mining focuses on the smaller components of a process or subprocess to drill into the granular clicks of an individual or compare clicks of multiple users to see how different people complete tasks. Task mining shows the frequency, complexity and cycle times by recording keystrokes and clicks, and copying and pasting activities to build a visual representation of how tasks are undertaken.

Workflow automation is setting up tasks to perform independently within set parameters to improve the flow of work. It’s the process of moving tasks from manual to automated digital labor.

Unattended robotics process automation (RPA) runs independently, completing tasks by following a rules-based process without requiring human intervention.

Attended robotics process automation (RPA) is a set automation that lives on a user’s device and won’t activate unless the user gives a command. It’s ideal for automation requiring security for sensitive data. SS&C | Blue Prism® Desktop is the only desktop automation solution delivering enterprise-level control, security, scalability and auditability for automating repetitive tasks on employee desktops. With Desktop, users can run centrally developed, validated processes in full compliance with an enterprise’s IT and security standards.

A robotics process automation (RPA) operating model is a system to help organizations deliver scaled deployment of their digital workers tailored to their needs, with comprehensive guides, templates and an automation maturity model. The SS&C | Blue Prism® Robotic Operating Model 2 (ROM™2)enables you to expand your intelligent automation (IA) capabilities with our strategic step-by-step guide, helping organizations start small and scale quickly.

Automation lifecycle management (ALM) is a scalable operating model for enterprise-ready automations. SS&C | Blue Prism® Automation Lifecycle Management (ALM) uses the power of Wireframer and reusable templates to help organizations design better quality automations quickly while simplifying complex and time-consuming process definition activities.

Still have RPA-related questions? Get in touch.

Alexis Veenendaal is an Associate Content Writer and Editor at SS&C Blue Prism. She’ll tell you all the cool tips and tricks for implementing intelligent automation into your workplace. She has lived and worked internationally as a professional writer and designer for nearly a decade after graduating from the University of Lethbridge for English Literature. Her personal pursuits include authoring books and digital cartography.