33 Automation Vocabulary Terms You Must Learn to End Confusion

Experts expect the robotic process automation (RPA) market alone to grow from $3.79 billion in 2024 to over $30 billion by 2030. Add the massive potential of agentic AI, and you have the makings of an automation transformation. But with all the automation vocabulary and abbreviations, where do you start? We can help.

In brief:

• Master automation vocabulary to navigate today’s rapidly evolving automation landscape, including terms like enterprise automation, hyperautomation, RPA, and generative AI.
• Distinguish between enterprise automation and hyperautomation by focusing on business processes first for enterprise automation and prioritizing technical solutions for hyperautomation to automate as many processes as possible.
• Use generative AI and AI agents to transform automation, enabling dynamic, autonomous decision-making and expanding intelligent automation across business operations.
• Engage employees early and often in automation initiatives, prioritize transparency, and use AI to augment human decision-making rather than replace it.

With today’s tight labor market, you can’t afford to burn out your employees. Enterprise automation and hyperautomation can help prevent burnout by filling gaps between automation systems, such as an enterprise resource planning (ERP) solution or customer relationship management (CRM) tool.

Recent advances in generative artificial intelligence (AI), autonomous AI, and AI agents exponentially increase the potential of enterprise automation and hyperautomation. However, knowing the differences, key terms, and buzzwords surrounding automation, enterprise automation, and hyperautomation lays the foundation for understanding how AI and generative AI fit into modern automation excellence.

In this blog post, we’ll break down the key terms and definitions related to enterprise automation and hyperautomation, as well as those evolving AI terms. Whether you’re a business leader, a technology professional, or simply someone who’s curious about the future of automation, we hope to provide you with a better understanding of the key concepts and trends shaping the industry.

Automation Vocabulary Foundational Terms

Let’s start by differentiating the terms automation and enterprise automation from hyperautomation.

While the terms may seem similar, the key differences are that automation is the general term for any task or process not requiring human intervention, enterprise automation focuses on the process first, and hyperautomation focuses on the technical requirements first. This change in focus may actually eliminate the need for more technology.

With this understanding in mind, we can define these terms as follows:

• Automation: The use of technology to perform tasks without human intervention.
• Enterprise Automation: The use of technology to automate business processes within an organization using the best tool for the process or job. Enterprise automation is achieved by first understanding the process and business requirements, fixing the process, and aligning technologies to fit the new requirements.
• Hyperautomation: Gartner defines hyperautomation as “a business-driven, disciplined approach that organizations use to rapidly identify, vet and automate as many business and IT processes as possible.” Typically, businesses achieve hyperautomation through a combination of robotic process automation (RPA) and AI.
• Digital Transformation: The approach, strategy, or journey that focuses on integrating digital technologies into all areas of a business. Digital transformation encompasses many additional terms, concepts, and ideas defined below that also help your company achieve its operating goals.
• Automation Strategy: The place where every automation effort should begin. Before you start investing in automation tools, you must define the problem you’re trying to solve, state how solving that problem aligns with business objectives, identify the best metrics for measuring success and return on investment (ROI), understand your technological infrastructure, and more.

Artificial Intelligence and Machine Learning Tools

• Artificial Intelligence (AI): The simulation of human intelligence in machines using predefined logic. AI is a broad field that includes other terms, such as natural language processing and computer vision.
• Machine Learning (ML): A subset of AI that is typically narrower in focus. ML allows machines to learn from data training. You can use ML anywhere you can test the population of inputs against a desired outcome.
• Natural Language Processing (NLP): Another branch of AI that specializes in taking input of words (either text or voice) and understanding the words’ intention.
• Computer Vision: Different from NLP because it’s less focused on words and more focused on images or objects that are presented to it. Its goal is to take a visual input and provide outputs of what was in the input.
• Intelligent Document Processing (IDP): Using AI to process documents, including those that contain handwriting or variable fields. IDP relies on optical scanning recognition and computer vision to scan PDFs and other documents. More advanced generative AI systems can generate responses to those emails based on context, while traditional AI could route different types of documents appropriately based on rules.

Generative AI and Advanced Systems Terms

• Generative AI (GenAI): Advanced AI that can compose contextual responses, generate documentation, synthesize information from multiple sources, or even write code for new situations. GenAI transforms intelligent automation from sets of predetermined paths to dynamically created solutions.
• Large Language Models (LLMs): Powerful AI systems trained on vast amounts of text data to understand and generate human language with remarkable fluency. LLMs enable advanced capabilities such as automating customer support, drafting communications, extracting insights from documents, and powering intelligent chatbots.
• AI Agents: Large language models (LLMs) equipped with tools to take on specific roles and autonomously make decisions. AI agents are like a digital workforce, because once they’re built, they work autonomously to make reasoned decisions without human intervention. Their autonomous nature, decision-making, and problem-solving abilities, as well as their ability to integrate outside sources of data and capabilities (extensibility), set them apart from chatbots.
• Multiagent Systems: Intelligent or AI-augmented solutions that orchestrate the work of two or more AI agents. For example, you might have an agent that opens, scans, and evaluates emails for routing. Another agent tags certain emails for follow-up, and a third agent composes the follow-up responses.
• Agentic Workflows: A structured sequence of tasks that multiple agents work together to perform. Agentic workflow differs from chatbots, traditional AI, and even ML because these more traditional approaches handle specific, isolated tasks with a limited scope and provide outputs based on input data. Agents in agentic workflows are autonomous, learn from their interactions, and interact with the outside world.
• Digital Workforce: All the chatbots and AI agents that empower your digital automation environment and work alongside your human workforce.

Process Management and Discovery Terms

• Business Process Management (BPM): The systematic approach to analyzing, designing, implementing, monitoring, and continuously improving and optimizing end-to-end business processes. Most modern BPM platforms have automation tools for building workflows and screens with which users and systems interact.
• Digital Process Management (DPM): The approach to managing processes is digitally inclusive of internal and external actors. While BPM helps your employees manage business processes, DPM also encapsulates customers and suppliers.
• Workflow Automation: A more granular component of BPM. For example, you may apply workflow automation to a part of a process, such as onboarding, by assigning it to teammates to train new employees. In comparison, a BPM project’s scope might have a broader focus on the onboarding process or other operations like recruiting, training and development, or retention.
• Business Process Automation (BPA) and Digital Process Automation (DPA): Two terms that could both be considered parts of BPM. BPA focuses on multistep processes that need humans as part of the process, such as our onboarding training example. DPA focuses on limiting the steps of the process to interactions between systems. You can achieve both in diverse ways and for varied reasons with BPM or RPA. BPM is differentiated from DPA because its methodology focuses more on a strategic initiative rather than automation processes. BPM automation makes use of BPA and DPA functionalities.
• Process Mining and Task Mining: While the two terms are similar, their methods and end goals are different. Process mining analyzes data in various systems, such as an ERP, to understand and improve business processes. In contrast, task mining, often called process discovery or task discovery, uses an agent installed on computers to understand and extract insights related to human-executed tasks. Process mining focuses on discovering patterns and insights about the performance of business data and processes, while task mining focuses on understanding how humans perform tasks.
• Orchestration: The coordinated management and automation of business processes, technology environments, and collaboration tools to drive efficiency and consistency across organizations. It ensures both IT administrators and end users can efficiently manage and use business applications, reducing complexity and improving productivity.

Traditional Automation Technology Terms

• Robotic Process Automation (RPA): UiPath defines RPA as software robots that “automate repetitive, rule-based tasks like data entry and system integration” and “mimic human actions in digital systems.” RPA software follows instructions to interact with computer screens (keyboard and mouse) in a programmatic way, allowing the robots to interact autonomously or directly on users’ workstations. RPA focuses on automating shorter tasks.
• Intelligent Automation: The integration of AI, machine learning (ML), and RPA for more advanced automation capabilities. Intelligent automation increases the productivity of normal RPA bots by using AI to help humans make decisions. Typical use cases involve classification, prediction, and rule-based decision-making (for example, fraud detection, predictive maintenance, or routing workflows based on data patterns).
• Low-Code/No-Code: Software platforms that deploy features such as user-friendly interfaces, drag-and-drop elements, and application templates so even non-developer users can develop advanced automation applications and solutions. “These incredibly powerful yet relatively simple-to-use citizen development tools put work automation into the hands of the average business user,” says Centric Consulting People and Change Practice Lead Hil Lee.

Human-Centered Approach Terms

• AI-Augmented Automation: A people-first approach to combining AI with business processes and automation. AI augments — but does not replace — the role of humans in your automations. For example, if a process required a human to sift through hundreds of potential sales leads for follow-up, AI could complete an initial pass to evaluate the list for previous activity and only serve the best leads up to the human to contact.
• Human-Centered Automation: An approach to automation that starts with people first, not technology. Engaging with employees early and often in your process automation initiative will not only increase the accuracy and efficiency of your results but also create a transparent environment in which new automations will stick with employees.
• Automation Bias: A phenomenon that can occur when humans do not monitor automation or AI outputs sufficiently. If not caught in time, small errors can escalate as automation systems incorporate the new, inaccurate outputs into their decisions. Over time, AI models can even start to “drift” from their intended purposes. “Policies and processes that ensure transparency, continuous monitoring, and feedback throughout the automation life cycle can help reduce risks while maintaining high-quality decision-making,” says Centric AI Strategy Director Joseph Ours.

Organizational and Strategic Automation Vocabulary Terms

• Automation Center of Excellence (CoE): A governing body that helps operationalize automations by ensuring stakeholder alignment, providing technical expertise, handling training and education, and establishing reporting mechanisms to measure automation performance and ROI. Key elements include establishing vision and strategy alignment, implementing formal intake processes with scoring rubrics for prioritization, creating standardized delivery and support frameworks, and developing governance structures with reusable component libraries.
• Business Enablement: The strategic integration of automation, digital technologies, and digital capabilities into business operations, processes, and strategies. Business enablement goals include improving overall efficiency, productivity, and outcomes while driving innovation, competitiveness, and adaptability. “Business enablement is not just a technology play,” says Centric National Operational Excellence Lead Kent Hansen. “Instead, business enablement seamlessly integrates business consulting, technology, and industry practices for long-term, sustainable change and continuous improvement.”
• Governance, Risk Management and Compliance (GRC): Your organization’s guardrails to ensure safe and effective automations, whether they are traditional RPA or advanced generative AI- or AI agent-based solutions.

Use Your Newfound Automation Vocabulary to Reap the Benefits of RPA

The many terms and abbreviations around automation can confuse teams and make it harder for them to understand the automation landscape. While this is certainly not a holistic glossary of every term that is and will be, our hope is that it sets you up for success.

Don’t let fears about required infrastructure and resources stop you from reaping the benefits of RPA.

Business process management, robotic process automation, and artificial intelligence come together to help you reach enterprise automation maturity. Talk to one of our experts about how you can get started. Contact us