The robotic process automation market alone is expected to grow by more than 30 percent by 2030. But with all the automation terminology and abbreviations, where do you start? We can help.
In 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 solution or customer resource management too.
However, the terms “enterprise automation” and “hyperautomation” can be hard to distinguish from each other. Plus, those terms have spawned many buzzwords and abbreviations that can further confuse — and hamper — your automation efforts. From robotic process automation (RPA), artificial intelligence (AI), machine learning (ML), business process management (BPM) and intelligent automation, to cognitive automation, digital process automation (DPA) and business process automation (BPA), keeping up with these abbreviations and understanding how they differ or work together can be overwhelming.
In this blog, we’ll break down the key terms and definitions related to enterprise automation and hyperautomation. 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.
Key Terms and Definitions
Let’s start by differentiating enterprise automation from hyperautomation. While the two terms may seem remarkably similar, the key difference is that enterprise automation focuses on the process first, while 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 two terms — and the general term “automation” — 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.”
Now, let’s look at some of the related terms and abbreviations that have spun out of these basic ideas in recent years:
- 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 that also help a company achieve its operating goals.
- Robotic Process Automation (RPA): UiPath defines RPA as a “software … that emulates humans’ actions interacting with digital systems and software.” RPA software follows instructions to interact with computer screens (keyboard and mouse) in a programmatic way, allowing them to interact autonomously or directly on users’ workstations. RPA focuses on automating shorter tasks.
- 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 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 like onboarding, such as assigning 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 multi-step processes that need humans as part of the process, such as our onboarding training example. DPA focuses on limiting 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.
- Intelligent Automation: The integration of artificial intelligence (AI) and RPA for more advanced automation capabilities. Intelligent automation increases the productivity of normal RPA bots because it leverages AI to assist in making human decisions.
- Artificial Intelligence (AI): The simulation of human intelligence in machines. 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.
- Process Mining: Often confused with task mining. While similar, their method and end goals are different. Process mining analyzes data in various systems, such as an ERP, to understand and improve business processes. 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.
Conclusion
The myriad of terms and abbreviations can be confusing for teams and individuals trying 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. In our on-demand webinar, our strategists highlight how RPA as a service requires fewer upfront resources and allows greater flexibility in scale and pricing.
