Automation refers to the use of technology to facilitate or perform tasks that originally required some form of human judgment or action. The term “tasks” refers not only to tasks and activities in the execution, working or operational environment, but it also encompasses tasks in thinking, discovering and designing these automations themselves.
Hyperautomation refers to the combination of multiple machine learning, packaged software and automation tools to deliver work. The propensity to use particular types of automation will be highly dependent on the organization’s existing IT architecture and business practices. Hyperautomation refers not only to the breadth of the palette of tools, but also to all the steps of automation itself (discover, analyze, design, automate, measure, monitor, reassess).
Hyperautomation is an unavoidable market state in which organizations must rapidly identify and automate all possible business processes with several implications:
The scope of automation changes. The automation focus now spans automating individual, discrete tasks and transactions based on static and rigid rules, to automating more and more knowledge work. In turn, those levels of automation enable enhanced and more dynamic experiences and better business outcomes.
A range of tools will be used to manage work and coordinate resources. Increasingly, organizations will use an evolving set of technologies to support an ever-expanding business scope. The tools include task and process automation, decision management, and packaged software — all of which will incorporate more and more machine learning technologies.
Architecting for agility is required. This means organizations need the ability to reconfigure operations and supporting processes in response to evolving needs and competitive threats in the market. A hyperautomated future state can only be achieved through hyperagile working practices and tools.
Workforce engagement is needed to reinvent how employees deliver value. Without engaging employees to digitally transform their operations, the organization is destined to gain only incremental benefits. This means overcoming the challenges associated with silos and the way the organization allocates resources and integrates the capabilities of its partners and suppliers.
RPA and iBPMS Are Key Components of Hyperautomation
Hyperautomation requires selection of the right tools and technologies for the challenge at hand. Understanding the range of automation mechanisms, how they relate to one another, and how they are combined and coordinated, is a major focus for hyperautomation. This is complicated because there are currently many multiple, overlapping and yet ultimately complementary technologies including:
Robotic process automation (RPA): RPA is a useful way to connect legacy systems that don’t have APIs with more modern systems. It will move structured data from system A to system B better than people and addresses integration challenges with legacy systems. The scope of these processes is typically a short-lived task associated with moving that data. RPA tools can also augment knowledge workers undertaking day-to-day work, removing mundane and repetitive tasks. Tightly defined integration scripts structure and manipulate data, moving it from one environment to the next. Because the integration is based on interacting with the metadata that drives the screens of existing applications, these tools are usually more accessible to business end users.
Intelligent business process management suites (iBPMSs). Beyond RPA, iBPMSs manage long-running processes. An intelligent business process management suite is an integrated set of technologies that coordinates people, machines and things. An iBPMS relies on models of processes and rules to drive a user interface and manage the context of many work items based on those models. Integration with external systems is usually achieved via robust APIs. Alongside the processes, strong decision models can simplify the environment and provide a natural integration point for advanced analytics and machine learning. iBPMS software supports the full life cycle of business processes and decisions: discovery, analysis, design, implementation, execution, monitoring and continuous optimization. An iBPMS enables citizen developers — most commonly business analysts — and professional developers to collaborate on iterative development and improvement of process and decision models.
These technologies are highly complementary, and Gartner increasingly sees them deployed side by side.
iBPMS platforms can choreograph complex styles of work — for example, adaptive case management or processes driven by complex events. This is increasingly important, particularly in the context of digitalized processes that coordinate the behaviors of people, processes and the “things” that are part of the Internet of Things. The rapidly changing operational context in a digitalized process requires actionable, advanced analytics to more-intelligently orchestrate business processes across the virtual and physical worlds.
A key thing to recognize is that the organization is becoming more model-driven, and the ability to manage the interconnected nature and complex versioning of these models is an important competency. To derive the full benefits of hyperautomation, organizations need an overarching view across their functional and process silos. Indeed, developing more and more sophisticated models is akin to developing a digital twin of an organization (DTO).
The Digital Twin of an Organization
A digital twin of an organization visualizes the interdependence between functions, processes and key performance indicators (KPIs). A DTO is a dynamic set of software models of a part of an organization. It relies on operational and/or other data to understand and provide continuous intelligence on how an organization operationalizes its business model connected directly to its current state and deployed resources. Critically, a DTO responds to changes in how expected customer value is delivered.
A DTO draws from a real-world environment with real people and machines working together to generate continuous intelligence about what is happening throughout the organization. Effectively, a DTO provides a contextual framework for business process and decision models. It helps capture where enterprise value links to the different parts of an organization and how its business processes impact value creation. As such, the DTO becomes an important element for hyperautomation. A DTO allows users to model and explore scenarios, choose one, and then make it real in the physical world.
Machine Learning and NLP Explode the Range of Hyperautomation Possibilities
AI techniques in various forms, including machine learning and Natural Language Processing (NLP), have rapidly expanded hyperautomation possibilities. Adding the ability to interpret human speech quickly at runtime, identify patterns in documents or data, and/or dynamically optimize business outcomes dramatically alters the range of automation possibilities. Indeed, when combined with RPA- and iBPMS-related products, they are starting to affect many industries and enable the automation of what was once deemed the exclusive domain of knowledge workers. But, rather than replacing those workers, AI technologies are mostly augmenting their ability to deliver value. These AI technologies have created a mini arms race as vendors attempt to leapfrog each other. AI technologies have:
Improved machine vision functionality in most RPA tools. Indeed, machine learning has enabled a major step forward in RPA through a type of computer vision. For example, it can recognize a “Submit” button and virtually press it, regardless of where it might appear on the screen. This has been extended to identifying all the text on screen (just like optical character recognition [OCR]). Taking this a step further, tools are emerging that can separate the labels on an image from the dynamically populated text fields. This innovation then enables the RPA tool to interact with image-based interfaces as though they were directly accessed applications.
Optimized business KPIs. An iBPMS or RPA tool can easily call a machine learning model or NLP functionality directly from within the short-term task or longer-running business process. Increasingly, machine learning and NLP are being directly embedded into iBPMS tools with preintegrated functionality making it easier to do the associated data science (plug-and-play machine learning) or call external services from the mega cloud vendors such as Amazon, Google, IBM and Microsoft.
Appeared in a plethora of adjacent and supporting technologies. These include advanced OCR and intelligent character recognition (ICR) to interpret handwriting. NLP is enabling more and more self-service automation as customers interact directly with chatbots and virtual personal assistants (VPAs).
Automated processes discovery. Machine learning is enabling vendors to discover working practices and their different variants in the workplace. Task mining tools, sometimes referred to as “process discovery,” help organizations achieve deep insight into their task flows to get the microview of the task steps or activities that could then be automated by RPA or an iBPMS.
The addition of machine learning and NLP to RPA and iBPMS tools provides the ability to industrialize the digital customer and employee experience by connecting those interactions directly to automated back-office operations and supplier ecosystems. Moreover, this enables a contextually aware, situationally adaptive approach — where the set and order of interactions among participants are uniquely choreographed based on the goals of the business, its partners, and its customers, and on operations intelligence that continuously updates and analyzes in real time. An iBPMS can proactively personalize contextually aware interactions at scale while supporting rapid transformation and/or improvement of the customer and employee journey. Furthermore, AI supports an iBPMS in automating and orchestrating business processes that shape themselves as they run. These processes can therefore be considered adaptive and intelligent, executing the best, next action instead of the same repeatable sequence of actions.
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