How to Achieve Better Outcomes with Hyperautomation Technology
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Hyperautomation and process orchestration: An optimal pairing
More and more organizations want to increase their degree of automation to achieve critical business imperatives. According to the 2023 State of Process Orchestration Report:
[ 95%]
say automation has helped their organizations achieve operational efficiency
[ 94%]
say automation has helped their organizations improve customer experiences
Many teams implement multiple automation technologies, tools, or platforms to get the job done. For example, increasing numbers of teams want to leverage AI-enabled automation to accelerate business outcomes. Regardless of which combination of solutions you use, the ultimate goal is achieving automation at scale. That is where hyperautomation comes in.
Simply defined, hyperautomation is a business approach organizations use to vet and automate their processes, and orchestrate the use of multiple automation technologies. It’s important to note: You can’t hyperautomate if you don’t orchestrate. Process orchestration is a catalyst to getting hyperautomation done right — leading to faster automation projects with higher value. This eBook will explore how to use hyperautomation technologies alongside process orchestration to drive better business outcomes.
What is process orchestration
Process orchestration coordinates the various moving parts (or endpoints) of a business process, and sometimes even ties multiple processes together. Process orchestration helps you work with the people, systems, and devices you already have – while achieving even the most ambitious goals around end-to-end process automation.
When integrated into a hyperautomation tech stack, process orchestration can drive better business outcomes, improving customer experiences and operational efficiency.
What is hyperautomation?
Hyperautomation is defined by Gartner as “a business-driven, disciplined approach that organizations use to rapidly identify, vet, and automate as many business and IT processes as possible. Hyperautomation involves the orchestrated use of multiple technologies, tools or platforms including: artificial intelligence (AI), machine learning, event-driven software architecture, robotic process automation (RPA), business process management (BPM) and intelligent business process management suites (iBPMS), integration platform as a service (iPaaS), low-code/no-code tools, packaged software, and other types of decision, process and task automation tools.”
Let’s dive a little deeper into the hyperautomation definition. First, it’s important to understand that hyperautomation is driven by business needs and pains. It doesn’t originate out of technical debt for instance. Second, it includes activities that span the entire Business Process Management (BPM) lifecycle. That includes identifying and capturing processes, as well as automating and monitoring them.
Typically, automated processes span various technologies and endpoints, which requires some orchestration capability. Endpoints include the people, systems, and devices that make up a process — which might include APIs, RPA bots, machine learning components, and many other technologies used in a corporate environment.
Last but not least – hyperautomation needs to happen fast. The main reason for choosing this strategy is to stay relevant and competitive in the digital age.
What are the benefits of hyperautomation?
Hyperautomation benefits and goals differ, depending on the organization’s business goals. However, some common benefits of hyperautomation include:
- Enhanced Efficiency: Hyperautomation empowers organizations to streamline manual processes, significantly boosting efficiency while minimizing the time and resources needed for task completion.
- Economical Advantages: Implementation of automation in specific processes leads to fewer errors and revisions, subsequently trimming operational costs.
- Elevated Competitiveness: Organizations proficient in process and task automation gain a market edge through enhanced operational efficiency compared to their counterparts.
- Optimized Customer Experiences: Embracing automated processes heightens the speed and precision of customer interactions, resulting in superior customer experiences.
- Improved Decision-Making: The automation of business processes frees employees to concentrate on strategic assignments, enhancing the quality of decision-making.
Hyperautomation challenges
Even with automation technology in place, business-critical processes are rarely straightforward or linear. Most organizations encounter:
- Endpoint diversity: Automated processes involve a diverse number of endpoints (people, systems, and devices). If these endpoints aren’t properly orchestrated, processes can be disjointed or siloed.
- Process complexity: Processes are coordinated based on a certain logic, which is rarely a linear set of steps. As a result, complex processes must be described by advanced workflow patterns, which ensure alignment, scalability and resilience of processes.
Fortunately, process orchestration can integrate diverse endpoints in a fast, flexible way, as well as execute across advanced workflow patterns.
Real-world hyperautomation examples
Many organizations are using hyperautomation to achieve a variety of business objectives and goals, including:
Streamlining human workflows
Automate assignments, escalations, notifications, recordings, etc. to improve the efficiency of human workflows in an automated process
Modernizing legacy systems
Embrace gradual digital transformation to transition from a legacy technology to more modern, cloud native software
Centralizing your process automation platform
Create a central, scalable process automation platform for your application delivery stack that lets you continue to deliver better software in less time
Orchestrating microservices
Ensure that the microservices involved in a business process are carefully monitored, managed, and analyzed — which is crucial for continued effectiveness
Replacing existing automation systems
Replace or make a homegrown automation system more efficient, untangle RPA bots, or migrate from a legacy BPMS
Which hyperautomation tools are most critical?
Hyperautomation tools and technologies are diverse, and achieve a variety of different objectives. The chart below provides an overview of the components of a hyperautomation tech stack, aligned with specific technical or business goals.
Read more: For a deeper explanation of the pros and cons of each of these technologies, along with vendor examples, download The Ultimate Guide to Building Your Hyperautomation Tech Stack.
Goals
- Set objectives and desired results
- Discover any existing inefficiencies
Technologies
Process mining tools, or a method for discovering, monitoring, and creating plans to improve real
Goals
- Achieve technical and business stakeholder alignment
- Use a common framework (BPMN, DMN) to expose process logic in a way everyone can understand
Technologies
Process modeling technology to visually create process models helps everyone better understand, discuss, and remember processes and decisions
Goals
- Improve customer experience
- Improve internal processes (e.g. digitizing legacy systems, increasing efficiency of existing processes or human workflows)
Technologies
- APIs and event streams – APIs and event streams help you get the data you need in the right system. They can interact with a workflow engine to kick off processes or integrate data from other systems
- RPA – These platforms automate work by simulating user interactions with software, or by using an API. The technology was a quick win for many early automation projects by reducing or eliminating manual tasks, although it is best not to rely on RPA in the long run due to process silos and broken/inefficient processes
- Integration platform as a service -These providers connect various systems through libraries of pre-packaged APIs to simplify integration efforts. These platforms are most effective when handling a series of data interactions by systems where human involvement isn’t needed.
- Low code tools – Low-code platforms emerged to reduce the time and skills needed to develop new mobile and web applications. Many of these vendors have extended their platforms to enable workflow-driven applications and process automation but have limitations for complex tasks, and can result in vendor lock-in.
- AI and machine learning – AutoML platforms and AI models can help inform processes, make decisions, and more.
- Front-end solutions – Many organizations will build out a custom front-end for their applications depending on the purpose and end user. Examples include project management, human task coordination, and communication tools.
- CI/CD tools – Continuous integration and continuous delivery (CI/CD) tools help teams streamline the development, deployment, and testing of critical deployments for developers.
Goals
- Provide an automation fabric to effectively stitch together a wide variety of endpoints and technologies.
- Automate processes from end to end, providing scalability and resilience
Technologies
Process orchestration platforms – These solutions are specifically designed to solve the challenges of orchestrating complex, business-critical processes. Empowers organizations to collaboratively build high-performance workflows that can enable true digital transformation.
Goals
- Provide visibility into end-to-end business processes
- Enable continuous improvement, by identifying bottlenecks and modifying processes until they’re at their optimal state
Technologies
Process intelligence – These solutions enable you to get detailed analytics on your running processes. Leveraging a tool that pairs BPMN diagrams with real-time analytics provides actionable context.
Hyperautomation trends to watch: The AI revolution
An explosion of emerging AI technologies has driven interest in AI-enabled automation. When paired with process orchestration, AI automation can be a powerful way to achieve business goals. Here are the three core areas where AI can be most effective in an automation context:
Read more: Get our Practical Guide to Leveraging AI Automation in Your Process Orchestration Workflows
- Predictive: Predictive AI can use data collected from process instances as these processes operate to make improvements to the models and overall process flow
- Generative: Generative AI can be leveraged to create a new process, refine existing processes, or generate code from natural language commands
- Augmented Intelligence: This style of AI can use machine learning to assist or automate task completion and decision-making
Process orchestration: The key to hyperautomation success
Process orchestration is a catalyst and force multiplier for hyperautomation. Organizations can be anywhere in their automation journeys to employ process orchestration — from unwinding a legacy BPMS, to patching together disparate RPA bots, to starting process automation from scratch. Regardless of where you are in your own journey, process orchestration can drive various processes between people, systems, and devices (agnostic of where they originate) — ensuring seamless end-to-end processes and accelerated digital transformation.
Think of process orchestration solutions like Camunda as the “brains” of a hyperautomation effort. Camunda 8 enables organizations to automate complex business processes using a variety of tools. This platform is designed to be highly scalable and flexible, allowing organizations to integrate a wide range of technologies into their process automation projects.
Camunda not only allows the automation of processes but also the continuous monitoring. The platform helps organizations to increase efficiency, reduce costs, and improve business outcomes.
