By Gordon McKenna, Chief Technology Officer, Ensono
The business case for hyperautomation is clear—make sure you’re prepared to fully utilize its promise.
Automation is an important feature of modern business—a trend that will only accelerate as time goes on. According to Gartner, “Organizations will require more IT and business-process automation as they are forced to accelerate digital transformation plans in a post-COVID-19, digital-first world.” But most of today’s automated tools continue to require triggers to launch subsequent events and they also lack complete automation, often requiring some level of human participation.
Hyperautomation represents an evolutionary step forward for automated tools in addressing this gap. It automates decision-making to adapt automated functions to changing environments without human intervention. In other words, hyperautomated processes are self-learning and self-adjusting. This makes hyperautomation more efficient and effective than traditional automation technologies. Gartner doesn’t mince words on this point: “Hyperautomation has shifted from an option to a condition of survival.” It anticipates the global market for hyperautomation technologies will reach close to $600 billion by the end of 2022, up from $481.6 billion in 2020.1 As a result, it’s critical that operational and IT leaders understand hyperautomation and its potential applications within their organizations. In this article, we define hyperautomation and the technologies that characterize it, highlight potential business use cases and share how IT and operational leaders can expect the hyperautomation market will evolve in the years to come.
In its glossary, 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.” Hyperautomation, therefore, involves more than a single technology. It’s an approach that encompasses multiple technologies, often working together. It’s these technologies’ use of artificial intelligence (AI) combined with machine learning (ML) that distinguishes hyperautomation from traditional automation. According to a recent Forbes article, “Using AI and ML, the practice of hyperautomation identifies patterns to create automations that can evolve and adapt on the fly. Hyperautomation processes are more self-sufficient and effective than traditional automation technologies as a result.”2
Companies may use one or several of these hyperautomated technologies, depending on their needs. For example, a company might use RPA to automate data entry into its customer relationship management (CRM) system. It could use iPaaS to connect its CRM system with its financial software, and LCAP to develop a mobile app for employees to access the CRM system on the go.
1 “Gartner Forecasts Worldwide Hyperautomation-Enabling Software Market to Reach Nearly $600 Billion by 2022,” Gartner.com Press Release, April 2021. 2 Ed Macosky, “From Traditional Automation To Hyperautomation” Forbes.com, June 2022.
Both the advantages and the universality of hyperautomation across industries are driving the growth of its associated technologies. Industries and potential use cases where hyperautomation can apply today include:
Banking and financial services – Can be used for processing loan applications, detecting fraud, and other focused tasks. Specifically, banks can use hyperautomation to automatically review customer transactions for signs of fraudulent activity. If it detects suspicious activity, the system can flag it for human review. Hyperautomation means the system can also learn and optimize its analysis over time.
Healthcare – Can streamline administrative tasks, such as insurance claims processing and appointment scheduling. It can also be used to automate clinical tasks, such as charting patient medical histories and ordering lab tests. Critically, hyperautomated systems can improve their performance based on novel situations, which is critical in the medical field.
Telecommunications – Can apply to a variety of tasks in telecommunications, including network management, customer service, and fraud detection. For example, hyperautomation can automatically identify and recommend fixes for network outages. It can also be used to route customer service calls to the best available agent in real time.
Retail – Can help with inventory management, order fulfillment, and marketing. For example, hyperautomation can automatically reorder products in an optimized way when inventory levels get low. It can also be used to fulfill orders from multiple channels (e.g., online, in-store) based on inventory availability and proximity.
Despite its diverse range of use cases, the most significant benefits of hyperautomation are somewhat uniform. First, it frees up employees’ time so they can focus on higher-level tasks. Personnel can focus on more strategic tasks that require human expertise as opposed to repetitive tasks that nonetheless require some degree of cognitive analysis.
Second, it improves accuracy and, in doing so, relieves personnel from the risk of human error. For example, if a company uses hyperautomation to automatically generate reports, it can eliminate the mistakes that might occur if these reports were generated manually.
When deciding whether or not to hyperautomate a business process, IT and operational leaders should consider the following factors.
Complexity – Individual tasks should be relatively simple and well-defined, but when multiplied and scaled up can become quite complex processes.
Frequency – Hyperautomation is most effective for tasks that are performed frequently.
Number of people – Hyperautomation can be used to automate tasks that are performed by a single person or by multiple people, so long as each of their functions is taken into account.
Level of human expertise – Like traditional automation, hyperautomation can be used to automate tasks that do not require human expertise, such as data entry. But it can also be used to automate tasks that require some human expertise, such as fraud detection.
If a business process meets all or most of these criteria, hyperautomation may be a good option. As a rule of thumb, hyperautomation is best suited for complex processes that are performed frequently and involve large amounts of data.
When implementing hyperautomation, businesses should be aware of several challenges. For example, not all IT departments have the inherent resources to implement hyperautomation successfully. Here is a closer look at what you should consider:
Specialized skills – Implementing hyperautomation requires a deep understanding of both business processes and technology. As such, businesses will need to invest in training their employees on these topics or partner with a third-party provider to carry out its implementation successfully. Due to a lot of this technology being fairly cutting edge, challenges can include a lack of ready skills available in the marketplace to both implement and support the implementation.
Organizational maturity – In addition to new IT resources and capabilities, personnel at all levels of the organization must be prepared to adapt. For example, line-of-business employees may have some of their tasks automated but may also need to adjust to new interfaces and systems. Non-technical users may need to build familiarity with analytics or low-code tools; they may need to become “citizen developers” or “citizen data scientists” for the first time.
Governance and security – When implementing hyperautomation, businesses will need to put in place proper governance mechanisms to ensure that automated processes are being executed correctly and efficiently. This is especially important given the high degree of complexity involved in hyperautomation. Security is another key consideration. Ensuring that the processes and associated technology and platform are locked down and monitored can prevent unnecessary exposure of data or man in the middle (MITM) attacks or even industrial sabotage.
Despite these challenges, hyperautomation can offer significant benefits to businesses that are willing to invest in it, including leaps in efficiency, reductions in business errors and a quicker time to market making the business far more competitive in the marketplace. They need only to plan correctly, gather the right skills, be fully aware of the challenges on day one, and be willing to learn and act upon necessary steps to mitigate them.
The success of your hyperautomation adoption initiatives depends on your organizational and IT maturity. For example, organizations with centralized IT departments, with employees familiar with modern automation, and who have adopted an agile methodology for software development and a cloud first approach are very good candidates.
But even if your organization doesn’t fit all these criteria, you can take steps to get started. Beginning with a hyperautomation proof of concept (POC) is a good way to get your feet wet and understand where your gaps may be. A POC will also help you understand the hyperautomation landscape and identify the right use cases for your organization.\\
