Hyperautomation is a term coined in 2019 by the IT research and advisory firm Gartner. Also referred to as “digital process automation” or “intelligent process automation,” hyperautomation provides a framework or an infrastructure of advanced technologies — including RPA, employed in tandem and augmented by artificial intelligence and machine learning — used for scaling automation capabilities in an enterprise. Its ultimate goal is to further automate already automated processes.
How Does It Differ from Traditional Automation?
Conventional approaches to enterprise automation focused only on the best way to implement automation within a particular context. These automation techniques were specific to a use case and were employed to conduct simple optimization of task processes. Hyperautomation adds another layer of AI to these techniques, making them even smarter. So, in this context, we can say that hyperautomation is an expansion of automation in both depth and breadth.
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How Does Hyperautomation Work?
A hyperautomation practice can be broken down into three steps:
- Using AI, it understands and identifies what processes are to be automated.
- The framework then chooses the appropriate automation tools required for the job.
- It drives agility through the reuse of the automated processes.
Hyperautomation makes it easier to infuse AI and machine learning capabilities, and in turn, simplifies the development and automatic generation of new automation prototypes. For example, under the umbrella of AI technologies, Natural Language Processing (NLP) lets the bot interpret human speech. Similarly, Optical Character Recognition (OCR) enables the bot to convert images to readable text. This capability allows hyperautomation to convert unstructured data into structured data, hence allowing access to data that has traditionally been inaccessible.
Apart from automation, Machine Learning (ML) tools of hyperautomation enable the bot to identify patterns in the data, offering an enterprise powerful analytical tools and capabilities that were previously unavailable.
With the process and task mining tools, hyperautomation can also provide a digital twin of the organization (DTO). A DTO virtually enables organizations to visualize how functions, processes, and key performance indicators interact and offers real-time information about the company.
This information can help organizations assess how new automation capabilities can help drive value, rapidly respond to bottlenecks, and identify new opportunities.
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Impact of Hyperautomation on Organizations
As per the Research Director at Gartner, Manjunath Bhat, “Robots aren’t here to take away our jobs; they’re here to give us a promotion.”
It is essential to establish that the focus of any kind of automation is not to replace humans but to augment their capabilities. Hyperautomation, therefore, needs to be combined with a wider range of tools to apply a broader systems-based approach to scaling automation efforts.
According to Gartner, By 2024, organizations combining hyperautomation technologies with redesigned processes will reduce their operational costs by as much as 30%. While several industries will benefit from adopting these advanced automation capabilities, hyperautomation is a critical trend around ITOps.
Hyperautomation for ITOps
The potential of using hyperautomation in ITOps is significant. For example, auto-discovery and dependency mapping (DDM) is a hyperautomation that can collect various data types on a defined schedule. Teams can use this data to uncover insights, populate a CMDB, and support ITSM processes. IT departments must track highly elastic infrastructures wherein the capacity needed for computation, storage, and network rise or fall based on demand.
Let’s consider a simple scenario. Due to COVID-19 pandemic and lockdown protocols, there is a sudden increase in home-delivery applications usage. This fluctuating demand can be effectively handled using rapidly scaling automation such as an agentless DDM. This facility can help IT incident managers quickly pinpoint the problems and improve the mean time to recovery (MTTR).
Another area of hyperautomation for a breakthrough in ITOps teams is implementing an AIOps solution that centralizes the data and then uses machine learning to correlate alerts from multiple systems into a time-sequenced incident.
In some cases, this data can be used to automate the most probable action. For instance, during a major incident, instead of numerous SMEs analyzing uncorrelated data from various monitoring tools, the hyperautomation in place can provide a centralized view of all the alerts and information from relevant systems.
Also read: AIOps Trends & Benefits for 2021
The Benefits of Hyperautomation
- Since hyperautomation adds more intelligence to existing automation processes and reuses the same, it lowers the overall cost of automation.
- It improves alignment between IT and business. It also reduces the need for shadow IT or third-party IT services, thus improving business security.
- Hyperautomation allows ITOps teams to achieve breakthrough results by automating common tasks and orchestrating more complex workflows through visual development tools and low-code methods. This, coupled with self-documentation, helps teams and employees understand the implementation.
- Hyperautomation improves an organization’s ability to measure the impact of digital transformation efforts by offering real-time updates. These updates also help them prioritize their future automation efforts.
- Companies could use RPA and machine learning to generate reports determining customer sentiment. The data for these reports can be gathered from various social platforms and be made readily available to the marketing team, who could then create real-time targeted customer campaigns.
Challenges with Hyperautomation
- Hyperautomation initiatives spanning multiple departments, services, and country boundaries can add a host of new privacy and security issues. In such a widely spread initiative, interoperability issues among various tools and seamlessness in integrating tools also creep up.
- Employing hyperautomation may sacrifice customer experience and satisfaction at times, especially during implementation.
- The tools for assessing the cost, the potential value of automation, and return on investment are still in their infancy.
- A survey by Forrester found that merely 40% – 60% of the code used for automation could be automatically generated using the existing tools. Manual augmentation is still required and needs to be accounted for when building robust automation capabilities at scale.
The Future of Hyperautomation
As per the current trends, the focus on augmentation over incremental productivity gains is where we see the most significant shift in how enterprises approach automation. In addition, the economic uncertainty of 2020 has led organizations to cut back on spending and increase their focus on operational efficiency. And as more organizations are moving towards this digital transformation, the volumes of processes and data that ITOps teams must manage will continue to surge.
Gartner predicts that by 2025, over 20% of products will get manufactured, packed, shipped, and delivered without being touched by anyone. And by 2024, 80% of hyperautomation offerings will have limited industry-specific depth mandating additional investment for IP, curated data, architecture, integration, and development.
The trends clearly show that hyperautomation is here to stay. Despite all its challenges, there are significant and notable benefits of using this advanced form of automation to boost productivity and provide higher-level functionality to your organization.
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