Introducing Hyperautomation to ITOps

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Greek mythology tells the story of Sisyphus, the founder, and king of Ephyra. As punishment for cheating death twice, he was forced to roll a massive boulder up a hill, only for it to roll back down when he neared the top. He repeated this for eternity. This situation isn’t dissimilar from what we see in most IT departments. Today’s IT managers and CIOs are tasked with a Sisyphean task. In an environment where every day is different, it’s difficult to know what you’ll need from your IT team tomorrow, let alone next week.

The answer? Hyperautomation — an approach that allows organizations to identify quickly, vet, and automate several processes using a raft of technologies such as Robotic Process Automation (RPA), artificial intelligence (AI), machine learning (ML), low-code application platforms (LCAP), and virtual assistants. With hyperautomation as the cornerstone of ITOps, organizations can take back control by significantly reducing manual tasks and increasing efficiency across all areas.

Automation tools have been around for decades, but they’ve never been more critical than they are today. The key to success in this new world is selecting the right tool or combination of tools for a given situation to help you get ahead of any issues before they start throttling your business.

The Rise of Hyperautomation

According to Gartner, the Hyperautomation software market is growing at a CAGR of just above 10 percent and is projected to hit $600 Billion by 2022 as more organizations transition to digital enterprises. Gartner predicts that by the end of 2024, businesses will use at least three out of the 20 process-agonistic types of software that support hyperautomation.

Organizations are moving from a loosely connected set of automation technologies to one that is more tightly connected. Vendors have responded by developing integrated products that combine RPA, LCAP, and business process management technologies into a single packaged tool.

Tools that give insight into company operations, automate and manage content ingestion, orchestrate work across many systems, and offer intricate rule engines are among the most popular categories of hyperautomation-enabling software.

Signing verification technologies, optical character recognition software, document ingestion systems, conversational AI, and natural language technology (NLT) will also be in high demand. Organizations will require technologies like this to automate the conversion and structuring of data and content, for example, sorting and converting paper records to digital format.

 Also read: Implementing Storage Automation in Data Centers

How Hyperautomation Is Different from RPA

A key differentiator between RPA and hyperautomation is that RPA automates repetitive rules-based processes while hyperautomation enables organizations to automate many different systems at a go. Thus, it is the ultimate automation toolbox.

Hyperautomation allows organizations to create, test, and re-use executable business logic in the form of microservices via a visual workflow designer. Essentially, it removes the need for IT managers to code these capabilities themselves, which helps reduce errors and speeds up development time.

AI and ML are paramount when it comes to fueling new forms of hyperautomation. As intelligent technologies evolve, they can make processes more efficient by mining data for patterns and responding quickly with suggestions that humans follow through on. They also have the capability of learning from their interactions with people or other machines.

The technology is adaptable to any process that requires repetitive steps or tasks that are more easily performed by machines than humans. This means it can be used for both simple manual tasks and highly complicated ones, where there’s no replacement for human intelligence. In addition, hyperautomation leads to stronger decision-making capabilities by automating routine tasks (or freeing up people to perform them), allowing IT professionals to focus on delivering the best possible experience to users.

Hyperautomation is Revolutionizing ITOps

Organizations planning to adopt hyperautomation technologies should start small and build a business case for implementation. Beginning with a small number of test cases will help companies understand how functional, technical, and organizational changes can affect their IT environment and the other systems their business relies on to benefit from hyperautomation.

As companies embrace hyperautomation, they should ensure that their IT architecture and organizational systems can support such rapid change. In addition, hyperautomation requires business units to work closely with the CIO and chief digital officer (CDO) as these new technologies permeate throughout an organization.

Hyperautomation will also require more collaboration between ITOps and AI/ML teams, given the need for these new technologies and processes to integrate and function seamlessly. It is essential for teams working on enterprise innovation projects to balance speed of delivery with technical excellence because customers expect high levels of functionality without significant disruption, which means there is a tight window for doing things right at the first go.

Some of the benefits of hyperautomation in ITOps include:

  • The use of hyperautomation in businesses lowers their overall cost of automation because it adds more intelligence to current processes and recirculates the same.
  • It aligns IT and business. It also reduces the need for shadow IT or third-party IT services, allowing companies to improve security.
  • Low-code tools and visual development technologies allow ITOps teams to break new ground by automating standard operations while orchestrating more complicated workflows through visual programming languages and low-code methods. This, in addition to self-documentation, aids teams and workers in comprehending the implementation process.
  • By providing real-time information, organizations can better assess the impact of IT initiatives. These updates also assist them in prioritizing their future automation projects.
  • RPA and machine learning may be used to create reports on consumer sentiment. The data for these reports can come from various social media sites, and it can be made available to the marketing team, who may use it to develop real-time targeted client campaigns.

Also read: 3 Reasons to Outsource the Management of Your Public Cloud

However, there are also a few challenges such as:

  • IT staff are typically not trained in the AI/ML technologies powering their company’s hyperautomation systems.
  • Data security may be compromised when data originates from multiple sources, complicating who owns what data and whether it is shared with third parties.
  • Many organizations lack the skill sets required to build out an IT environment that can support hyperautomation. Smaller companies may find it challenging to keep up with rapidly changing enterprise demands without the necessary resources or experience. Additionally, larger companies often find it hard to secure high-caliber employees who understand both today’s technological advancements and traditional processes for updating current technology. Another challenge enterprises face using hyperautomation in their business model is how quickly they must adapt to new technology updates, which could affect their hyperautomation platform.
  • Many companies testing hyperautomation solutions will need a team of business process managers and business analysts to help define the rules for how work gets done and testers or software developers to code what happens when certain events occur. To implement a successful hyperautomation solution, an IT department must also have a solid understanding of automation tooling and other technologies such as virtualization and cloud computing. 
  • In addition, the need for enterprises to stay up-to-date with multiple technology changes complicates implementation efforts by increasing costs from both a financial perspective and a time perspective. These factors make it difficult for smaller companies with limited resources to experiment with this rapidly advancing technology.

The Future of Hyperautomation in ITOps

The current trend suggests a significant shift in how businesses approach automation due to the focus on augmentation rather than incremental productivity gains. In addition, economic anxiety in 2020 has compelled businesses to cut back on spending while emphasizing operational efficiency. And as more companies embrace digital transformation, the volumes of processes and data that IT Operations teams manage will continue to increase.

If Gartner’s IT Automation Predictions for 2021 are anything to go by, hyperautomation will soon be the new normal. Customers will be the first humans to touch more than 20% of all manufactured goods and produce by 2025, and 80% of hyperautomation solutions will have an industry-specific depth that necessitates further investment in IP, integration, curated data, development, and architecture by 2024.

Despite a few disadvantages, there are several significant advantages of adopting this sophisticated automation to increase output and provide greater-level functionality to your company.

Read next: Best Machine Learning Tools for Automated Insights 2021

Kihara Kimachia
Kihara Kimachia
Kihara Kimachia has been a professional tech writer and digital marketing consultant for more than ten years. He has a great passion for technology and currently works freelance for several leading tech websites.

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