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Hyperautomation in ITSM...and where AI fits in

Martin Stewart -

What is hyperautomation? And what does it mean to ITSM? Hyperautomation means automation that goes above and beyond current, individual technology capabilities by combining the power of several automation technologies. In ITSM, it means bringing AI capabilities into the mix to enable automation scenarios that are beyond the reach of traditional workflow and task automation capabilities alone.

What is hyperautomation?

Gartner Inc., who coined the term hyperautomation define it 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.”

Hyperautomation is not a technology. It’s a strategy for the automation of as much of the routine work as possible in an organization – using a set of appropriate tools for each situation. It’s a strategic mindset focused on efficiency. It’s an awareness that manual work is inherently inefficient when there are tools that are already available to automate that work. And it’s a commitment to boost efficiency – knowing that each efficiency gain makes time for more gains. Collectively, over time, these gains transform the focus and performance of the organization, and the productivity and happiness of employees. When the majority of routine operations tasks are automated, employees can focus on building the future of your organization. That's critical in a business environment where the pace of innovation is the number one predictor of survival and success.


How hyperautomation strategy works

Hyperautomation involves looking at the work that an organization does and matching that work with a combination of suitable automation technologies to turn human work into machine work. As manual work across an organization is highly variable, it follows that a set of automation technologies are needed to effect end-to-end automations. It isn’t about one technology. There is no single hyperautomation product.

This means using different technology building blocks (including BPM, low-code/no-code tools, AI/ML, integrations/API calls to apps, and more) to stitch together the actions needed to fully automate business and IT processes (and more granular tasks that fly under the BPM radar).

The goal is to release people from mundane work, so they can focus on higher value work.


Hyperautomation and ITSM: AI raises the bar of what's automatable

In Gartner’s definition above, “orchestrated use of multiple technologies” is the key phrase. Workflow automation (guiding processes) and task automation (automating tasks within processes) are already an established part of the ITSM tech toolbox. When combined, they automate the flow of the process and some of the tasks in the process. In many cases, workflows and task automation can drive fully automated services.

Adding AI into the mix makes the automation of many more processes and tasks possible – meaning more services can now be fully automated (end-to-end, with no human tasks). Where service delivery processes still rely on human actions, organization can now harness AI solutions to fill gaps and automate the remaining manual tasks – those that were standing in the way of end-to-end service automation. It’s frustrating when a single human-reliant task slows down an otherwise fully automated service delivery process. AI bots can alleviate these manual task bottlenecks to accelerate service delivery from human speed to digital speed. Service delivery teams get time back. Service customers get what they need faster. It's a win-win opportunity that people on both sides of the service transaction can get behind.


How hyperautomation in ITSM works

The type of AI we’re talking about here is predominately machine learning (ML) - specifically decision engines which evaluate structured and unstructured data to understand the context and make a decision. An AI bot, trained on a dataset can learn about past decisions and how successful they were in delivering the right outcomes. Once trained, the AI can evaluate the a broad range of situations and decide how to act. This gives them decision-making capabilities above and beyond the simple logical rules that have traditionally driven workflows and task automations. Across many scenarios, they are as good as (or even better than) people at making decisions on what actions need to be taken.

As a starting point, organizations should look at which manual tasks are the most repetitive and time-consuming. Then, it is time to evaluate feasibility. Is there sufficient historic data to train an AI bot to handle this task? Is the complexity and variability of the scenario within reach of an AI bot? How does the effort required to train a bot compare to the effort saved?


Hyperautomation for advanced self-healing

We have talked about using combinations of automation technology to make service automation happen, but there are other major automation scenarios that AI can drive in the IT space - specifically AIOps.

For example, self-healing. This is where we get into the overlap between ITSM and ITOM. Where ITSM is focused on services and support, ITOM looks after underlying infrastructure and operations (I&O). They need each other. ITSM ties elements of I&O together to provide services of value. ITOM looks after those elements to ensure they are always available and accessible when called upon.

The injection of machine learning into ITOM (commonly called AIOps) means self-healing infrastructure can mature from simple, rule-based self-healing (which is limited by the need for each error condition and resolution process to be manually matched) to a machine-learning AI which uses historic records to understand error conditions and responses across a much broader set of scenarios - without the need for manual "programming" of rules by system admins. As machine learning AIs continue to learn from a dynamic set of operational data, more and more self-healing scenarios can be covered. That means progressively more time can be reclaimed by people in ITOM roles (and the service desk, because there are fewer incidents) to spend on new technology and innovation projects that will make a real difference to the organization.


Expanding hyperautomation scenarios

Expanding service automation and advancing self-healing aren’t the only hyperautomation scenarios. These are just two areas of ITSM and ITOM where automation can be applied to transform efficiency. Now that AI is rapidly expanding what is possible, the target for hyperautomation is anywhere that people are doing regular, repetitive, and time-consuming work. That means there are opportunities to be found across support, analytics and insights, risk analysis, root cause analysis, change planning, and more.


Benefits of hyperautomation in ITSM

  • Achieve end-to-end automation of complex services.
  • Drive faster delivery for customers.
  • Reduce daily operations workload for IT people.
  • Focus attention on higher value work.
  • Boost engagement among IT teams.
  • Cut stress in your service desk and ITOM teams. Reduce staff churn and end the brain-drain.
  • Reduce service downtime from days/hours to minutes/seconds.
  • Improve employee up-time and productivity. Boost profitability.
  • Deliver noticeable advancements that the whole organization will notice.

Find out more


Hornbill ESM

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