The hyperautomation market is already worth $481.6 billion, according to Gartner, and it’s set to rocket to nearly $600 billion by the end of the year. More to the point, all organizations that want to win will have to go all in on it, according to Gartner’s Fabrizio Biscotti. “Hyperautomation,” he says, “has shifted from an option to a condition of survival.”
To underscore this, Garner expects organizations that successfully introduce hyperautomation to their ITOps, along with redesigning their operational processes, will be able to reduce their operational costs by as much as 30%.
What is Hyperautomation?
Hyperautomation is a term first coined by Gartner a couple of years ago, and the research house defines it as an approach that enables organizations to rapidly identify, vet, and automate as many processes as possible using technologies such as robotic process automation (RPA), low-code application platforms (LCAP), artificial intelligence (AI), and virtual assistants.
Automation, as opposed to hyperautomation, is largely carried out using a few relatively simple tools. By contrast, organizations getting started with hyperautomation in earnest need to adopt a wide selection of separate complex tools, and in the past these have had very little integration between them.
Hyperautomation Platforms for ITOps
What’s beginning to change, as increasing numbers of organizations turn to hyperautomation in their ITOps, is that the toolset is becoming more sophisticated and, crucially, far more integrated. “Vendors are developing integrated offerings that combine technologies like RPA, LCAP and business process management into one, packaged, tool,” explains Cathy Tornbohm, another Gartner analyst.
Thus we are beginning to see the emergence of hyperautomation platforms, which the research house describes like this: “Hyperautomation today involves a combination of tools, including robotic process automation (RPA), intelligent business management software (iBPMS) and AI, with a goal of increasingly AI-driven decision making.”
This also includes tools that provide visibility to map business activities, automate and manage content ingestion, orchestrate work across multiple systems, and provide complex rule engines, according to Gartner.
Read more: The Growing Relevance of Hyperautomation in ITOps
One particular area that is expected to be in high demand is the field of technologies that can support IT departments looking to hyperautomate staff-facing interactions. More sophisticated than a simple chatbot, these sorts of interactions could involve things like staff members requesting access to an in-house application or to a cloud-based resource or service.
In order to achieve this, the hyperautomated process will need access to technologies which can automate “content ingestion”. This will involve chatbot technologies such as conversational AI and natural language processing (NLP), but also optical character recognition, signature verification, document ingestion, and other parts of the process.
Another important technology enables the robotic execution of actions, or playbooks of actions, usually via an application’s UI, that mimic a human’s action during a transaction, such as processing the staff member’s request.
Digital IT Staff
Robotic process automation is complex, but in itself it is not particularly cutting edge. However, what will really makes hyperautomation a star in the field of ITOps is the combination of RPA with intelligence — that is, artificial intelligence.
By combining these two technologies, it will be possible to create digital IT staff to take some of the pressure of existing (human) IT staff, many of whom are overworked due to the worldwide shortage of staff with specialist IT skills in many areas.
These “digital staff” will be able to help by taking on many of the most repetitive IT tasks, but increasingly they will also be able to handle vital specialist tasks such as detecting and reacting to security incidents. They will be able to connect to different security applications, operate with structured and unstructured data from device logs and help tickets, analyze the data that they have access to, and then make decisions and take actions.
The beauty of this approach is that in doing so they will also discover new processes that are ripe for hyperautomation, so that the scale of hyperautomation can expand. That’s the promise, at least.
And that’s important because the point of hyperautomation in ITOps is not just to reduce operational costs significantly, but also to discover where it is possible to redesign and improve other operational processes.
It’s only when organizations both hyperautomate and improve their existing processes that the massive cost savings in ITOps and elsewhere in the organization can be achieved.
Read next: Data Center Automation Will Enable the Next Phase of Digital Transformation