According to a recent McKinsey research, around 70 percent of organizations across all industry verticals are adopting hyperautomation technologies to achieve operational excellence and optimize costs. Yet, 70 percent of all such digital transformation initiatives fail.
In the context of ITSM, hyperautomation is key to operate an effective IT Service Desk focused on reducing ticketing workload and IT incidents, while maximizing customer satisfaction and ultimately, improving your business bottomline.
However, hyperautomation is more than deploying the latest tooling to replace repetitive manual tasks. The objective of an effective IT Service Desk is not limited to resolving customer ticketing requests, but to facilitate an end-to-end intelligence-driven ITSM operational workflow of the organization. And since the modern business is inherently data-driven, organizations need Hyperautomation Intelligence capabilities to overcome their ITSM automation challenges and mistakes.
Let’s explore the top 5 ITSM automation mistakes and how Hyperautomation Intelligence solves the underlying challenges:
A traditional ITSM automation strategy is designed around isolated manual and repetitive tasks. The goal is to simply replace manual efforts with automated technologies, with minimal regard for the broader context. In doing so, organizations end up automating different segments of the IT Service Desk pipeline independently and in silos. The overall process fails to integrate as a whole and the performance bottleneck persists.
The knowledge gained within those silos remains hidden and concentrated within disparate teams. The automation process then becomes highly dependent on specific individuals without a knowledge base available to the wider organization. New service resolution requests and incidents correlating with a broad problem root cause are naturally seen as independent problems and any correlation is only observed after several ticketing hops between the resolution teams. As a result, the overall IT Service Desk operation remains inefficient and largely bottlenecked by isolated automation tools and an outdated knowledge base.
The robot- and ticket-first frameworks handle the commoditised work that makes up but a small percentage of help desk effort time.
What’s needed is dynamic hyperautomation that provides people with real time information that can improve execution of their complex work. The Swish platform takes this approach by embedding a “people-first” lens into our technology.
Hyperautomation Intelligence solves these issues by focusing on end-to-end automation. The goal of hyperautomation is to drive intelligence across the service desk and the wider enterprise IT infrastructure. This requires exhaustive data collection and analysis across all information nodes, and then building a consumable knowledge base for the entire ITSM organization.
Your ITSM capabilities directly impact end-user experience and expectations about your company services. Any change to your IT Service Desk can have a measurable impact and potentially guide the future strategy of your IT operations. A common mistake when automating ITSM workflows is the lack of end-user focus. Organizations fail to understand exactly how their services are consumed, unable to identify the problem root cause of repetitive service requests and lack the technology capabilities required to effectively manage service requests. Additionally, many organizations employ technologies that only add to the complexity of the ITSM process: a high learning curve, inadequate information-driven collaboration and complicated automation workflows make it challenging for the workforce to deliver the desired IT Service Desk experience to end-users.
Hyperautomation Intelligence solves this problem by focusing automation efforts from an end-user perspective. The automation process is driven by historical insights on user interactions with the service desk and is designed to identify an optimal resolution path for recurring request categories. Automation tools are tightly integrated and easier to use, making it easier for the IT Service Desk to meet end-user expectations at scale.
ITSM organizations can maximize their ability to understand customer sentiment, usage and behavioral patterns by analyzing the variety of unstructured data available through user interactions with the Service Desk. These interactions and the underlying structure of the data is generally dynamic in nature and changes unpredictably. Instead of relying on traditional models, your ITSM can take advantage of advanced NLP capabilities to extract the insights hidden in highly dynamic and unstructured data assets.
Many business organizations are compelled to track a deluge of IT Service Desk metrics and log data as they follow the popular business adage: What Gets Measured, Gets Managed. While the underlying premise is true – infrastructure and operations management is a highly data-driven function – knowing what to measure is key to managing the most impactful characteristics of your ITSM organization. For instance, metrics such as First Call Resolution (FCR) describe how fast a ticket is resolved, but improving FCR without reducing ticketing volume on specific incident categories only suggests a repetitive incident impact due to the unresolved problem root cause. Similarly, many organizations aim for a low Mean Time to Recovery (MTTR) – duration of outage – without adequately addressing the Mean Time to Failure (MTTF) – frequency failure.
Hyperautomation Intelligence requires organizations to set metrics benchmarks that represent a larger picture of your ITSM performance. Hyperautomation takes a holistic view of the enterprise-wide network infrastructure and establishes a decision framework that can prioritize service use cases based on alignment with organizational goals. Automation is orchestrated to account for the changing circumstances of user demands and infrastructure performance based on proactive and contextual insights across multiple metrics and KPIs.
Although the goal of technology is to save time by automating manual tasks, automating purely for the sake of it doesn’t always translate into productivity improvements. Many organizations automate ITSM processes without careful evaluation of their operational workflows. If the service pipeline is already bottlenecked, automating processes will deliver diminishing returns for the end-to-end ITSM operations. Secondly, if an operational workflow is automated without first eliminating waste processes, the technology will ultimately automate waste and slow down the service pipeline. Furthermore, if automation doesn’t simplify the ITSM service pipeline, your IT Service Desk teams may be forced to take extra steps and possibly circumvent automation protocols to make their job easier. And when it comes to IT Service Desk that is continuously overwhelmed by service requests and incidents that occur unpredictably within a complex network infrastructure, knowing what to automate becomes exceptionally important.
Hyperautomation Intelligence answers exactly that question: identifying performance bottlenecks, problem root cause and patterns of incidents before simply automating the service resolution process. By helping understand why incidents occur in the first place, hyperautomation intelligence allows the IT to make well informed decisions, shift-left the resolution process, optimize the operational workflows and ultimately, focus on delivering value to end-users.
Advanced process mining capabilities can help replace traditional manual process analysis techniques and deliver accurate visualization of process workflows and performance of your IT Service Desk. Process mining tools can analyze vast volumes of the historical ticketing data archives, create process maps to assist IT teams in fast issue resolution and identify tasks that bottleneck the resolution process.
Organizations fail to yield a rhythm of continuous improvement when they treat ITSM hyperautomation as a one-time technology investment. In practice ongoing improvements in your ITSM process come from accounting for the changing operational dynamics and end-user feedback on an ongoing basis. Although automation technologies deliver a provision for ongoing improvement, many organizations operate a rigid ITSM framework with strict governance models. Inadequate agility to continuously transform and prepare for the changing user demands ultimately prevents them from maximizing the value potential of the automation tooling.
Hyperautomation Intelligence is not only focused on the tooling, but also on the organizational culture, governance models and the ability of employees to make use of the technology. It supports an agile governance mindset driven by cross-functional collaboration, high stakeholder support and behavioral change without losing sight of organizational policies, goals and inherent risks.