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Automation and Autonomy: the two terms that are often used incorrectly and interchangeably, can make a significant difference to the ROI of ITSM processes and technologies at organizations buying-in to the marketing and media hype.
In this article, we will discuss the difference between automation and autonomy in context of ITSM technology solutions and how Swish.ai has taken the charge of the AI revolution in ITSM.
Advances in technology are typically nonlinear. At various levels of technology maturity, organizations are able to automate tasks under certain assumptions and constraints, which can be increased in complexity to reflect real-life human-like intelligence capabilities. Fully autonomous capabilities means that a system can demonstrate a high degree of self-governance and adaptability in highly uncertain and ever-evolving environments.
In the enterprise IT industry however, poor marketing lingo often blurs out the difference between automation and autonomy.
Let’s compare the two definitions:
Automation refers to the introduction of technology to reduce human intervention.
Autonomy refers to intelligent capabilities that allow systems to respond and adapt to changes while satisfying a set of constraints.
The result is an overpromising technology solution delivering underwhelming results. Instead of fully replacing human intervention, isolated automation components make systems complex in dynamic operational environments and overburden limited staff with firefighting IT issues and simply keeping the systems alive.
This issue holds particularly true for ITSM delivery organizations. In the IT Service Desk use cases for instance, automating individual and disparate components without driving end-to-end automation rarely improves First-Level Resolution or ticket volume metrics. A truly autonomous ITSM Delivery Organisation automatically and intelligently routes tickets in real-time to self-drive and self-optimize, enables organizations to reach unprecedented levels of efficiency and productivity!
The Technology — AI Clustering: Traditional IT Service Desk solutions assign tickets to predefined categories. In practice, the categorization process is static in response to the dynamic ITSM needs of the organization. Swish.ai introduces clustering of IT issues and service requests that help guide tickets to appropriate resolution teams with minimal hops and optimal shift-left ticket routing. Category assignment is performed autonomously using an NLP engine that compares an individual ticket text to historical data archives. The NLP Clustering technology reads the unstructured free-text component of the ticket and applies smart labels that are used to track trends, such as specific IT issues and anomalies that are driving the ticket volume.
The People — Skillset Mapping and HR Management: Consumer-facing enterprises are often overwhelmed by the number of tickets and service requests by end-users. Despite ongoing investments in human resources, the number of service agents available always seem to be insufficient. The problem however, is not always about the number of Service Desk staff available, but the distribution of HR resources based on the type and quantity of service requests. Swish.ai optimizes HR allocation across IT service support domains and levels based on skills, experience and time to resolve for maximum resource utilization.
The Process — Ticket Routing: Swish.ai uses a range of AI-enabled technology features to ensure intelligent ticket routing, maximizing shift-left resolution. The platform captures a 360-degree view of the request streams, identifies anomalies and problem root-causes before the impact reaches end-users. The knowledge is captured and managed in real-time, making it easy to share insights across teams and organizational departments. The Smart Agent feature empowers the support personnel with a personalized AI assistant that complements the optimal distribution of skills necessary to resolve a variety of service requests at scale.
