3 Sides of the Autonomous Routing Triangle

Colin Howes | April 13th, 2021

Traditional IT Service Desk is outdated. The advent of technology solutions has enabled organizations to engage in multiple transformation initiatives simultaneously in order to solve a variety of Service Desk challenges. However, technology-driven transformation often fails to translate into a systematic, process-driven and scalable mechanism for Service Management organizations. IT Leaders buy into the marketing hype and promises in solving specific challenges associated with scalable and agile IT Service Desk operations. The solution therefore lies in addressing a variety of constraints as they optimize ITSM operations for business goals.

In this post, we will focus on ticket routing operations within the IT Service Desk domain. Organizations typically model business decisions against three key constraints: Problem Identification, Ticket Distribution and Workforce Assignment. The goal of an autonomous ticket routing technology is to optimize for all three of these key pillars.

The Iron Triangle of IT Service Desk

Service Desk organizations are constantly evolving and adapting to manage support and services for an ever-growing user base. They operate in narrowly defined constrained environments, where teams are required to meet specific KPI objectives. Once the KPIs are defined, pre-defined frameworks and operational models are adopted to reach optimal performance against the said constraints: the ability to  identify IT issues; routing tickets to the right resolution teams and distributing HR assignments based on skill set availability, types of IT incidents and other business goals. In practice, the scale of business operations make it impossible to address all three constraints, so the decision makers are compelled to adopt intuitive ideas and technology solutions as the path of least resistance. These methods work well under specific circumstances and eventually, must integrate with existing workflows as well as technology solutions. The end result is that the Service Desk staff is forced to perform a growing list of tasks manually, ensuring all solutions work well to meet the predefined KPIs, which in fact may not be optimized for the global objective. This practice denies the often overlooked opportunity for employees to adapt their workforce capabilities necessary to address new challenges facing the dynamic IT Service Desk landscape. A global view of Service Desk operations is key to optimally address these multiple challenges simultaneously. This is where the role of disruptive AI solutions comes into play: the ability to investigate the trends in data, discovering patterns in support requests and modeling operational workflows to meet all constraints. This capability is key to solving a myriad of Service Desk issues across the three constraints:  

1. Problem Identification:

The traditional process requires users to classify IT issues under known problem categories. Users are often unaware of the correct categories corresponding to their support requests, which may in fact fall under multiple overlapping support categories. The lack of accurate and precise problem classification makes it further difficult to categorize the support request, compelling users to submit tickets under the Other category, which does little to help reach the right resolution teams promptly.

Solution: AI Clusters

NLP technologies analyze historical ticket archives and correlate unstructured and vague tickets to find the most accurate identifiers and features describing a problem classification. The AI technology then generates clusters of similar issue categories, allowing the right resolution teams to find solutions for newly discovered issues faster by addressing the underlying problem root cause.

2. Ticket Distribution:

The scale and variety of support requests and IT issues often makes it challenging to automate the ticket distribution and routing process optimally. Organizations require Service Desk teams to resolve tickets based on business objectives and KPIs such as MTTR and MTTD on issues most impactful toward these goals. As a result, ticket distribution becomes a manual exercise that is detrimental to the overall performance of the Service Desk, despite the availability of traditional automated routing tools.

Solution: Adaptive Load Balancing​

Load balancing capabilities based on AI technologies can incorporate a variety of KPIs and constraints to ensure optimal ticket distribution. This results in reduced MTTD and MTTR on mission-critical issues and maintaining highly dependable IT services.

3. Workforce Assignment:

Service Desk organizations are not only overwhelmed by the sheer volume of support requests, but also struggle to make optimal use of the skills and knowledge already available to them. Suboptimal ticket routing, lack of skill set mapping between resolution teams and the ability to accumulate and share the acquired knowledge greatly reduces the ability of Service Desk organizations to scale their operational capacity.

Solution: AI Skill Set Mapping

AI based skill set mapping technologies bridge the skills gap by routing tickets based on the most relevant and available team members as well as making it easier to identify, extract and share applicable knowledge across the ticket lifecycle.

Modern IT Service Desk must constantly evolve and enhance their ability to meet organizational goals based on the constraints associated with ticket routing, skills availability and their ability to shift left resolution through fast problem resolution. In order to address these limitations, the IT Service Desk needs intelligence embedded into decision making as well as the execution process. Optimizing for all three sides of the autonomous routing triangle therefore becomes an ongoing process based on AI models that continuously learn, adapt and implement decisions based on the new ticketing information.

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