Quantum-Inspired Optimization for IT Service Management (ITSM) Workflows

Abstract

IT Service Management (ITSM) workflows are characterized by complex, evolving decision-making processes, including incident routing, task sequencing, and coordinating thousands of configuration items [1]. As business environments increase in scale, traditional methods of optimization, such as rule-based and heuristic methods, are no longer sufficient due to increasing mean-time-to-resolution (MTTR), poor use of resources, and increased operational risk [4]. The paper suggests a framework for optimizing ITSM using quantum-inspired optimization techniques [6][7], including simulated annealing and tensor routing models, to provide predictive, near-optimal task allocation in real time [9]. A case study of a large company shows that the framework resulted in a 35 percent reduction in incident MTTR, increased first-contact resolution rates, and a reduction in conflict due to changes. Additionally, this research includes a quantum-inspired routing algorithm that is suited to large enterprises, validating this model as a \"next-generation\" optimization approach for digital service operations [3].

Introduction

Modern IT Service Management (ITSM) environments are highly complex, dynamic, and combinatorial, involving thousands of tickets, agents, and interdependent configuration items. Traditional ITSM systems rely on static rules, priority matrices, and heuristics for routing incidents, sequencing changes, and managing problems. While effective at small scale, these approaches break down in large enterprises, leading to increased mean time to resolution (MTTR), frequent ticket reassignments, analyst burnout, SLA violations, and higher rates of failed or rolled-back changes.

To address these limitations, the article proposes the Quantum-Inspired ITSM Optimization Framework (QITOF), a ServiceNow-native solution that unifies incident, problem, change, and request management into a single, continuously optimized decision process. QITOF applies quantum-inspired optimization techniques—such as simulated annealing and QAOA-style probabilistic search—formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem. This allows the framework to evaluate global interactions across skills, workloads, service dependencies, SLAs, and real-time operational conditions, rather than treating tickets as isolated entities.

The framework ingests real-time events, uses natural language understanding to extract semantic signals from tickets, and performs graph-based analysis of CMDB dependencies. Optimization decisions are executed seamlessly through existing ServiceNow APIs, without disrupting user workflows. Unlike static models, QITOF continuously adapts routing and sequencing decisions as conditions change.

Empirical analysis of 36 months of historical ITSM data (1.8 million tickets), simulation via a digital twin, and A/B testing in production demonstrate significant improvements. In a global financial services case study, QITOF reduced incident MTTR by ~35%, cut reassignment rates, achieved over 99% first-contact resolution, and substantially reduced change conflicts and rollbacks.

The results show that quantum-inspired optimization provides near-optimal, real-time routing and sequencing decisions at enterprise scale, outperforming traditional heuristic methods. The article concludes that dynamic, mathematically grounded optimization is essential for next-generation ITSM platforms operating in continuously changing, high-complexity environments.

Conclusion

The Quantum-Inspired ITSM Optimization Framework [9] has shown that we are no longer looking at quantum-inspired algorithms [6][7] as theoretical concepts, but as a viable methods for use within enterprise service operations in production. With the introduction of near-optimal routing and sequencing decisions within real time, we have been able to produce quicker resolutions, provide a higher level of stability, and produce greater service level outputs. What we have demonstrated through results is that the definition of the Next Generation ITSM Platforms [3] will not be defined by static rules but rather by adaptive optimization. \"In ITSM, the Shortest Path is Not A Straight Line, But Instead A Quantum Superposition.\" [9]

References

[1] Gartner, \"Magic Quadrant for ITSM Tools,\" 2025. [2] ServiceNow, \"ITSM Architecture Guide,\" Vancouver Release, 2025. [3] Forrester, \"The Future of AI in ITSM,\" 2025. [4] IDC, \"Worldwide ITSM Software Forecast,\" 2025. [5] HDI, \"State of Technical Support,\" 2025. [6] E. Farhi et al., \"A Quantum Approximate Optimization Algorithm,\" arXiv:1411.4028, 2014. [7] L. Zhou et al., \"Quantum Approximate Optimization Algorithm,\" Phys. Rev. A, 2020. [8] D-Wave Systems, \"Leap Quantum Cloud Service,\" 2025. [9] S. K. Prasad, \"Quantum-Inspired ITSM Routing,\" in ServiceNow Knowledge 2025, 2025. [10] A. Lucas, \"Ising formulations of many NP problems,\" Front. Phys., 2014.

Copyright

Copyright © 2026 Saikrishna Tarakampet. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.