/ Case Studies / Building AI Into the Flow of Service Operations Case Studies Building AI Into the Flow of Service Operations How a Global Data and Analytics Organization Used Microsoft AI to Modernize IT Service Management Critical Issue As organizations scale, service teams often become burdened by repetitive, manual activities that slow response times and consume valuable employee capacity. Ticket routing, assignment, documentation searches, response creation, and resolution support frequently require significant analyst involvement, even when many of the underlying activities follow repeatable patterns. A global data and analytics organization operating a large Global Client Service Center sought to modernize its service operations through practical AI adoption. Its existing IT service management environment processed approximately 2,000 service tickets per week, but analysts spent substantial time performing repetitive administrative work instead of focusing on higher-value problem solving. The organization wanted to explore how AI could streamline ticket management while preserving appropriate human oversight and governance. Rather than pursuing broad automation all at once, the organization chose a phased pilot approach focused on targeted service desk use cases that could demonstrate measurable value and establish a foundation for future AI-powered service operations. Customer Profile A global market research and data analytics organization supporting retail and consumer packaged goods industries. The company operates a Global Client Service Center responsible for managing a significant volume of service tickets through its enterprise IT service management platform and sought to improve operational efficiency through AI-driven automation and intelligent assistance. Key Problem The organization needed to reduce the manual effort involved in managing service tickets while maintaining quality, consistency, and operational control. Analysts were spending time on repetitive activities such as ticket routing, assignment, documentation searches, response drafting, and ticket administration. Leadership wanted to improve responsiveness and scalability without increasing headcount while establishing a governance-driven approach to implementing AI within enterprise service management workflows. BUSINESS CHALLENGES High-Volume Manual Service Processes Many service management activities relied on manual intervention, from ticket routing and assignment to initial communication and research. These repetitive tasks consumed analyst capacity and introduced inconsistency across service workflows. Scaling Service Operations Efficiently As ticket volumes grew, leadership wanted to improve operational efficiency without relying on proportional increases in staffing. The organization needed a solution capable of supporting future growth while maintaining service quality. Balancing Automation with Governance Enterprise service processes often include approvals, exception handling, and operational controls that cannot simply be removed. The organization needed an approach that incorporated AI assistance while preserving human review, governance requirements, and controlled decision-making. Outcomes AI at Enterprise Scale Supporting a service organization that manages approximately 2,000 tickets per week, the initiative established a framework for applying AI to high-volume service management operations. The solution was designed to automate key portions of the ticket lifecycle while maintaining appropriate human oversight and governance. Intelligent Knowledge Utilization The solution leveraged a substantial service knowledge base consisting of more than 6,300 historical tickets, including over 5,700 completed resolutions, helping analysts quickly access relevant precedent, documentation, and troubleshooting insights during ticket resolution activities. Foundation for Measurable Efficiency Gains Business analysis identified the opportunity to reduce manual effort by 21 to 42 minutes per ticket through automation of routing, assignment, response generation, and resolution-support activities. The pilot established a foundation for evaluating these opportunities through phased deployment and operational measurement. our solution Concurrency partnered with the organization to design, build, and deploy an AI-powered service management solution focused on practical automation, analyst assistance, and long-term scalability. AI Service Desk Automation Designed a Ticket Resolution Copilot focused on high-value service desk workflows. Automated ticket routing and assignment activities based on available context. Enabled AI-assisted response drafting for service interactions. Introduced intelligent workflow orchestration across ticket management processes. Analyst Assistance & Knowledge Management Surfaced relevant documentation and prior incident knowledge to support analysts. Provided resolution assistance through contextual recommendations. Incorporated human-in-the-loop workflows for review and approval. Reduced time spent searching for historical information and support content. Microsoft AI Platform Architecture Leveraged Microsoft Copilot Studio and Azure AI technologies. Implemented an architecture designed to support future expansion. Incorporated orchestration, governance, and monitoring capabilities. Established reusable integration patterns with enterprise systems. Governance & Operational Readiness Conducted discovery workshops and use-case prioritization sessions. Defined success criteria, operational processes, and governance models. Delivered proof-of-concept, pilot deployment, and rollout planning activities. Supported training, adoption, and operational transition readiness. Lessons Learned & Next Steps Start with Operational Friction, Not Technology The most effective AI use cases emerged from understanding how analysts actually worked rather than beginning with a technology-first mindset. By focusing on repetitive service desk activities, the project identified opportunities where AI could provide meaningful operational value while preserving governance requirements and existing support processes. Human Oversight Remains Critical The project reinforced the importance of responsible AI adoption within enterprise operations. Human approval gates, exception handling, and governance controls remained key components of the solution design, ensuring that automation enhanced analyst effectiveness rather than replacing operational decision-making. The organization is positioned to build on the initial AI foundation by expanding intelligent automation into additional service workflows and business processes while maintaining consistent governance and operational controls. Conclusion For many organizations, the challenge is not finding opportunities for AI. It is identifying where AI can create measurable business value while fitting within existing operating models. This engagement focused on applying Microsoft AI technologies to a real-world service management environment where repetitive work, knowledge discovery, and ticket administration created opportunities for improvement. By partnering with Concurrency, the organization established a practical framework for integrating AI into service operations through intelligent automation, analyst assistance, and governance-driven deployment. With a service center supporting approximately 2,000 tickets each week, a knowledge foundation built from more than 6,300 historical tickets, and identified efficiency opportunities of 21 to 42 minutes per ticket, the initiative created a scalable foundation for future AI adoption across the enterprise. AI Service Management & Copilot Agent FAQs How can AI improve IT service management operations? AI can help automate repetitive service desk activities such as ticket routing, categorization, response drafting, and knowledge discovery. In this engagement, Microsoft AI technologies were applied to service workflows to reduce manual effort, improve consistency, and support faster ticket resolution while maintaining appropriate human oversight. Why use a human-in-the-loop approach for service desk AI? Human-in-the-loop AI allows organizations to gain efficiency benefits while preserving operational control. This project incorporated review processes, approval gates, exception routing, and analyst decision-making to ensure AI-supported workflows aligned with business requirements and governance expectations. How does Microsoft Copilot Studio support enterprise agent development? Microsoft Copilot Studio provides a platform for building intelligent business agents that can interact with enterprise data, workflows, and business processes. This engagement combined Microsoft AI technologies, orchestration capabilities, and governance controls to support practical service management automation. What should organizations consider before implementing AI-powered service management? Organizations should evaluate process maturity, governance requirements, knowledge availability, approval workflows, and operational readiness before deploying AI solutions. Successful implementations typically begin with clearly defined use cases, measurable objectives, and controlled pilot deployments before expanding to broader business scenarios. What should organizations consider before expanding an existing automation solution? Organizations should evaluate current automation accuracy, exception volumes, data quality, business process consistency, and stakeholder readiness before scaling automation initiatives. A phased approach allows teams to validate results, manage risk, and identify opportunities for continuous improvement while maintaining business continuity.
