Modernizing Support Operations with Generative AI on AWS

Scaling support operations involves managing increasing ticket volumes, meeting SLAs, and adapting to evolving compliance requirements, all without proportional staffing increases. Traditional knowledge management relies heavily on fragmented SOPs, recordings, and tribal expertise, leading to inefficiencies and delays. To address these challenges, AWS has developed a comprehensive support operations platform leveraging generative AI, which automates the capture of operational knowledge, provides real-time guidance, and offers predictive insights.

The platform integrates several advanced AI components on AWS, including Amazon Bedrock for multimodal video understanding and generative modeling, and AWS Strands Agents SDK for agentic workflows. One key feature is the Video-to-SOP tool, which automatically converts training videos into structured SOPs, capturing visual, spoken, and interaction data to produce accurate, up-to-date procedures. These SOPs are linked to specific video segments, allowing reviewers to verify steps against source recordings, ensuring accuracy and traceability.

Another core component is the Ticket Analyzer, which uses natural language processing and Retrieval-Augmented Generation (RAG) to identify relevant procedures and generate contextual guidance for ticket resolution. This reduces the time analysts spend searching for instructions and helps ensure consistent, accurate responses. The system also automates operational tasks such as ticket tagging, comments, and status updates through agentic workflows, with human oversight to maintain control.

The Value Stream Intelligence feature visualizes how work flows across teams and systems, highlighting bottlenecks and dependencies. This end-to-end visibility enables managers to better understand workload distribution, identify delays, and optimize processes proactively. Complementing this, the analytics layer built on Amazon Quick provides dashboards for workload management, SLA risk prediction, and workload rebalancing recommendations, transforming reactive reporting into proactive operational management.

This integrated architecture creates a feedback loop where each resolved ticket and generated SOP enhances the system’s knowledge base, enabling faster, more accurate resolutions over time. Early pilots have demonstrated significant improvements, including a fourfold return on investment, reduced inaccuracies in ticket handling, improved SLA compliance, and increased documentation efficiency.

By adopting this AI-driven approach, support organizations can achieve faster resolution times, lower operational risks, and more consistent service quality. The platform’s flexibility allows adaptation across various industries such as financial services, healthcare, logistics, manufacturing, and energy, making it a versatile solution for enterprise support transformation.