Modernizing and scaling support operations with generative AI on AWS
Learn how to build a generative AI-based support operations platform on AWS that converts training videos into structured SOPs, applies Retrieval-Augmented Generation to guide ticket resolution, and uses machine learning to predict SLA risk and prioritize work.
Modernizing and scaling support operations to meet rising ticket volumes, stringent Service Level Agreements (SLAs), evolving compliance requirements, and outdated documentation is a challenging task. Traditional approaches rely on fragmented knowledge spread across SOPs, recordings, and tribal expertise, forcing analysts to spend excessive time searching for guidance instead of resolving issues promptly.
To overcome these constraints, organizations can leverage generative AI on AWS to capture knowledge from operational workflows, apply it during ticket resolution, and proactively identify risks before they impact SLAs. This approach focuses on enhancing underlying processes and streamlining work flows across teams.
The solution involves designing and implementing a generative AI-based support operations system on AWS. It automates the creation of Standard Operating Procedures (SOPs) from training videos using Retrieval Augmented Generation (RAG), guides ticket resolution by applying AI, and optimizes workload distribution using machine learning (ML).
Additionally, the system automates tasks like ticket tagging, commenting, and status updates through agentic workflows while maintaining human oversight for control and accuracy. This architecture is illustrated through a real-world operational use case and can be adapted to various industries, including financial services, healthcare, logistics, manufacturing, and energy.
Enterprise support operations depend on process knowledge, which becomes fragmented as organizations grow. While documentation exists, it rarely reveals the end-to-end process flow. SOPs are developed sporadically for specific functions without a systems perspective, resulting in documentation that covers individual tasks but not the broader workflow.
Knowledge is often shared through training calls, walkthroughs, and troubleshooting sessions, with most of it lost after meetings end. This leads to repeated rediscovery of information rather than continuous knowledge accumulation.
As incoming tickets arrive faster than guidance can be found, ticket backlogs accumulate, and delays compound. Analysts spend considerable time searching for SOPs across various platforms, often reconstructing resolution paths based on incomplete or inconsistent information. Only a portion of procedures are formally documented, with the rest held as tribal knowledge by a few experienced analysts.
Junior staff rely on escalation, while senior staff become bottlenecks for routine questions. Improving consistency requires visibility into the full process, which is currently lacking due to the way SOPs are created without mapping the complete workflow. Additionally, automatic priority assignment based on ticket details often fails to identify risky tickets before they breach deadlines, further complicating workload distribution and decision-making.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
