Aderant transforms cloud operations with Amazon Quick

· Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

Aderant, a global provider of legal business management software, transformed its cloud operations by implementing Amazon Quick to enhance support for its Expert Sierra platform. Facing challenges with information scattered across six disconnected systems, Aderant deployed Quick in October 2025, achieving full rollout by November 2025. The solution unified AI-powered search across Confluence, SharePoint, Git, Jira, Teams, and Quick Sight, reducing search times by over 90% (from 30-45 minutes to 3-5 minutes). Additionally, Amazon Quick Flows automated knowledge base article creation, cutting creation time by 75% (from one hour to 15 minutes) and increasing output by 200%. The CloudOps Helper bot achieved 95% active use among the 38-person engineering team, demonstrating significant efficiency gains and improved collaboration for Aderant's global team.

Key takeaway

For MLOps Engineers or IT Professionals managing complex cloud environments with fragmented information, adopting an AI-powered unified search and automation platform like Amazon Quick can drastically reduce operational friction. You can expect to accelerate troubleshooting, improve documentation quality and output, and reclaim thousands of hours annually, enabling your team to focus on problem-solving rather than information retrieval. Consider integrating such a solution to streamline workflows and enhance global team collaboration.

Key insights

Unified AI-powered search and workflow automation significantly improve operational efficiency and knowledge management.

Principles

Method

Deploy an AI-powered platform like Amazon Quick to unify search across existing systems, automate documentation workflows via Quick Flows, and leverage research capabilities for insights into operational data.

In practice

Topics

Best for: MLOps Engineer, IT Professional, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.