The use of AI to improve search over enterprise content using natural language understanding and semantic retrieval. It addresses slow or inaccurate keyword search across large, fragmented information stores.
Use cases
11
Examples
11
Industries
7
Timeline
6 mo
Data updated 1 day ago
Adoption over time
Documented cases per month
By case publish month · completed months only
10 cases documented across 37 months (Jul 23 – Jul 26), peaking at 6 in June 2026.
AI Use Cases Hub
1 earlier case before Jul 23 not shown
Each column counts every documented case of this type by its publish month, across the full corpus. The in-progress current month is excluded from columns and surfaced separately, and cases published before the charted window are summarized as earlier cases instead of plotted.
2.6Innovativeness2.6/5Differentiated2.6/5 - Differentiated. This is a fairly standard retail search modernization using managed Alibaba Cloud services, similar to recent retail search and platform modernization cases rather than a novel AI-native architecture.
Ztore, a Hong Kong-based online shopping platform, used Alibaba Cloud OpenSearch and SMS API to improve e-commerce search and customer communication.The implementation aimed to replace legacy search limitations and increase efficiency and accuracy while strengthening marketing outreach.
3Innovativeness3/5Differentiated3/5 - Differentiated. Similar recent Azure OpenAI cases score around 3; this is a strong domain-specific search and validation design, but not a novel model architecture or multi-agent system.
FM (property insurance) built a secure AI-powered search solution on Microsoft Azure to help engineers retrieve engineering standards faster and more accurately across tens of thousands of pages of complex PDFs, diagrams, flow charts, and tables.The solution uses Azure OpenAI in Foundry Models and Azure AI Search, applies structured chunking aligned to engineering logic, validates answers against ground-truth examples, and continuously evaluates performance through governance and feedback loops.
3.5Innovativeness3.5/5Advanced3.5/5 - Advanced. This is a differentiated but familiar cloud AI pattern: a scalable search and RAG stack combining Vertex AI with Elastic and data pipelines. It is more involved than a simple assistant, but the architecture is still an applied enterprise implementation rather than a frontier design.
Sightly built a scalable search solution using Google Cloud and Elastic to support advertising and marketing offerings.The platform processes massive volumes of news, social media, and video data and uses Vertex AI to enrich vendor data, while RAG features power new generative AI capabilities.The system aims to provide fast query speeds, flexible development, and real-time brand intelligence at scale.
3.8Innovativeness3.8/5Advanced3.8/5 - Advanced. The re-architecture is more advanced than a basic search deployment because it combines serverless search infrastructure, vector search grounding, and deep platform integration with Gemini Enterprise Agent Platform, but the article still describes pragmatic cloud modernization rather than a breakthrough AI architecture.
Elastic fundamentally re-architected its Elastic Cloud offering to Elastic Cloud Serverless on Google Cloud, transforming its ability to provide lightning-fast search and AI capabilities at scale while eliminating operational complexity for customers and significantly improving its own software delivery performance through DORA practices.The solution uses Google Compute Engine, Google Kubernetes Engine, and Cloud Storage, and integrates Elastic vector search with Gemini Enterprise Agent Platform UI and SDK for prompt grounding.
3Innovativeness3/5Differentiated3/5 - Differentiated. This is a differentiated but fairly common enterprise generative AI pattern: two domain assistants on Amazon Bedrock with grounding and guardrails. It looks similar to other recent AWS customer stories for SAP/document search, so it does not justify a higher novelty score.
Cosmos Aluminium, a Greek aluminum extrusion company, worked with AWS Partner LCM Go Cloud to build two specialized generative AI agents on AWS for HR and accounting.The CV Intelligence Application evaluates résumés against job requirements, and the Accounting Manual Application answers questions using SAP ERP manuals.The solution used Amazon Bedrock, Amazon Bedrock Guardrails, Amazon Bedrock Knowledge Bases, Amazon S3, Amazon DynamoDB, AWS Lambda, and AWS Site-to-Site VPN with a bilingual Greek and English interface.
3.8Innovativeness3.8/5Advanced3.8/5 - Advanced. This is more inventive than a standard AI search rollout because it combines a full microservices modernization with Bedrock-based Arabic/English natural-language search and moderation, but it remains a pragmatic production system rather than a novel AI architecture. Compared with similar recent assistant/search cases in the calibration set, it is clearly more complex than a basic copilot yet not as advanced as multi-agent or heavily engineered platforms.
Gathern modernized its infrastructure on AWS by moving from a monolithic system to a microservices-based architecture.It added Amazon Bedrock-powered natural language search so travelers could describe accommodation needs in Arabic or English and have them converted into structured search filters.
2.7Innovativeness2.7/5Differentiated2.7/5 - Differentiated. This is a practical retail search modernization using multimodal search, tagging, and centralized analytics. Compared with recent Gemini/Vertex AI commerce cases, it is similar in pattern and not notably more advanced, though the scale and user reach are strong.
THE ICONIC is Australia and New Zealand's leading fashion and lifestyle platform.It used Google Cloud data and AI services to improve search and discovery, connect data across sources, and deliver more relevant recommendations and image-based browsing.
3.7Innovativeness3.7/5Advanced3.7/5 - Advanced. Compared with recent legal and enterprise assistant cases, this is an advanced but not breakthrough deployment: it combines enterprise search, workflow automation, and MCP-based integrations across many systems, yet uses packaged Amazon Q capabilities rather than a novel custom AI architecture.
Aderant, a global provider of business management software for the legal industry, transformed how its 38-person Cloud Engineering team supports Expert Sierra, its cloud-based legal practice management solution.The company used Amazon Q Quick capabilities to unify search across six internal knowledge systems and automate documentation workflows for CloudOps and Product Support.
2.8Innovativeness2.8/5Differentiated2.8/5 - Differentiated. This is a practical enterprise search and assistant rollout using packaged Google Agentspace features, similar in novelty to other common enterprise assistant deployments. The Chrome Enterprise integration and no-code agent creation add some differentiation, but the architecture is still largely configuration-led rather than bespoke.
Gordon Food Service began rolling out Google Agentspace to US employees to improve access to enterprise knowledge across silos.The implementation uses grounded enterprise search and synthesis across Google Workspace and ServiceNow, with Gemini multimodal intelligence and Chrome Enterprise integration.It is intended to help employees search multiple systems in one place, improve decision-making, reduce effort to find information, and enhance internal operations and product development.
3Innovativeness3/5Differentiated3/5 - Differentiated. Comparable to other retail AI search and operations cases scored around 3; this is a broad Azure-based transformation with generative AI, real-time shelf insights, and Microsoft 365 integration, but no unusual architecture or quantified breakthrough outcomes are shown.
Sainsbury's will use Microsoft AI and machine learning tools to improve store operations, customer online shopping search, and in-store shelf replenishment.The retailer plans to deliver the experience through Microsoft Azure and Microsoft 365 collaboration tools over the next five years.
How many search modernization use cases are documented?
The AI Use Case Hub documents 11 real search modernization deployments across 7 industries, with 11 detailed company examples you can browse.
Which industries adopt search modernization the most?
Search modernization is most common in Retail (36%), Professional Services (18%) and Other (9%).
Which countries lead in search modernization?
United States leads documented search modernization deployments, followed by Saudi Arabia and Greece.
What technologies are used for search modernization?
Teams most often build search modernization with Amazon Bedrock, Vertex AI and Google Kubernetes Engine.
What AI capabilities power search modernization?
Across the documented deployments, the most common capability patterns are RAG (27%), Agent (18%) and Sustainability (18%).
What results do companies report from search modernization?
Across the 11 deployments reporting outcomes, companies most often cite customer experience & trust (64%), cost efficiency (45%) and speed & agility (36%). Where impact is quantified, the strongest evidence is in cost savings: a median −50% across 3 reported metrics.