This category uses AI to monitor systems, classify incidents, and assist with troubleshooting and support workflows. It helps teams resolve technical issues faster and manage operational workloads more efficiently.
Use cases
37
Examples
37
Industries
11
Timeline
18 mo
Data updated 1 day ago
Adoption over time
Documented cases per month
By case publish month · completed months only
31 cases documented across 37 months (Jul 23 – Jul 26), peaking at 4 in June 2026.
AI Use Cases Hub
2 earlier cases 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.
2.8Innovativeness2.8/5Differentiated2.8/5 - Differentiated. A solid enterprise AI assistant build with Azure OpenAI/Azure AI Foundry and deep integration into Teams and ticketing, but it is a relatively common multilingual self-service pattern rather than a novel AI architecture; compared with recent Microsoft copilots, it sits in the lower-differentiated band.
G-STAR built Maia, an AI assistant powered by Azure OpenAI and Azure AI Foundry. Developed with CloudNation and integrated with Microsoft Teams, SharePoint, and the IT ticket system, Maia delivers secure, multilingual self-service for retail and corporate teams.The assistant was designed to guide employees through processes, policies, tutorials, and company objectives, help them solve problems on their own, and find information quickly across departments.G-STAR expects Maia to reduce IT tickets, simplify workflows, and improve resolution accuracy while scaling adoption across the enterprise.
4.2Innovativeness4.2/5Advanced4.2/5 - Advanced. This is more advanced than a standard support chatbot because it is an agentic operations assistant integrated into network workflows with graph-connected data and multiple foundation models, similar in ambition to other recent applied agent cases but with deeper operational context.
C Spire built an agentic AI assistant on AWS to help network technicians solve issues more efficiently and keep its network resilient.The solution gives technicians natural-language access to equipment manuals, technical documentation, and network schematics while combining alarms, logs, and configurations to support diagnosis and resolution.
3.8Innovativeness3.8/5Advanced3.8/5 - Advanced. Compared with common AWS assistant or summarization cases, this is a more advanced production operations pattern because it combines AI workload observability, model routing, and cloud-native orchestration. The closest calibration cases suggest advanced modernization rather than a breakthrough architecture.
Mediacorp needed accurate, cost-efficient automation for large-scale generative AI and multimodal metadata enrichment across millions of media assets. It also wanted better visibility into AI model performance, token cost, accuracy, and incident debugging.Mediacorp worked with SoftwareOne to build an observability layer for AI workloads on AWS, extending Amazon CloudWatch to track model accuracy, token consumption, latency, and cost. The solution used AWS Lambda, Amazon ECS, and Amazon Bedrock model selection and routing using CloudWatch metrics.
2.8Innovativeness2.8/5Differentiated2.8/5 - Differentiated. This is a production Azure-based ITSM agent rollout with grounded retrieval, routing, and orchestration, but it follows a fairly common enterprise RAG/automation pattern rather than a novel multi-agent or fine-tuned architecture. It sits slightly above recent no-code assistant cases, but below truly advanced orchestrations.
TeamDynamix, a US-based SaaS provider of ITSM, automation, and AI software, extended its Azure-based platform with AI capabilities to automate routine service requests and scale support without adding complexity or cost.The company embedded AI agents into its no-code ITSM and ESM platform, using Azure OpenAI, Azure AI Search, Azure Machine Learning, Azure Cosmos DB, and Azure Kubernetes Service to ground responses in customer data and manage agent workloads at scale.
3Innovativeness3/5Differentiated3/5 - Differentiated. Compared with recent AWS agentic observability cases, this is a practical but not especially novel implementation: it combines cloud monitoring, agentic investigation, and dashboard integration, but the architecture remains a standard enterprise observability pattern rather than a leading-edge agent system.
United Express worked with Caylent to build an agentic AI observability solution for its weight-and-balance system.The system ingests millions of log entries, uses Amazon CloudWatch for near real-time alerting, and uses Amazon Bedrock to support conversational investigation of events and API data.The goal was to detect anomalies earlier and reduce manual log analysis across regional airline operations.
3.5Innovativeness3.5/5Advanced3.5/5 - Advanced. Compared with recent AWS Bedrock workflow cases, this is a strong but not breakthrough agentic RCA deployment: multi-agent orchestration and log analysis are more advanced than a basic assistant, yet similar to other recent production Bedrock automation patterns.
Formula 1 (F1) and AWS built a root cause analysis workflow that uses Amazon Bedrock and a multi-agent system to analyze logs, summarize findings, correlate incidents, and recommend fixes.The workflow helps engineers resolve race-day issues faster and supports proactive prediction and prevention.
2.4Innovativeness2.4/5Incremental2.4/5 - Incremental. A focused SOAR automation deployment using Chronicle SOAR to streamline threat response and reduce false positives; similar in ambition to common managed security automation cases and less novel than advanced agentic architectures in recent calibration cases.
Evolutio, a cybersecurity services provider in Spain and Portugal, implemented Google Cloud Chronicle SOAR to mature and automate its security orchestration, incident response, and threat monitoring workflows.The solution groups and tiers customer service levels based on cyber maturity, automates parts of incident and remediation workflows, and reduces false positives so analysts can focus on higher-value investigation and playbook development.
3.7Innovativeness3.7/5Advanced3.7/5 - Advanced. More advanced than a basic chatbot because it combines conversational agents, RAG, workflow orchestration, analytics, and operational routing across multiple channels, but it is still a pragmatic enterprise implementation rather than a novel frontier architecture.
Grupo Falabella (Falabella Retail / Sodimac / Tottus) built TARS, a multi-agent conversational AI workflow for incident reporting and ticket creation across retail and help desk operations in Latin America.The solution uses Google Cloud Contact Center AI Platform, Dialogflow/Conversational Agents, Gemini models, Vertex AI Search for RAG over internal process documentation, and supporting services including BigQuery, Pub/Sub, Cloud Run, Firestore, Cloud Storage, Dataform, Looker, and Cloud Logging.The system captures key incident fields, auto-creates and routes tickets, centralizes communication across channels, and supports regional standardization of support processes.
3Innovativeness3/5Differentiated3/5 - Differentiated. A solid serverless edge-to-cloud AWS implementation with measurable operational impact, but the architecture is a practical pattern rather than a novel one, so it is comparable to other differentiated deployments around level 3.
Juniper Networks built Juniper Support Insights (JSI) to securely automate the customizable collection and reporting of customer device data at scale.The solution uses a serverless edge-to-cloud architecture on AWS to give network teams better device-level insight across tens of thousands of devices and reduce the back-and-forth required for troubleshooting.Juniper says the approach improves customer support and operational experience while enabling proactive issue prediction, device end-of-life planning, and secure data handling.
3Innovativeness3/5Differentiated3/5 - Differentiated. A practical AIOps MVP with real operational scale and measurable time savings, but it is still a fairly common enterprise predictive-analytics deployment rather than a novel architecture; it aligns with recent differentiated cases around score 3.
Telecom Argentina, a connectivity and technology provider serving more than 30 million customers in Argentina, is implementing an AI initiative focused on customer experience and IT operations.The company built an AIOps MVP from scratch in Google Cloud to improve network assurance, QA, and customer service.The solution processes 3.8 TB of data per day and supports a predictive model for customer service and incident management.
The AI Use Case Hub documents 37 real it operations deployments across 11 industries, with 37 detailed company examples you can browse.
Which industries adopt it operations the most?
IT operations is most common in Tech & Comms (32%), Professional Services (16%) and Manufacturing (14%).
Which countries lead in it operations?
United States leads documented it operations deployments, followed by Global and United Kingdom.
What technologies are used for it operations?
Teams most often build it operations with Azure OpenAI, Amazon Bedrock and Microsoft Teams.
What AI capabilities power it operations?
Across the documented deployments, the most common capability patterns are Agent (57%), Multi-agent (24%) and RAG (22%).
What results do companies report from it operations?
Across the 37 deployments reporting outcomes, companies most often cite speed & agility (70%), new product / capability (62%) and customer experience & trust (54%). Where impact is quantified, the strongest evidence is in time & speed: a median −84.5% across 6 reported metrics.
