What challenges did TeamDynamix face before AI implementation?

Rising IT ticket volumes outpaced rule-based automation. TeamDynamix needed to resolve routine service requests faster while keeping AI behavior reliable, secure, and grounded in customer data.

What was the impact of the AI solution?

Customers can deflect 30% to 60% of help desk tickets through automation and AI. Tickets that route to the service team can be resolved up to 90% faster. Support workload is reduced by up to 70%. The solution can eliminate two to three months of manual tasks per technician each year.

Source

MicrosoftScaled productionEvidence: Medium65/100

TeamDynamix builds agent-led ITSM automation with Azure OpenAI and Azure AI Search

Use case typeIT operationsUpdated Jul 6, 2026

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.

Organization: TeamDynamix
Industry: Tech & Comms
Location: United States
Published: July 2026

Reported outcomes

Ticket resolution speed: Up to 90% higher

Time & speed

Support workload: Up to 70% lowerManual tasks per technician: 2.5 months per year

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Ticket deflection: 30-60% increase

Microsoft Customer StoriesJul 6, 2026Customer storyExplicit claimMedium evidence strength

deflecting 30%–60% of IT tickets

Normalized claim

Ticket resolution speed: 90% increase

Microsoft Customer StoriesJul 6, 2026Customer storyExplicit claimMedium evidence strength

resolve issues up to 90% faster

Normalized claim

Support workload: 70% decrease

Microsoft Customer StoriesJul 6, 2026Customer storyExplicit claimMedium evidence strength

reduce support workload by up to 70%

Normalized claim

Manual tasks per technician: 2.5 months per year decrease

Microsoft Customer StoriesJul 6, 2026Customer storyInferred claimMedium evidence strength

eliminating as much as two to three months of manual tasks per technician each year

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: TeamDynamix
Provider: Microsoft
Maturity: Scaled Production
Linked source: Microsoft Customer Stories

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

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1IT operations
2Workflow automation
3Agent orchestration

Built AI agents into its no-code ITSM platform.
Used Azure OpenAI in Foundry Models to interpret requests and generate responses.
Used Azure AI Search and Azure Cosmos DB vector search to ground responses in customer data and surface related tickets and knowledge articles.
Used Azure Machine Learning to classify, prioritize, and route requests.
Ran the agent workloads on Azure Kubernetes Service to operationalize the workflow at scale.

Technologies

Azure OpenAI Azure AI Search Azure Machine Learning Azure Cosmos DB Azure Kubernetes Service Microsoft Azure

Customers can deflect 30% to 60% of help desk tickets through automation and AI.
Tickets that route to the service team can be resolved up to 90% faster.
Support workload is reduced by up to 70%.
The solution can eliminate two to three months of manual tasks per technician each year.

Architecture

TeamDynamix built agent-led workflows on Azure. Azure OpenAI in Foundry Models interprets requests and generates responses, Azure AI Search and Azure Cosmos DB provide retrieval-augmented generation grounded in customer data, Azure Machine Learning classifies and routes tickets, and Azure Kubernetes Service runs the agent workloads in production.

Sources & evidence1

Evidence: Medium65/100Evidence strength