What was the impact of the AI solution?

Reported approximately 57% growth in operational productivity in 2025 Reported a 13% increase in customer satisfaction Reported around 10% faster service resolution time

Source

MicrosoftScaled productionEvidence: Medium65/100

DANA Indonesia uses Azure OpenAI and Copilot for agentic customer service (DIANA)

Use case typeCustomer service agentUpdated Mar 6, 2026

DANA, one of Indonesia’s largest digital wallet platforms, has collaborated with Microsoft since 2024 to integrate AI across business operations, product development, and customer experience. The company built an end-to-end AI foundation using Azure OpenAI, GitHub Copilot, and Microsoft 365 Copilot, and introduced DIANA, a task-oriented virtual assistant for customer service powered by Azure OpenAI. DIANA uses multiple specialized AI agents for dialogue and empathy, contextual understanding, knowledge-base retrieval, and insight analysis, with layered integration, governance, and failover mechanisms to maintain reliability during spikes.

Organization: DANA
Industry: Finance
Location: Indonesia
Published: March 2026

Reported outcomes

+57%

operational productivityProductivity & throughput

+13%customer satisfaction+10%service resolution time

Strategic outcomes

Customer experience & trustMore responsive customer serviceRisk & complianceSecure, reliable, responsible AI foundation

Catalog median for productivity & throughput deployments: +40% across 108 reported metrics. Compare benchmarks →

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

Normalized claim

Operational productivity: 57% increase

Microsoft SourceMar 6, 2026News articleExplicit claimMedium evidence strength

57% growth in operational productivity

Normalized claim

Customer satisfaction: 13% increase

Microsoft SourceMar 6, 2026News articleExplicit claimMedium evidence strength

13% increase in customer satisfaction

Normalized claim

Service resolution time: 10% increase

Microsoft SourceMar 6, 2026News articleExplicit claimMedium evidence strength

around 10% faster service resolution time

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

Customer: DANA
Provider: Microsoft
Maturity: Scaled Production
Linked source: Microsoft Source

Maintain secure and reliable customer experience at digital scale Improve customer service quality, operational productivity, and fraud-risk awareness Support enterprise-grade security, privacy, and governance while adopting AI Built an end-to-end AI foundation with Azure OpenAI, GitHub Copilot, and Microsoft 365 Copilot Introduced DIANA, a task-oriented customer service assistant powered by Azure OpenAI Orchestrated multiple specialized agents for dialogue and empathy, contextual understanding,

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Customer service agent
2AI agents
3Developer productivity

Maintain secure and reliable customer experience at digital scale
Improve customer service quality, operational productivity, and fraud-risk awareness
Support enterprise-grade security, privacy, and governance while adopting AI

Built an end-to-end AI foundation with Azure OpenAI, GitHub Copilot, and Microsoft 365 Copilot
Introduced DIANA, a task-oriented customer service assistant powered by Azure OpenAI
Orchestrated multiple specialized agents for dialogue and empathy, contextual understanding, knowledge-base retrieval, and service-improvement insight analysis
Used layered data integration, robust governance, and failover mechanisms to keep service reliable at scale

Technologies

Azure OpenAI GitHub Copilot Microsoft 365 Copilot

Reported approximately 57% growth in operational productivity in 2025
Reported a 13% increase in customer satisfaction
Reported around 10% faster service resolution time

Architecture

DIANA is described as a task-oriented dialogue system powered by Azure OpenAI and orchestrated by multiple AI agents with distinct roles, supported by layered integration, governance, and failover mechanisms for reliability at scale.

Sources & evidence1

Evidence: Medium65/100Evidence strength