Information Technology / Enterprise IT Operations

AI-Driven IT Service Management Analytics Platform

AI-powered ITSM analytics platform reduced incident resolution time by 79%, improved SLA compliance to 99.2%, and automated 32% of recurring tickets.

softreetechnology.com/case-studies
AI-Powered ITSM Analytics & Process Automation
0%
Faster Incident Resolution
0%
SLA Compliance
0x
Annual ROI
Use Cases
Operations Analytics & Intelligent IT Service Management
Industry
Information Technology / Enterprise IT Operations
Project Type
Enterprise ITSM Analytics Platform Modernization
Scale of Operation
Supports thousands of IT incidents, service requests, and operational events daily across multiple business units and global regions.
End Users
12,000+ IT Operations & Service Management Users
Service Provided
Power Platform Development
The Client Challenge

Business Process Challenges

A multinational enterprise operating across 18 countries managed over 12,000 IT service tickets each month through multiple disconnected platforms, including ServiceNow, Jira, Azure Monitor, and Confluence. The absence of a centralized analytics layer created visibility gaps and delayed operational decision-making.

Incident resolution processes relied heavily on manual triage and escalation workflows. Critical incidents frequently breached SLA targets, while support teams spent significant time consolidating data from multiple systems before they could begin root-cause investigations.

The organization also lacked predictive capabilities. Recurring service disruptions were only identified after user impact had already occurred, resulting in prolonged outages, increased operational costs, and reduced service reliability.

Compliance reporting presented an additional challenge. Audit preparation for ISO 20000 and SOC 2 required weeks of manual effort due to fragmented evidence collection and limited governance visibility.

Our Approach

Our Strategic Approach

Softree designed and delivered a unified AI-powered ITSM analytics platform using Microsoft Fabric, Power BI, Power Automate, Azure Machine Learning, and Azure SQL.

The solution consolidated data from ServiceNow, Azure Monitor, Jira, Confluence, Active Directory, and other enterprise systems into a centralized Microsoft Fabric Lakehouse. This created a single source of truth for operational reporting, analytics, and automation.

Machine learning models were implemented to classify tickets, detect anomalies, forecast SLA risks, and identify root causes across interconnected infrastructure components. Real-time dashboards provided executives and operations teams with actionable insights across incident management, service performance, compliance, and operational health.

Power Automate workflows enabled automated ticket routing, escalation management, and recurring issue resolution, reducing manual intervention and accelerating response times.

Our Solution Architecture

How we delivered it.

AO
Azure OpenAI
Integrated Azure OpenAI layer in the solution architecture.
P
Python
Integrated Python layer in the solution architecture.
R
RAG
Integrated RAG layer in the solution architecture.
MF
Microsoft Fabric
Integrated Microsoft Fabric layer in the solution architecture.
R(G
RAG (Retrieval-Augmented Generation)
Integrated RAG (Retrieval-Augmented Generation) layer in the solution architecture.
O
OneLake
Integrated OneLake layer in the solution architecture.
P
PySpark
Integrated PySpark layer in the solution architecture.
AML
Azure Machine Learning
Integrated Azure Machine Learning layer in the solution architecture.
Visual Proof

Explore the Solution Through visuals

Application dashboard
Click to expand
Operations command center
Application dashboard
Screenshot 01Operations command center
Workflow designer
Screenshot 02Automated approval flows
Analytics view
Screenshot 03Executive KPI dashboard
Mobile app
Screenshot 04Field inspection experience
The Outcome

What changed for the client.

The platform transformed enterprise IT operations by enabling proactive service management and AI-assisted decision-making.

Mean Time to Resolve (MTTR) decreased from 18 hours to 3.8 hours, representing a 79% improvement. SLA compliance increased from 77% to 99.2%, significantly reducing service disruptions and contractual risk.

AI-driven anomaly detection reduced Priority 1 incidents by 73%, while intelligent automation resolved 32% of recurring tickets without human intervention. Monthly reporting effort was reduced from 320 hours to 28 hours, allowing teams to focus on higher-value operational initiatives.

The organization achieved approximately $2.8 million in annual operational savings and realized a 3.2x return on investment within the first year. The platform now serves as the foundation for future AI-driven IT operations, autonomous remediation, and business-impact intelligence initiatives.

Results & Business Impact

The numbers behind the rollout.

01
Report generation
Same-day automated
02
Approval cycle
Under 4 hours
03
Data accuracy
99%+ governed
04
App deployment
8–12 weeks low-code
Reference Tech Stack

The full integration layer.

Azure OpenAI
Python
RAG
Microsoft Fabric
RAG (Retrieval-Augmented Generation)
OneLake
PySpark
Azure Machine Learning
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FAQ

Frequently asked questions.

The platform addressed slow incident resolution, fragmented data sources, manual reporting processes, SLA breaches, and the lack of predictive insights across enterprise IT operations.
AI models automated ticket classification, detected anomalies before service disruptions occurred, predicted SLA risks, and accelerated root cause analysis, enabling faster and more proactive IT operations.
The organization achieved a 79% reduction in incident resolution time, increased SLA compliance to 99.2%, reduced manual reporting efforts by more than 85%, and significantly improved enterprise-wide operational visibility.

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What we offer

Enterprise Integration
Cloud Architecture
AI & Automation
Microsoft Solutions
Offshore Engineering

Offices

  • Bengaluru

    11th Floor, Prestige Tech Park, Platina 2 · Outer Ring Rd, Kadubeesanahalli · Bengaluru, Karnataka 560087, India

  • Cuttack

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  • San Francisco

    San Francisco, CA 94108 · United States

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