Agentic AI Factory Cuts Monitoring Resolution to 2 Days for a Global EPC Firm
Client Overview
A global EPC firm with more than 100 years of experience delivers infrastructure across energy, power, water, telecommunications, and smart cities for thousands of projects worldwide. Sales, finance, procurement, and safety teams make decisions on those projects, and AI models now inform them. The initiatives grew separately inside each function, so the client wanted one standard way to deliver and govern AI across the organization.
Isolated AI Solutions Held Back Enterprise-Wide Adoption
The firm wanted production-ready AI to sharpen decisions across business functions. Existing AI work had to keep running through the ownership transition, with shared standards and governance taking shape alongside it.
01
Siloed AI Initiatives
Business functions across the firm ran AI initiatives on their own, which limited scalability and enterprise-wide adoption.
02
Governance Gaps
Governance remained limited across the separate initiatives, which restricted how far the firm could scale them.
03
Operating Model Gap
Existing AI initiatives needed a standardized operating model and delivery engine to keep running. Future initiatives needed the same engine to get started.
04
Decisions Waiting on Production-Ready AI
Decision-making across sales, finance, procurement, safety, and client engagement waited on production-ready AI solutions and a modernized AI foundation.
Agentic AI Factory Delivers Production-Ready AI Across Business Functions
Delivery squads on an Azure-native platform take AI requests from intake to production and keep models improving after launch.
01
Central AI Factory
A central factory builds, deploys, and manages enterprise-grade AI for every business function. Cross-functional squads of data scientists, ML and data engineers, MLOps specialists, and domain consultants run delivery.
02
Azure-Native Architecture
Azure-native architecture builds deployment, monitoring, retraining, and governance into each solution. Microsoft Fabric and Azure AI modernize the foundation underneath.
03
Intake and Value Tracking
Structured intake and prioritization decide which AI requests to move forward. Value tracking ties each solution to business KPIs.
04
AI CoE
An AI CoE applies industry best practices and standardizes governance and ownership across initiatives, so delivery stays consistent.
Production-Ready Solutions That Turn Project Data into Decisions
The factory turned existing AI initiatives into production-ready solutions on MLOps and a governance framework. Continuous model improvements helped stabilize them throughout the transition.
Win Rate Prediction
Predicts the chance of winning each sales opportunity from historical Salesforce and business data. Teams prioritize opportunities and allocate resources, which keeps sales forecasts accurate.
Cash Flow Forecasting
Forecasts cash inflows and financial trends across ongoing projects. Finance teams identify risks early and make informed planning decisions under stronger financial governance.
Job Site Safety
Predicts high-risk safety incidents from historical project data. Early warnings let teams act on risks proactively and run safer projects. Continuous model enhancements refine the predictions.
Supplier Risk Intelligence
Identifies supplier risks to project schedules and material availability, including cost escalation and delivery risks. Procurement teams make better decisions and strengthen supply chain reliability.
Client Behavior Analytics
Analyzes customer engagement patterns and turns them into actionable insights. Teams improve service delivery and strengthen customer relationships, and the insights point to growth opportunities.
Faster Resolution and Sharper Forecasts from Agentic AI
1 Month to 2 Days Faster Microsoft Monitoring Resolution
The team resolves monitoring issues far faster, which keeps production AI stable.
56% Stronger Cash Flow Forecasting
Mean Absolute Percentage Error fell down to 17%. Finance teams plan liquidity on more accurate forecasts.
15% Sharper Job Site Safety Prediction
Continuous model enhancements improved high-risk incident prediction. Teams prioritize preventive actions with better insight.
>$8,880 a Year Lower Cloud Costs Each Year
Cloud cost optimization saves more than $740 per month.
Zero Disruption Business Continuity Through Transition
Existing AI solutions kept running through the AI transition.
Digital Backbone
A Shared Delivery Model Keeps AI Useful After Launch
Individual models answer individual business questions, but those answers go stale as conditions change, and someone has to keep them current. A shared delivery model gives that work a clear home. New initiatives join through reusable frameworks and standard processes, so the next model starts on a working foundation.