Introduction:

Artificial Intelligence represents a transformative framework for enhancing enterprise systems through predictive models, intelligent automation, and scalable performance structures. Its integration within the Microsoft ecosystem provides a powerful platform for aligning business operations with advanced analytical and automation tools. This training program emphasizes institutional models and technical structures that support optimization, resilience, and long term efficiency in digital environments. It also highlights the role of managers and technical leaders in shaping strategies that maximize the value of AI adoption.

Program Objectives:

By the end of this program, participants will be able to:

• Analyze foundational concepts of AI and their integration with enterprise platforms.

• Evaluate predictive analytics models for data-driven insights and decision support.

• Classify intelligent automation frameworks for business process optimization.

• Examine performance scaling strategies supported by AI in cloud environments.

• Gain the skills to design strategic roadmaps for sustainable AI adoption within organizational structures.

Targeted Audience:

• IT Managers and Program Directors.

• Software Architects and Enterprise Developers.

• Data Analysts and Machine Learning Specialists.

• Digital Transformation Leaders.

• Project Managers responsible for AI adoption.

Program Outline:

Unit 1:

Foundations of AI and Microsoft Ecosystem Integration:

• Institutional concepts of AI relevant to enterprise system performance.

• Overview of AI services in Microsoft Azure: Cognitive Services, Machine Learning, Synapse Analytics.

• Frameworks for AI integration within .NET Core enterprise applications.

• Strategic role of managers in driving AI adoption across organizations.

Unit 2:

Predictive Analytics and Data-Driven Insights:

• Structured processes for data preparation and preprocessing using SQL and EF Core.

• Implementation of predictive analytics models with ML.NET frameworks.

• Structures for building dashboards that translate data into actionable insights.

• Case frameworks: forecasting demand, analyzing usage patterns in enterprise portals.

Unit 3:

Intelligent Automation for Business Processes:

• AI driven workflow structures using Power Automate and .NET APIs.

• Chatbot and intelligent assistant design through Azure Bot Service.

• Models for automating repetitive processes in Blazor-based enterprise portals.

• Change management structures supporting adoption of intelligent automation.

Unit 4:

Performance Scaling and Optimization:

• Techniques for performance tuning in .NET Core enterprise applications.

• AI-enabled monitoring frameworks for anomaly detection and risk alerts.

• Scaling Blazor applications using AI-based load prediction structures.

• Institutional considerations for cloud deployment in Microsoft Azure.

Unit 5:

Capstone Workshop and Strategy Session:

• Project application: optimizing a sample enterprise system using AI models.

• Best practices for embedding AI initiatives into Agile workflows.

• Strategy session: frameworks for managers to scale and sustain AI adoption.

• Structured Q&A and knowledge transfer models for institutional capacity building.