Course Outline

🔹 Day 1: Foundations of AI and Enterprise Analytics

Session 1: The Role of AI in Enterprise Data Strategy

• What is AI-powered analytics?
• Evolution from descriptive to predictive and prescriptive analytics
• Benefits and challenges for enterprises
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Session 2: Enterprise Data Ecosystem

• Data sources: ERP, CRM, IoT, and external APIs
• Data lakes, warehouses, and data integration tools
• Data quality, governance, and real-time streaming basics

Session 3: Introduction to AI Tools and Platforms

• Overview of platforms: Power BI + AI, Tableau + Einstein, Google Looker, Azure Synapse, BigQuery ML
• Intro to AutoML and embedded ML in analytics tools
• Use cases: churn prediction, sales forecasting, anomaly detection

Lab Activities:

• Connect enterprise-style datasets to Power BI or Google Looker
• Build an AI-driven sales or operations dashboard
• Explore sample use cases with AI insights (e.g., Q&A, anomaly detection)

🔹 Day 2: Predictive Analytics and Business Intelligence with AI

Session 1: Predictive and Prescriptive Analytics

• Forecasting models (ARIMA, Prophet, ML-based models)
• Classification and clustering in enterprise scenarios
• What-if analysis and scenario simulation

Session 2: No-Code and Low-Code AI for Business Users

• Using Power BI AI Visuals (Key Influencers, Decomposition Trees)
• Google AutoML Tables and BigQuery ML
• Salesforce Einstein Discovery for augmented analytics

Session 3: Applied Use Cases Across Departments

• Finance: cash flow prediction, fraud detection
• HR: attrition risk, skill gap analysis
• Operations: inventory forecasting, supply chain optimization
• Customer service: sentiment analysis, case routing

Lab Activities:

• Build a customer churn prediction model using AutoML
• Run a time series forecast in Power BI or BigQuery ML
• Create a "Key Drivers" analysis dashboard using enterprise data

🔹 Day 3: Custom AI Integration and Strategy Alignment

Session 1: Embedding AI into Business Processes

• Embedding ML models into dashboards and applications
• Integrating with RPA and business workflows (e.g., Power Automate, Zapier)
• Triggering alerts and decisions from real-time analytics

Session 2: From Data to Action – Enterprise AI Strategy

• Aligning AI analytics with business KPIs
• Data literacy and adoption across teams
• AI governance and model monitoring

Session 3: Capstone Project + Executive Insights

• Teams design and build an AI-powered enterprise analytics solution
• Presentation to “executive panel”
• Strategic roadmap planning for AI-driven transformation

Lab Activities:

• Create a full enterprise analytics workflow: ingest → analyze → alert
• Set up automatic decision triggers based on AI predictions
• Present a use-case-based prototype with measurable outcomes