AI in the Enterprise: Platforms, Integration & Adoption

This comprehensive one-day course provides business leaders and program owners with a practical platform overview and adoption playbook for enterprise AI deployment. Students will learn to compare major AI platforms including Microsoft 365 Copilot, Amazon Bedrock, Azure OpenAI, and supporting services, focusing on integration patterns, value identification, and scaled adoption strategies. The course emphasizes collaboration between business and IT teams to ensure successful, sustainable AI implementation.

Participants will gain hands-on experience comparing platform capabilities and organizational fit, identifying high-value use cases with ROI estimation and complexity assessment, aligning with IT on access controls, data governance, and monitoring, designing integration patterns including RAG over SharePoint/S3, and building comprehensive rollout plans with enablement assets and success metrics. Through labs and a capstone project, students will develop a complete adoption plan ready for organizational implementation.

Audience

This course is designed for business leaders and program owners partnering with IT and vendor teams to implement enterprise AI solutions. Product managers, operations directors, digital transformation leads, and business analysts responsible for AI adoption will benefit from this practical, implementation-focused approach.

Objectives

Upon completion of this course, students will be able to:

• Compare enterprise AI platform capabilities: Microsoft 365 Copilot, Amazon Bedrock, Azure OpenAI, and GCP Vertex AI
• Evaluate platform fit based on content requirements, chat interfaces, workflow needs, and API integration
• Identify and prioritize 3-5 high-value use cases with ROI estimates and complexity assessments
• Design integration patterns including connectors, RAG implementations, and event-driven workflows
• Align with IT teams on access controls, data boundaries, logging, and compliance requirements
• Build scalable adoption strategies including licensing, centers of excellence, and prompt operations
• Create comprehensive enablement programs with playbooks, office hours, and KPI tracking

Topics

• Enterprise AI platform landscape: strengths, tradeoffs, and selection criteria
• Microsoft 365 Copilot capabilities and deployment patterns
• Amazon Bedrock foundation models and agent frameworks
• Azure OpenAI and GCP Vertex AI comparison
• Integration patterns: connectors, plugins, and custom APIs
• RAG (Retrieval-Augmented Generation) over enterprise content (SharePoint, S3, etc.)
• Event-driven workflows and automation triggers
• Security and compliance: identity management, data boundaries, and audit logging
• Data access and connector matrix design
• Centers of Excellence structure and prompt operations
• Enablement strategies: playbooks, training programs, and office hours

Prerequisites

Students should have:

• No specific technical prerequisites required
• Understanding of enterprise software systems and organizational processes
• Familiarity with collaboration platforms (SharePoint, Teams, etc.) helpful but not required
• Interest in AI adoption and organizational change management
• Modern web browser with internet access
• Access to vendor consoles (Microsoft 365, AWS, Azure) helpful for demos but not required

Outline

I. Platform Landscape and Selection

1. Enterprise AI Platform Overview
   1. Microsoft 365 Copilot: integrated productivity AI
   2. Amazon Bedrock: foundation model marketplace and agents
   3. Azure OpenAI: enterprise-grade OpenAI deployment
   4. GCP Vertex AI: Google’s AI platform and services
2. Platform Capabilities and Fit
   1. Content grounding and knowledge base integration
   2. Chat interfaces and conversational AI
   3. Workflow automation and orchestration
   4. API access and custom development
3. Selection Criteria and Decision Framework
   1. Organizational readiness assessment
   2. Technical requirements and constraints
   3. Cost models and licensing considerations
   4. Vendor ecosystem and support

II. Integration Patterns

1. Connector Architecture
   1. Pre-built connectors and plugins
   2. Custom connector development
   3. Authentication and authorization patterns
2. RAG over Enterprise Content
   1. SharePoint and Microsoft 365 integration
   2. Amazon S3 and document repositories
   3. Chunking strategies and indexing
   4. Citation and source attribution
3. Event-Driven Workflows
   1. Trigger patterns and event sources
   2. Workflow orchestration tools
   3. Real-time vs. batch processing
4. Hands-On Lab 1: Use Case Integration Design
   1. Select organizational use case
   2. Choose appropriate integration pattern
   3. Document architecture with pros and cons
   4. Estimate implementation complexity and timeline

III. Security and Compliance Alignment with IT

1. Identity and Access Management
   1. Single sign-on (SSO) and authentication
   2. Role-based access control (RBAC)
   3. Conditional access policies
2. Data Boundaries and Governance
   1. Data residency requirements
   2. Information barriers and sensitivity labels
   3. Data loss prevention (DLP) integration
3. Logging and Monitoring
   1. Audit log requirements and retention
   2. Usage analytics and reporting
   3. Compliance dashboards
4. Hands-On Lab 2: Data Access and Connector Matrix
   1. Identify data sources for use case
   2. Map access requirements and permissions
   3. Design connector architecture with security controls
   4. Create data access matrix documentation

IV. Adoption at Scale

1. Licensing and Deployment Models
   1. User-based vs. consumption-based licensing
   2. Pilot program sizing and cost estimation
   3. Scaling strategies and budget planning
2. Centers of Excellence
   1. COE structure and responsibilities
   2. Champion networks and peer support
   3. Governance and oversight
3. Prompt Operations (PromptOps)
   1. Prompt library management
   2. Version control and testing
   3. Sharing and reuse strategies

V. Enablement and Measurement

1. Enablement Programs
   1. User onboarding and training pathways
   2. Playbook development and documentation
   3. Office hours and support structures
2. Change Management
   1. Communication strategies and stakeholder engagement
   2. Resistance management and adoption drivers
   3. Feedback loops and continuous improvement
3. KPI Framework
   1. Usage metrics and adoption rates
   2. Productivity gains and time savings
   3. User satisfaction and NPS
   4. Cost savings and ROI calculation
4. Hands-On Lab 3 – Capstone: One-Page Adoption Plan and 90-Day Roadmap
   1. Create concise one-page adoption plan
   2. Define pilot scope and participant selection
   3. Design 90-day implementation roadmap with milestones
   4. Identify enablement assets and training requirements
   5. Define success metrics and measurement approach
   6. Present adoption plan to group