Objective

Build foundations in AI thinking, solution architecture, and implementation planning – even without a technical background.

Topics Covered

• Why AI succeeds or fails in organizations
• AI fundamentals for enterprises
• Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings – practical business implications
• From business idea to executable AI roadmap
• Process modeling and workflow mapping
• Enterprise AI architecture patterns and documentation standards
• Data flows and integration logic
• Build vs buy decisions
• Prompt engineering for structured systems

Hands-On

• Translate a business case into an AI architecture diagram
• Design an AI workflow and implementation canvas

Objective

Diagnose organizational needs and identify high-impact AI opportunities across core business functions.

Topics Covered

• Discovery: stakeholder interviews and process inventory
• As-is process mapping and technical audit
• Challenge-to-opportunity framework
• Department-specific use-case libraries: Marketing, Sales, HR, Finance, Customer Support, Operations, and Product
• Root cause analysis (RCA) and bottleneck identification
• Prioritization methods: matrices, voting, heat maps, and the Center of Excellence model
• Quantitative and qualitative ROI estimation
• AI readiness assessment and data sensitivity checks
• Roadmap development, executive pitching, and the AI business partner framework

Hands-On

• Run a discovery workshop and build an AI opportunity map
• Develop an ROI hypothesis and quick-win shortlist
• Pitch a prioritized AI roadmap to an executive panel

Objective

Learn how to secure AI solutions from the outset, so that every application, automation, and agent developed later in the program is built with security in mind from the first version.

Topics Covered

• Zero Trust principles for AI security and data handling
• Data classification, PII handling, and rules for what can be shared with AI models
• Prompt security, prompt injection awareness, and guardrails
• Access control and exposure risks for internal AI tool

Hands-On

• Harden a prompt-based workflow against prompt injection and data leakage
• Build a data classification and guardrail checklist for a business use case

Objective

Build practical solutions using vibe coding and automation platforms, with enterprise-ready documentation and handoffs.

Topics Covered

• Vibe coding foundations and the platform landscape
• From business need to specification: writing an executable build brief
• Building and publishing a functional internal application
• UX and UI improvement for internal tools
• Application-level security: roles, permissions, and exposure
• Automation design patterns using Zapier, Make, and n8n
• Automations, APIs, and embedded AI capabilities
• Internal and knowledge assistants, including RAG when needed
• Human-in-the-loop workflows and escalation paths
• Pre-launch testing, go-live, and documentation for handoff

Hands-On

• Take an internal application from written brief to a published, working tool
• Build an end-to-end automation that improves a real business process
• Build an internal assistant prototype and document it for deployment

Objective

Design and implement agentic workflows that handle complex tasks reliably and safely.

Topics Covered

• Agent design patterns and tool usage
• Orchestration and multi-step workflows
• Memory, context, and knowledge access patterns
• Monitoring and evaluation of agent outputs
• When to use agents vs simpler automation

Hands-On

• Build a single-agent workflow for a business function use case
• Extend to multi-agent collaboration with evaluation checkpoints

Objective

Operate AI solutions safely in real organizations with governance, testing, and stability practices.

Topics Covered

• Testing checklists and evaluation workflows
• Governance models: committees, policies, and review cycles
• Operational stability: monitoring, incident basics, and maintenance routines

Objective

Lead adoption inside the organization through stakeholder alignment, champions, rollout strategy, and measurable impact.

Topics Covered

• Stakeholder mapping and resistance management
• Building an internal AI champions network
• Rollout strategy: communications, enablement, and training
• Adoption and impact metrics

Students apply everything learned throughout the program to design and build a deployable AI solution for a real business challenge.

They select a predefined or approved scenario, such as an HR Onboarding Assistant or Customer Support Triage Bot, and take it from diagnosis and architecture through prototype, testing, governance, and executive presentation.

Key Deliverables

• AI Diagnosis Report – named department, current process with time/cost estimate, and a definition of success at 90 days
• Solution Architecture – data flow diagram, tool selection with justification, failure modes, and escalation path
• Working Prototype – minimum of 5 connected nodes, handling 2+ edge cases, with a setup README
• Test Report – 10+ test cases, including 2 adversarial inputs, with known limitations documented
• Security and Governance Checklist – data classification by data type, access control, PII handling decisions, and human escalation triggers
• ROI Model + Executive Presentation – break-even month, 3 measurable KPIs, and a 10-slide deck with a live demo