Course Overview
This course prepares learners for the AWS Certified AI Business Strategist (AIB-C01) exam, designed for professionals who make strategic AI decisions rather than build AI systems. It covers evaluating AI investments, building business cases, designing governance, and scaling AI adoption within organizations. The course includes 63 focused lessons, quizzes, downloadable templates, and a final assessment aligned with the official exam domains.
Key Takeaways
- Classify AI use cases and defend the choice of AI solution type.
- Diagnose issues such as model drift, bias drift, hallucinations, and pipeline breaks.
- Make informed build-buy-partner decisions and prioritize AI portfolios effectively.
- Develop AI business cases with KPIs, ROI models, and total cost of ownership considerations.
- Apply responsible AI principles including human oversight and governance.
- Assess organizational readiness and close data ownership and silo gaps.
- Scale AI initiatives from pilot to enterprise using structured methodologies and centers of excellence.
Prerequisites
- No coding or hands-on AWS experience required.
- Basic familiarity with AI concepts and general awareness of AI in business recommended.
- No AWS account or lab work needed.
Target Learners
- Product and program managers responsible for AI decision-making.
- Sales, business development, and marketing professionals evaluating AI offerings.
- Line-of-business leaders and operations managers accountable for AI outcomes and workforce changes.
- Consultants and business analysts building AI business cases and governance programs.
Welcome: The Beta Exam and the Early Adopter Badge 6:15
Exam Blueprint: Questions, Timing, Scoring, and Domain Weights 5:24
Who This Exam Is For and What Is Deliberately Out of Scope 6:43
AI vs Machine Learning vs Generative AI: The Three Circles 7:01
Algorithms, Models, Training, Inference, and Predictions in Business Terms 8:37
Structured vs Unstructured Data and Why Data Quality Decides Outcomes 6:52
Global AI Standards: ISO/IEC 42001 and ISO/IEC 23053 8:41
Domain 1A quiz: Core AI Concepts and Standards
Rule-Based Automation or AI? The Decision Test 7:52
AI Agents: Autonomy, Tool Use, Agent-to-Agent Communication, and Orchestration 8:50
Why AI Needs Ongoing Monitoring: Model Drift and Performance Changes 9:05
Shadow AI and the Approved, Blocked, Under-Evaluation Tool Register 8:13
Prompt Engineering Principles Every Strategist Should Know 6:24
Tokens and Context Windows: When Limits Break Your Use Case 6:40
RAG vs Fine-Tuning: Choosing the Right Model Adaptation 9:31
Domain 1B quiz: Solution Types and GenAI Techniques
High-Impact AI Use Cases by Business Function 7:32
Build, Buy, or Partner: Evaluating AI Implementation Options 8:20
Prioritizing the AI Portfolio: Scale, Pause, or Terminate 7:02
When AI Is the Wrong Answer 7:15
Transitioning Processes to AI and Migrating Between AI Platforms 6:52
Domain 2A quiz: Developing AI Strategy
Defining KPIs for AI: Tangible and Intangible Benefits 6:21
Baselines First: Measuring Before You Deploy 6:22
Calculating AI ROI: A Comprehensive Framework With Real Arithmetic 7:52
Leading Indicators That Predict AI Project Success 5:34
Competitive Landscape and Sustainable AI Advantage 7:15
Transforming Business Models With AI 7:05
Matching AI Spend to Industry Maturity and Competitive Dynamics 7:20
AI Cost Controls: Pricing Models, Savings Plans, Pricing Calculator 7:57
Domain 2B quiz: Measuring Value and Competitive Positioning
The Responsible AI Dimensions 9:00
Navigating Tradeoffs When Business Goals Collide With Responsible AI 8:48
Governance by Design: Building Responsible AI Into the Project Plan 8:28
Human Oversight, Guardrails, Hallucination Detection, and Escalation Criteria 11:28
Domain 3A quiz: Responsible AI Principles
AI Governance Structures 9:41
Regulatory Compliance Risks for AI-Enabled Business Processes 8:21
Access Controls, Data Security, and the AWS Shared Responsibility Model for AI 7:16
AI Risk Classification Frameworks Across the Lifecycle 7:58
Risk Controls and Monitoring for AI Systems in Production 7:25
Bias Across the AI Lifecycle and Monitoring for Bias Drift 7:56
Harmful Content and Intellectual Property Risks in AI Systems 7:35
AI Reliability Risks: Hallucinations, Data Quality Degradation, and Model Drift 7:57
Domain 3B quiz: Governance, Compliance, and Risk
Assessing AI Readiness Across Five Dimensions 5:57
AI Maturity Models: Locating the Enterprise on Its Journey 7:23
Finding Capability Gaps Across People, Process, Technology, and Governance 9:13
Data Readiness: Quality, Accessibility, and the Cost of Silos 6:44
Data Strategy, Data Ownership, and Data Sharing Frameworks 7:40
Foundational Technology and Infrastructure Requirements for AI 7:39
Domain 4A quiz: Readiness and Foundations
Executive Sponsorship, Leadership Alignment, and Empowering AI Champions 6:10
Building Cross-Functional AI Teams With Clear Accountability 7:29
Communicating AI Change and Overcoming Cultural Barriers 8:55
POC Programs, Hackathons, Training, and Responsible AI Literacy 7:35
Redesigning Roles: From Manual Operations to Human Oversight and Collaboration 7:34
Domain 4B quiz: Leading Change and Workforce
Iterative Transformation: Envision, Experiment, Launch, Scale, and the AWS CAF 9:24
Scaling Methodologies: Quick Wins That Build Toward Enterprise Deployment 6:47
The AI Center of Excellence and Cross-Functional Collaboration Mechanisms 7:39
Feedback Loops and Success Metrics That Track Long-Term Value 7:38
From Experiment to Production-Grade: Governance and Operational Requirements 7:34
Domain 4C quiz: Scaling from Pilot to Enterprise
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