AWS Certified AI Business Strategist (AIB-C01)

About this course

AWS Certified AI Business Strategist (AIB-C01) is the first AWS certification built for the people who decide about AI rather than build it: product and program managers, sales and BD leads, line-of-business heads, consultants, analysts, and marketers. It validates the judgment that moves AI from experiment to production — evaluating investments, building business cases, designing governance, and scaling adoption. Until now there has been no structured way to prepare for it.

The exam is currently in beta, and that matters. Earn the certification by February 15, 2027 and you receive an additional Early Adopter digital badge that later candidates cannot earn. Beta pricing is 50 USD instead of the standard 100 USD. But AWS's Official Practice Exam is not available during beta, so this course is designed to be your complete preparation: 63 focused lessons of 6–8 minutes each, 30 downloadable templates, 10 section quizzes, and a 15-question timed final assessment.

Every lesson maps to a numbered skill in the official exam guide, across all four domains and their exact weights: AI Fundamentals and Literacy (24%), AI Strategy and Business Value Creation (28%), AI Governance and Responsible AI Leadership (24%), and Business Readiness, Leadership, and AI Transformation (24%). You will learn the exam's own vocabulary — build-buy-partner, scale-pause-terminate, envision-experiment-launch-scale, approved-blocked-under-evaluation — and the decision patterns examiners reward.

This is not a services course. The exam explicitly excludes coding, data engineering, model tuning, and configuring or administering AWS. Instead you will learn Amazon Bedrock, Amazon SageMaker AI, and Amazon Quick at the strategic level the exam tests — pricing tiers, Guardrails, Knowledge Bases, managed-versus-custom, seat-versus-consumption pricing — plus the AWS Cloud Adoption Framework, the shared responsibility model, the Well-Architected Responsible AI Lens, ISO/IEC 42001 and 23053, and the complete list of in-scope and out-of-scope services so you can spot a distractor instantly.

You also get the exam mechanics most candidates never hear about: 85 questions, a 700 passing score on a 100–1,000 scale, compensatory scoring with no per-domain minimum, no penalty for guessing, and why the beta form runs 170 minutes while the exam guide lists 130. A dedicated exam-technique module teaches a repeatable elimination method for scenario questions, and a capstone walks one enterprise AI journey across all four domains so the pieces connect.

Everything you build here works on Monday morning, not just on exam day. The portfolio scorecard, ROI model with adoption adjustments, AI tool register, governance charter, risk classification matrix, readiness rubric, COE charter, and production readiness checklist are the same artifacts you will use to run AI in your own organization.

What you'll learn

• Classify any use case as rules, classical ML, generative AI, or an AI agent — and defend the choice
• Choose between prompt engineering, RAG, and fine-tuning, and spot when tokens or context windows are the constraint
• Diagnose model drift, bias drift, hallucinations, and pipeline breaks from their symptoms
• Contain shadow AI with an approved, blocked, and under-evaluation tool register
• Make defensible build-buy-partner decisions and prioritize a portfolio with scale, pause, or terminate verdicts
• Recognize when AI is the wrong answer and propose the alternative
• Build an AI business case with KPIs, baselines, ROI adjusted for adoption and lag, leading indicators, and TCO
• Match consumption, instance, and seat-based AWS pricing to usage, using Pricing Calculator and Cost Explorer
• Apply the responsible AI dimensions and design human oversight with guardrails and escalation criteria
• Stand up three-tier AI governance with risk classification and regulatory mapping (PH Data Privacy Act, EU AI Act)
• Assess organizational readiness and maturity, and close data ownership and silo gaps
• Lead AI change with sponsorship, champions, transparent communication, and role redesign
• Scale from pilot to enterprise via envision-experiment-launch-scale, quick-win waves, a COE, and readiness reviews
• Position Bedrock, SageMaker AI, and Amazon Quick strategically and spot every out-of-scope service distractor

Requirements

• No coding, no hands-on AWS experience, and no prior AWS certification are required — exactly as the exam guide states
• Basic familiarity with AI concepts and a general awareness of what AI can do in business; about six months working with or alongside AI initiatives is helpful but not mandatory
• No AWS account is needed. There are no labs; the course teaches what Amazon Bedrock, SageMaker AI, and Amazon Quick are for, not how to configure them
• A copy of the official AIB-C01 exam guide, which is free from AWS and referenced by skill number in every lesson
• Access to the AWS Certification Official Practice Question Set on AWS Skill Builder is recommended for final preparation, since the Official Practice Exam is not available during beta
• A spreadsheet application to use the downloadable ROI model, portfolio scorecard, and readiness rubric templates

Who this course is for

• Product and program managers who need to say yes, no, or not yet to AI proposals and defend the decision
• Sales, business development, and marketing professionals positioning AI-enabled offerings or evaluating vendors
• Line-of-business leaders and operations managers accountable for AI outcomes, budgets, and workforce change
• Consultants and business analysts who build AI business cases, readiness assessments, and governance programs for clients
• Technology professionals who want to add strategic AI business judgment alongside AWS Certified AI Practitioner or other technical certifications
• Anyone who wants to be among the first in the world to hold this credential and earn the Early Adopter badge before February 15, 2027
• This course is not for you if you want to build, train, or deploy models, write production prompts, or configure AWS services — those skills are explicitly out of scope for this exam and are not taught here

Curriculum