AWS Certified AI Business Strategist (AIB-C01)

What you'll learn

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Classify any use case as rules, classical ML, generative AI, or an AI agent - and defend the choice
Diagnose model drift, bias drift, hallucinations, and pipeline breaks from their symptoms
Make defensible build-buy-partner decisions and prioritize a portfolio with scale, pause, or terminate verdicts
Build an AI business case with KPIs, baselines, ROI adjusted for adoption and lag, leading indicators, and TCO
Apply the responsible AI dimensions and design human oversight with guardrails and escalation criteria
Assess organizational readiness and maturity, and close data ownership and silo gaps
Scale from pilot to enterprise via envision-experiment-launch-scale, quick-win waves, a COE, and readiness reviews
Requirements

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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
Description

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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.
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
Homepage

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