AWS Certified AI Business Strategist (AIB-C01)

Learning plan

About 30 hours of focused study, split into 5 phases. Pick your weekly pace and the timeline adjusts.

Estimated finish: 3 weeks

What the exam covers

Source: official exam guide. Weights can change, so check before you start.

Before you start

• No coding or AWS console experience needed. AWS suggests about 6 months working with or alongside teams adopting AI
• Business-level awareness of Amazon Bedrock, SageMaker AI and Amazon Quick (what each is for, not how to configure it)

1. Week 1 · 7 h

   AI literacy for decision-makers

   • Distinguish AI, ML and GenAI, structured vs unstructured data, training vs inference, and explain why data quality drives outcomes
   • Decide when rule-based automation beats AI, and what makes an AI agent different (autonomy, tool use, agent-to-agent communication)
   • Explain prompt engineering, context-window limits, RAG and fine-tuning in plain business terms
   • Know what ISO/IEC 23053 and ISO/IEC 42001 cover and why an approved/blocked/under-evaluation tool list reduces shadow AI

   Use: AWS Skill Builder – AIB-C01 exam prep · AIB-C01 official exam guide · Generative AI for Everyone (DeepLearning.AI)

2. Weeks 1–2 · 9 h

   Strategy, ROI and competitive advantage (largest domain)

   • Map AI use cases in customer operations, sales and marketing, R&D and software development to measurable outcomes, and spot when AI is the wrong answer
   • Make build-buy-partner calls (Bedrock vs SageMaker AI vs an AWS Marketplace product) using budget, timeline, capability and compliance
   • Define KPIs with a pre-AI baseline, separate tangible from intangible benefits, and calculate ROI from time savings, cost reduction and revenue growth
   • Compare consumption-based, instance-based and seat-based pricing and use Savings Plans, the AWS Pricing Calculator and Cost Explorer for cost planning

   Hands-on: Pick one real process at your company (for example support-ticket triage). Write a one-page business case with a baseline metric, three KPIs, a build-buy-partner recommendation and a 12-month cost estimate built in the AWS Pricing Calculator for Bedrock on-demand usage.

   Use: AWS Pricing Calculator · AWS Executive Insights

3. Weeks 2–3 · 7 h

   Responsible AI and governance

   • Apply the responsible-AI dimensions (fairness, explainability, privacy, safety, transparency, robustness) to scenarios where they conflict with a business goal
   • Design a governance structure with cross-functional representation and clear accountability, and classify AI use cases by risk tier
   • Know when human oversight is required and which safeguards apply (Bedrock Guardrails, hallucination detection, escalation criteria)
   • Identify reliability, IP and harmful-content risks, plus bias drift, and use the AWS shared responsibility model and the Well-Architected Responsible AI Lens

   Use: AWS Responsible AI resources · Well-Architected Responsible AI Lens

4. Week 3 · 4 h

   Readiness, change leadership and scaling

   • Score an organisation's AI readiness across people, process, technology and governance, and place it on a maturity model
   • Use the AWS Cloud Adoption Framework (AWS CAF) and its envision, align, launch, scale phases to plan a pilot-to-enterprise rollout
   • Plan an AI centre of excellence, executive sponsorship, champions, and workforce upskilling (POCs, hackathons, responsible-AI training)

   Use: AWS Cloud Adoption Framework

5. Week 3 · 3 h

   Practice exams and gap review

   • Take the official practice question set in Skill Builder and review every explanation, not just the wrong answers
   • Re-read the task statements for your weakest domain and write one example scenario for each skill
   • Practise answering under time pressure; the beta has 85 questions

   Use: AWS Skill Builder – AIB-C01 exam prep

You're ready to book when…

• You can turn a vague AI idea into a business case with a baseline, KPIs, ROI and a build-buy-partner decision
• You can explain the difference between RAG and fine-tuning and between an AI agent and a chatbot to a non-technical executive
• You can name the governance roles, risk tiers and human-oversight triggers for a high-risk use case
• You score 80% or higher on the official practice question set

Exam tips

• The exam favours strategic decisions over implementation. If an answer requires configuring a service or tuning a model, it is probably out of scope and wrong.
• Passing by February 15, 2027 earns the Early Adopter badge. Beta takers get one attempt and must wait for the standard exam to retake.
• The passing score is 700 of 1,000, and the scoring is compensatory, so a weak domain can be offset by strong ones.