Overview
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This program equips you with the knowledge and skills an AI Business Strategist needs to know and to help you prepare you for the AWS Certified AI Business Strategist Certification.
Somewhere between the executives asking what AI can deliver and the technical teams building it, someone has to translate in both directions. That work usually lands on a business professional with no engineering background, and it is what this learning plan prepares you for.
You start by building enough AI literacy to ask sharp questions and hear when a vendor is overselling. From there you decide what deserves funding, and you prove the value afterward in terms a CFO accepts.
Then the work turns to keeping it defensible. That means putting governance in place before anyone asks, weighing responsible AI against business pressure, and knowing where a human stays in the loop.
The hardest part comes last, because most programs stall after a successful pilot. You assess whether the organization is genuinely ready, close the gaps in data, skills, and sponsorship, then scale what works.
AWS services and frameworks are covered at a business level, not an implementation level.
Syllabus
- Course 1: AI Business - Core AI Concepts and Terminology
- Course 2: AI Business - Generative AI Concepts and Techniques
- Course 3: AI Business - AI Solutions and Enterprise Considerations
- Course 4: AI Business - Develop AI strategies that Align with Business
- Course 5: AI Business - Measure and Demonstrate AI Business Value
- Course 6: AI Business - Position AI for Competitive Advantage
- Course 7: AI Business - Responsible AI Principles and Dimensions
- Course 8: AI Business - AI Governance and Regulatory Compliance
- Course 9: AI Business - AI Risks and Direct Mitigation Strategies
- Course 10: AI Business - Assess Business AI Readiness and Maturity
- Course 11: AI Business - Establish Data and Infrastructure for AI
- Course 12: AI Business - Lead Change and Build an AI-ready Workforce
- Course 13: AI Business - Scale AI to Enterprise-Wide Deployments
Courses
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Learn how to govern AI systems by composing a cross-functional governance structure with clear accountability and spotting which stakeholder is missing, differentiating the compliance risks that attach to AI across industries, and matching access controls to user roles and data sensitivity. You will also classify systems by risk tier so governance attention goes where the exposure is.
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Learn how to identify enterprise AI risks and direct mitigation by tracing where bias enters the AI lifecycle, weighing harmful content and intellectual property risks by impact and likelihood, and distinguishing reliability failures such as hallucinations, data quality degradation, and model drift, and matching controls and production monitoring to each risk type.
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Learn how to select the right AI solution type by distinguishing when rule-based automation fits better than AI, and when an agent's autonomy and tool use make it the right approach. You will also cover how multi-agent patterns are orchestrated, why deployed systems need monitoring for model drift, and how a transparent tool classification framework limits shadow AI risk.
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Learn how to assess organizational AI readiness by evaluating the dimensions that decide whether an initiative succeeds, placing your organization on the maturity spectrum and reading what that implies for next steps, and separating the root cause blocking progress from the symptoms it produces. You will also prioritize where to invest when the budget covers only one gap at a time.
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Learn how to describe core AI concepts and terminology by classifying algorithms, models, training, and inference in business descriptions, distinguishing general AI from machine learning and generative AI, and recognizing the purpose of global standards. You will also classify data as structured or unstructured, connect data quality defects to their effects on AI performance, and judge whether historical data can support a use case.
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Learn how to develop AI strategies that align with business objectives by mapping AI capabilities to specific business outcomes and recognizing when a rule-based approach fits better. You will also weigh build, buy, and partner options against budget, timeline, and compliance requirements, decide which initiatives to scale, pause, or end, and evaluate what a transition to AI requires of data readiness, cost, and business continuity.
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Learn how to establish the data and infrastructure foundations AI depends on by differentiating data readiness gaps in quality and accessibility and prioritizing the improvements that matter most, applying data strategy components such as ownership and sharing frameworks at enterprise scale, and identifying the technology gaps that would stop an initiative before it starts.
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Learn how to put generative AI to work by applying prompt engineering for reliable output, recognizing when token limits and context windows affect performance, and knowing when RAG or fine-tuning is the right adaptation technique for a business need.
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Learn how to lead enterprise-wide AI change by building executive sponsorship and champion networks, and assembling cross-functional teams with real accountability rather than standing meetings. You will also communicate openly about AI's effect on jobs, diagnose which cultural barrier is blocking adoption before choosing an intervention, match workforce development to different employee segments, and lead role transitions that balance AI capabilities against human strengths.
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Learn how to measure and demonstrate AI business value by defining KPIs that capture both tangible gains and intangible ones like customer satisfaction, and establishing baseline metrics before launch so improvement can be proven rather than assumed. You will also calculate ROI across time savings, cost reduction, and productivity gains, identify leading indicators that predict success, and control the cost drivers that grow after deployment.
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Learn how to position AI for competitive advantage by assessing what rivals have actually built rather than announced, and distinguishing an operational improvement competitors can copy from a durable advantage. You will also recognize when AI can change the business model rather than just the process, and set investment levels by reading industry maturity and competitive dynamics.
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Learn how to apply the responsible AI dimensions to described system scenarios, and choose a decision framework when business objectives conflict with those principles. You will also build governance into a project plan from the start rather than bolting it on, and judge which applications need human oversight and what safeguards they require.
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Learn how to scale AI from pilot to enterprise deployment by identifying which phase an initiative is in, from envision through experiment, launch, and scale, and sequencing short-term wins that build toward enterprise deployment. You will also stand up a Center of Excellence without creating a new silo, set metrics that track value beyond the pilot, clear the barriers that block production grade, and manage the continuity risks of scaling across an enterprise.
Taught by
AWS Instructor