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Complete learning course

Business Analyst + AI

Requirements, process analysis, and AI use-case discovery

A path for business analysts covering requirements, documents, processes, gap analysis, AI use cases, risks, and output quality verification.

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7–12 weeks3 modules10 lessons5 assessments

Module 1

Shared AI core

Model limits, structured outputs, and verification for evidence-based analysis.

OutcomeThe analyst uses AI without replacing evidence with assumptions.

  1. AI literacy and model limits

    Core

    Capabilities, hallucinations, context limits, privacy, and responsible use.

    Material →

  2. Prompt and context engineering

    Core

    Instructions, examples, constraints, context, and output verification.

    Prerequisites: AI literacy and model limits

    Material →

  3. Structured outputs and evaluation

    Core

    Response schemas, deterministic checks, test cases, and acceptance criteria.

    Prerequisites: Prompt and context engineering

    Material →Resource →

Module 2

AI-assisted analysis workflows

Requirements, documents, processes, contradictions, and traceability.

OutcomeThe analyst structures source material faster while preserving traceability and evidence boundaries.

  1. Requirements and user stories with AI

    Core

    Drafting, ambiguity detection, acceptance criteria, and traceability.

    Material →

  2. Document analysis and gap detection

    Core

    Compare versions, surface conflicts, identify missing rules, and preserve evidence.

    Material →

  3. Process mapping and automation candidates

    Recommendedpractice + assessment

    As-is/to-be flows, bottlenecks, controls, and AI opportunities.

    Material →Practice →Assessment →

  4. Project: AI-assisted requirements audit

    Projectpractice + assessment

    Build a requirements package with traceability, gaps, risks, and acceptance criteria.

    Prerequisites: Requirements and user stories with AI, Document analysis and gap detection

    Material →Practice →Assessment →

Module 3

AI use-case discovery

Value, feasibility, risk, controls, and ROI for AI initiatives.

OutcomeEach use case has an explicit value hypothesis, data readiness, risks, controls, and success criteria.

  1. AI use-case scoring

    Corepractice + assessment

    Assess value, frequency, data readiness, risk, and automation potential.

    Material →Practice →Assessment →

  2. ROI, risk, and controls

    Corepractice + assessment

    Costs, human review, privacy, failure impact, and monitoring requirements.

    Material →Practice →Assessment →

  3. Milestone: validated AI business case

    Milestonepractice + assessment

    A prioritized use case with requirements, risks, controls, and measurable success metrics.

    Prerequisites: AI use-case scoring, ROI, risk, and controls

    Material →Practice →Assessment →

Final practice and assessment

The course ends with a practical artifact and an acceptance rubric. The result is considered complete after the acceptance criteria are met, not merely after reading the materials.

Process mapping and automation candidates →Project: AI-assisted requirements audit →AI use-case scoring →ROI, risk, and controls →Milestone: validated AI business case →Open roadmap →