AI Business Analyst

• Bachelor's degree in Business, Computer Science, Data Analytics, or a related field.

• Bachelor's degree in Business, Computer Science, Data Analytics, or a related field.

Master's degree is a plus.

• 3-6 years' experience in business analysis, with at least 2 years supporting AI, analytics, or digital transformation projects.
• Strong foundational understanding of AI concepts including machine learning, NLP, LLMs, vector databases, RAG, and prompt engineering.
• Experience working within Microsoft's AI and productivity ecosystem (Azure AI, Microsoft 365, Power Platform, Copilot).
• Proven ability to document and present complex ideas in a clear, engaging, and executive-friendly format.

• AI Use Case Definition & Impact Framing
• Identify, qualify, and refine AI use cases across business units using structured frameworks and value-mapping techniques.
• Formulate clear, well-structured problem statements and define success criteria aligned to efficiency, customer experience, or revenue growth.
• Assess use case feasibility based on technical complexity, data availability, integration needs, and business readiness.
• Documentation & Analysis
• Translate complex business problems into AI requirements and solution hypotheses.
• Prepare high-quality business cases, value assessments, process flows, and technical documentation to support experimentation and implementation.
• Collaborate with data scientists, solution architects, and platform leads to validate solution approaches and ensure technical fit.
• Stakeholder Engagement & Communication
• Facilitate cross-functional workshops with business units, IT, and Digital teams to surface pain points and explore AI opportunities.
• Clearly communicate AI concepts, limitations, and trade-offs to non-technical stakeholders in a way that drives alignment and action.
• Act as the voice of the user in solution design - ensuring outcomes are human-centric, ethical, and usable.
• Critical Thinking & Conceptual Rigor
• Analyze processes, data flows, and decision points to pinpoint where AI can generate meaningful business impact.
• Apply systems thinking and root cause analysis to ensure AI interventions are both sustainable and scalable.
  
  Evaluate and prioritize ideas using scoring models, effort/impact grids, and strategic alignment checks.
• AI Knowledge Integration
• Stay up to date with the latest in generative AI, machine learning, LLMs, semantic search, RPA, and Microsoft AI tools (e.g., Azure AI, Copilot Studio).
• Contribute to refining the organisation's AI playbook, use case library, and intake methodology.
• Consider business use case alignment with the organisation's AI operating model and the organisation's enterprise architecture.