AI Business Analyst

Required • 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. Key Responsibilities • 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. Show more Show less

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.