Description

Job Description

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 

1. 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. 

2. 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. 

3. 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. 

4. 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. 

5. 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

Key Skills

Education

Any Graduate