AI Business Analyst - Umhlanga

Purpose of the Role

The AI Business Analyst connects business strategy to practical AI outcomes. The role identifies and prioritises AI use cases, translates business needs into clear requirements for AI solutions, quantifies the value of each initiative, and ensures delivered solutions are adopted, governed and measured.

The role does not build models or write production code. It owns the problem definition, the requirements, the success criteria and the business case, and works closely with technical specialists who deliver the solution.

KEY RESPONSIBILITIES

3.1 AI Opportunity Discovery

• Run discovery sessions and workshops with business stakeholders to understand processes, pain points and goals.
• Identify, document and prioritise AI use cases based on value, feasibility, risk and effort.
• Distinguish between problems suited to AI, automation, analytics or simple process change, and recommend the right approach.
• Assess organisational readiness for AI, including data, security, skills and change capacity.

3.2 Requirements and Solution Design

• Translate business needs into functional and non-functional requirements, user stories and acceptance criteria.
• Document current-state and future-state processes, including where human review and approval remain in the loop.
• Define prompt, agent, workflow and integration requirements in collaboration with solution engineers and developers.
• Define data requirements, including sources, quality, access and sensitivity.
• Support design decisions on platform and tooling selection, for example Microsoft 365 Copilot, Copilot Studio and Power Platform.

3.3 Business Case and Value Realisation

• Build business cases that quantify expected benefits, costs, licensing and risk.
• Define KPIs and baseline measures before delivery so that value can be demonstrated afterwards.
• Track and report realised benefits against the business case and recommend adjustments.

3.4 Data, Security and Responsible AI

• Work with security and compliance specialists to confirm that proposed AI use cases respect data classification, access controls and retention requirements.
• Identify privacy, bias, accuracy, intellectual property and regulatory risks early, and document mitigations.
• Contribute to AI usage policies, acceptable-use guidance and governance frameworks, including alignment with POPIA where applicable.

3.5 Delivery, Testing and Adoption

• Act as the link between business stakeholders and technical teams throughout delivery.
• Define and coordinate user acceptance testing, including evaluation of AI output quality against agreed criteria.
• Develop adoption and change plans, training content and user guidance in collaboration with enablement teams.
• Gather user feedback after go-live and manage the backlog of improvements.

3.6 Monitoring and Continuous Improvement

• Monitor usage, quality and performance of deployed AI solutions and escalate drift, errors or declining value.
• Maintain a repository of use cases, patterns, lessons learned and reusable artefacts.
• Keep up to date with developments in AI platforms and best practice and advise stakeholders on relevant changes.

3.7 Stakeholder and Client Engagement

• Communicate findings, risks and recommendations clearly to executives and non-technical audiences.
• Manage stakeholder expectations on what AI can and cannot deliver.
• Prepare proposals, scopes of work, assessment reports and presentations as required.

• 1 Minimum Qualifications and Experience
• Bachelor's degree or national diploma in Information Systems, Business Administration, Commerce, Engineering, Data Science or a related field.
• 1-3 years of experience as a Business Analyst, Business Systems Analyst, Functional Consultant or similar.
• Demonstrated experience gathering requirements and documenting processes for technology solutions.
• Practical, hands-on experience using generative AI tools in a professional setting.

4.2 Technical Knowledge

• Working understanding of how large language models, Copilots and agents function, including their limitations such as hallucination, grounding and context.
• Strong knowledge of Copilot, Copilot Studio, Work IQ, Cowork, Credits, etc.
• Familiarity with prompt design and evaluation of AI output quality.
• Understanding of data concepts: data quality, structured and unstructured data, classification and access control.
• Proficiency with business analysis techniques and tools: BPMN or equivalent process modelling, user stories, backlog tools, and Excel.
• Working knowledge of the Microsoft 365 ecosystem, including Microsoft 365 Copilot, Teams, SharePoint and Power Platform.

4.3 Core Competencies

• Analytical and structured problem solving.
• Clear written and verbal communication, including the ability to explain technical concepts in plain language.
• Facilitation skills and confidence engaging with senior stakeholders.
• Commercial awareness, with the ability to link initiatives to measurable value.
• Sound judgement on risk, ethics and responsible use of AI.
• Curiosity and the discipline to keep learning in a fast-moving field.

4.4 Advantageous

• Microsoft certifications, for example AI-900, PL-200, PL-300, MS-900 or SC-900.
• Experience with Copilot Studio, Power Automate or similar agent and workflow tools.
• Basic SQL or Python for data exploration.
• Experience in a consulting or client-facing environment.