Business Analyst – AI Automation at Rand Refinery

SUMMARY OF KEY RESPONSIBILITIES INCLUDED BUT NOT LIMITED TO:

• Maintain the enterprise AI roadmap and implementation pipeline.
• Develop AI-enabled business solutions that improve operational efficiency and decision-making.
• Conduct AI risk and impact assessments.
• Ensure compliance with cybersecurity, privacy, regulatory, and governance standards.
• Establish controls to manage confidentiality, bias, accuracy, accountability, and data protection risks.
• Design and develop solutions using approved low-code technologies.
• Configure AI agents, workflows, forms, approvals, and knowledge repositories.
• Leverage Microsoft Copilot Studio, Power Automate, Power Apps, SharePoint, Dataverse, and Microsoft 365 Copilot.
• Ensure solutions are fully documented and aligned to governance requirements.
• Track and report on productivity gains, risk reduction, process improvements, and cost savings.

Requirements

MINIMUM REQUIREMENTS:

• A completed Matric qualification.
• A diploma/degree in Information Systems, Computer Science, or related qualification.
• Minimum of 3 years’ relevant experience in business analysis, product management, digital transformation, process automation, enterprise applications or intelligent automation.
• Experience delivering workflow or automation solutions from concept through implementation and adoption.
• Previous experience within an IT environment will be advantageous.

SKILLS/COMPETENCIES:

• Intermediate/Advanced proficiency—Microsoft suite (Excel, Email, Word) and SAP.
• Proficiency with Microsoft Copilot Studio, Power Automate, Power Apps, SharePoint, Dataverse, and Microsoft 365 Copilot.
• Excellent analytical, problem-solving, planning, and organisational skills.
• Knowledge of Generative AI, AI agents, prompt engineering, and AI governance principles.
• Understanding of APIs, integrations, identity and access management, and application lifecycle management.

KNOWLEDGE:

• Knowledge of advanced clerical, operational, or customer service skills acquired through education, experience, or on-the-job training.
• AI governance, Responsible AI, and regulatory compliance principles.
• Cybersecurity, privacy, data governance, and information classification requirements.
• Software development and solution delivery lifecycles.
• Enterprise applications, automation platforms, and digital transformation practices.
• Testing, monitoring, change management, and operational support processes.