Role Purpose

The Business Analyst – AI & Automation is responsible for identifying, analysing, qualifying, prioritising, and supporting the delivery of Artificial Intelligence (AI), Generative AI, Automation, and Digital Transformation initiatives across the organisation.

This role serves as the critical link between business stakeholders and technology delivery teams, ensuring business challenges are translated into clear requirements, measurable outcomes, and successful AI-enabled solutions.

The successful candidate will combine strong business analysis expertise with practical AI literacy, including an understanding of Generative AI, Large Language Models (LLMs), Microsoft Copilot, AI Agents, Prompt Engineering, and Responsible AI principles.

The role focuses on business value realisation by ensuring AI investments solve real business problems, improve customer experiences, increase productivity, reduce operational costs, and support strategic business objectives.

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Role Focus

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Key Responsibilities

1. AI Opportunity Discovery

• Facilitate AI opportunity discovery workshops with business stakeholders.
• Identify business processes suitable for AI and automation.
• Analyse current business operations to uncover optimisation opportunities.
• Develop AI business cases and value assessments.
• Maintain an AI opportunity pipeline and prioritised use-case backlog.
• Perform initial assessments covering business value, feasibility, complexity, risks, and dependencies.

Expected Outcome

A healthy pipeline of qualified and prioritised AI opportunities aligned with business strategy.

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2. Requirements Engineering

• Gather, analyse, and document business, functional, and non-functional requirements.
• Produce Business Requirements Documents (BRDs), Business Specifications (BRSs), User Stories, Acceptance Criteria, Process Maps, Use Cases, and Customer Journey Maps.
• Translate business requirements into delivery-ready specifications for AI and automation initiatives.
• Ensure all requirements align with business objectives, governance standards, and measurable KPIs.

Expected Outcome

Well-defined, high-quality requirements that enable efficient delivery and minimise rework.

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3. AI Product & Delivery Support

• Support Agile ceremonies, including backlog refinement, sprint planning, sprint reviews, and retrospectives.
• Collaborate with Product Owners, Solution Architects, Data Engineers, AI Engineers, Developers, and business stakeholders.
• Participate in solution design sessions to ensure business alignment and user experience.
• Coordinate User Acceptance Testing (UAT) and support issue resolution.
• Assist with solution deployment, adoption, and post-implementation support.

Expected Outcome

Successful delivery of AI-enabled solutions that meet agreed business objectives.

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4. AI Literacy & Innovation

• Build practical knowledge of Artificial Intelligence, Machine Learning, Generative AI, and Conversational AI.
• Develop an understanding of:
  • Large Language Models (LLMs)
  • AI Agents
  • Retrieval-Augmented Generation (RAG)
  • Vector Search
  • Knowledge Bases
  • Prompt Engineering
• Leverage Microsoft Copilot and approved AI productivity tools to support business analysis and use-case discovery.
• Understand AI capabilities sufficiently to identify business opportunities without developing AI models or production code.

Expected Outcome

Business and technology teams share a practical understanding of AI capabilities and opportunities.

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5. Benefits Realisation

• Define business KPIs for AI initiatives.
• Establish performance baselines and target outcomes.
• Measure realised benefits including:
  • Productivity improvements
  • Cost reduction
  • Customer experience enhancement
  • Revenue growth
  • User adoption
• Report benefits and business outcomes to leadership.

Expected Outcome

Clear visibility of business value delivered through AI initiatives.

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6. AI Governance & Responsible AI

• Ensure AI initiatives comply with governance, security, privacy, and regulatory requirements.
• Support AI intake processes, approvals, and risk assessments.
• Identify operational, compliance, security, and reputational risks.
• Document human oversight, audit requirements, and escalation procedures.
• Promote responsible and ethical AI practices across projects.

Expected Outcome

AI solutions are delivered in a compliant, secure, and responsible manner.

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Minimum Qualifications

Essential

• Bachelor’s degree in Business Analysis, Information Systems, Computer Science, Engineering, Business Management, Commerce, or a related discipline.
• Strong understanding of Business Analysis methodologies, Agile delivery, and requirements engineering.

Preferred Certifications

• ECBA, CBAP, PMI-PBA, or Agile Business Analysis certification.
• Microsoft AI Fundamentals (AI-900)
• Microsoft Azure Fundamentals (AZ-900)
• Microsoft Copilot Fundamentals
• Training in Responsible AI, Product Ownership, Data Governance, or Design Thinking is advantageous.

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Experience

Essential

• Minimum 5 years’ experience as a Business Analyst.
• Experience delivering Digital Transformation initiatives.
• Experience working within Agile delivery environments.
• Strong stakeholder management and workshop facilitation skills.
• Proven experience producing:
  • BRDs
  • BRSs
  • User Stories
  • Acceptance Criteria
  • Process Maps
  • Test Scenarios

Preferred

Experience supporting projects involving:

• Artificial Intelligence
• Automation
• Analytics
• Digital Platforms
• Financial Services
• Banking
• Telecommunications
• Enterprise Technology

Experience with Microsoft Copilot, AI Agents, Azure AI Services, Power Platform, or similar AI technologies is advantageous.

Experience using Jira, Azure DevOps, Confluence, Miro, Visio, Power BI, or comparable collaboration and delivery tools.

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Technical Knowledge

AI Fundamentals

• Artificial Intelligence
• Machine Learning
• Generative AI
• Conversational AI

Large Language Models

• Enterprise applications
• Business capabilities
• Practical limitations

Prompt Engineering

• Prompt creation
• AI output evaluation
• Business context validation

AI Agents

• Agent workflows
• Human oversight
• Business automation

Retrieval-Augmented Generation (RAG)

• Grounded AI responses
• Knowledge-based AI