The AI Business Analyst supports AI innovation delivery by helping teams convert business problems intoresearch-backed requirements, testable hypotheses, and well-documented outcomes.
This role works with business partners, product owners, service designers, and technical teams to runstructured discovery, support rapid experimentation (including lightweight prototypes), and maintainstrong documentation and reporting.
The purpose of the role is to reduce ambiguity early-so AI initiatives are built on evidence, clear userneeds, and measurable success criteria, which is critical to delivering innovation that actually works in realworkflows.
AI Discovery Documentation and Evidence-Based Reporting
- Support the Business Partner and Product Owner in creating and maintaining innovation documentation, including problem statements, hypotheses, user stories, product backlog items, PRDs, and basic evaluation criteria.
- Maintain detailed documentation of requirements, assumptions, decisions, experiment outcomes, and test results.
- Support data-informed decision-making by developing simple dashboards/reports to track experiment progress, adoption signals, and delivery milestones.
- Provide regular progress reporting to PMO, including what was tested, what was learned, and what decisions/risks are emerging.
Support Agile Team Delivery for AI Use Cases
- Work closely with the Product Owner and squad to clarify requirements during sprints, ensuring work items are testable and measurable.
- Participate in agile ceremonies (planning, stand-ups, retrospectives, showcases) and support sprint readiness through clear requirements and acceptance criteria.
- Collaborate with Service Designer/UX to map user journeys and identify where AI can create value (and where it needs guardrails).
- Support sprint tracking (velocity, burndown, outcomes) and highlight blockers-especially those tied to data availability, workflow fit, or model/tool limitations.
- Use agile tools (e.g., JIRA) to maintain clear and traceable requirements.
Stakeholder Research, Workshop Facilitation, and Problem Framing
- Conduct interviews, workshops, and lightweight research to understand business needs and translate them into AI-ready problem statements.
- Help define use cases, edge cases, exceptions, and constraints, including operational realities and user trust expectations.
- Translate needs into detailed specifications/user stories with acceptance criteria that include both functional outcomes and basic AI success measures (where relevant).
- Support the Business Partner, Product Owner, and Service Designer in workshops and meetings, capturing decisions and follow-ups clearly.
Solution Analysis, Prototyping Support, and Testing
- Support early validation through simple prototypes, feasibility checks, and gap analysis, using available tools to make concepts tangible.
- Assist in defining acceptance criteria and supporting UAT, ensuring validation includes user usefulness and quality expectations, not just functionality.
- Prepare UAT scripts with QA testers collate results and consolidate defects/feedback for Product Owner/Tech Lead triage.
- Document test findings, key issues, and recommended next steps.
Support Change Management and Continuous Improvement
- Collaborate with stakeholders to prepare SOPs, training guides, and user communications for AI-enabled workflow changes.
- Gather feedback post-implementation and support continuous improvement loops (e.g., workflow tweaks, clarification of requirements, backlog enhancements).
- Identify opportunities for process optimization and improved user experience.
- Participate in team discussions and knowledge sharing contribute to a positive, solution-oriented culture.
Skills for Success:
- Bachelor's degree from an established university
- At least 2-4 years of relevant experience in business analysis / product support / project delivery
- Exposure to agile delivery in a squad/team setting (writing stories, supporting ceremonies, basic sprint reporting).
- Experience gathering requirements through interviews/workshops and producing clear documentation (BRD/PRD/user journeys/user stories).
- Some involvement in testing support (UAT coordination, test scripts, defect logging) and stakeholder follow-ups.
- AI innovation discovery basics: can help frame problems into hypotheses, define simple success measures, and capture assumptions/constraints clearly.
- Requirements & documentation quality: strong in user stories, acceptance criteria, process maps/user journeys, and traceability from need build test.
- Agile BA toolkit: comfortable with JIRA (or similar), sprint support, dependency tracking, and producing clear status reporting/dashboards.
- Structured communication: writes and speaks clearly can summarise workshops into decisions, actions, and next steps.
- Stakeholder handling: confident working with multiple teams, following up, and keeping people aligned on requirements.
- Curiosity & learning mindset: asks good questions, is comfortable with ambiguity, and iterates based on new information.
