Role fit · AI Business Analysis
Business analysis for AI-enabled workflows, with validation and responsible-use thinking.
The foundations are professional work. The AI-specific practice is being built, and this page says which is which.
Why this fits
Most of what makes AI projects succeed or fail is ordinary business analysis: framing the problem, judging whether the data is ready, deciding where a human has to stay in the loop, and defining what a good output looks like. That part is verified work. The AI-specific practice on top of it is in progress.
Verified foundations
Professional work, verified against the résumé. This is business analysis and data work — not AI experience.
Gathers and documents business requirements with HR, Sales, and Operations stakeholders, translating them into structured analytical deliverables and KPI dashboards.
Cleans, validates, and preprocesses large operational datasets, then applies trend and regression analysis to surface patterns for non-technical stakeholders.
Acts as the bridge between technical teams and business stakeholders, translating analysis into executive-level briefings.
Built and evaluated classification and regression models during a data science internship, documenting findings in structured reports.
Building capability in AI opportunity framing, AI-specific requirements, human-in-the-loop design, and evaluation criteria for AI-assisted analysis.
Developing
Not experience. Currently being built, and listed so the boundary is visible.
- AI opportunity discovery and use-case prioritisation
- Framing problems for AI versus rules-based automation
- AI-specific functional and non-functional requirements
- Prompt design for analytical and documentation workflows
- Human-in-the-loop workflow design
- Data readiness and grounding requirements
- Evaluation criteria for accuracy, usefulness, consistency, and traceability
- Hallucination and uncertainty handling
- Privacy, security, bias, and responsible-use considerations
- Acceptance criteria for AI-assisted features
- Change impact, adoption, and stakeholder training
- Monitoring feedback and defining escalation paths
Relevant work
No case studies are published yet. What is in preparation, and the work itself on Experience.
Planned work
AI-assisted requirements and KPI triage
Proposed — not yet builtA planned prototype that turns synthetic stakeholder notes into candidate requirements and KPI mappings, with ambiguity flags and a human approval step before anything is finalised.
Can an AI assistant produce a useful first-pass requirements set without a human losing control of what gets agreed?
Roles this suits
- Junior AI Business Analyst
- AI Transformation Analyst
- GenAI Business Analyst
- AI Operations Analyst