AI Business Analyst

About the role

CoorB is looking for an experienced AI Business Analyst with hands-on AI experience to identify, assess and shape practical AI-enabled solutions. You will work closely with business and technical stakeholders to turn real operational needs into clear requirements, measurable success criteria and responsible implementation approaches.

Key responsibilities

• Assess business processes to find automation and AI opportunities, weighing benefits, costs, feasibility, dependencies and risks.
• Lead discovery sessions with business stakeholders on pain points, process variations, data availability, controls and desired outcomes.
• Write functional requirements and user stories for AI features such as document intelligence and extraction, conversational assistants, classification and decisioning, and intelligent routing.
• Define quality standards for AI features, including accuracy thresholds, confidence handling, fallback paths, human-in-the-loop processes, exception management and audit requirements.
• Support model evaluation by checking AI outputs against real cases, building representative test datasets, documenting results and noting limitations.
• Turn AI findings into recommendations, acceptance criteria, process changes, operating procedures and implementation priorities.
• Explain AI capabilities, assumptions, risks and limitations to non-technical stakeholders, and recommend rules-based or conventional solutions where appropriate.
• Work with product, engineering, data, security, compliance and operations teams through design, delivery, testing, rollout and continuous improvement.

Required profile

• Hands-on experience with AI-enabled products, prototypes or solutions, including use-case discovery, requirements definition, testing or evaluation.
• 5+ years in business analysis, process improvement, product analysis, automation, digital transformation or a related role.
• Strong ability to map processes, identify inefficiencies, assess options and build business cases covering value, cost, risk and feasibility.
• Experience writing functional requirements, user stories, acceptance criteria, process flows and exception scenarios.
• Practical understanding of AI concepts such as model outputs, confidence levels, evaluation datasets, accuracy, false positives and negatives, human oversight and responsible fallback handling.
• Familiarity with data privacy, security, compliance or audit considerations is an advantage.