Job Description

Job Summary

Business Analyst (BA) – Agentic AI / Autonomous Process Automation

Role Summary

The Business Analyst – Agentic AI bridges business operations and AI engineering teams to identify, define, and deliver agentic AI solutions that automate multi-step enterprise workflows. This role owns process discovery, requirements, controls, and value realization for autonomous agents—ensuring solutions are feasible, governed, auditable, and adoption-ready.

Key Responsibilities

Key Responsibilities

Key Responsibilities

1) Opportunity Identification & Feasibility

• Identify and prioritize processes suitable for agentic automation based on business value, feasibility, and risk.
• Lead structured assessments covering process boundaries, triggers/endpoints, stakeholders, and exception complexity.
• Evaluate systems/data integration readiness and document dependencies that affect automation design.

2) Process Analysis & Requirements Engineering (Agentic-Specific)

• Produce As‑Is / To‑Be process maps and define agentic workflow steps including decision points and exception paths.
• Translate business needs into backlog-ready epics, user stories, and acceptance criteria suitable for AI build teams.
• Define agent roles, responsibilities, handoffs, and orchestration logic (multi-agent sequencing, escalation, retries, fallbacks).
• Document tool/system actions agents will perform (APIs, UI automation, mailbox/doc intake, system updates) and required integrations.

3) Human-in-the-Loop (HITL), Controls & Governance

• Design HITL checkpoints (approve/reject/override) for high-risk actions and specify confidence thresholds and guardrails.
• Partner with Risk/Compliance and IT to ensure solutions meet traceability, auditability, and security-by-design expectations.
• Define monitoring requirements: agent performance metrics, exception tracking, drift signals, and operational runbooks.

4) Data / Knowledge Readiness for Grounded Automation

• Specify requirements for enterprise knowledge grounding (SOPs, policy docs, structured/unstructured sources) and validation rules.
• Collaborate with data/engineering teams to define data quality needs, lineage, and access controls for automation outcomes.

5) Testing, UAT & Adoption

• Own business testing strategy: UAT planning, scenario design, exception test sets, and acceptance thresholds for AI outputs.
• Support rollout readiness through change impact assessment, training content, communications, and adoption feedback loops.

6) Value Tracking & Continuous Improvement

• Define and track KPIs such as touchless rate, cycle time reduction, accuracy, exception rate, and business impact; maintain value realization reporting.
• Drive continuous improvement by analyzing exceptions and recommending workflow tuning and policy updates.

Skill Requirements

Core Deliverables

• Agentic AI Feasibility / Readiness Assessment (process boundary + risk + value)
• Process Maps (As‑Is/To‑Be), Decision Logs, Exception Taxonomy
• Agent Specs: roles, orchestration flow, integration points, tool actions
• HITL + Controls Matrix (approval points, thresholds, audit fields)
• UAT Plan + Test Scenarios + Acceptance Criteria
• KPI / Value Dashboard + Post-go-live Optimization Backlog

Other Requirements

Required Qualifications

• 7+ years experience in Business Analysis / Process Transformation / Automation
• Strong expertise in process mapping, requirements elicitation, stakeholder management, and Agile delivery.
• Working understanding of agentic AI concepts (multi-step workflows, autonomous actions, orchestration, human oversight).
• Ability to translate business requirements into clear functional specs for AI/engineering teams.

Preferred Qualifications

• Experience delivering AI/automation initiatives with HITL controls and governance.
• Familiarity with RAG / enterprise knowledge grounding, document automation, and workflow orchestration patterns
• Exposure to agentic/LLM tooling ecosystems (examples may include orchestration frameworks and copilots).

Key Skills

Business & BA Skills

• Process discovery, SIPOC / BPMN thinking, requirements (BRD/FRD), user stories, acceptance criteria
• Value case development, KPI definition, benefits tracking

Agentic AI Skills

• Agent workflow decomposition, decision boundary definition, exception design, escalation logic
• HITL design: approval checkpoints, confidence thresholds, audit trails
• Governance mindset: traceability, responsible AI, security and compliance-by-design