Key responsibilities

- Run inception workshops with business teams.
- Document current-state process maps.
- Identify high-value agentic AI use cases.
- Define success metrics, ROI assumptions, and adoption goals.
- Help business teams redesign workflows around human-in-the-loop AI.
- Support pilot users, collect feedback, and drive adoption.
- Build prompt literacy and agent literacy across business teams.
- Identify role-level AI skills gaps as part of the paired engineering phase.
- Co-design AI agents with business and engineering teams to automate or augment workflows.
- Translate business processes into agent workflows, including task decomposition, decision logic, and tool usage.
- Define agent roles, boundaries, escalation paths, and human-in-the-loop controls.
- Collaborate with engineering teams on agent requirements, data needs, APIs, and integrations.
- Develop and iterate prompts, instructions, and evaluation criteria for AI agents.
- Establish guardrails for responsible AI use, compliance, and risk mitigation in agent design.
- Test, validate, and monitor AI agent performance against defined KPIs and business outcomes.
- Drive continuous improvement of deployed agents based on feedback, telemetry, and usage patterns.





Skills required

- Business process mapping and facilitation.
- Design thinking and value-stream analysis.
- KPI and ROI definition.
- Solid communication and training skills.
- Familiarity with AI capabilities, limitations, and responsible-use practices.
- Change management and adoption planning.
- Ability to work with non-technical business stakeholders.
- Understanding of agentic AI concepts (multi-step reasoning, tool use, orchestration, memory, autonomy levels).
- Experience designing or contributing to AI agent workflows or automation solutions.
- Prompt engineering and prompt orchestration for task execution.
- Ability to translate business requirements into agent specifications and interaction flows.
- Familiarity with AI agent frameworks and platforms (e.g., Copilot Studio, Lang Chain, Semantic Kernel, or similar).
- Knowledge of API integration concepts, data flows, and system interactions.
- Ability to define evaluation frameworks for agent quality, accuracy, and reliability.
- Awareness of AI governance, safety, and risk controls specific to autonomous or semi-autonomous systems.

📌 AI Enablement Coach (Tamil Nadu)
🏢 NCR Voyix
📍 Tamil Nadu