AI Enablement Coach

Job description

**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. - Strong 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.