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.