AI Enablement Coach
On-site · Atlanta, Georgia, United States
Job Summary
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.
Required Qualifications
- 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, LangChain, 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
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