AI Adoption Lead

Major Responsibilities

• Capacity Building & Adoption
• Provide hands-on coaching, training, and feedback loops that build staff comfort and competence with AI tools.
• Support teams in developing sophisticated, multi-step prompting workflows against real operational data.
• Monitor adoption and usage patterns, adjusting approach to ensure sustained uptake rather than one-off experiments.
• Identify and develop AI superusers within each team who can champion and sustain AI-enabled practices after direct engagement ends.
• Workflow Diagnosis & AI Solution Design
• Work alongside pillar teams to map and diagnose existing workflows, identifying high-value opportunities for AI-enabled improvement.
• Build and scale reusable AI-enabled solutions (e.g., prompt libraries, tools, workflow templates) that can be deployed across teams.
• Prototype and iterate quickly, moving from diagnosis to working solution with minimal overhead.
• Ensure solutions are practical, sustainable, and designed for handoff (not dependent on ongoing central support).
• Quality Assurance & Responsible AI Use
• Establish and maintain quality standards for AI-generated content and outputs across the department, ensuring accuracy, consistency, and alignment with Airbel’s mission.
• Develop review protocols and guardrails that help teams use AI confidently while managing risks around hallucination, bias, and data sensitivity.
• Contribute to organizational thinking about responsible AI use, ensuring Airbel’s practices reflect both ambition and rigor.
• AI Systems, Intake & Scalable Infrastructure
• Build and maintain repeatable playbooks, prompt libraries, and workflow templates that allow AI-enabled practices to scale across the department.
• Establish and run a lightweight intake and prioritization process for AI work across the department (e.g., request intake, triage criteria, and a clear queue/roadmap) so effort is driven by impact rather than volume or urgency of requests.
• Define “what good looks like” for AI-enabled solutions (e.g., quality checks, human review expectations, evaluation approaches, and documentation requirements) and ensure new solutions meet these standards before broad rollout.
• Document lessons learned, edge cases, and best practices in accessible formats that enable self-service adoption.
• Continuously update resources as AI capabilities evolve, ensuring the department’s toolkit reflects what’s currently possible.
• Organizational Learning & AI Transformation
• Feed insights, best practices, and emerging AI capabilities from departmental work into IRC’s broader AI Transformation efforts as directed by the Director of Strategy & Delivery.
• Stay current with frontier AI developments and continuously reassess what’s possible, proactively identifying new opportunities for the department.

Job Requirements

• 4–7 years of relevant professional experience (e.g., process improvement, product management, solutions engineering, research operations, or a related field), with increasing recent focus on applying AI tools to personal and/or team workflows.
• Deep fluency with frontier AI platforms (e.g., Claude’s full suite of tools and capabilities), including comfort executing sophisticated, multi-step prompting against real operational data / context and using AI to generate and iterate on functional tools (e.g., dashboards, automations, lightweight applications).
• Demonstrated ability to decompose how work actually gets done, determine which tasks AI should own versus humans, and reconstruct workflows accordingly.
• Strong facilitation skills, with the ability to bring non-technical colleagues along and build their confidence with AI tools.
• Strong written communication skills, particularly the ability to create clear, usable documentation and instructional materials.

Application Requirements

• Along with your resume and cover letter, please submit two examples of AI tools, workflows, or applications you have personally built in the past 6 months.
• This could include: Links to custom GPTs you have created, Screenshots or documentation of automation workflows you have built, Demos or code samples of agents you have configured, Brief descriptions of AI implementations you have led, including the tools used and outcomes achieved.
• We are specifically looking for evidence of recent hands-on building experience with current AI tools and platforms. The goal is to see how you approach solving problems with AI, not to evaluate you on years of experience in a field where many of these tools have only existed for months.