AI Enablement Lead

How to Apply

A cover letter and resume are important submissions for the hiring team to get a sense of your experience. In the cover letter, in two pages or less, please let us know how this role aligns with your career aspirations and skill set. Submit both a cover letter and resume as one file (because of system limitations).

Who We Are

About The Center For Academic Innovation Through curricular innovation, tools for student success, and educational research and analytics, the University of Michigan Center for Academic Innovation is building the future of education. Our vision is a future in which education connects and empowers learners everywhere to reach their full potential throughout their lives. To realize our vision, we make it our mission to collaborate across campus and around the world to create equitable, lifelong educational opportunities for learners everywhere.

About The Technology Infrastructure And Support Team The TIS team builds and maintains the technology behind CAI’s learning, media, and IT work. Our work spans Michigan Online infrastructure, cloud storage, cybersecurity, disaster recovery, studio production, XR, AV systems, media asset management, and internal support. TIS is also home to the AI Enablement Team, which partners with teams across the Center to test promising ideas and turn the ones that work into tools and workflows people can rely on. Working closely with Creative Studios, Operations, and product engineering teams, we keep CAI’s systems dependable and secure so learners, faculty, and staff can focus on their work.

For more information, please visit our website: Academic Innovation.

Responsibilities*

• Lead structured intake and discovery with faculty and staff, map current workflows, and identify the problem, users, constraints, and baseline performance
• Translate validated needs into engagement briefs with scope, owners, success measures, risks, dependencies, and a clear handoff plan
• Plan and lead cross-functional AI enablement engagements from discovery through prototype, evaluation, adoption, and operational handoff
• Build or coordinate prototypes using approved AI tools, APIs, data sources, and workflow platforms
• Define evaluation plans that test quality, reliability, efficiency, accessibility, and user adoption before scaling a solution
• Coordinate privacy, security, data governance, accessibility, procurement, and policy reviews with the appropriate CAI and university partners
• Facilitate working sessions, demonstrations, documentation, and role-specific learning that help teams adopt new workflows responsibly
• Create reusable patterns, templates, and decision records so successful approaches can be applied across CAI
• Track engagement capacity, progress, outcomes, and risks, and provide concise portfolio reporting to the AI Council and TIS leadership
• Guide the work of the Applied AI Engineer (post-graduate fellow), student fellows, and rotating domain experts assigned to engagements

What You’ll Do

The Center for Academic Innovation at the University of Michigan is seeking an AI Enablement Lead to turn high-value workflow problems into responsible, measurable AI-enabled solutions. Reporting to the Director of Technology Infrastructure and Support, you will lead engagements from discovery through evaluation, adoption, and operational handoff. You will combine consultative practice, hands-on prototyping, stakeholder facilitation, and disciplined delivery. The successful candidate is pragmatic, collaborative, and able to translate among domain experts, technologists, data and policy partners, and executive sponsors.

Required Qualifications*

• Bachelor’s degree or an equivalent combination of education and experience
• Three or more years leading applied technology, product or service design, process improvement, innovation, or consulting projects
• Demonstrated experience designing or building AI-enabled workflows with large language model tools, APIs, or automation platforms
• Experience mapping workflows, facilitating colleagues, turning ambiguous needs into clear requirements and a manageable delivery plan, and supporting teams through the workflow and role changes that come with new ways of working
• Experience setting evaluation criteria and using qualitative and quantitative evidence to decide whether to continue, revise, scale, or stop a project
• Ability to lead several workstream…