AI Enablement Lead

AI Enablement Lead

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.

Responsibilities include leading structured intake and discovery with faculty and staff, mapping current workflows, and identifying the problem, users, constraints, and baseline performance. You will translate validated needs into engagement briefs, plan and lead cross-functional AI enablement engagements, build or coordinate prototypes, define evaluation plans, coordinate privacy, security, data governance, accessibility, procurement, and policy reviews, facilitate working sessions, demonstrations, documentation, and role-specific learning, create reusable patterns, templates, and decision records, track engagement capacity, progress, outcomes, and risks, and guide the work of the Applied AI Engineer, student fellows, and rotating domain experts assigned to engagements.

Required qualifications include a 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 workstreams and cross-functional contributors with clear ownership and follow-through, including presenting progress, results, and recommendations to executives and leadership groups, working knowledge of responsible AI, privacy, security, accessibility, and data governance, and ability to learn and appropriately use new technologies to improve processes, communication, and outcomes, including AI-enabled tools where relevant to the role, consistent with university policy and job responsibilities.