AI Change Management & Education Specialist

Tampa General Hospital•Tampa, FL

About The Position

The AI Change Management & Education Specialist drives adoption of AI tools across clinical and operational teams at Tampa General Hospital. Working within the Office of the Chief Medical Informatics and AI Strategy Officer, the specialist develops and delivers training, communication, and change management programs that help clinicians and staff use AI capabilities safely and effectively. The role partners with deployment teams, clinical leadership, and end users to prepare the organization for new AI tools, support workflow integration, and gather feedback that improves adoption and outcomes. The role is responsible for performing job duties in accordance with the mission, vision, and values of Tampa General Hospital.

Requirements

Bachelor's degree in Instructional Design, Nursing, Education, Health Informatics, Healthcare Administration, Communications, or a related field.
Five years of experience in change management, training and education, clinical informatics adoption, or healthcare operations, including experience supporting technology or workflow change in a healthcare setting.
Expertise in change management methodology and adult learning or instructional design.
Strong communication, facilitation, and relationship-building skills.
Ability to translate technical AI concepts for clinical and operational audiences.
Understanding of clinical workflows and healthcare operations.
Comfort working across multidisciplinary teams in a fast-changing environment.
Change Management certification (Prosci or ADKAR).
Relevant instructional design credential.
Clinical licensure for candidates with a clinical background; candidates with an RN degree need an RN license.

Responsibilities

Develop and execute structured change management plans for AI deployments, aligned with implementation timelines, operational readiness, and the organization's AI governance framework.
Conduct readiness and impact assessments; identify affected stakeholders, workflow changes, adoption barriers, and role-specific learning needs.
Build and sustain executive sponsorship, local champion, super-user, and peer-support networks across clinical and operational areas.
Distinguish change-related resistance from substantive workflow, safety, privacy, security, or ethical concerns and route issues to the accountable owner.
Design and deliver role-based, accessible training in classroom, virtual, self-paced, and at-the-elbow formats using adult-learning and instructional-design practices.
Develop, version, and maintain curricula, simulations, quick-reference guides, tip sheets, workflow job aids, and other educational assets.
Teach responsible and appropriate AI use, including intended and prohibited uses, known limitations, verification expectations, human accountability, privacy requirements, and escalation pathways.
Define learning objectives and competency measures; track completion, proficiency, appropriate use, common errors, and refresher-training needs.
Develop and execute clear, audience-specific communications about AI capabilities, workflow changes, expectations, implementation plans, and support resources.
Provide go-live and stabilization support across hospital, clinic, and ambulatory settings, including nonstandard hours when required for high-impact deployments.
Partner with deployment, informatics, clinical, operational, governance, privacy, security, and support teams to align education and change activities with safe workflow integration.
Collect, synthesize, prioritize, and route feedback from users and leaders to product, deployment, engineering, validation, support, and governance owners.
Measure adoption, sustained use, proficiency, user confidence, support burden, workflow adherence, satisfaction, and benefits realization; communicate findings and risks to stakeholders.
Update communications and education when tools, models, workflows, policies, risks, or known limitations change.
Continuously improve change, communication, and education approaches using measured outcomes, user feedback, incident learnings, and deployment experience.