AI Trainer

HonorHealth•Virtual - Arizona, AZ

2d•Remote

About The Position

The AI Trainer owns the human side of HonorHealth's AI rollout: turning a powerful set of tools into confident, safe, and productive daily habits across the organization. This is a hands-on enablement role, not a one-time training gig. The AI Trainer designs and delivers tiered curriculum, runs live sessions and office hours, builds a self-serve learning library, cultivates a network of departmental champions, and continuously refines what we teach based on what's actually landing with users. Working alongside the AI Director and Program Manager – Workforce Productivity, the AI Trainer ensures that adoption, governance, and enablement move as one coordinated program. Because HonorHealth operates in a healthcare environment, responsible and compliant use—including HIPAA considerations and safe data-handling practices—is woven into everything this role teaches. The AI Trainer is accountable for measurable adoption outcomes, user confidence, and sustainable behavior change across all AI-enabled workflows.

Requirements

Bachelors in Business or 4 years equivalent experience, Communications, Linguistics, Human Resources, Computer Science, Analytics, Education, Healthcare Administration, or related field Required
2 years, Experience in training, enablement, learning & development, instructional design, or closely related role Required
Demonstrated ability to teach software/technology to non-technical audiences and meet people at their skill level Required
Hands-on experience with prompt engineering, M365 Copilot, or applied AI tools (or demonstrated rapid ability to master) Required

Nice To Haves

Masters in related field Preferred
Experience supporting enterprise software or AI tool rollouts at scale (hundreds of users) Preferred
Familiarity with healthcare operations and data-sensitivity requirements (HIPAA/PHI awareness) Preferred
Experience building or running a champions/ambassador program Preferred
Comfort working with adoption analytics and using data to guide enablement decisions Preferred
AI, prompt engineering, change management, or related coursework/certification Preferred

Responsibilities

Build a tiered curriculum (beginner through power-user) that progressively develops AI proficiency across the organization.
Develop role-based training paths so clinicians, analysts, operations managers, and other roles each receive content relevant to their actual workflows.
Deliver live training sessions, recorded modules, quick-reference guides, and maintain a curated prompt library.
Run recurring office hours and drop-in sessions to unblock users in real time and reinforce learning.
Create clear, engaging learning materials from scratch that meet users at their current skill level.
Onboard each Wave cohort with a structured ramp: kickoff, hands-on sessions, follow-up, and reinforcement.
Track where each cohort is on the adoption curve and intervene where usage stalls or confidence lags.
Capture and recycle real internal success stories to build momentum for later Waves.
Support end-user adoption through practical guidance, examples, coaching materials, and usage recommendations.
Help users understand where AI adds value, where human review is required, and where escalation remains necessary.
Identify, train, and support a network of departmental AI champions who extend enablement beyond what one trainer can reach.
Equip champions with materials, talking points, and a feedback channel back into the program.
Coordinate regular champion check-ins to share best practices, address emerging challenges, and scale adoption.
Recognize and celebrate champion contributions to build engagement and program visibility.
Teach safe data-handling practices with clear guidance on PHI, HIPAA considerations, and what should and shouldn't be put into AI tools.
Partner with Information Security to translate governance policy into plain-language, practical user guidance.
Make responsible-AI practices a default part of every training module, not a footnote.
Reinforce human review requirements, scope limits, and role clarity in all AI-supported work.
Define and track enablement metrics: active usage, feature adoption, session attendance, satisfaction scores, and self-reported productivity gains.
Use adoption data and user feedback to continuously refine content and target underperforming areas.
Report progress and insights to program leadership on a regular cadence.
Maintain the program's living documentation so the enablement system keeps running as the team and tools evolve.
Work in lockstep with the AI Director and Program Manager – Workforce Productivity so enablement, communications, and governance stay aligned.
Performs other duties as assigned.