Job Description:
Training & Development Designer – AI Enablement
Location: Hybrid (4 days/week onsite)
Position Overview
The Training & Development Designer will lead the design, development, and delivery of enterprise-scale learning experiences that build AI fluency, accelerate adoption of Microsoft 365 Copilot, and help employees responsibly integrate approved AI tools into their daily work.
This role requires a strategic learning and organizational change leader who can translate complex technologies into practical, role-based learning experiences; anticipate adoption barriers; drive behavior change; and support a global workforce of 150,000+ employees through sustained transition and enablement efforts.
The ideal candidate will facilitate engaging learning experiences for employees, leaders, champions, and specialized user groups while ensuring training initiatives align with broader change management, communications, readiness, and adoption strategies.
Key Responsibilities
Learning Strategy & Curriculum Development
- Develop enterprise-wide training and change enablement strategies to support large-scale AI adoption across regions, functions, roles, and varying levels of digital maturity.
- Design role-based learning curricula for Microsoft 365 Copilot, Copilot Studio concepts, and other approved AI tools.
- Create learning journeys that guide employees from awareness to confidence, practical application, responsible usage, and sustained behavior change.
- Build scalable, reusable learning assets that can be delivered consistently across global audiences.
Training Delivery & Facilitation
- Facilitate virtual and in-person training sessions, workshops, office hours, labs, and learning events for employees, leaders, champions, and targeted user groups.
- Create engaging and polished learning experiences that encourage hands-on practice and real-world application.
- Develop high-quality video content that reinforces AI learning concepts and accelerates user adoption.
Change Enablement & Adoption
- Identify audience-specific change impacts, adoption barriers, resistance points, capability gaps, and reinforcement needs.
- Translate change insights into targeted learning and enablement plans.
- Support champion networks, peer-learning communities, and train-the-trainer initiatives to scale learning and reinforce adoption.
- Equip managers, champions, and facilitators with tools, messaging, and resources needed to drive change within their organizations.
Stakeholder Collaboration
- Partner with technology, change management, communications, HR, security, and business stakeholders to align training content with enterprise priorities, governance requirements, and change plans.
- Translate technical AI capabilities, risks, and use cases into accessible training materials for both technical and non-technical audiences.
Measurement & Continuous Improvement
- Measure training effectiveness using adoption metrics, learner feedback, readiness indicators, sentiment analysis, and usage data.
- Continuously refine curriculum, delivery methods, and enablement strategies to improve adoption outcomes and business impact.
- Monitor emerging AI capabilities, governance updates, and employee needs to keep learning content current and relevant.
Core Competencies
Enterprise Learning & Change Strategy
Designs scalable learning and adoption programs that meet the needs of a large, geographically dispersed workforce.
AI Fluency
Understands how Microsoft Copilot and other AI tools can improve productivity, collaboration, creativity, and decision-making.
Audience-Centered Design
Creates content tailored to various roles, functions, skill levels, regions, use cases, and levels of change readiness.
Facilitation Excellence
Develops hands-on learning experiences that build confidence through practical application and ongoing practice.
Change Leadership
Anticipates adoption challenges, supports leaders and champions as change advocates, and implements reinforcement strategies that sustain new behaviors.
Responsible AI Enablement
Promotes secure, ethical, policy-compliant, and responsible use of AI technologies.
Data-Driven Decision Making
Uses learner insights, readiness signals, adoption data, and performance metrics to optimize learning outcomes.
