Senior Manager, AI Training & Enablement

About the Role:

Wiley Research is executing a three-year transformation, from a strong publishing foundation, through a connected intelligence layer, to an essential research infrastructure. AI runs through every stage. The value comes when more than 1,500 Research colleagues use it well in their daily work, and when teams are ready to adopt the agents and workflows Wiley builds.

The Senior Manager, AI Training & Enablement leads AI adoption across Research. The role has two focus areas: (1) moving colleagues toward everyday use of Copilot and Claude, and (2) deepening adoption of agents and AI-enabled workflows in the teams ready for them. With a population this size, focus is central to the job. The role chooses where to invest, builds simple and repeatable plans, and extends its reach through managers and peer champions.

The role works closely with the AI Center of Excellence, which owns governance, controls, and the building of agents, and with the BM&SP crew team, which experiments with operational solutions enabled by AI. It does not set AI policy, approve tools, or build agents. It makes sure colleagues know what is available and permitted, how to use it in their own work, and what is changing in how their team operates.

The role is hands-on: it designs and produces the learning materials and leads live sessions directly, concentrating that effort on priority segments. Materials are deliberately light: one-page role-based starter guides, short live and recorded sessions, example prompts and workflows, and manager talking points. Full curricula are not expected.

This role will evolve as Research evolves. Wiley is in the middle of a transformation, so the scope and priorities described here will be further defined with the VP once the person starts, and will keep changing as the organization does. The right person is flexible and comfortable adjusting course.

The responsibilities below describe what this role may take on. They are possibilities rather than a fixed list. We will confirm scope and sequencing with the person in the role, and refine them as we go, as the organization and the transformation evolve.

• Build and maintain a Research adoption plan: role-family segments, target use cases, sequencing of waves, and success measures. Review it with the VP each quarter.

• Apply clear prioritization criteria, such as volume of repeatable work, leader sponsorship, team readiness, and availability of proven use cases. State plainly what is deferred and why.

• Establish baseline usage with data from the AI Center of Excellence and tool reporting, and define a short set of adoption measures.

• Keep the plan aligned with Research strategic priorities and with the operating rhythm run by BM&SP.

• Define learning objectives and key learning points for each priority audience with Research leaders and subject-matter experts, so every asset targets a specific use case and behavior.

• Develop short, practical materials in plain language that make AI tools and workflows simple: one-page guides, example prompts and workflows, recorded demos and short videos, and micro-learning modules where useful. Use Wiley templates and standards.

• Extract core information from AI Center of Excellence guidance, existing procedures, subject-matter experts, and crew team pilots, and turn it into role-ready material.

• Pilot materials with a small group and obtain the needed approvals, including AI Center of Excellence review of policy content, before release.

• Design for accessibility and varied learning styles, including assistive technologies.

• Research learner and business needs for each audience, and refine materials from feedback. Keep content current as tools, features, and guardrails change, and retire what is out of date.

• Build role-based starter plans for each priority role family: top use cases, example prompts, short sessions, and office hours.

• Schedule, coordinate, and deliver short live and virtual sessions and office hours, timed to tool rollouts and key points in the Research calendar.

• Track training requests, attendance, and completion, and use the data to decide what to build, adjust, or retire next.

• Build and support a network of peer champions across Research teams, with a light toolkit and regular check-ins. Coach champions and any colleagues supporting this work on content and delivery standards.

• Equip people managers with talking points and small team activities so adoption continues after launch.

• Translate guardrails from the AI Center of Excellence into plain-language guidance colleagues can follow in their daily work.

• Collect questions and barriers from colleagues. Resolve what content can fix and escalate the rest to the AI Center of Excellence.

• Identify teams ready to move from individual use to shared agents and redesigned workflows, and sequence that work with the AI Center of Excellence and the crew team.

• Prepare receiving teams for new agents and workflows with quick-start materials and hands-on walkthroughs: what changes, who does what, how to escalate issues, and how success will be measured.

• Turn proven crew team pilots into short, repeatable guides covering what the solution does, who it is for, and how to start.

• Gather user feedback and return it to the AI Center of Excellence and the crew team to improve agent and workflow design.

• Support the clean transition of successful pilots into their permanent organizational home, with documented handoff.

• Apply a change approach proportionate to the size of each change: impact assessment, sponsor engagement, attention to concerns, and reinforcement after launch.

• Work with HR and Research leaders when AI adoption affects roles, responsibilities, or ways of working. HR owns workforce decisions.

• Coordinate with BM&SP communications and Research leadership so AI updates arrive as a coherent story, not a stream of separate announcements.

• Maintain a single, easy-to-find home for Research AI guidance, examples, and upcoming sessions, for example a SharePoint site.

• Track adoption breadth (who is using), depth (how often and for which use cases), and workflow adoption. Add short pulse surveys and colleague stories.

• Assess whether the materials and sessions work: completion, feedback, and change in use. Use the findings to improve the next wave.

• Report monthly to the VP and quarterly to the Research Leadership Team, with recommendations on where to invest, adjust, or stop.

• Retire or revise approaches that are not driving adoption.

• AI Center of Excellence: agree a working model for how adoption needs, agent releases, and policy updates flow between teams.

• BM&SP crew team: stay close to active experiments so adoption planning starts before solutions are ready.

• HR, Research communications, Technology & Operations, and Legal: engage as needed, and reuse existing training and change resources before building new ones.

• 5+ years of experience in technology adoption, change management, internal training, learning design, or enablement in a large, matrixed organization.

• Demonstrated change management experience: a track record of shifting behavior and ways of working across large, varied groups.

• Hands-on experience designing and developing learning content in multiple formats (written guides, recorded video, e-learning), including defining learning objectives, piloting, and improving from feedback.

• Hands-on fluency with Copilot, Claude, or similar generative AI tools, and the ability to show practical use credibly to non-technical colleagues.

• Skill at designing simple, role-based adoption plans and materials that colleagues use, rather than full curricula.

• Experience delivering engaging live and virtual training to varied audiences.

• Strong prioritization judgment: able to focus limited capacity on the segments and use cases with the most impact.

• Ability to influence and align leaders, managers, and technical partners without direct authority.

• Excellent written and verbal communication in plain, direct language, with the ability to make complex or technical subjects simple.

• Comfort working with usage data and simple adoption and training measures, such as attendance, completion, and feedback.

• Flexibility and adaptability: comfort in a role that will evolve as the organization and the transformation evolve, and the judgment to adjust priorities as they change.

• A qualification in training, teaching, or learning design, or equivalent experience.

• Experience with a learning management system and with building SharePoint pages or similar guidance sites.

• Experience designing accessible learning content for varied learning styles.

• Experience in research publishing, scholarly communication, or information services.

• Experience supporting adoption of AI agents, workflow automation, or similar process change.

• Experience building and running champion or peer-network programs.

• Familiarity with Wiley or similar globally distributed organizations.

• Formal change management training or certification.

Salary Range: