AI Learning and Enablement Lead

Level: Director L5

Function: Learning and Talent Development / People Function

Reports to: Head of Learning and Development

Matrix / line management: Learning Managers supporting AI deployment and delivery

Location: Contract London

Core outcome

Build practical, responsible AI capability across the firm and help colleagues apply approved AI tools in ways that improve quality, consistency, and productivity.

Role Purpose

The AI Learning and Enablement Lead is responsible for building the learning and adoption system that helps Kroll colleagues use approved AI tools safely, confidently, and effectively in the flow of work. The role translates enterprise AI strategy, approved tools, responsible use expectations, business priorities, and service-line needs into practical learning pathways, deployment playbooks, role-based resources, champion, citizen development, builder enablement, manager toolkits, and adoption measurement.

This is not a traditional training role with AI added on. It is an enterprise enablement and behaviour-change role, with a clear focus on real workflows, measurable impact, and responsible use.

Key Responsibilities

• Develop and maintain a practical AI learning and enablement roadmap aligned to Kroll’s enterprise AI strategy, approved tools, governance expectations, and service-line priorities.
• Own the AI learning deployment model across onboarding, activation, role-based application, workflow integration, champion, citizen development, builder enablement, manager support, adoption reinforcement, and impact measurement.
• Design, build, and deliver AI learning pathways covering AI literacy, responsible use, prompt practices, role-based use cases, workflow application, advanced features, and practical adoption for priority teams.
• Embed AI learning into Kroll University, Oracle Learning, onboarding journeys, learning taxonomy, content governance, learning communications, and reporting.
• Develop and maintain AI Deployment Playbooks.
• Create practical learning assets including prompt packs, first-five-prompts resources, role-based workflow guides, use case exercises, manager toolkits, leader briefing materials, champion, citizen development, builder enablement resources, responsible AI guidance, and adoption communications.
• Translate technical AI concepts into clear, usable guidance for non-technical audiences, including job aids, live sessions, digital resources, leader talking points, and learner communications.
• Partner with IT, Data/AI, Risk, Legal, Compliance, Privacy, HR, Communications, and business leaders to ensure learning content reflects approved tools, safe-use guidance, data protection expectations, escalation routes, and governance requirements.
• Support tool adoption for approved enterprise AI tools, including ChatGPT Enterprise, Microsoft Copilot, GitHub Copilot, and future approved platforms where learning support is required.
• Design structured enablement for new AI license users, including launch communications, feature awareness, onboarding, responsible use reinforcement, prompt practice, use case discovery, and impact survey loops.
• Support the AI champion citizen development, builder, and practitioner ecosystem by designing champion, citizen development, builder onboarding, learning toolkits, use case discovery templates, prompt challenge materials, barrier reporting templates, and outcome reporting guidance.
• Work with the Community Manager, AI Champions, Citizen Developers, Builders Business Engagement Lead, Learning Managers, and Change/Communications partners to support peer learning, use case sharing, prompt libraries, showcases, and adoption campaigns.
• Support managers and leaders with practical enablement resources that help them set expectations, model responsible AI use, review AI-assisted outputs, and embed AI into team workflows.
• Use data and feedback to measure learning reach, confidence, adoption, active use, quality of use, business impact, time saved, workflow improvement, and content effectiveness.
• Monitor external AI learning trends, market practice, and vendor resources, bringing practical recommendations back into LTD without chasing novelty for its own sake.
• Maintain AI learning content governance, including version control, review cadence, refresh cycles, retirement of outdated guidance, taxonomy tagging, and alignment with approved tools and policies.