As the AI Adoption & Enablement Lead, this role is the primary change agent driving the human adoption of AI across the organization – turning the AI platform’s capabilities into real, everyday productivity gains as part of the organization's multi-year AI Workforce Transformation. The role bridges the AI engineering team and the wider business, translating what is technically possible into what is practical and valuable for teams.
The position requires a blend of technology fluency, communication, training and change management skills. It exists to accelerate safe, responsible AI adoption – cultivating a network of AI champions, sourcing and sheheparding high-impact use cases, and embedding AI copilots into daily workflows – so that the organization becomes a genuinely AI-augmented & human-led.
Key Responsibilities
- Adoption Strategy & Planning: Develop and own the AI adoption and enablement roadmap aligned to the transformation Blueprint, with clear targets for the AI Augmentation Index.
- Training Programme Delivery: Design and run training curricula, workshops, demos and onboarding for AI copilots and tools across business units.
- Enablement Content: Produce playbooks, quick-start guides, prompt libraries, FAQs and success stories that make AI easy to adopt and reuse.
- Champions Network: Build and coordinate a cross-functional AI champions network and community of practice; equip champions to drive adoption locally.
- Use-Case Pipeline: Source, qualify and prioritise AI use cases with business owners and the AI engineering team; track them from idea to adoption.
- Adoption Measurement: Define adoption KPIs, instrument usage tracking with the engineering team, and report progress and impact to leadership and the AI Steering Committee.
- Responsible-AI Enablement: Embed human-in-the-loop, transparency and responsible-AI guidance into all enablement; help users understand controls and escalation paths.
- Stakeholder Engagement: Partner with business-unit leaders, HR/L&D, Risk and Compliance and Internal Communications to land adoption initiatives smoothly.
- Feedback Loop: Gather user feedback and adoption barriers and channel them back to the AI engineering team to improve tools and experience.
- External Thought Leadership: Represent the organization selectively at partner forums and industry events and through content, strengthening thought leadership and the employer brand.
- Continuous Improvement: Stay current on AI adoption best practice and continuously refine enablement approaches.
Qualifications and Requirements
- Education: A Bachelor’s degree in a relevant field (Computer Science, Business, Communications or related; a Master’s is an added advantage).
- Experience: 5+ years in technology adoption, enablement, developer relations, change management or technical training – ideally including AI/ML or digital-transformation programmes.
- AI & Technology Fluency: Strong working understanding of AI/ML and Large Language Models – what they can and cannot do, prompt design, copilots and common enterprise use cases – sufficient to translate capabilities into practical business value (hands-on coding is not required).
- Change Management & Adoption: Proven track record of driving technology adoption or transformation – changing how people work, not just informing them – using recognised change-management approaches.
- Training & Facilitation: Excellent ability to design and deliver engaging training, workshops and demos for technical and non-technical audiences; skilled at producing playbooks and enablement content.
- Communication & Influence: Outstanding communication, storytelling and stakeholder-influencing skills; able to build trust and rally diverse teams around AI initiatives.
- Community Building: Experience building and energising communities of practice, champion networks or developer / user communities.
- Measurement & Insight: Ability to define and track adoption metrics (usage, proficiency, impact) and turn insight into action; comfortable with dashboards and simple analytics.
- Responsible AI & Domain Awareness: Awareness of responsible-AI, privacy and compliance principles and good knowledge of the financial-services context; able to advocate safe, ethical AI use.
- Certifications: Change-management (e.g. PROSCI), training / facilitation, or AI/ML foundational certifications are advantageous.
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