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 shepherding high-impact
use cases, and embedding AI copilots into daily workflows – so that the organization becomes a genuinely AI-augmented & human-led.
Requirements
Technical Competencies
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
Education Requirements
A
Bachelor’s degree in a relevant field (Computer Science, Business,
Communications or related; a Master’s is an added advantage), with 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
