Define and execute the roadmap for internal AI tooling and capabilities; identify opportunities to improve productivity and business outcomes through AI; evaluate emerging technologies and recommend adoption approaches; and align AI initiatives with organizational objectives and technology strategy.
Own the lifecycle management of internal AI platforms and tools (development, deployment, monitoring); coordinate feature evaluations, pilot programs, rollout activities, and operational improvements; manage platform relationships and licensing considerations; and ensure services are reliable, secure, and delivering measurable value.
AI Governance & Risk Management
Establish and maintain governance frameworks, standards, policies, and guardrails for secure AI usage; collaborate with InfoSec, Data Protection, Legal, and Architecture teams to ensure AI adoption aligns with organizational requirements; and promote responsible, transparent, and ethical use of AI technologies.
Monitor AI platform consumption and costs; analyse usage trends and value realization; identify optimization opportunities; support budgeting and forecasting activities; provide recommendations for efficient resource utilization; and ensure AI investments are managed responsibly and sustainably.
Testing, Evaluation & Quality Assurance
Lead structured testing and validation of new AI capabilities and model releases; evaluate functionality, performance, accuracy, usability, security, and business value; document findings and recommendations; and ensure new capabilities meet agreed standards before wider adoption.
Training, Workshops & Community Building
Design and deliver AI training programs, workshops, demonstrations, and hands-on learning sessions tailored to Technology teams; develop learning resources and best practice guidance; establish communities of practice; and encourage knowledge sharing across the organization.
Backlog, Delivery & Stakeholder Alignment
Own and prioritise the backlog and roadmap for AI tooling and initiatives; gather requirements and feedback from stakeholders; manage dependencies, risks, and delivery plans; communicate priorities and outcomes clearly; and ensure delivered capabilities address real user needs.
Use data, feedback, and adoption metrics to drive continuous improvement; identify barriers preventing effective AI usage; introduce improvements to tooling, processes, and governance; and continuously enhance the user experience.
Documentation & Standards
Ensure clear, accessible documentation covering AI tooling, governance requirements, usage guidelines, best practices, operating procedures, and decision records; support self-service enablement and maintain knowledge repositories.
Provide technical leadership and subject matter expertise relating to AI platforms, model capabilities, integrations, prompt engineering approaches, automation opportunities, and adoption patterns; guide technical decision-making and help teams maximize value from AI technologies.