Overview
Design integrated learning interventions combining AI literacy, data literacy and core business skills, translating strategic concepts such as tool readiness, professional judgement and safe AI use into practical, applied experiences that build confidence and capability. Sequence learning alongside tool and technology adoption by collaborating with HR, DTI and business areas, ensuring training reinforces trust, enables process and automation thinking, and improves workflow design capability. Define and communicate tool readiness requirements across roles and cohorts, aligning capability development with organisational needs to support effective and scalable AI and data adoption. Develop scenario-based, role-specific learning grounded in real organisational workflows, enabling colleagues to apply AI and data skills directly within their day-to-day responsibilities. Embed behavioural and judgement-focused learning that promotes safe and ethical AI use, deepens understanding of data governance and accountability, and supports effective manager role modelling. Pilot targeted learning interventions with defined cohorts, evaluating effectiveness through behavioural change, process efficiency and quality outcomes, and refining approaches using data-driven feedback. Engage and align stakeholders across HR, DTI and business functions to co-create learning solutions that are strategically aligned, operationally relevant and scalable across the organisation. Contribute to organisational capability building by shaping integrated AI, data and digital upskilling approaches, helping shape how training, tooling and governance combine to enable safe adoption and positioning the role as a key driver of future skills development.
Responsibilities
- Design integrated learning interventions combining AI literacy, data literacy and core business skills, translating strategic concepts into practical, applied experiences.
- Sequence learning alongside tool and technology adoption by collaborating with HR, DTI and business areas, ensuring training reinforces trust and improves workflow design capability.
- Define and communicate tool readiness requirements across roles and cohorts, aligning capability development with organisational needs to support AI and data adoption.
- Develop scenario-based, role-specific learning grounded in real organisational workflows, enabling colleagues to apply AI and data skills in day-to-day responsibilities.
- Embed behavioural and judgement-focused learning that promotes safe and ethical AI use, data governance and accountability, and supports manager role modelling.
- Pilot targeted learning interventions with defined cohorts and evaluate effectiveness through behavioural change, process efficiency and quality outcomes, refining approaches with data-driven feedback.
- Engage and align stakeholders across HR, DTI and business functions to co-create learning solutions that are strategically aligned and scalable.
- Contribute to organisational capability building by shaping integrated AI, data and digital upskilling approaches to enable safe adoption and position the role as a driver of future skills development.
Requirements
- Proven experience designing learning interventions for digital, data, or AI capability building
- Experience embedding learning within technology adoption or transformation programmes
- Demonstrable ability to conduct skills assessments and define capability pathways
- Integrated AI and data capability design – experience designing learning that can build on and combine data literacy, AI literacy, and business skills
- Learning design aligned to capability maturity – ability to design interventions across different maturity levels, such as awareness and foundational understanding; applied process and automation thinking; and advanced capability (e.g. workflow or agent design)
- Scenario-based and applied learning – strong experience in scenario-based, role-specific learning design
- Ability to translate abstract concepts into practical, work-based use cases and understanding of AI, automation and data in context
- Working knowledge of tools and technology in this space, such as data visualisation platforms; workflow tools (e.g. Power Automate); AI tools (e.g. Copilot, agent-based systems)
- Understanding of how these tools are applied in business processes, rather than just technical capabilities
- Stakeholder engagement – experience working across HR, technology, and business teams; ability to communicate with senior stakeholders and align learning to strategic objectives
- Evaluation and continuous improvement – experience evaluating learning effectiveness and iterating based on feedback and data
Notes
ATS Optimization Keywords (optional): Hard Skills — Scenario-Based Learning Design; Capability Pathway Definition; Skills Assessment; Process and Automation Thinking; Workflow Design. Soft Skills — Communication with Senior Stakeholders; Collaboration Across Teams. This section is for ATS visibility and does not affect core responsibilities.
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