Introduction

 

Apprenticeship units are short flexible training courses designed to support employers to upskill their workforce in critical skill shortage areas. Units are for employed learners aged 19 and over, where their employer has identified that they need to upskill quickly to respond to emerging skills gaps and to support business growth and productivity.

Apprenticeship units are based on relevant knowledge and skills in existing employer-led occupational standards to ensure relevant high-quality, targeted training. Each unit is short, with the length of training ranging from 30 and 140 hours delivered over a period of 1 to 16 weeks. This enables employers to have maximum flexibility to select a unit that meets their specific skill need and to deliver the training in a way that fits around their business.

 

Who is it for?

 

This apprenticeship unit is for individuals in leadership roles responsible for setting direction, governance and oversight for AI use who, with the support of their employer, need upskilling in AI leadership literacy, including the capabilities and limitations of AI and the opportunities it presents to their organisation.
This unit is particularly relevant for individuals in organisations at an early or exploratory stage of AI adoption, where there is a need to build a foundational understanding and identify viable opportunities.

 

Learning outcomes

 

A learning outcome is a concise statement that describes what an individual should be able to do by the end of their course. It summarises a cluster of knowledge and skills in the course and provides a foundation for assessment.

Learning outcomes:

• Organisational leadership in setting AI policy and strategy. Including business cases, detailing implications for the workforce, organisational perception and sustainability to inform decision making.

• Evaluate opportunities for AI-driven improvement using qualitative and quantitative evidence, including the assessment of risk.

• Define, document and communicate an AI strategy aligned to organisational goals, values and risk appetite.

• Assess viability and risk through AI use cases and pilots, identifying investment areas balancing productivity gains against feasibility, impact and organisational readiness.

• Engage stakeholders to build support for AI strategy and adoption, including non-technical audiences.

• Critically evaluate and monitor. Implementing adaptations, including responses to emerging AI technologies and trends.

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