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 shaping, influencing, or supporting decisions about the adoption of AI systems within their organisation. These individuals, with support of their employer, need upskilling in adopting AI systems and governing them responsibly. It is suited to those involved in evaluating options, developing business cases, and establishing governance and assurance approaches for AI and digital technologies.

 

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:

• Evaluate AI solutions and vendors using structured criteria following organisational process (cost, performance, risk, data readiness).

• Make procurement decisions based on testing, benchmarking and user validation following organisational policy and process. 

• Assess risks associated with AI acquisition, including vendor lock-in, data, IP, and sustainability. 

• Design and implement AI governance frameworks, including roles, responsibilities and escalation pathways.

• Embed ethical, legal and regulatory considerations into AI decision-making processes.

• Define assurance and compliance processes, including documentation, auditability and transparency.

• Design and implement human oversight mechanisms for AI systems.