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, or aspiring to, 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, defining organisational AI priorities and leading organisational change.
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:
- Make evidence-based investment and procurement decisions for AI tools, platforms and suppliers, applying fair evaluation criteria and addressing long-term risks for example vendor lock-in, contractual assurance, data, resilience and exit planning.
- Sponsor and operate a governance framework that safeguards lawful, ethical and responsible AI use, embedding ethical principles, accountability, transparency and assurance into organisational decision-making.
- Oversee AI risk management within the organisation’s enterprise risk framework, ensuring audit readiness, effective incident response, and continuous monitoring of AI-related risks.
- Lead and coordinate cross-functional delivery of AI-enabled organisational change, including assessing workforce impacts, engaging stakeholders, and supporting targeted upskilling and reskilling, while balancing innovation with practical implementation constraints.
- Represent the organisation credibly in external AI discussions with regulators, partners, sector bodies and the public. Anticipate and prepare for evolving AI regulation by monitoring regulatory change, embedding compliance readiness into business processes, and maintaining up-to-date knowledge of emerging AI technologies and practices.
| Occupational standard |
Related apprenticeships
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Occupational map link
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| Artificial intelligence (AI) and automation practitioner | ST1512 V2.0 | OCC1512 |
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ST1512 V2.0
Occupational map link:
OCC1512
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| Chartered manager (degree) | ST0272 V1.1 | OCC0272 |
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| Machine learning engineer | ST1398 V1.0 | OCC1398 |
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ST1398 V1.0
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OCC1398
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| Senior leader (degree) | ST0480 V1.2 | OCC0480 |
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ST0480 V1.2
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OCC0480
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Entry requirements
Learners must be employed and must be 19 years or over.
Must be working or aspiring to a leadership position within an organisation with autonomy to deliver technological change and inform investment decisions.
Technical knowledge
K1: AI and automation concepts and models that support leadership decision-making, and their limitations. The impact adoption may have on workplace culture and wellbeing.
K2: The capabilities, benefits and risks of automation, AI and digital tools including responsible use, ethical considerations and the potential impact on the workforce.
K3: The role of organisational leadership in responsible AI adoption, including setting values, policy, and strategy. The business case for ethical AI adoption, including reputational risk, staff engagement and morale, and long-term sustainability.
K4: Understand how to develop and implement organisational AI strategy and plans, including approaches to, workforce development taking and managing risk, monitoring and evaluation, and quality assurance.
K5: How to assess the viability of solutions when making acquisition decisions, for example testing and evaluating solutions, using test data and results, feasibility (time, cost, data quality and process maturity), and user testing.
K6: The capabilities, risks and implications of adopting on-premise, cloud-based and third-party solutions.
K7: Legislation, regulation, governance and assurance frameworks that support the safe adoption of artificial intelligence.
K8: Governance principles to ensure accountability and compliance, including defining roles and responsibilities to identify, escalate and mitigate threats or risks to assets, data and cyber security.
K9: Assurance and compliance arrangements, including documentation expectations, structured risk assessments, aligning with recognised AI assurance and governance frameworks. The importance of auditability, transparency, and accountability in organisational contexts.
K10: Crisis and risk management strategies including accountability and technological implications.
K11: Principles and practices for the long-term monitoring of AI and automation solutions to ensure organisational learning. Including detection and mitigation of risks such as model drift, emerging bias, degraded performance, and security vulnerabilities.
K12: Principles of human oversight and human AI collaboration to achieve shared outcomes.
K13: Principles for operationalising sustainable AI solutions to support organisational strategies and objectives.
K14: Approaches to maintaining awareness of existing, evolving and emerging AI technologies and sector trends for example peer learning, online forums, AI tool release notes, to inform strategic AI decisions.
K15: Engagement and training approaches used with non-technical staff to understand their roles, responsibilities, and concerns when AI automation solutions are proposed, in support of strategic AI governance decisions.
Technical skills
S1: Identify organisational improvements and opportunities for innovation and growth, using qualitative and quantitative analysis of information and data.
S2: Set strategic direction for AI and gain support for it from key stakeholders.
S3: Commission analysis to identify if AI adoption is viable. Evaluate assessments of risks and unintended consequences of AI automation projects, such as the impact on job roles.
S4: Use evidence to inform governance of AI adoption, outcomes and facilitate improvement.
S5: Ensure sustainable and efficient AI and automation solutions.
S6: Ensure business needs are aligned with technical capabilities, to ensure solutions are scalable, efficient, and aligned with the organisation’s strategic objectives.
S7: Keep up to date with existing, evolving, emerging technologies and sector trends in AI, automation and technology to support the evaluation of vendor and supplier solutions.
S8: Apply principles relating to ethics and values-based leadership and governance and regulatory compliance.
S9: Apply regulatory, legal, ethical and governance considerations when evaluating AI recommendations at each stage of the AI adoption process.
S10: Define expectations for testing and feedback to ensure reliability, security, accessibility of AI systems, and alignment with organisational needs.
S11: Lead and respond in a crisis situation using risk management techniques.
S12: Use project management principles, techniques and tools to support the development of clear, balanced AI communications and briefings, articulating both opportunities and risks.
S13: Lead deployment of AI and automation strategies, including measures to deal with the impact of automation for example workforce engagement, retraining, redeployment, or upskilling of affected staff.
S14: Horizon scan to identify new developments that have implications for AI use.
Knowledge and skills outcomes
| Function | Learning Outcome | K+S Mapping |
|---|---|---|
| AI strategy and organisational direction | Define, document and communicate an organisation-wide AI strategy aligned to organisational goals, values and risk appetite, including prioritised use cases, benefits realisation measures and delivery milestones. | K1, K3, K4, S2, S3, S5 |
| AI procurement, investment and adoption | Make evidence-based investment and procurement decisions for AI tools, platforms and suppliers, applying fair evaluation criteria and addressing long-term risks (e.g., vendor lock-in, contractual assurance, data/IP terms, resilience and exit planning). | K5, K6, K7, K8, K9, S4, S6, S7, S8 |
| Governance, ethics and responsible AI | Sponsor and operate a governance framework that safeguards lawful, ethical and responsible AI use, embedding ethical principles, accountability, transparency and assurance into organisational decision-making. | K9, K10, K11, K12, S4, S9, S10 |
| Enterprise risk, audit and incident readiness | Oversee AI risk management within the organisation’s enterprise risk framework, ensuring audit readiness, effective incident response, and continuous monitoring of AI-related risks. | K11, K12, K13, K14, K15, S1, S11, S12 |
| Leadership, delivery and organisational change | Lead cross-functional teams to deliver AI-enabled organisational change, balancing innovation with practical implementation constraints and aligning delivery with strategic objectives. | K2, K5, S3, S4, S5, S13 |
| Communication, trust and external engagement | Represent the organisation credibly in external AI discussions (regulators, partners, sector bodies and the public). Anticipate and prepare for evolving AI regulation by monitoring regulatory change, embedding compliance readiness into business processes, and maintaining up-to-date knowledge of emerging AI technologies and practices. | K3, K15, S8, S12, S13 |
| Workforce transformation and capability building | Plan and manage workforce transformation resulting from AI adoption, including role impact assessment, engagement strategies, and targeted upskilling and reskilling to support accountable AI-enabled work. | K2, S3, S13, S14 |
Funding
This apprenticeship unit is currently eligible for public funding.
The funding rate for this apprenticeship unit is [X].
Skills England will provide the Department for Work and Pensions with ongoing advice on critical skills needs, and the affordability and prioritisation of funding for apprenticeship units will remain under review.
The Department will give notice if funding for this apprenticeship unit is to be withdrawn. Following which, funding for new starts will not be available after four weeks from that notice being given.
Validation and assessment
Mandatory: As a minimum, learners will need to pass a skills test delivered by the training provider, to demonstrate that they have acquired the skills and knowledge set out in the apprenticeship unit. Employers will need to validate the result to confirm the learner has been successful.
Extended: In addition, employers (or learners) have the option to choose independent external assessment where they feel it is appropriate, for example through use of a non-mandatory qualification.
If the apprenticeship unit is in a regulated occupation and the role requires adherence to industry recognised standards and procedures, we would expect employers to choose an extended assessment.
Version log
| Version | Change detail | Earliest start date | Latest start date |
|---|---|---|---|
| 1.0 | Not set | Not set |
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