Knowledge (K) – The theoretical understanding an apprentice needs to perform their role effectively. This includes industry-specific principles, regulations, and best practices.
- K1: 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 morale, and long-term sustainability.
- K2: Legal and regulatory frameworks including employment rights, equality, and responsible automation, data protection and GDPR. Ethical principles and professional standards relevant to AI development such as fairness, transparency, and accountability.
- K3: Understand the potential social and economic impacts of AI and automation on different roles, particularly for non-technical staff including change management principles.
- K4: Approaches for identifying and implementing incremental change, including piloting, evaluating solutions in relation to organisational constraints such as budget, time, and resources.
- K5: Methods to identify opportunities to enhance productivity such as improve processes, reduce waste, increase user or customer satisfaction or optimise outcomes.
- K6: The importance of designing AI and automation systems that augment rather than replace human work, where feasible.
- K7: The capabilities, benefits and risks of automation, AI and digital tools including responsible use, ethical considerations and the potential impact on the workforce.
- K8: The capabilities, risks and implications of on-premise, cloud-based and third party solutions.
- K9: AI and automation concepts, models and limitations. The impact adoption may have on workplace culture and wellbeing.
- K10: Sources of error and algorithmic bias, including how they may be affected by choice of dataset and methodologies applied, and the impact on the user and or organisation. Fairness metrics and mitigation approaches.
- K11: User requirements when designing and implementing AI and automation solutions including accessibility considerations.
- K12: Product development lifecycle including consideration of user experience (UX) principles such as user centred design (UCD), data informed design and experimental testing.
- K13: How to assess the viability of solutions, for example testing and evaluating solutions, using test data and results, feasibility (time, cost, data quality and process maturity), and user testing.
- K14: Principles and application of testing methodologies and their application in practice.
- K15: Principles of human oversight and human AI collaboration to achieve shared outcomes.
- K16: Feedback and evaluation loops to improve systems, processes, productivity and performance including human in the loop safeguards.
- K17: Principles for designing sustainable solutions to support organisational strategies and objectives.
- K18: Governance principles to ensure accountability and compliance, including methods to identify system vulnerabilities and mitigate threats or risks to assets, data and cyber security.
- K19: Engagement and training approaches used with non-technical staff to understand their roles, responsibilities, and concerns when AI automation solutions are proposed. Including best practice and methods to deliver training.
- K20: Methods to develop resources such as manuals, short explainers, chat-based guidance, interactive wikis and training materials.
- K21: Strategies for inclusive communication with stakeholders from diverse and non-technical backgrounds.
- K22: Collaborative working principles to explore AI and automation solutions and implement prototypes, pilots or proof of concepts.
- K23: Mitigation strategies for post-deployment issues such as overreliance and automation bias.
- K24: Principles to support project and change management delivery.
- K25: Approaches to maintaining up-to-date knowledge of existing, evolving and emerging technologies and sector trends for example peer learning, online forums, AI tool release notes.
- K26: The benefits of wellbeing and safe working practices.
- K27: Methods for assuring compliance in AI and automation projects, including documentation of model decision-making, conducting structured risk assessments, and aligning implementation with recognised AI assurance and governance frameworks. The importance of auditability, transparency, and accountability in organisational contexts.
- K28: Principles and practices of algorithmic impact assessment and workforce equality monitoring, including methods to identify, assess, and mitigate potential disproportionate impacts of automation and AI systems on different workforce groups. Organisational responsibilities under equality and employment law, and methods to evidence fairness and transparency in adoption.
- K29: Principles and practices for the long-term monitoring of AI and automation solutions, including detection and mitigation of risks such as model drift, emerging bias, degraded performance, and security vulnerabilities.
You can view the standard here.