Selecting and Applying Generative AI Tools in the Workplace

This standard outlines the skills and knowledge required to effectively select and apply generative AI (GenAI) tools within workplace environments. It encompasses assessing the suitability of GenAI tools for specific tasks, integrating them into workflows to enhance efficiency and productivity, and ensuring their safe and responsible use. Key focus areas include quality control, data security, and adherence to ethical principles, enabling individuals to manage GenAI tools with confidence and accountability.

The standard is designed to equip professionals with the expertise to make informed decisions about GenAI technologies, aligning their application with organisational objectives, policies, regulatory requirements, and ethical standards. It is particularly relevant for those seeking to adopt GenAI tools as part of their workplace activities and responsibilities.

1. Identify tasks, projects, or processes where generative AI (GenAI) can improve efficiency, quality, creativity, or value in line with organisational goals.
2. Collaborate with team members and stakeholders to evaluate specific organisational use cases where GenAI can be adopted.
3. Asses GenAI use cases within the context of job roles, organisational objectives, and ethical considerations.
4. Evaluate GenAI tools by comparing their functionality, usability, output quality and compatibility with existing systems.
5. Select GenAI tools that best meet organisational requirements.
6. Verify that selected tools comply with data privacy, security, and relevant ethical standards and policies for responsible GenAI use.
7. Configure and deploy GenAI tools to meet task requirements while minimising workflow disruption.
8. Use GenAI tools to obtain improved information relevant to tasks or projects.
9. Monitor and review the output of GenAI tools, to validate response accuracy and address any issues as they arise.
10. Complete approved cybersecurity training for GenAI, to support productivity initiatives in line with organisational requirements.

1. What is meant by GenAI.
2. The basic concepts of large language models (LLMs) and their functionality.
3. The core capabilities of GenAI, including text generation, image and video creation, data synthesis, and their practical relevance to workplace adoption.
4. The types of GenAI tools and their applications for different workplace tasks.
5. The advantages and disadvantages of GenAI.
6. Key factors to consider when applying GenAI tools, including functionality, accuracy, security, compatibility with existing systems, and availability of user support.
7. Methods for evaluating the effectiveness and ROI (Return on Investment) of GenAI tool adoption in workplace contexts.
8. How to use GenAI tools to generate improved information.
9. The steps involved in applying GenAI tools.
10. Best practices for applying GenAI tools to existing tasks and processes.
11. Techniques for configuring GenAI tools to align with task-specific needs.
12. Methods for reviewing, validating, and refining GenAI outputs to meet project requirements and quality benchmarks.
13. Standards for evaluating the accuracy, relevance, and context-appropriateness of GenAI outputs.
14. Key privacy laws and regulations relevant to GenAI usage including GDPR (General Data Protection Regulations) and internal data protection policies.
15. Strategies for managing data privacy and cybersecurity risks associated with GenAI tools and their application.
16. Ethical considerations in GenAI usage, including bias prevention, transparency, and the societal and environmental impact of GenAI.
17. Organisational and industry standards for ethical GenAI, focusing on fair use, accountability, and mitigating potential biases in GenAI outputs.
18. Approaches for encouraging team adoption of GenAI tools, fostering a collaborative environment that incorporates GenAI responsibly.
19. Techniques for balancing generative GenAI assistance with human judgment to verify that GenAI serves as a complement to, not a replacement for, human expertise.
20. The need to balance generative AI assistance with human judgment, to collaboratively enhance human expertise, in line with principles of responsible and sensible GenAI usage.