D.10 Information and Knowledge Management

4.1 Identify AI-related knowledge and information assets, such as datasets, models, documentation, and policies, relevant to defined organisational processes.

Assessed by Assessment of an AI knowledge asset inventory and AI asset classification document, assessed for correct identification and classification of datasets, models, documentation, and policies relevant to defined organizational processes.

4.2 Analyse operational processes to determine AI-related information and knowledge requirements in support of AI-enabled tasks or decision-making.

Assessed by Evaluation of a written AI information needs analysis and AI process–knowledge mapping, assessed for analysis of operational processes and determination of AI-related information and knowledge requirements supporting AI-enabled tasks or decision-making.

4.3 Apply appropriate methods and tools to organise, document, and maintain AI knowledge and information using structured repositories and standardized documentation practices.

Assessed by Review of a structured AI knowledge repository with AI documentation templates and version control evidence, assessed for appropriate organization, documentation, and maintenance of AI knowledge and information using standardized documentation practices.

4.4 Ensure the accessibility, quality, and consistency of AI knowledge and information by applying established validation, versioning, and basic governance procedures.

Assessed by Assessment of an AI knowledge quality checklist and documented AI knowledge governance procedures, assessed for accessibility, quality, consistency, validation, versioning, and basic governance of AI knowledge and information.

4.5 Share and reuse AI knowledge effectively within teams or projects to support operational efficiency and informed use of AI systems.

Assessed by Evaluation of an AI knowledge sharing package with documented AI knowledge reuse evidence, assessed for effective sharing and reuse of AI knowledge within teams or projects to support operational efficiency and informed use of AI systems.

6.1 Identify AI-related knowledge and information assets, such as datasets, models, documentation, and policies, relevant to defined organisational processes.

Assessed by Assessment of an AI knowledge asset inventory and AI asset classification document, assessed for correct identification and classification of datasets, models, documentation, and policies relevant to defined organizational processes.

6.2 Analyse operational processes to determine AI-related information and knowledge requirements in support of AI-enabled tasks or decision-making.

Assessed by Evaluation of a written AI information needs analysis and AI process–knowledge mapping, assessed for analysis of operational processes and determination of AI-related information and knowledge requirements supporting AI-enabled tasks or decision-making.

6.3 Apply appropriate methods and tools to organise, document, and maintain AI knowledge and information using structured repositories and standardized documentation practices.

Assessed by Review of a structured AI knowledge repository with AI documentation templates and version control evidence, assessed for appropriate organization, documentation, and maintenance of AI knowledge and information using standardized documentation practices.

6.4 Ensure the accessibility, quality, and consistency of AI knowledge and information by applying established validation, versioning, and basic governance procedures.

Assessed by Assessment of an AI knowledge quality checklist and documented AI knowledge governance procedures, assessed for accessibility, quality, consistency, validation, versioning, and basic governance of AI knowledge and information.

6.5 Share and reuse AI knowledge effectively within teams or projects to support operational efficiency and informed use of AI systems.

Assessed by Evaluation of an AI knowledge sharing package with documented AI knowledge reuse evidence, assessed for effective sharing and reuse of AI knowledge within teams or projects to support operational efficiency and informed use of AI systems. DEVELOPMENT & OPERATIONS [2]

4.1 Identify AI-related knowledge and information assets, such as datasets, models, documentation, and policies, relevant to defined organisational processes.

Assessed by Assessment of an AI knowledge asset inventory and AI asset classification document, assessed for identification and classification of datasets, models, documentation, policies, and other AI-related assets relevant to defined organizational processes.

4.2 Analyse operational processes to determine AI-related information and knowledge requirements in support of AI-enabled tasks or decision-making.

Assessed by Evaluation of a written AI information needs analysis and AI process–knowledge mapping, assessed for analysis of operational processes and identification of AI-related knowledge requirements supporting AI-enabled tasks or decision-making.

4.3 Apply appropriate methods and tools to organise, document, and maintain AI knowledge and information using structured repositories and standardized documentation practices.

Assessed by Review of a structured AI knowledge repository with AI documentation templates and version control evidence, assessed for organization, documentation, and maintenance of AI knowledge and information.

4.4 Ensure the accessibility, quality, and consistency of AI knowledge and information by applying established validation, versioning, and basic governance procedures.

Assessed by Assessment of an AI knowledge quality checklist and documented AI knowledge governance procedures, assessed for accessibility, quality, consistency, validation, versioning, and basic governance of AI knowledge.

4.5 Share and reuse AI knowledge effectively within teams or projects to support operational efficiency and informed use of AI systems.

Assessed by Evaluation of an AI knowledge sharing package with documented AI knowledge reuse evidence, assessed for effective sharing and reuse within teams or projects to support operational efficiency and informed AI use.

5.1 Design and evaluate AI knowledge management structures for complex or cross-unit AI initiatives involving multiple stakeholders.

Assessed by Evaluation of an AI knowledge management architecture and knowledge structure design, including data/knowledge models, metadata structures, and integration with AI workflows; assessed for coherence, scalability, traceability, and alignment with organisational and analytical requirements.

5.2 Advise on the AI knowledge lifecycle, including creation, validation, governance, maintenance, and reuse of AI models, data, and related artifacts.

Assessed by Assessment of an AI knowledge lifecycle framework and lifecycle guidelines, including processes for knowledge creation, validation, storage, reuse, and retirement; evaluated for completeness, feasibility, and alignment with AI system lifecycle and organisational needs.

5.3 Develop and improve governance approaches for AI knowledge and information by integrating organisational, regulatory, ethical, and strategic considerations.

Assessed by Review of an AI knowledge governance framework and knowledge risk & compliance assessment, including policies, roles, controls, and risk mitigation strategies; assessed for robustness, regulatory alignment, and effectiveness in ensuring trustworthy and compliant knowledge management.

5.4 Facilitate collaborative knowledge sharing and learning across interdisciplinary teams engaged in AI development, deployment, or oversight.

Assessed by Evaluation of an AI knowledge sharing strategy and collaboration model, including mechanisms for knowledge dissemination, stakeholder engagement, and cross-functional collaboration; assessed for effectiveness, inclusiveness, and alignment with organisational culture and workflows.

5.5 Align AI knowledge management practices with organisational objectives to support innovation, effectiveness, and sustainable use of AI technologies.

Assessed by Assessment of an AI knowledge management strategy and strategic alignment report, including alignment with business objectives, AI strategy, and value creation goals; evaluated for strategic coherence, justification of design choices, and integration with organisational governance and decision-making processes.

5.1 Design and evaluate AI knowledge management structures for complex or cross-unit AI initiatives involving multiple stakeholders.

Assessed by Evaluation of an AI knowledge management architecture and AI knowledge structure design, assessed for suitability for complex or cross-unit AI initiatives involving multiple stakeholders.

5.2 Advise on the AI knowledge lifecycle, including creation, validation, governance, maintenance, and reuse of AI models, data, and related artifacts.

Assessed by Assessment of an advisory AI knowledge lifecycle framework and AI lifecycle guidelines, assessed for coverage of creation, validation, governance, maintenance, and reuse of AI models, data, and related artifacts.

5.3 Develop and improve governance approaches for AI knowledge and information by integrating organisational, regulatory, ethical, and strategic considerations.

Assessed by Review of an AI knowledge governance framework and AI knowledge risk and compliance assessment, assessed for integration of organizational, regulatory, ethical, and strategic considerations and evidence of improvement.

5.4 Facilitate collaborative knowledge sharing and learning across interdisciplinary teams engaged in AI development, deployment, or oversight.

Assessed by Evaluation of an AI knowledge sharing strategy and AI collaboration model, assessed for support of collaborative knowledge sharing and learning across interdisciplinary AI development, deployment, or oversight teams.

5.5 Align AI knowledge management practices with organisational objectives to support innovation, effectiveness, and sustainable use of AI technologies.

Assessed by Assessment of an AI knowledge management strategy and strategic AI knowledge alignment report, assessed for alignment with organizational objectives and contribution to innovation, effectiveness, and sustainable use of AI technologies.

4.1 Design and evaluate AI knowledge management structures for complex or cross-unit AI initiatives involving multiple stakeholders.

Assessed by Evaluation of an AI knowledge management architecture and AI knowledge structure design, assessed for suitability for complex or cross-unit AI initiatives involving multiple stakeholders.

4.2 Advise on the AI knowledge lifecycle, including creation, validation, governance, maintenance, and reuse of AI models, data, and related artifacts.

Assessed by Assessment of an advisory AI knowledge lifecycle framework and AI lifecycle guidelines, assessed for coverage of creation, validation, governance, maintenance, and reuse of AI models, data, and related artifacts.

4.3 Develop and improve governance approaches for AI knowledge and information by integrating organisational, regulatory, ethical, and strategic considerations.

Assessed by Review of an AI knowledge governance framework and AI knowledge risk and compliance assessment, assessed for integration of organizational, regulatory, ethical, and strategic considerations and evidence of improvement.

4.4 Facilitate collaborative knowledge sharing and learning across interdisciplinary teams engaged in AI development, deployment, or oversight.

Assessed by Evaluation of an AI knowledge sharing strategy and AI collaboration model, assessed for support of collaborative knowledge sharing and learning across interdisciplinary AI development, deployment, or oversight teams.

4.5 Align AI knowledge management practices with organisational objectives to support innovation, effectiveness, and sustainable use of AI technologies.

Assessed by Assessment of an AI knowledge management strategy and strategic AI knowledge alignment report, assessed for alignment with organizational objectives and contribution to innovation, effectiveness, and sustainable use of AI technologies.

5.1 Design and evaluate AI knowledge management structures for complex or cross-unit AI initiatives involving multiple stakeholders.

Assessed by Evaluation of an AI knowledge management architecture and AI knowledge structure design for complex or cross-unit AI initiatives involving multiple stakeholders.

5.2 Advise on the AI knowledge lifecycle, including creation, validation, governance, maintenance, and reuse of AI models, data, and related artifacts.

Assessed by Assessment of an advisory AI knowledge lifecycle framework and AI lifecycle guidelines covering creation, validation, governance, maintenance, and reuse of AI models, data, and related artifacts.

5.3 Develop and improve governance approaches for AI knowledge and information by integrating organisational, regulatory, ethical, and strategic considerations.

Assessed by Review of an AI knowledge governance framework and AI knowledge risk and compliance assessment, evaluated for integration of organizational, regulatory, ethical, and strategic considerations.

5.4 Facilitate collaborative knowledge sharing and learning across interdisciplinary teams engaged in AI development, deployment, or oversight.

Assessed by Evaluation of an AI knowledge sharing strategy and AI collaboration model, assessed for facilitation of collaborative learning across interdisciplinary teams engaged in AI development, deployment, or oversight.

5.5 Align AI knowledge management practices with organisational objectives to support innovation, effectiveness, and sustainable use of AI technologies.

Assessed by Assessment of an AI knowledge management strategy and strategic AI knowledge alignment report, evaluated for alignment with organizational objectives and support for innovation, effectiveness, and sustainable use of AI technologies.

5.1 Design and evaluate AI knowledge management structures for complex or cross-unit AI initiatives involving multiple stakeholders.

Assessed by Evaluation of an AI knowledge management architecture and AI knowledge structure design for complex or cross-unit AI initiatives involving multiple stakeholders.

5.2 Advise on the AI knowledge lifecycle, including creation, validation, governance, maintenance, and reuse of AI models, data, and related artifacts.

Assessed by Assessment of an advisory AI knowledge lifecycle framework and AI lifecycle guidelines covering creation, validation, governance, maintenance, and reuse of AI models, data, and related artifacts.

5.3 Develop and improve governance approaches for AI knowledge and information by integrating organisational, regulatory, ethical, and strategic considerations.

Assessed by Review of an AI knowledge governance framework and AI knowledge risk and compliance assessment, evaluated for integration of organizational, regulatory, ethical, and strategic considerations.

5.4 Facilitate collaborative knowledge sharing and learning across interdisciplinary teams engaged in AI development, deployment, or oversight.

Assessed by Evaluation of an AI knowledge sharing strategy and AI collaboration model, assessed for facilitation of collaborative learning across interdisciplinary teams engaged in AI development, deployment, or oversight.

5.5 Align AI knowledge management practices with organisational objectives to support innovation, effectiveness, and sustainable use of AI technologies.

Assessed by Assessment of an AI knowledge management strategy and strategic AI knowledge alignment report, evaluated for alignment with organizational objectives and support for innovation, effectiveness, and sustainable use of AI technologies.

5.1 Design and evaluate AI knowledge management structures for complex or cross-unit AI initiatives involving multiple stakeholders.

Assessed by Evaluation of an AI knowledge management architecture and AI knowledge structure design for complex or cross-unit AI initiatives involving multiple stakeholders.

5.2 Advise on the AI knowledge lifecycle, including creation, validation, governance, maintenance, and reuse of AI models, data, and related artifacts.

Assessed by Assessment of an advisory AI knowledge lifecycle framework and AI lifecycle guidelines covering creation, validation, governance, maintenance, and reuse of AI models, data, and related artifacts.

5.3 Develop and improve governance approaches for AI knowledge and information by integrating organisational, regulatory, ethical, and strategic considerations.

Assessed by Review of an AI knowledge governance framework and AI knowledge risk and compliance assessment, evaluated for integration of organizational, regulatory, ethical, and strategic considerations.

5.4 Facilitate collaborative knowledge sharing and learning across interdisciplinary teams engaged in AI development, deployment, or oversight.

Assessed by Evaluation of an AI knowledge sharing strategy and AI collaboration model, assessed for facilitation of collaborative learning across interdisciplinary teams engaged in AI development, deployment, or oversight.

5.5 Align AI knowledge management practices with organisational objectives to support innovation, effectiveness, and sustainable use of AI technologies.

Assessed by Assessment of an AI knowledge management strategy and strategic AI knowledge alignment report, evaluated for alignment with organizational objectives and support for innovation, effectiveness, and sustainable use of AI technologies.