CWA 18398 · e-CF areas A–E

41 competences, and the levels each one defines

A competence does not exist at every level. Writing a requirement at a level a competence does not define is a malformed requirement, and the engine refuses it rather than quietly searching for something that cannot be held. Each entry below states exactly which of e-1 to e-5 it defines, with the standard's descriptor for each, and the evidence floor a claim must clear at that level before it may be shown as verified.

A Plan (10)

A.1 IS and Business Strategy Alignment

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e-4 · evidence floor V3: Aligns IS and AI strategy with business strategy, integrating AI capabilities into enterprise architecture while managing risk, sourcing, and governance.
e-5 · evidence floor V3: Leads enterprise-wide IS and AI strategy; anticipates long-term business needs and drives innovation.

A.2 Service Level Management

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e-3 · evidence floor V2: Defines and monitors SLAs for AI services with moderate autonomy. Negotiates AI performance targets with stakeholders, considering business needs and technical capacity. Tracks AI-specific indicators such as drift, cost, throughput, and availability. Ensures that AI monitoring pipelines generate actionable alerts for SLA violation.
e-4 · evidence floor V3: Independently establishes and enforces SLAs for complex AI services across multiple teams or domains. Evaluates and adjusts AI performance targets based on operational data and stakeholder feedback. Integrates AI-specific requirements into contracts and vendor agreements. Implements continuous improvement processes for AI service reliability and efficiency.

A.3 Business Plan Development

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e-3 · evidence floor V2: Independently designs and justifies business plans for AI- enabled initiatives, evaluating alternative approaches, quantifying AI-specific costs and benefits, managing risks, and communicating value to stakeholders.
e-4 · evidence floor V3: Leads and governs the development of complex AI business plans, defining evaluation frameworks, integrating regulatory and ethical considerations, guiding others, and managing stakeholder expectations on AI value and risk.
e-5 · evidence floor V3: Shapes organisational strategy for AI investment through business planning, setting enterprise-level principles, prioritisation criteria, and governance models that align AI initiatives with long-term business objectives.

A.4 Product/Service Planning

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e-3 · evidence floor V2: Plans and manages product or service activities, including AI- Service enabled elements, ensuring alignment with business objectives, delivery constraints and regulatory requirements.
e-4 · evidence floor V3: Leads and optimises product or service planning for AI- enabled solutions. Ensures effectiveness, compliance and sustainability across the lifecycle, balancing value, risk and resource constraints.

A.5 Architecture Design

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e-3 · evidence floor V2: Independently implements and adapts AI-enabled architectural solutions. Ensures alignment with enterprise architecture and policies. Identifies minor risks and recommends improvements.
e-4 · evidence floor V3: Leads the design of complex AI-enabled enterprise architectures. Ensures AI solutions are scalable, secure, interoperable, and ethically aligned. Provides guidance to teams on AI integration.

A.6 Application Design

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e-3 · evidence floor V2: Designs AI applications and data structures independently for routine or well-defined tasks. Selects suitable AI methods and technologies to meet project requirements, balancing cost, performance, and quality.
e-4 · evidence floor V3: Leads the design of complex AI applications and system architectures. Integrates AI models into larger systems, ensuring scalability, interoperability, and ethical compliance. Advises stakeholders on AI design trade-offs and performance optimisation.

A.7 Technology Trend Monitoring

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e-3 · evidence floor V2: Independently monitors ICT and AI technology trends and evaluates innovation opportunities. Ensures the feasibility and relevance of AI adoption initiatives within a defined domain.
e-4 · evidence floor V3: Provides leadership in the monitoring, evaluation and exploitation of ICT and AI technological developments. Advises on AI innovation strategies and ensures alignment with organisational objectives.
e-5 · evidence floor V3: Provides strategic vision and authority for ICT and AI innovation. Initiates and governs transformational AI initiatives and shapes long-term AI adoption strategies.

A.8 Sustainability Management

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e-3 · evidence floor V2: Applies established methods to monitor and improve the sustainability performance of AI systems and projects.
e-4 · evidence floor V3: Leads initiatives to assess, advise, and implement sustainable AI practices across projects and organisational units

A.9 Innovating

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e-3 · evidence floor V2: Independently develops and adapts AI-based solutions to address defined business, societal, or research needs. Combines AI with other technological advances to enhance products, services, or processes creatively.
e-4 · evidence floor V3: Leads innovation projects that exploit AI to create novel solutions or new business/research opportunities. Advises teams or stakeholders on the strategic use of AI to drive value, efficiency, or societal benefit.
e-5 · evidence floor V3: Defines the strategic direction for AI-driven innovation across the organisation or research domain. Envisions transformative AI applications that create new markets, services, or societal impact.

A.10 User Experience

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e-2 · evidence floor V1: Applies established user experience design principles and methods to AI-enabled digital products and services under guidance. Contributes to the design of usable and understandable AI interactions by following defined standards, patterns, and ethical guidelines.
e-3 · evidence floor V2: Selects, adapts, and applies user experience design approaches for AI-enabled systems to meet user, business, and ethical requirements. Ensures that AI interactions are understandable, usable, and aligned with user expectations.
e-4 · evidence floor V3: Leads the definition and governance of user experience design strategies for AI-enabled products and services. Ensures consistency, trustworthiness, and responsible use of AI across user journeys and platforms.

B Build (6)

B.1 Application Development

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e-3 · evidence floor V2: Develops AI-enhanced applications according to specifications, integrating AI/ML models into software solutions. Applies established AI platforms and frameworks to achieve functional requirements.
e-4 · evidence floor V3: Leads the design and development of AI-enhanced applications, optimising performance, cost, and quality. Selects appropriate AI/ML models, platforms, and algorithms to meet business needs. Guides others in AI integration and validation.

B.2 Component Integration

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e-3 · evidence floor V2: Independently integrates AI components into systems, ensuring interoperability, performance and security. Selects appropriate integration approaches and contributes to AI system design.
e-4 · evidence floor V3: Leads and coordinates AI component integration activities. Ensures consistency, quality and compliance of AI-enabled system architectures across projects or services.

B.3 Testing

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e-2 · evidence floor V1: Supports testing of ICT and AI-based systems. Executes predefined test procedures and records results. Applies basic AI evaluation methods and assists in documenting model behaviour.
e-3 · evidence floor V2: Independently conducts systematic testing of ICT and AI systems. Evaluates AI models for correctness, robustness, fairness, and compliance with standards. Provides preliminary reports and recommendations for improvements.
e-4 · evidence floor V3: Leads testing activities for complex ICT and AI systems. Integrates AI model evaluation with broader system testing. Ensures adherence to regulatory, ethical, and internal standards. Advises teams on mitigation of AI risks and improvement of system robustness.

B.4 Solution Deployment

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e-3 · evidence floor V2: Implements AI solutions reliably in production, integrating models with systems and workflows. Monitors performance and executes predefined updates or retraining as required.

B.5 Documentation Production

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e-2 · evidence floor V1: Produces simple AI-related documents following established templates and guidelines.
e-3 · evidence floor V2: Independently produces and maintains AI documentation that integrates technical and compliance requirements.

B.6 Systems Engineering

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e-3 · evidence floor V2: Independently engineers AI infrastructure components and integrates AI systems into existing IT environments. Applies standard methods to ensure performance, security, and compliance, and contributes to AI system design.
e-4 · evidence floor V3: Leads the design and optimisation of complex AI infrastructure. Anticipates AI system behaviour, implements advanced integration solutions, and ensures high performance, security, and ethical compliance. Provides guidance to others.

C Run (5)

C.1 User Support

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e-2 · evidence floor V1: Provides user support for standard AI-enabled systems and services under supervision. Addresses routine user requests related to AI functionalities and follows established procedures to resolve or escalate incidents.

C.2 Change Support

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e-2 · evidence floor V1: Applies defined change management procedures to ICT and AI solutions under guidance. Supports implementation of approved AI changes and monitors their operational impact.
e-3 · evidence floor V2: Plans and executes AI-related changes. Evaluates impact and coordinates changes to minimise service disruption and risk.

C.3 Service Delivery

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e-2 · evidence floor V1: Provides service delivery support for AI-enabled services under guidance. Ensures AI services operate according to defined procedures, service levels and security requirements. Identifies and escalates AI-related incidents and performance deviations.
e-3 · evidence floor V2: Ensures reliable and secure delivery of AI-enabled services. Takes responsibility for operational performance, monitoring and incident management of AI components. Proactively addresses service risks to maintain agreed service levels.
e-4 · evidence floor V3: Leads service delivery for AI-enabled systems. Ensures continuity, resilience and trustworthiness of AI services across the organisation. Aligns AI service delivery with business objectives, security policies and governance requirements.

C.4 Problem Management

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e-2 · evidence floor V1: Supports resolution of ICT and AI incidents under guidance. Management Helps collect data on AI system errors and monitors alerts.
e-3 · evidence floor V2: Independently diagnoses and resolves routine ICT and AI incidents. Applies established procedures to prevent recurrence.
e-4 · evidence floor V3: Leads problem management across ICT and AI systems. Proactively prevents incidents and optimises system and AI model performance.

C.5 Systems Management

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e-3 · evidence floor V2: Manages and maintains IT and AI systems independently, ensuring reliable and secure operation. Resolves operational issues and contributes to continuous improvement of AI-enabled services.

D Enable (11)

D.1 Information Security Strategy Development

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e-4 · evidence floor V3: Develops and integrates AI security strategy across projects or business units. Aligns AI security objectives with organisational goals, manages AI risks proactively, and ensures ethical AI deployment.

D.2 ICT Quality Strategy Development

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e-4 · evidence floor V3: Defines, evolves, and integrates AI quality objectives across projects and value streams. Guides teams to ensure trustworthy and resilient AI systems.

D.3 Education and Training Provision

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e-3 · evidence floor V2: Implements AI training programs and integrates AI learning into development plans. Provision
e-4 · evidence floor V3: Defines and manages AI training programs that enhance organisational AI capability.

D.4 Purchasing

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e-3 · evidence floor V2: Applies standard procurement processes to acquire AI products and services that meet organisational requirements.

D.5 Sales Development

Scoped out. Not applicable in this CWA (Annex J, p.409). Reject any claim on this competence with a scope error, not a level error.

D.6 Digital Marketing

Scoped out. Not applicable in this CWA (Annex J, p.409). Reject any claim on this competence with a scope error, not a level error.

D.7 Data Science and Analytics

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e-2 · evidence floor V1: Applies basic data collection, cleaning, and analysis techniques to support AI-driven projects under supervision.
e-3 · evidence floor V2: Independently conducts data analysis and analytics tasks to support AI model development and AI-enhanced decision-making.
e-4 · evidence floor V3: Leads data science and analytics initiatives that enable AI solutions, optimizing AI-supported processes and organisational decision-making.
e-5 · evidence floor V3: Defines and drives organisational strategy for AI-enabled data analytics, shaping AI adoption and governance.

D.8 Contract Management

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e-2 · evidence floor V1: Supports the management of AI-related contracts under supervision, ensuring compliance with basic organisational processes, ethical AI principles, and regulatory requirements.
e-3 · evidence floor V2: Independently manages AI contract activities, ensuring clarity on data ownership, model training and performance standards, liability, and regulatory compliance while monitoring supplier delivery and escalating risks as needed.

D.9 Personnel Development

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e-3 · evidence floor V2: Independently identifies AI skill gaps, contributes to AI-focused learning initiatives, and applies AI competences in role-specific contexts.
e-4 · evidence floor V3: Integrates AI competence development into team/organisation processes, guides others in AI adoption, and evaluates learning impact.

D.10 Information and Knowledge Management

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e-3 · evidence floor V2: Independently manages AI knowledge and information within defined processes. Analyses business processes to identify AI information needs. Applies tools and techniques to organise, access, and share AI knowledge effectively.
e-4 · evidence floor V3: Designs and improves AI knowledge management processes and structures across multiple teams or business units. Advises on AI knowledge lifecycle, governance, and strategic use. Promotes collaborative knowledge sharing and innovation.
e-5 · evidence floor V3: Leads organisational AI knowledge strategy, ensuring alignment with enterprise objectives, regulatory requirements, and innovation goals. Shapes AI knowledge policies, oversees lifecycle governance, and drives AI-enabled value creation.

D.11 Needs Identification

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e-3 · evidence floor V2: Independently engages stakeholders to identify and validate needs, translating them into AI solution requirements.
e-4 · evidence floor V3: Leads the identification and articulation of AI-related needs, ensuring alignment with business strategy, regulatory compliance, and ethical principles.

E Manage (9)

E.1 Forecast Development

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e-4 · evidence floor V3: Provides expert guidance on AI forecasting, influencing strategic decision-making. Evaluates multiple AI adoption scenarios and recommends actions across organisational functions.

E.2 Project and Portfolio Management

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e-3 · evidence floor V2: Manages small AI projects or well-defined components of larger portfolios, applying standard project management practices with some autonomy.
e-4 · evidence floor V3: Leads complex AI projects or portfolios, integrating multiple AI initiatives to deliver strategic business value.
e-5 · evidence floor V3: Drives strategic AI project and portfolio management across the organisation, shaping innovation, governance, and AI capability development.

E.3 Risk Management

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e-2 · evidence floor V1: Applies defined risk management practices to AI systems, supporting the identification, documentation, and monitoring of AI-related risks within established governance and regulatory frameworks.
e-3 · evidence floor V2: Enables and coordinates AI risk management activities across AI lifecycle phases, ensuring that AI-related risks are systematically assessed, treated, and communicated in alignment with organisational risk appetite.
e-4 · evidence floor V3: Ensures the effectiveness and consistency of AI risk management across the organisation by defining AI risk controls, governance mechanisms, and assurance processes aligned with enterprise risk frameworks.
e-5 · evidence floor V3: Initiates and influences strategic AI risk management practices, shaping organisational risk posture for AI and aligning innovation, governance, and compliance objectives at enterprise and ecosystem levels.

E.4 Relationship Management

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e-3 · evidence floor V2: Independently maintains and nurtures relationships with stakeholders involved in AI projects. Coordinates routine AI- related tasks and facilitates communication across teams.
e-4 · evidence floor V3: Guides and coordinates stakeholders across multiple functions or teams in AI initiatives, fostering alignment and trust. Proactively manages expectations, addresses concerns, and resolves conflicts.

E.5 Process Improvement

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e-3 · evidence floor V2: Independently performs and monitors AI development and operational processes. Contributes to AI process evaluation, improvement suggestions, and risk awareness.
e-4 · evidence floor V3: Leads the design, implementation, and continuous improvement of AI processes. Ensures alignment with organisational objectives, ethical standards, and compliance.

E.6 ICT Quality Management

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e-3 · evidence floor V2: Ensures the quality of AI systems within a defined scope by applying and adapting quality governance practices. Defines and monitors AI quality indicators, evaluates AI outcomes, and identifies improvement actions. Contributes to risk-based decisions related to AI performance, reliability, and compliance.
e-4 · evidence floor V3: Leads AI quality management activities across projects or services. Establishes AI quality governance, policies, and assurance practices aligned with business and AI strategy. Evaluates AI quality outcomes, drives corrective actions, and ensures adherence to ethical, regulatory, and organisational standards.

E.7 Business Change Management

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e-3 · evidence floor V2: Manages small-scale AI transformation initiatives and defines business requirements for AI adoption. Monitors AI adoption metrics, identifies deviations, and conducts risk assessments addressing bias, model drift, and cybersecurity. Communicates AI benefits and limitations to stakeholders.
e-4 · evidence floor V3: Leads AI-driven transformation programs, designing change strategies covering structural, cultural, and stakeholder factors. Evaluates outcomes using AI metrics, integrates governance, ethics, and compliance, and promotes AI literacy and organisational readiness.
e-5 · evidence floor V3: Shapes enterprise-wide AI transformation strategy and embeds AI-driven change in long-term planning. Guides executive decisions, sets standards for ethical and sustainable AI adoption, and influences industry-wide practices.

E.8 Information Security Management

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e-2 · evidence floor V1: Applies defined AI security policies, procedures, and controls to AI systems and components under guidance. Contributes to the secure operation of AI-enabled services by following established governance, risk management, and incident response processes related to AI.
e-3 · evidence floor V2: Enables the effective security management of AI systems by implementing and adapting AI-specific security measures. Ensures that AI risks are identified, assessed, and mitigated within projects and operational environments, working with stakeholders to support secure and compliant AI use.
e-4 · evidence floor V3: Ensures the security, resilience, and governance of AI systems across organisational domains. Defines AI security strategies, governance frameworks, and risk management approaches aligned with business and AI strategies. Advises stakeholders on AI-related security, compliance, and trustworthiness requirements.

E.9 IS Governance

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e-4 · evidence floor V3: Leads the definition, implementation, and evolution of AI governance frameworks, ensuring consistent control of AI systems across the organisation. Integrates AI governance into enterprise risk management, compliance, and strategic decision-making.
e-5 · evidence floor V3: Shapes and ensures enterprise-wide AI governance strategy, aligning AI capabilities with long-term business objectives, societal expectations, and regulatory evolution. Exercises authority over AI governance direction and influences external stakeholders and ecosystems.