- Foundational: Basic understanding of AI concepts, common applications, and general principles of how AI systems work.
- Comprehensive: Strong working knowledge of AI concepts, applications, and limitations, with ability to apply this knowledge in practical scenarios.
- Expert: Comprehensive understanding of AI principles, architectures, and applications across multiple domains, with ability to solve complex AI problems and guide teams.
- Visionary: Ability to predict and shape future AI developments, with deep understanding of how AI will transform industries and society. Can articulate long-term implications and guide strategic direction.
- Novice: Basic understanding of how AI can create business value and awareness of common use cases.
- Competent: Understands AI's business implications and can contribute to AI strategy discussions and implementation planning.
- Proficient: Can identify and execute AI business opportunities, develop business cases, and lead implementation projects.
- Strategic Leader: Ability to develop and implement AI transformation strategies at enterprise level, with proven track record of driving organizational change.
- Awareness: Basic knowledge of major AI players, tools, and platforms in the market.
- Navigating: Understands the AI vendor landscape and can select appropriate tools and partners for specific needs.
- Integrating: Can effectively combine multiple AI tools, platforms, and services to create comprehensive solutions.
- Shaping: Ability to influence and create AI ecosystems, build strategic partnerships, and drive industry standards.
- Basic: Can understand and convey fundamental AI concepts and requirements.
- Proficient: Strong ability to explain AI concepts and project outcomes to technical and non-technical audiences.
- Expert: Can effectively communicate AI strategies, implications, and results to various stakeholders at all organizational levels.
- Thought Leader: Ability to articulate complex AI concepts to diverse audiences and shape public discourse on AI developments.
- Basic Understanding: Awareness of common ethical issues in AI like bias, privacy, and transparency.
- Applied Ethics: Able to identify ethical concerns in AI projects and implement appropriate safeguards and mitigation measures.
- Ethical Leadership: Can develop and implement organizational AI ethics policies, lead ethics committees, and ensure ethical compliance across projects.
- Thought Leader: Shapes ethical AI frameworks and policies at industry/global level, anticipating future ethical challenges and developing mitigation strategies.
- Awareness: Basic familiarity with the structure and key AI regulations.
- Understanding: Good knowledge of the AI act-related regulations, ability to identify risk categories and their compliance requirements.
- Implementation: Can develop and execute compliance programs for AI systems after a risk assessment to ensure regulatory adherence.
- Policy-making: Ability to shape AI regulation and compliance frameworks, with deep understanding of international AI governance.
- Basic Data Literacy: Understanding of data types, basic statistics, and data visualization principles.
- Applied Statistical Analysis: Can perform statistical analysis, create data models, and derive and explain meaningful insights with data visualization.
- Advanced Analytics: Strong capability in complex statistical analysis, predictive modeling, and advanced data visualization for exploration and reporting.
- Data Science Integration: Expert-level ability to architect complex data solutions and integrate advanced analytics across an organization.
- Basic: Understanding of programming concepts and ability to write simple scripts.
- Proficient: Good coding skills in relevant languages, can implement AI algorithms and modify existing code.
- Advanced: Strong programming skills in multiple languages, ability to develop sophisticated AI applications.
- Master: Expert-level programming skills across multiple languages, can architect complex AI systems and optimize code performance.
- Beginner: Basic understanding of ML concepts and ability to use ML libraries/frameworks.
- Intermediate: Good understanding of common ML algorithms and ability to implement standard solutions.
- Advanced: Strong knowledge of ML algorithms, can design and implement complex ML solutions.
- Expert: Deep understanding of ML theory and ability to develop novel algorithms and architectures.
- Junior: Basic software development skills, can work on assigned tasks under supervision.
- Mid-level: Good software development skills, can independently implement features and solve technical problems.
- Senior: Strong software engineering skills with ability to lead development teams and design complex features.
- Architect: Can design and oversee large-scale AI software systems, making high-level technical decisions.
- Foundational: Understanding of basic MLOps concepts and tools for model deployment.
- Intermediate: Can implement and maintain standard ML deployment pipelines and monitoring systems.
- Advanced: Strong ability to set up and maintain ML pipelines and infrastructure at scale.
- Specialist: Expert in designing and implementing enterprise-scale ML operations infrastructure and practices.
- Basic User: Can effectively use common generative AI tools and understand their basic capabilities and limitations.
- Advanced User: Proficient in using and combining various generative AI tools to create sophisticated solutions.
- Innovator: Strong ability to identify and implement novel applications of generative AI across different domains.
- Developer: Can develop and customize generative AI models, understanding the underlying architectures and techniques.