Course Objective & Scope
In many organisations AI skill is reduced to a binary between "writing basic prompts" and "coding in Python", ignoring the distinct capabilities required by sales specialists, analysts, executives and engineers. Without a shared skills language, training budgets and hiring criteria miss their mark.
This course guides participants through building an enterprise AI skills taxonomy across technical, functional, governance and human-judgment dimensions, complete with a four-level proficiency rubric (Awareness, Practitioner, Builder, Mastery). Participants produce a role-to-skill matrix and a skill-gap assessment blueprint for their organisation.
Who Should Attend?
- Talent management, learning and development (L&D) and corporate academy leaders
- Organisational development and HR architecture/HRIS managers
- AI Center of Excellence and workforce transformation project teams
- Functional leaders defining career frameworks across business and tech teams
Key Takeaways
- Design a multi-tiered AI skills taxonomy spanning everyday users, citizen builders, AI engineers and executives
- Write observable, behaviour-based L1–L4 proficiency rubrics for each competency
- Embed critical verification, data literacy, responsible AI and agent oversight alongside prompt/workflow skills
- Measure unit-level skill gaps by comparing target role profiles against baseline assessments
- Connect the AI skills taxonomy to LMS learning paths, hiring rubrics and career progression frameworks
Curriculum
Positioning the AI Taxonomy Within Enterprise Competency Architecture
- Clarifying skills, competencies, capabilities and role profiles
- Integrating the AI layer cleanly into existing corporate competency models
- Drawing on global reference frameworks such as SFIA, ESCO, WEF and OECD
Designing the Four-Pillar AI Competency Tree
- Usage & Workflow: prompt/context engineering, enterprise assistants, low-code agent orchestration
- Data & Technical: data literacy, RAG/LLM architecture, evals, MLOps/LLMOps
- Governance & Risk: KVKK/privacy, security, hallucination/bias auditing, responsible AI
- Human & Cognitive: critical thinking, problem framing, decision quality and ethical judgment
Proficiency Rubrics (L1–L4) and Observable Behaviours
- L1 Awareness, L2 Daily Practitioner, L3 Workflow/Solution Builder, L4 Expert/Strategic Leader
- Writing evidence-based "can-do" descriptors rather than vague adjectives
- Mapping target proficiency profiles across job families and seniority levels
Competency Assessment and Skill-Gap Diagnostics
- Combining scenario-based practical tests, self-assessments and manager calibration
- Generating unit- and role-level AI Skill Heatmaps
- Separating fast-depreciating tool syntax from durable cognitive and architectural skills
Workshop: Building the Organisation's AI Competency Matrix
- Coding target AI skill levels for 6–8 core job families in the organisation
- Integration checklist for LMS, talent acquisition and career progression systems
- Peer review and calibration of the completed taxonomy and matrix
Other Courses in This Category
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For corporate academy, L&D and HR leaders: translating skill-gap analysis into persona-based upskilling journeys and structured reskilling bridge programmes for transitioning roles.
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For department managers and team leads: designing work allocation, orchestration, daily management rituals and trust calibration in hybrid teams where people work alongside autonomous AI agents.
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For professionals and team leads: writing unambiguous task briefs for AI agents, setting human-in-the-loop approval gates, defining escalation triggers and systematically auditing agent outputs.
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For HR leaders, line managers and compliance teams: redesigning OKRs/KPIs, fair performance appraisal, incentives and human accountability chains when work is co-produced with AI and agents.
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For knowledge workers, specialists and leaders: preventing cognitive offloading, automation complacency and skill atrophy while sharpening critical thinking and human judgment alongside AI.
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Transforms pioneering business-unit employees into AI Champions who drive grassroots adoption, discover micro-use cases, build safe prompt/workflow templates and coach peers across their teams.
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