AI Skills Intelligence & Workforce Reskilling
Identify the skills your workforce needs as AI changes how people work. This training course develops the ability to map changing tasks, assess evidence of proficiency and prioritise reskilling pathways that help employees apply new skills in their roles.
Prepare yourself for the AI Skills Intelligence & Workforce Reskilling course
Identify where AI changes the skills people need and decide which capability gaps to address first. This AI Skills Intelligence & Workforce Reskilling training course helps participants examine work at the task level, define the skills a role requires and assess how well existing evidence supports those requirements. It gives workforce and transformation teams a basis for deciding where AI literacy is sufficient and where employees need more applied or specialist development.
Participants use representative role profiles to design learning pathways and define what successful application looks like in the workplace. They consider how proficiency can be demonstrated, how employees can contribute to decisions about role redesign and how skills information should be reviewed. The training course is jointly marketed by Excentra AI Academy and Oxford Management Centre and uses a shared curriculum centred on evidence-based workforce planning.
Highlights of this AI Skills Intelligence & Workforce Reskilling training course includes:
- Task-level analysis of AI exposure and changing skills
- Role-based skills taxonomies and observable proficiency levels
- Evidence quality, fairness and privacy in skills assessment
- Reskilling pathways, internal mobility and human–AI role design
- Roadmap ownership, workplace application and performance measures
The learning journey
Course Summary Outline
Day 1
AI, Job Tasks & Future Skills
Day 2
Skills Intelligence & Capability Assessment
Day 3
Workforce Reskilling & Role Redesign
Day 4
AI-Enabled Learning & Skills Development
Day 5
AI Workforce Readiness & Transformation Roadmap
Is This Course Right for You?
The AI Skills Intelligence & Workforce Reskilling training course is designed for professionals who assess changing role requirements, lead AI capability development or plan workforce transitions.
- Strategic Workforce Planning Managers
- HR Analytics Managers
- Learning and Development Managers
- Organisational Design Managers
- Talent Development Managers
- AI Transformation Managers
- Digital Adoption Managers
- Workforce Capability Leads
Course Summary
- Generative and agentic AI across job tasks
- Task exposure, automation potential and human judgement
- Role-based skills taxonomies and proficiency levels
- Job profiles, assessments, workforce and labour-market data
- Reskilling priorities by value, feasibility and workforce impact
- Related skills, redeployment options and learning prerequisites
- AI literacy, applied practice and specialist pathways
- Simulations, coaching and workplace projects
- Reskilling phases, owners and dependencies
- Budgets, learner capacity, trainers and technology
Want the full outline?
Key Learning Outcomes and Objectives?
The AI Skills Intelligence & Workforce Reskilling training course equips participants to link changes in job tasks to decisions about workforce development. Participants learn to define observable proficiency, assess evidence of capability gaps and prioritise learning for specific job families. They also develop the ability to plan responsible changes to roles and track whether employees apply new skills at work, using measures that managers and workforce teams can review.
- Map the effect of AI on job tasks, role responsibilities and skills.
- Build a role-based AI skills taxonomy with observable proficiency levels.
- Assess skills-gap evidence for quality, bias and privacy risks.
- Prioritise reskilling and role redesign options by value and workforce impact.
- Design learning pathways that assess proficiency and workplace application.
- Develop a reskilling roadmap with accountable owners, milestones and performance measures.
The Excentra Learning Approach
Participants work with representative job-task profiles and synthetic or approved skills data. They map where AI could change work, then compare role requirements with evidence such as assessment results, work samples and self-reported confidence.
Participants use those findings to prioritise learning, assess how responsibilities could change and define how managers would evaluate workplace application. Illustrative scenarios can span public-service administration, energy operations, financial services, telecommunications and corporate support functions.
Six capabilities, and how far this course takes each one
Data Intelligence
Intelligent Monitoring
Document Intelligence
Workflow & Agent Design
Responsible Implementation
Business Transformation
Ready to take the next step?
Reserve your place, ask us a question, or arrange private training for your team.
Frequently Asked Questions
AI skills intelligence is the structured use of job-task, role and workforce evidence to identify the skills people need as AI changes their work. It helps teams distinguish demonstrated capability from a skill inferred from a job title or profile. Managers can use the findings to set development priorities and decide where further evidence is needed.
An introductory AI course builds familiarity with AI concepts and applications. This training course starts with the tasks employees perform and examines how AI may change the skills those tasks require. Participants then design role-specific learning pathways and define how they would assess proficiency and application at work.
Yes. AI can suggest skills associated with the tasks in a job description. Those suggestions describe possible role requirements, and evidence such as assessments or work samples helps establish what an employee can demonstrate. The course shows how to review inferred skills for omissions, bias and outdated assumptions.
Participants consider which workforce data is necessary, who should see it and how employees can query or correct an inaccurate skills inference. They examine how incomplete records or uneven access to development opportunities can affect an assessment. The course also covers human review before skills evidence is used in workforce decisions.
Yes. Excentra can discuss adapting the examples to your organisation’s job families, AI adoption priorities and skills assessment practices. Synthetic or approved workforce information can be used to reflect the roles you want to examine. Excentra Discuss your organisation’s requirements with Excentra.
Yes. The course explores how to identify transferable skills and assess how closely they match the requirements of a revised role. For example, an analyst’s experience checking data quality can support the review of AI-generated outputs. Participants use this analysis to identify learning prerequisites and assess potential redeployment options.
Participants map who performs each task, who reviews the output and who remains accountable for the decision. They consider how changing responsibilities can affect workload, job quality and the need for employee consultation. The resulting task map helps identify where people need new skills to work effectively with AI.
Participants develop a task-level AI exposure map, a role-based skills taxonomy and a capability gap matrix. They use these to prioritise reskilling, explore role redesign and design a learning pathway with proficiency assessments. The final AI Workforce Skills & Reskilling Roadmap brings these decisions together with owners, resource requirements and performance measures.
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