Workforce development in the age of AI can help people learn to use new tools safely, adapt as tasks change, and demonstrate skills employers need. Effective program design connects learning to real work while accounting for participants’ varied access, experience, and support needs.
Consider Change at the Task Level
AI exposure does not by itself show that a particular job will disappear or that a worker’s duties will change in a specific way. The International Labour Organization’s 2025 global index of occupational exposure assesses exposure using task-level data and notes that transformation of jobs is the most likely impact of generative AI because most occupations include tasks requiring human input. Its findings are broad estimates, not a prediction for an individual employer or worker.
For workforce planning, examine the tasks in local occupations, identify which may change, and ask employers and workers what skills are needed. Revisit the assessment as tools and job practices change.
Four Priorities for Workforce Programs
Build practical AI literacy
Participants should practice identifying appropriate uses, protecting information, checking outputs against reliable sources, and recognizing when a result needs human review or escalation.
Teach through relevant workflows
Use practice activities that reflect the tools, documents, and decisions participants may encounter. Explain the limits of a demonstration: completing a training exercise alone does not establish readiness for every workplace or role.
Make access part of program design
Ask participants about device and connectivity access, language, disability-related accommodations, digital confidence, and the cost of required tools. Plan support and alternatives rather than assuming everyone begins with the same resources.
Keep employer input current
Invite employers and workers to review changing tasks, the skills required, and how participants can demonstrate them. Employers can also explain their AI-use expectations, data safeguards, and where human accountability remains.
What Employers Can Contribute
Employers may share appropriately anonymized workflows, clarify job requirements, offer work-based learning, and help assess whether training applies to the work. Set clear limits on what information can be shared, and do not expose confidential or personal information in training exercises.
Frequently Asked Questions
What AI skills do workers need?
Needs vary by occupation and task. Useful foundations may include safe use, clear instructions, source verification, workflow judgment, information protection, and knowing when to ask for help.
Will technical training alone be enough?
Training should combine tool familiarity with relevant domain knowledge, communication, critical evaluation, and practice handling exceptions. The balance depends on the job and the tools used.
How should programs measure success?
Use measures connected to program goals, such as demonstrated skills, completion, placement, retention, wage progression, employer feedback, accessibility, and application of learning at work. Interpret results in context and avoid attributing changes to training alone without evidence.