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As organizations expand AI use, governance depends on people who can apply the rules to real decisions—not just on written policies. Training needs to match the learner’s role: broad, practical guidance for staff using AI, and deeper technical, risk, legal, and governance skills for specialists. Survey findings point to rising governance activity and skills concerns, but they do not establish one global training gap or prove that training alone solves governance problems.
Why AI governance is creating new training demands
AI governance covers how an organization develops, selects, uses, evaluates, and oversees AI. That work crosses functions: privacy, cybersecurity, data governance, IT, security, legal and compliance, and ethics may all have a role. Training is therefore not a single course for a single team. People need to understand what they are responsible for, how to identify risks in their work, and when to escalate an issue.
The IAPP and Credo AI AI Governance Profession Report 2025 is based on a spring 2024 survey of more than 670 respondents from 45 countries and territories, as well as seven company case studies. In that survey, 77% of organizations were working on AI governance; the figure was nearly 90% among organizations already using AI. These are survey results, not a census of organizations worldwide.
The same report found that 23.5% of respondents named finding qualified AI professionals as one challenge in delivering AI. Asked about future staffing, only 10 of 671 respondents (1.5%) said their organization would not need additional AI governance staff in the following 12 months. That answer reflects respondents’ expectations, not a measured hiring outcome.
What different groups need to learn
Staff who use AI in everyday work
Most employees do not need specialist-level knowledge of AI law or model evaluation. They do need role-relevant guidance on approved tools, appropriate data handling, checking outputs, recognizing errors or bias, and knowing when a task requires human review or escalation. Training is more useful when examples resemble the employee’s actual work than when it stops at general definitions.
Managers and workflow owners
Managers need to connect permitted AI use to how work is assigned, reviewed, and documented. They should understand which decisions require additional scrutiny, how to route questions, and how to identify when a workflow has changed enough to require refreshed guidance. Training cannot substitute for clear ownership, adequate resources, or operational controls; it should help people use those structures.
Rank #2
Technical and governance specialists
Specialists need a broader, interdisciplinary skill set: understanding AI systems, assessing risk, applying governance and compliance requirements, and translating legislation into actionable policies. Smaller organizations may ask one person to cover several areas; larger ones may distribute responsibilities among specialists. The IAPP and Credo AI report does not identify one team structure as universally best. It also notes that red teaming is likely to become a more necessary skill.
In the report, half of AI governance professionals were typically assigned to ethics, compliance, privacy, or legal teams. Reported primary functions also included privacy (22%), legal and compliance (22%), IT (17%), data governance (10%), ethics and compliance (6%), and security (5%). These categories describe respondents’ reported organizational placement; they are not a recommended staffing blueprint.
Rank #3
What the training evidence shows—and what it does not
Government training coverage varies by task
The OECD Digital Government Outlook 2026 reports 2025 data from 36 OECD countries. Thirty-two countries (89%) reported AI training programs for government employees. Coverage was more common for practical AI use and ethical use than for applying AI to public services or policymaking:
| Training area | Countries reporting programs | Share of 36 OECD countries |
|---|---|---|
| Practical AI use | 28 of 36 | 78% |
| Ethical AI use | 22 of 36 | 61% |
| Data privacy and security | 20 of 36 | 56% |
| AI use in public services | 13 of 36 | 36% |
| AI use in policymaking | 13 of 36 | 36% |
The figures count countries that reported training programs; they do not measure how many public servants completed training or whether it improved outcomes. The OECD describes approaches ranging from broad foundational learning and government-tailored fundamentals to specialized technical courses. Its point is that general familiarity does not automatically prepare someone for a particular public-sector task. As the OECD puts it, “Strong governance frameworks for AI must be matched by the skills and confidence of public servants who use AI tools in their daily work.”
UK employers report skills gaps and informal learning
UK evidence gives a separate view of workforce readiness. The UK Department for Science, Innovation and Technology’s AI Labour Market Survey 2025 executive summary says 97% of respondents identified at least one AI labour-market skills gap, while 88% of organizations relied on on-the-job training rather than structured education and training programs. These figures describe the UK survey, not employers globally.
A related UK government AI upskilling insight briefing reports employer training gaps in technical skills (67%), responsible and ethical AI (32%), and non-technical skills (10%). These categories point to different needs; they should not be treated as interchangeable measures of governance expertise. The briefing identifies common shortcomings in provision, including limited flexibility and accessibility, unclear skills frameworks, and insufficiently practical or contextualized learning.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose or design useful AI training
Use the training decision to specify who must do what differently at work. Compare options against these criteria rather than choosing a course by title alone:
- Audience and role: Is the material for all staff, managers, technical teams, or governance specialists?
- Learning outcome: Does it teach basic AI literacy, responsible use, applied workflow skills, risk assessment, or legal and governance implementation?
- Delivery: Is self-paced, live online, in-person, group, or embedded on-the-job learning accessible to the people who need it?
- Practice transfer: Will learners apply the material to actual decisions, tools, and workflows rather than only discuss hypothetical cases?
- Currency and jurisdiction: Does the content address the laws and policies relevant to the organization, and is there a process to refresh it when tools, tasks, or requirements change?
The UK government briefing says “the most successful approaches are those that are embedded in day-to-day work, easy to access, and designed to grow over time.” Treat this as practical guidance, not a guarantee of effectiveness for every organization. In practice, organizations can connect learning to approved workflows, make support easy to find, and revisit materials as roles and AI use evolve.
Where specialist training fits
For people responsible for AI governance and risk management, the IAPP offers AIGP training. Its AIGP training page lists online, live online, in-person, and group formats, with content covering AI technology, current law, risk management, and governance. The AIGP certification page lists digital study resources. This is one specialist option, not a universal requirement for everyone who uses AI or participates in governance.
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