Research · AI Strategy & Adoption

AI Workforce & Skills

AI changes how work gets done in almost every role. Organizations that invest in skills, redesign roles and set clear rules for AI use get more value and less risk than those that simply hand out tools.

Why it matters

Employees are already using AI tools, often without guidance. Without training, they may trust incorrect output, share sensitive data or fail to use the tools at all. Regulation is also paying attention: the EU AI Act addresses AI literacy, although 2026 amendments shifted more of the responsibility for promoting it to the European Commission and Member States. Beyond compliance, the value of AI depends on people changing how they work.

Skill layers

• AI literacy for everyone: what AI can and cannot do, how to check outputs and what data may be used.
• Role-specific skills: using AI effectively in sales, service, finance, legal, engineering and other functions.
• Specialist skills: AI engineering, data, security, governance and evaluation.
• Leadership skills: redesigning processes and measuring AI outcomes.

Role and process redesign

Adding AI to an unchanged process tends to produce small gains. Larger gains come from redesigning workflows around what AI does well, clarifying where human judgment is required, and adjusting performance measures so people are rewarded for outcomes rather than volume of manual work.

Common pitfalls

• Rolling out tools without training or usage guidelines.
• Training that is generic rather than tied to real tasks.
• Cutting entry-level roles without a plan for developing future talent.
• Ignoring employee concerns about job security, which slows adoption.

How to get started

• Publish clear, simple rules for acceptable AI use.
• Provide baseline AI literacy training for all staff.
• Pick a few roles and redesign their workflows with AI, measuring results.
• Identify the specialist skills you must hire or develop.