"Let's give all employees AI training" is the right starting point. But the next question comes immediately: should everyone get the same training? The answer is no. A CEO, an HR specialist and a software developer expect different things from AI, face different risks and need different skills.
This guide maps roles to AI training needs so you can build the right programme.
The common base: awareness and safe use for everyone
Whatever their role, every employee needs two things:
- What AI is, and what it can and can't do: a shared language and realistic expectations.
- Safe-use rules: which data can go into which tool, and how to verify output.
This base can be built with short 2–3 hour sessions and should be a prerequisite for every other programme.
Role-based training map
| Role | What they want from AI | Priority training need |
|---|---|---|
| Senior leadership | Strategy, competitive advantage, investment decisions | AI strategy, return and risk assessment, regulation |
| Middle managers | Team productivity, process improvement | Identifying use cases, change management, guiding teams |
| HR | Job ads, interview prep, training content, employee communications | Productivity with GenAI, bias and privacy risks in hiring |
| Finance and accounting | Report interpretation, analysis, regulatory tracking | Analysis with GenAI, data literacy, verifying output |
| Marketing and sales | Content, customer analysis, proposals | Content and analysis with GenAI, brand and copyright risks |
| Legal and compliance | Contract summaries, legal research | Safe use, regulation (data protection, EU AI Act), governance |
| Operations and customer service | Automating repetitive work | AI agents and process automation |
| Analysts and reporting teams | Turning data into insight | Data literacy, machine learning without code |
| Software developers | Building AI-powered products | LLM application development (RAG, agents, evaluation) |
| Data scientists | Building and deploying models | Applied machine learning, MLOps |
| IT and infrastructure teams | Secure, scalable infrastructure | MLOps, DevOps and cloud, on-premise LLM infrastructure |
Managers: the decision-makers
Managers don't need to know how to build models. But they do need to answer:
- In which processes does AI create real value?
- How do we assess the cost and return of an AI project?
- What are the risks and how do we manage them?
That's why executive training should be strategic and decision-focused, not technical. A workshop built around the organisation's own use cases helps managers take ownership of the transformation.
Business units: where productivity is won
The fastest returns from AI usually show up in business units. But saying "use ChatGPT" isn't enough. Effective business-unit training:
- Uses examples from the department's real work
- Teaches effective prompting through practice
- Leaves the team with a reusable prompt library
- Covers department-specific risks: bias in hiring for HR, copyright for marketing, wrong figures for finance
Technical teams: the builders
Needs vary widely across technical roles. For software developers, LLM-based applications (RAG, agents, evaluation) are the priority; for data scientists, taking models to production (MLOps); for infrastructure teams, secure and scalable model-serving infrastructure.
The right sequence
For most organisations we recommend this order:
- All employees: awareness and safe use
- Managers: strategy and decision-focused training
- Business units: hands-on, department-specific GenAI training
- Technical teams: in-depth programmes chosen according to the organisation's AI roadmap
This sequence reduces risk early and lets each layer build on the previous one.
Conclusion
The right AI training starts with the right content for the right people. Our corporate training catalogue is built around these four layers, and every programme is tailored to your roles and processes. Contact us to work out which programmes fit which roles.
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