Guides
What AI training should be given to employees?
Short answer
AI training for employees should be determined by role. A short common foundation covering data rules and output verification for all employees; generative AI tools and prompt writing for business units; strategy and risk for managers; AI-assisted development for software teams; and application development training such as RAG and agent architectures for data teams are appropriate.
Key concepts
Generative AI: The general term for AI tools that produce new content such as text, code, and images.
RAG (Retrieval-Augmented Generation): An architecture in which the model finds and uses relevant information from an organization's own documents before generating a response.
Which role should receive which AI training?
Every role has different needs; giving the same training to the entire company both bores technical teams and leaves business units behind.
| Role | Training focus | Learning goal | Example content |
|---|---|---|---|
| All employees | Literacy and usage rules | Using the tool safely, verifying output | Short internal module |
| Managers | Strategy, use cases, risk | Prioritization and investment decisions | Microsoft AI Strategies |
| Business units | Generative AI tools, prompt writing | Saving time in daily work | Generative AI Tools, Prompt Engineering & OpenAI |
| Software teams | AI-assisted development | Integrating an assistant into code, testing, and review | AI-Assisted Software Development with Claude |
| Data and AI teams | Enterprise LLM applications | Building systems that work with organizational data | Advanced RAG & Enterprise LLM Applications |
| Security and compliance | AI security | Managing model attacks and data leakage | Custom enterprise program |
Academy Club's AI catalog includes content covering most of these roles, from tool training for business units to advanced technical topics such as agent architecture and MLOps.
Is a common foundational training necessary for all employees?
Yes. Regardless of which tool is used, there is a short common foundation every employee should know:
- Data rules: Personal data, customer information, and trade secrets should not be entered into publicly available tools.
- Output verification: The model can produce convincing but inaccurate answers; responsibility lies with the user.
- Approved tools: Which tool can be used for which task is determined by organizational policy.
For organizations operating in the EU, this is a legal obligation: Article 4 of the EU AI Act requires that staff have sufficient AI literacy (Regulation (EU) 2024/1689).
Which trainings should be prioritized for technical teams?
For software teams, the priority is integrating assistants into existing steps such as testing, code review, and documentation; for data teams, it is building systems that work with organizational data and whose accuracy can be measured.
In regulated industries, security should be a separate topic. For example, the AI Security program Academy Club designed for Fibabank covered types of model attacks and the integration of generative AI tools into banking systems without data leakage (news).
How is priority decided among trainings?
Priority is determined by starting with the role that has the highest business impact and the most readiness.
- List the use cases. Write down which task AI will accelerate in each unit.
- Prepare the tools and policy. Ensure approved tools and rules are ready before training begins.
- Give everyone the common foundation. Start with a short, mandatory module.
- Deepen with a pilot role, measure, and scale. Start with the team where business impact will be most clearly visible, then move to other roles based on results.
Observations from the field: Where do organizations miss the role distinction?
Organizations most often struggle because they put all employees through a single awareness training and consider the matter closed. The observations below are based on recurring situations Academy Club has encountered in corporate technical training.
Awareness training doesn't change behavior. Participants learn what AI can do but don't see how to apply it to their own work. If a role-based hands-on session doesn't follow the common foundation, usage remains low.
Software teams want process training, not tool training. Most developers have already tried code assistants. The real value comes from the team deciding together how the assistant will fit into review and testing steps.
[EDITOR: A 1-2 sentence opinion from an Academy Club instructor on designing role-based AI training should be added to this section.]
Conclusion
AI training for employees should be planned with a common foundation for everyone, followed by programs tailored to each role's own work. If you would like to design a custom program for the roles in your organization, you can fill out Academy Club's training request form.
Frequently Asked Questions
Should AI training be mandatory?
It is recommended that the common foundation module be mandatory, as it directly reduces data security risk. Role-based trainings should be selected according to need.
Which tool should training be based on?
It is most practical to deliver training through the tool the organization has approved. If a tool hasn't been chosen yet, opt for content that teaches concepts independently of any specific tool.
How often should training content be updated?
It should be reviewed at least once a year. An interim update is needed whenever the organization's tool or policy changes.
Can employees without coding knowledge build automation?
Employees without coding knowledge can set up simple workflows using no-code tools. However, agents that access organizational data require security and permission design, so they should be set up together with the technical team.
Should managers attend technical trainings?
Technical depth isn't necessary, but it's important for the team to know the scope of the training they've received. A short management session makes it easier to allow time and permission for usage after training.