AI competence Checklist for companies

> Key Takeaway: This checklist helps companies assess their AI readiness: data quality and availability, technical infrastructure, AI competency in the team, defined use cases with measurable ROI, and a clear governance structure for responsible AI deployment.

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The introduction of artificial intelligence in the company can seem overwhelming.

Where do you start?

What must be considered?

This practical checklist takes you step by step through the process – from the first inventory to the successful implementation.

Use them as your personal roadmap for AI competence.

Phase 1: inventory & strategy

Creating basic principles

Strategic preparation

Define #### AI vision

Create clear goals: What do you want to achieve with AI? Efficiency enhancement, innovation, cost reduction?

Conduct actual analysis

Capture the current status: What AI tools are already used? What competences are there?

Identify Use Cases

Find specific applications with high benefits and low risk for entry.

Plan your budget

Calculate costs for tools, training, external advice and internal resources.

Phase 2: Governance & Compliance

Set the framework

Legal and organizational basics

EU AI Act Check Requirements

Make sure that your AI usage meets the requirements of the EU AI Act (Article 4: AI Literacy).

Create AI guidelines

Develop internal guidelines for responsible handling of AI tools.

Ensure data protection

Check GDPR compliance and define which data may be entered in AI systems.

Determine responsibilities

Name an AI officer or a competence team as a central point of contact.

Phase 3: Qualification & Training

Building skills

Employees empower

Determining training needs

Analyze which groups of employees need which AI skills.

Select training formats

Decide between presence training, e-learning, workshops or blended learning.

Basic training for all

Run a basic training on AI foundations for the entire workforce.

Offer special training

Provide deeper training for power users and specialist departments.

forming AI-champions

Identify and train internal multipliers that support colleagues.

Phase 4: Implementation & Rollout

Translating into practice

introduce AI tools

start a pilot project

Start with a manageable pilot project in a selected department.

Write feedback

Create feedback channels and evaluate experiences systematically.

Stepwise rollout

Expand the deployment based on the learnings from the pilot project.

Communicating successes

Share positive results and best practices across the company.

Phase 5: Continuous improvement

Optimizing the long term

Ensure sustainability

define and measure KPIs

Set measurable success criteria and track them regularly.

Regular refreshments

Plan continuous Wei

References and further reading

Short: The following independent references complement the topics in this article:

The following independent references complement the topics in this article:

• Bitkom – German digital industry association
• German Federal Office for Information Security (BSI)
• European Commission – Digital strategy
• MDN Web Docs (Mozilla)
• W3C – World Wide Web Consortium

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