Are you wondering where to start with your AI strategy? Do you have the feeling that you lack the necessary expertise or strategy to successfully integrate AI into your company? Don’t worry – this is exactly where we come in. Here is your personal checklist. Do you need help with implementation? Get in touch with us!
Checklist: 7 steps to your AI strategy (2026)
Checklist: Is your company ready for AI?
1. Lay the foundations
- Define corporate goals: Establish clear goals that are to be supported by AI (e.g. cost reduction, increased efficiency, new business models).
- Budget planning: Provide funds for AI initiatives, infrastructure and training.
- Involve stakeholders: Involve internal and external stakeholders at an early stage.
2. Employees and expertise
- Promote further training: Prepare employees for working with AI solutions.
- Promote internal collaboration: Intensify cross-departmental collaboration.
- Get experts on board: Build up internal AI experts or bring in external specialists -> This is where we come in!
3. Organisation and structures
- Analyse existing processes: Identify which processes can be optimised using AI.
- Develop a data strategy: Evaluate data sources, quality and storage.
- Define responsibilities: Set up AI managers and teams.
- Plan change management: develop strategies to promote the acceptance of AI in the company.
4. Technology and infrastructure
- Check IT infrastructure: Ensure that the existing infrastructure is suitable for AI applications (e.g. cloud, hardware).
- Establish data platform: Set up databases, data warehouses or data lakes.
- Ensure integration: Connect existing systems and new AI solutions.
5. Select AI solutions
- Define areas of application: Identify potential areas of application such as automation, customer analysis or predictive modelling.
- Evaluate tools and providers: Decide between ready-made AI solutions and customised development.
- Develop prototypes: Launch pilot projects to test the benefits and feasibility.
6. Legal and ethical aspects
- Ensure compliance: Comply with data protection and security requirements.
- Define ethical principles: Formulate guidelines for the responsible use of AI.
- Assess risks: analyse and minimise potential dangers (e.g. discrimination, wrong decisions).
7. Measurement and optimisation
- Determine success criteria: Define KPIs (e.g. productivity increase, sales growth).
- Monitor results: Regularly review the progress of AI implementation.
- Iterative approach: Continuously adapt and optimise projects.
Are you missing a partner for your AI strategy?
Creating and implementing a well-thought-out AI strategy can be complex – especially if the necessary expertise is not available internally. We support your company at every step of this checklist and accompany you from analysis to implementation. Our approach is clear, focussed and practical – far removed from the AI hype.
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The field of Artificial Intelligence (AI for short) deals with the automation of intelligent behaviour and machine learning. AI is used today in many different forms and "strengths" and is becoming more precise and widely applicable thanks to intensive research. Why does soxes use Artificial Intelligence? By using artificial intelligence in software or, for example, IoT systems, complex problems can be simplified or even completely automated. Repetitive or lengthy tasks can be taken over by AI, for example to free up resources for more effective activities. In addition, AI programmes can be used to support information-intensive decisions. Where has soxes used Artificial Intelligence? With the application Assessment Gate, soxes has implemented an AI-based personnel selection tool for precise suitability predictions with individual candidate assessments. The tool was "trained" with corresponding data about positions in companies and their requirements so that it can make recommendations about the suitability of potential candidates. Thus, Assessment Gate can be used as a support in the recruitment process of new employees.
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Artificial intelligence and related fields such as machine learning, image recognition or robotics form one of the most important innovation trends in IT today. They have been finding their way into software solutions for a long time, but are now also increasingly being used in smaller projects, start-ups and SMEs. AI has entered the mainstream and opens up many new application possibilities and thus business areas. For many decision-makers, it is clear that AI can now be used as an economic option, but how does AI work in the first place and what are the prerequisites for meaningful use?
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