On the targeted use of artificial intelligence in companies; [Vom zielführenden KI-Einsatz in Unternehmen]
Keywords:
Artificial Intelligence, Business, AI Strategy, Digital Transformation, Automation, Data Management, Corporate InnovationAbstract
This article discusses the targeted use of artificial intelligence in companies and considers how organizations can gain practical value from AI rather than adopting it simply because the technology is available. Artificial intelligence can support many business activities, including information processing, forecasting, automation, customer services and internal decision-making. However, successful implementation depends on identifying suitable tasks and matching them with realistic technical and organizational requirements. The article focuses on the importance of selecting clear use cases in which AI can solve an existing problem or improve an established process. Data quality is a central factor because unreliable, incomplete or poorly structured information can limit the usefulness of AI-based systems. Companies also need to consider data protection, transparency, security and the responsibilities of employees when automated systems are introduced. A targeted approach can help organizations avoid unnecessary investments and make it easier to evaluate whether an AI application delivers measurable benefits. The article emphasizes that AI should be integrated into business processes with a clear purpose and appropriate human oversight. Employees remain important because they provide professional knowledge, evaluate results and handle situations that cannot be solved automatically. The discussion therefore places AI within a broader digital transformation process rather than treating it as an isolated technology. Practical planning, suitable data, responsible implementation and continuous evaluation are presented as important conditions for using artificial intelligence effectively in companies. Clear governance is equally important, because companies need defined responsibilities for AI systems, regular review of performance and a realistic understanding of where automated methods can and cannot be trusted.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). This permits anyone to share, copy, redistribute, remix, transform, and build upon the material for any purpose, including commercial purposes, provided that appropriate credit is given to the original author(s), a link to the license is provided, and any changes made to the material are indicated.



