Artificial intelligence is no longer a dream of the future, but an everyday reality in many companies. However, anyone introducing AI quickly realizes that the requirements for such systems differ significantly from traditional IT solutions. It is not enough to simply describe functions; instead, topics such as data quality, transparency, ethics and responsibility come to the fore.
In this article, you will learn how requirements for AI systems differ, which new perspectives are becoming important and how a structured approach, for example according to IREB® or ISO/IEC 42001, clarity and security.
What distinguishes traditional IT from AI?
Traditional IT systems follow fixed rules. Functions are described, processes are defined and results are checked. Requirements can be formulated precisely: what should happen, when, how and by whom?
AI systems are different. This is because they are systems that learn, recognize patterns and make decisions independently. Their behavior is often probabilistic instead of deterministic. This brings with it new challenges:
- The results may vary depending on the data available.
- Decisions are not always transparent.
- Training data and its quality have a significant influence on system behavior.
- There is a risk of discrimination, bias or undesirable side effects.
The requirements for AI systems must therefore also be rethought.
New perspectives in requirements management
When companies plan AI systems, they should expand their requirements in a targeted manner. Traditional functional and non-functional requirements remain important, but they are no longer enough. Other dimensions need to be added:
- Transparency: How can we make it clear how decisions are made?
- Sustainability: What impact does its use have on people, society and the environment?
- Data responsibility: What are the requirements for data sources, anonymization and training processes?
- Control mechanisms: How can it be ensured that the system behaves as expected?
Such requirements cannot be dealt with using a checklist. They require interdisciplinary thinking and a structured, methodical approach.
Standards for orientation: IREB and ISO/IEC 42001
The good news is that there are now also proven frameworks for the special requirements of AI systems. Two of them are particularly relevant:
- IREB provides a sound methodical approach for professional requirements engineering. It helps to properly elicit and document requirements and to coordinate them with stakeholders, even in complex projects.
- ISO/IEC 42001 is the new international standard for an AI management system. It provides guidance on ethical, legal and organizational requirements and creates the structural basis for trustworthy AI.
Both standards complement each other. Together, they make it possible to implement AI projects in a transparent, responsible and targeted manner.
Conclusion: Thinking ahead about requirements pays off
Artificial intelligence opens up many opportunities - but also poses new questions for companies. Those who formulate requirements for AI systems with foresight create the basis for sustainable success.
Good requirements management creates clarity, promotes trust and prevents risks. It combines technical feasibility with social responsibility - and ensures that AI systems will continue to serve people in the future, not the other way around.
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Would you like to know why requirements are the basis for successful projects and how the IREB standard supports this? Then read the article:
IREB simply explained: Why requirements are the basis for successful projects
Training tip: IREB or ISO/IEC 42001 training courses at SERVIEW
With the IREB Foundation Level (CPRE FL) training or the ISO/IEC 42001 Foundation trainingat SERVIEW, you will be optimally prepared for the special requirements of AI projects. Learn how to professionally collect and document requirements and anchor them in the company.
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