Requirements engineering in the world of AI: what matters
Artificial intelligence is fundamentally changing how systems are developed, operated and further developed. As the dynamics and complexity increase, so does the importance of clearly defined requirements. Particularly in the context of AI, the following applies: if you start unclear, you quickly risk losing control.
Even in the age of artificial intelligence, requirements engineering remains the key to designing resilient, comprehensible and verifiable systems. But what exactly is important - and how do traditional requirements differ from those for intelligent systems?
Why AI has different requirements
In classic IT systems, requirements can often be described precisely: What functions should the system fulfill? What data is processed? What does the process look like?
AI systems are different: many of these aspects cannot be fully planned in advance. AI systems learn from data, react flexibly to changes and make decisions based on complex algorithms. This fundamentally changes the requirements process:
- Expected behavior must be thought of statistically, not deterministically.
- The quality and origin of the training data becomes a critical success factor.
- Traceability and fairness are just as important as efficiency and performance.
- Regulatory requirements, e.g. according to ISO/IEC 42001must be taken into account at an early stage.
In short: requirements engineering remains - but the context is becoming much more demanding.
New focal points in AI requirements engineering
Effective requirements engineering in the field of AI goes beyond traditional methods and asks additional questions:
- What are the ethical and social requirements?
- How is it ensured that the system works fairly and without discrimination?
- What are the requirements for data sources, training processes and models?
- How can transparency be guaranteed even though the system makes complex decisions?
It's not just about technical specifications - it's also about responsibility, trust and traceability.
Structured approach more important than ever
Especially with new technologies, teams tend to just get started. But this can be expensive - due to a lack of documentation, unclear objectives or decisions that are difficult to understand. Structured requirements engineering according to established standards such as IREB® helps to minimize risks.
Professional requirements engineering makes it possible to ...
- identify and involve relevant stakeholders at an early stage,
- systematically record and document requirements,
- to set up well-founded validation processes,
- to flexibly version and further develop requirements.
This creates clarity - and security in dealing with a young but highly regulated technology.
Conclusion: AI requires a new awareness of requirements
Artificial intelligence is not only changing technical processes, but also the requirements for planning, implementation and responsibility. Requirements engineering is becoming a bridge between technology, ethics, business and compliance.
If you want to successfully develop and implement AI systems today, you need one thing above all: clear, well thought-out and coordinated requirements - and people who know how to develop them professionally.
Reading tip: Getting started with requirements engineering
Would you like to know how good requirements are created and how to get started with requirements engineering? Read the article:
What makes good requirements? Getting started with requirements engineering
Training tip: IREB training at SERVIEW
SERVIEW's IREB training courses teach you how to capture, document and manage requirements in a structured way, even in the context of modern technologies such as AI. Ideal for anyone taking on responsibility in the digital transformation.
Find out more now: IREB training courses at SERVIEW
