Requirements Engineering in the World of AI: What Matters Most
Artificial intelligence is fundamentally changing the way systems are developed, operated, and refined. As systems become increasingly dynamic and complex, the importance of clearly defined requirements grows. This is especially true in the context of AI: if you start out with a lack of clarity, you quickly risk losing control.
Even in the age of artificial intelligence, requirements engineering remains the key to designing robust, traceable, and verifiable systems. But what exactly matters—and how do traditional requirements differ from those for intelligent systems?
Why AI Has Different Requirements
In traditional IT systems, requirements can often be described precisely: What functions should the system perform? What data is processed? What is the workflow?
With AI systems, it’s different: Many of these aspects cannot be fully planned in advance. AI systems learn from data, respond flexibly to changes, and make decisions based on complex algorithms. This fundamentally changes the requirements process:
- Expected behavior must be viewed in statistical, not deterministic, terms.
- The quality and origin of the training data are becoming critical success factors.
- Transparency and fairness are just as important as efficiency and performance.
- Regulatory requirements, such as those set forth in ISO/IEC 42001, must be taken into account at an early stage.
In short: Requirements engineering is here to stay—but the context is becoming significantly more challenging.
New Focus Areas in AI Requirements Engineering
Effective requirements engineering in the field of AI goes beyond traditional methods and raises additional questions:
- What are the ethical and social requirements?
- How is it ensured that the system operates fairly and without discrimination?
- What are the requirements for data sources, training processes, and models?
- How can transparency be ensured even though the system makes complex decisions?
It's not just about technical specifications—it's also about responsibility, trust, and traceability.
A Structured Approach Is More Important Than Ever
Especially when it comes to new technologies, teams tend to just jump right in. But that can end up being costly—due to a lack of documentation, unclear objectives, or decisions that are difficult to understand. Structured requirements engineering based on established standards such as IREB® helps minimize risks.
Professional requirements engineering makes it possible to …
- to identify and involve relevant stakeholders early on,
- Systematically collect and document requirements,
- to establish robust validation processes,
- To version and further develop requirements in a flexible manner.
This provides clarity—and certainty—when dealing with a technology that is still in its infancy but heavily regulated.
Conclusion: AI requires a new awareness of requirements
Artificial intelligence is transforming not only technical processes but also the requirements for planning, implementation, and accountability. Requirements engineering serves as a bridge between technology, ethics, business, and compliance.
Anyone who wants to successfully develop and deploy AI systems today needs one thing above all else: clear, well-thought-out, and coordinated requirements —as well as people who know how to develop them professionally.
Recommended Reading: An Introduction to Requirements Engineering
Would you like to know how good requirements are developed and how to get started with requirements engineering? Read the article:
What Makes for Good Requirements? An Introduction to Requirements Engineering
Training Tip: IREB Training at SERVIEW
In SERVIEW’s IREB training courses, you’ll learn how to systematically capture, document, and manage requirements—even in the context of modern technologies such as AI. Ideal for anyone taking on a leadership role in digital transformation.
Learn more now: IREB Training Courses at SERVIEW
