Artificial intelligence is changing the way we work. Tools like ChatGPT and GitHub Copilot can write entire passages of text, generate code, and interpret data. Companies hope these technologies will make their processes more efficient and help them bring their products to market faster. However, this raises a legitimate question: Will these tools and automated assistants render traditional requirements engineering obsolete? In a world full of prompts and machine learning, do we still need standards like the International Requirements Engineering Board (IREB) or the Business Analysis Body of Knowledge (BABOK)?
The tension between human analysis and AI-supported automation underscores the importance of stable foundations. It’s not an either/or question, but rather a question of a new interplay.
Standards Provide Clarity for Both People and Machines
For years, IREB and BABOK have represented structured approaches to requirements management. IREB provides a methodological foundation for those who systematically collect, document, review, and manage requirements. BABOK goes one step further, focusing not only on correctly recording requirements but also on anchoring them in the corporate context. What are the strategic goals behind this? How does a feature generate real business value?
These questions are more relevant today than ever before. AI systems do not operate in a vacuum; they need guidance. If clear instructions are not formulated, it should not be surprising if the AI misses the mark. The old saying “garbage in, garbage out” still applies, but the output is much more complex today.
Is the Role of a Prompt Engineer Similar to That of a Requirements Engineer?
Although prompt engineering is often considered a new discipline, upon closer inspection, it shares many similarities with traditional requirements engineering. A good prompt must be clearly formulated, contextually embedded, and precise. These are characteristics with which certified requirements engineers and business analysts are already familiar when it comes to requirements. This leads to a blurring of roles. Those who can define requirements precisely are well-suited to control AI models effectively.
In practice, the ability to break requirements down into understandable, structured units is not obsolete; it’s becoming more important. This is because modern AI systems require more structure, not less. The apparent intelligence of machines relies on clear inputs, comprehensible expectations, and a reliable framework. IREB and BABOK provide precisely this framework.
Furthermore, the project team remains responsible for the outcome. Even if AI provides suggestions or prepares decisions, they must be interpreted and evaluated by humans. This requires technical understanding and methodological foundations conveyed through standards.
Why AI Alone Is Not Enough and How Standards Make a Difference
AI can perform repetitive tasks, summarize texts, and deliver initial drafts. However, it cannot understand implicit expectations. It cannot recognize unspoken interests in stakeholder interviews. It also cannot make conscious strategic decisions. Even the best models have their limits.
Standards such as IREB and BABOK precisely fill these gaps. These standards provide project teams with concrete tools to understand the context, identify conflicting goals, and gain an overview of requirements. These standards also offer structures for managing new requirements in a flexible yet traceable manner, which is a key prerequisite for dynamic projects.
Projects Require Rules, as well as Individuals Who Can Apply Them
Projects that use AI also raise new questions. For example, how transparent are decisions based on machine learning? How can ethical responsibility be integrated into automated systems?
While standards such as IREB and BABOK do not provide definitive answers to these questions yet, they create a methodological framework for addressing them systematically. These standards provide project teams with the knowledge necessary to document requirements comprehensibly, record decision paths, and comply with regulatory requirements. With sensible further development, they can also integrate ethical guidelines.
Conclusion
AI is changing the way we plan and execute projects, formulate requirements, and make decisions. However, AI does not replace the fundamentals of good requirements management. In fact, because AI tools are only as good as their inputs, standards such as IREB and BABOK have become prerequisites for sustainable projects.
Therefore, certifications remain relevant because of, not despite, the use of AI models. They qualify individuals to formulate requirements in a comprehensible, strategic, and goal-oriented manner. They also help companies use new technologies responsibly.
Those who want to prepare their teams for future requirements should rely on proven standards and combine them with new skills. Our CPRE-Seminar bei microTOOL provides a practical foundation for doing so. It is ideal for those who want to apply requirements engineering in a professional and future-proof manner.