The engineering profession, in its broadest sense, revolves around transforming a need into a product. Whether we’re talking about software or hardware, requirements gathering is an essential step to bridge the gap between those who know the need and those who provide a solution.
With the rapid evolution of AI, it’s clear that many tasks currently performed by engineers can and will be automated to varying degrees. However, one step remains indispensable: the articulation of the need itself. Even in an AI-driven world, defining what the AI is supposed to do — and under which constraints— will always be a human responsibility
The Essential Role of Requirements in the Age of AI
AI excels at automating tasks: generating code, analyzing data, optimizing processes, and even designing systems. But it can’t (at least for now) autonomously determine the fundamental “why” behind a project — why we’re building something, what problem it’s supposed to solve, and what success looks like. This foundational understanding must come from humans: stakeholders, product owners, engineers, and users.
Consequently, requirements management tools — such as Reqchecker, Jama, and DOORS — will become even more critical in this context. These tools help organize, trace, and validate requirements to ensure that the product aligns with stakeholder expectations. In a future where AI is heavily involved in design and development (for instance, generative AI that codes directly from a set of requirements), it will be essential to define these requirements with precision and clarity.
The Probabilistic Nature of AI and Its Impact on Traceability
Another key challenge is that AI algorithms are inherently probabilistic and, to some extent, stochastic. Unlike classical deterministic algorithms, AI models — especially deep learning and generative models — often involve randomness in training and inference. Their outputs are thus subject to variability and may produce slightly different results under similar conditions.
This probabilistic nature limits their ability to guarantee 100% exactness in any domain, whether it’s software, hardware, or complex systems. Even with sophisticated interpretability methods (like SHAP or LIME), AI systems can rarely provide complete and deterministic traceability from input to output in the same way that a formal algorithm can.
As a result, even the most advanced AI systems cannot fully replace the need for a rigorous formalization of requirements and explicit traceability. Without this foundation, it would be impossible to ensure that the AI’s outputs truly meet stakeholder needs — especially in critical contexts like safety, security, or compliance.
AI as a Partner in Requirements Engineering
Interestingly, AI can also assist in the requirements process itself. For example:
- Structuring unstructured needs into formal requirements.
- Analyzing requirements for inconsistencies or ambiguities.
- Suggesting missing requirements or dependencies.
- Supporting impact analysis and prioritization.
This partnership between human expertise and AI assistance will elevate the quality and coherence of requirements engineering. But it’s important to remember that while AI can help improve requirements, it cannot create them from scratch in the absence of human guidance.
Shifting Roles for Engineers
As AI increasingly takes over execution tasks (coding, testing, simulating, etc.), engineers’ roles will inevitably shift toward:
- Understanding and articulating stakeholder needs.
- Verifying that solutions meet these needs.
- Managing requirements changes and ensuring traceability.
- Assessing risks, trade-offs, and impacts.
In essence, the human contribution will be less about “doing the work” and more about defining, supervising, and validating it.
What future awaits us?
The rise of AI will not eliminate the need for clear, structured requirements — on the contrary, it will make them even more essential. Requirements management tools will become critical hubs where humans define what AI must do. Meanwhile, engineers will evolve to focus more on capturing, analyzing, and refining the needs that drive product development.
Furthermore, because AI systems are inherently probabilistic and lack deterministic guarantees of exactitude, rigorous requirements management and explicit traceability will remain indispensable. This is the key to ensuring that even the most advanced systems continue to serve human goals effectively, reliably, and safely.