AI in Requirements Engineering: Useful — But Only If the Foundations Exist
By A. Perico
2 min read
AI can accelerate requirements work—but it cannot fix poor system definition or weak processes.
AI in Requirements Engineering: Useful — But Only If the Foundations Exist
AI is already useful in requirements engineering. It can summarize inputs, suggest structure, identify vague language, generate alternatives, compare versions, and accelerate review. The mistake is assuming that because it can improve requirement text, it can also repair a weak engineering operating model. It cannot.
If the underlying system definition is poor, AI usually produces a cleaner-looking version of the same confusion. That is why AI adoption in this space often creates the wrong kind of optimism. Teams get more content, faster, and mistake that for better engineering.
What AI is genuinely good at
AI is strong at pattern work. It can detect repetition, propose consistent phrasing, expose missing elements, and increase throughput for routine authoring and review tasks. In mature environments, that is valuable because it reduces manual friction around already-understood engineering work.
NIST’s AI RMF is helpful here because it frames AI in terms of trustworthiness and responsible use rather than magical replacement.
NIST says the AI RMF is intended to improve the ability to incorporate “trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems.”
That is the right mindset for requirements engineering as well. AI is a capability multiplier, not a substitute for sound definition.
Where AI reaches its limit
AI does not actually understand your product context the way your organization needs it understood. It does not own the tradeoffs, the stakeholder politics, the safety implications, or the operational consequences of a wrong assumption. It can infer patterns from your inputs. It cannot take engineering accountability for what the system is supposed to become.
That is why AI often produces “clearer ambiguity.” The wording improves, but the unresolved question remains unresolved. If the project has not agreed on boundaries, priorities, or verification logic, the model cannot conjure real agreement into existence.
AI works best where the foundations already exist
Where teams already have a stable system definition, explicit requirements structure, and usable traceability, AI becomes genuinely powerful. It can speed up reviews, support consistency, and highlight gaps that people would otherwise miss under time pressure.
But if traceability is weak, requirements are inconsistent, and change control is informal, AI mostly amplifies the current state of disorder. More automation then just means the organization can create bad artifacts faster.
Final thought
AI in requirements engineering is useful, but only under the same condition that applies to most engineering automation: the fundamentals have to exist first.
AI can help teams write, review, and scale requirements work. It cannot define the system for an organization that has not done the thinking.