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Quality, precision and speed for requirements engineering: AI agents as support

Requirements engineering is one of the most important phases in product development. It defines what the product does, who it is intended for, and under what conditions it meets technical, regulatory and user-driven requirements. When requirements engineering fails, the entire project can collapse — which is why quality is critical. With AI-based agents, requirements work can be streamlined, enabling projects to progress faster. AI doesn’t replace experts, but it gives them better tools to succeed.

AI enhances requirements engineering

AI enables faster, more accurate and more consistent requirement development. It supports engineers by organizing information, ensuring completeness and improving documentation quality. The following examples illustrate how AI can strengthen key parts of the requirements process:

Structuring information with AI

In the early stages of product development, information accumulates from many sources: standards databases, customer meetings and internal team documentation. Searching, combining and documenting data takes time, and forming a clear overview can be challenging. Generative AI (GenAI) can process and structure all relevant materials quickly and clearly. This way, all critical project information can be gathered in one place in a consistent format.

Navigating the regulatory jungle

Product development must take legislation, quality assurance and risk management into account. Documentation related to these areas can be difficult to interpret and time-consuming. GenAI helps identify essential regulations and automatically extracts the key points from large data sets. Users receive suggestions for relevant source materials and can focus on decision-making.

Quality and completeness

Good requirements documentation is based on clear, unambiguous and concise requirements. GenAI can express needs, expectations and constraints as requirements using natural language. It recognizes both explicit and implicit requirements and flags missing perspectives. AI leverages industry best practices and adapts requirement formulation to project needs.



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Traditional vs. AI agent–assisted requirements engineering

Traditionally, requirements engineering is performed manually, which takes time and exposes the process to errors. Documentation quality varies between individuals, and deficiencies are often discovered only in later stages — when correcting them is both expensive and slow.

AI brings speed, precision and continuous learning to the process:

  • Efficient data processing

    AI agents can quickly handle large amounts of information. They utilize project-specific data as well as previous requirement documents and public sources.

  • Consistent documentation

    The system operates objectively and consistently, producing high-quality, clear documentation regardless of the author.

  • Continuous learning

    The agent analyzes previous project outcomes and learns from them — identifying past errors, gaps and successes that it applies in future work.

  • Project-specific adaptability

    AI agents apply industry best practices and agile methods, such as user stories or user-need simulations.

High-quality requirements engineering is rooted in collaboration

AI agents support human-centered design. They use the material provided by users, identify missing information and request additional detail when necessary. Draft documents are generated automatically, and a human expert can review, refine and approve them. This enables specialists to focus on prioritizing requirements and making decisions.

Fewer errors — more value

AI-assisted requirements engineering improves project efficiency and reduces the risk of errors. When key requirements are identified early:

  • Projects progress faster, speeding up product launch and increasing sales.
  • Costs are reduced by avoiding late fixes and unnecessary work effort.
  • Products meet expectations more accurately, increasing customer satisfaction and trust.

Key takeaways: How AI improves requirements engineering

In AI-assisted requirements engineering, GenAI mines relevant data from large information sources, applies insights from previous projects to support new requirement sets, and automatically produces key documents on behalf of the user. This frees the project team to focus on more strategic tasks such as planning, decision-making and prioritization.

When human expertise is combined with the efficiency of AI, a collaboration emerges that improves product development quality, accelerates project progress and reduces errors. The result is products that accurately meet customer requirements — while saving time and cost.

About the author

Nea Mattila

Master's Thesis Worker

I’m a Biomedical Engineering student at Aalto University, graduating with my MSc in early 2026, with a background in Bioinformation Technology. I’m fascinated by the intersection of humans and technology, and how AI and medical data can be used to build tools that genuinely support people. I am currently working on my Master’s thesis for Etteplan, where I explore how large-language models can support real-world product development. I’m especially passionate about designing AI that enhances, rather than replaces, human expertise.