Automatic Import and AI-Based Evaluation of Requirements in RM Tools

Why many companies believe automation is the solution

Many organizations today are under enormous pressure: increasing customer demands, shorter development cycles, and growing documentation requirements.

As a result, teams spend most of their time on administrative tasks instead of actual analysis or coordination.

Typical symptoms include:

• Requirements are manually transferred from Excel lists or PDF documents into tools.
• Different formats and spellings lead to chaos in the structure.
• Content evaluation remains superficial because there is simply not enough time.
• Changes or new versions regularly lead to confusion.

In this situation, the idea of automation sounds tempting. AI that automatically imports, sorts, and evaluates seems like the perfect answer to the increasing workload.

But this is precisely where the misunderstanding begins.

The reason for this lies not in the technology, but in the mindset. Many companies understand requirements management primarily as data management, not as a decision-making process. As a result, they look for solutions that take away tasks instead of improving them — even though these are tasks that have to be done anyway.

This leads to three typical misconceptions:

1. “Once everything has been imported, the work is done.”
   → But the real work only begins after the import: understanding, evaluating, coordinating.
2. “AI can objectively assess the quality of requirements.”
   → AI recognizes patterns, but it does not understand intentions, contexts, or dependencies. A requirement can be perfectly formulated in formal terms — but completely wrong in terms of content.
3. “Automation replaces know-how.”
   → In fact, it only replaces manual labor. Technical understanding remains indispensable.

An example illustrates this: As long as machines do not independently design, develop, and build products, humans must interpret requirements and derive solutions from them anyway. Fully automating the evaluation of incoming requirements is therefore understandable, but in the long run, it is the wrong approach. In short, anyone who believes that automation will make requirements engineering redundant has never really understood it.

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How to use automation and AI correctly

Automation in requirements engineering is not a panacea — but it is a powerful lever when used in a targeted and integrated manner. The key is that it reduces routine work without replacing the knowledge process or methodological control.

A tried-and-tested approach consists of three consecutive steps:

1. Automatic import — structure instead of chaos

The first step is to automatically import and structure incoming requirements documents (e.g., Excel, Word, or ReqIF files).

Modern tools such as reqSuite® rm recognize not only formal structures but also semantic relationships. This allows requirements to be automatically assigned to subject areas, components, or responsible parties — all within a consistent, versioned project.

This is crucial because only in an integrated RM environment do imported data remain isolated, but immediately become part of a traceable requirements model with links, histories, and responsibilities.

2. AI-based evaluation — recognizing quality instead of guessing it

In the second step, artificial intelligence takes over the preliminary content review. For example, it recognizes:

• incomplete formulations (“The system should be flexible...”),
• ambiguous terms (“fast,” “simple,” “cost-efficient”),
• or inconsistencies in content relating to specialist areas, system components, or projects.

This creates a data-based picture of the quality of the requirements — long before a human has read everything. However, the goal is not to make decisions, but to point out specific issues that require human attention. The key point is that these analyses are only useful if they take place directly in the context of a complete RM process — i.e., with traceability, versioning, workflows, and impact analyses. Only then can a selective evaluation become a truly useful result for the project team.

3. Rule-based processes — consistency throughout the entire RE cycle

Clearly defined rules are needed to ensure that the results of AI analysis have an impact in everyday life:

• Which requirements need to be reworked?
• Which ones can be processed directly?
• Which ones are transferred to which department?

Rule-based workflows allow these transitions to be seamlessly automated — for example, by automatically triggering quality checks, review processes, or approval steps.

But here, too, the same applies: such rules only have an effect if they are part of a complete RM process that links requirements with tests, risks, standard requirements, or design artifacts. Only an integrated solution can create the connection between analysis, tracking, and implementation that modern product development requires.

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Best practices: How AI and humans can work together optimally

Automation only unleashes its full potential when it is embedded in a clear methodology, a networked tool landscape, and a culture of responsibility.

Five proven principles have emerged in this regard:

1. “Automate preparation, not decisions.”
   Let AI do the groundwork, but keep specialist decisions in human hands.
2. “Remain critical of results.”
   AI is only as good as the data it has been trained on. Regular validation is essential.
3. "Make the process transparent.”
   All participants should be able to understand how evaluations are made and where the data comes from.
4. “Use AI for training, not for relief.”
   AI analyses can help improve writing quality and structuring skills within the team.
5. “Think in terms of systems, not tools.”
   An isolated AI tool can only solve subtasks. Real added value can only be provided by a system that integrates analysis, traceability and governance.

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Practical example: From Excel chaos to a structured basis for decision-making

A medium-sized supplier regularly received extensive lists of requirements from its OEM customers — mostly in the form of Excel spreadsheets with sometimes over 1,000 entries. Manual transfer and sorting used to take several days.

With automatic import into reqSuite® rm, these documents are now read in just a few minutes. AI-supported logic recognizes redundant requirements, flags unclear wording, and automatically assigns content to the correct components.

The result:

• Preparation time has been reduced by more than 70%.
• Errors caused by manual transfers have been virtually eliminated.
• Technical evaluation can be carried out directly in the tool — based on a clear structure and consistent relationships.

However, the final decision on which requirements are implemented remains with humans. Automation creates the basis — not the judgment.

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Why an AI tool alone is not enough

Many AI startups and specialized providers today promise to revolutionize requirements engineering with smart text analysis. However, they usually view requirements as isolated texts — without process, without history, without context.

In practice, however, requirements management is a networked system: Requirements are linked to test cases, risks, standards, architecture components, and stakeholders. Without these links, any AI analysis loses its connection to reality.

An AI tool can say that a requirement is unclear. An RM system also shows the impact of this ambiguity — for example, on functions, scope of delivery, or test cases. It is this connection that makes automation truly valuable.

That is why it is not enough to use a single “hip” AI tool. What modern product development needs is an integrated overall solution that combines automation, methodology, and process logic — like in an orchestrated platform where AI is not a foreign body but a natural element.

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Implementation: How to get started with AI-supported automation

A successful start depends less on the technology than on the approach.

A step-by-step approach is recommended:

1. Select a pilot project
   Start with a realistic but manageable set of requirements that reflect typical challenges.
2. Define process goals
   Do you want to reduce import time? Increase quality? Or improve transparency?
3. Check data quality
   AI can only work with consistent data. That's why it's worth cleaning up existing documents before using them for the first time.
4. Analyze and adjust results
   Initial results should be evaluated together with the specialist departments in order to identify misclassifications and refine rules.
5. Integrate in the long term
   Once the process is working reliably, it can be gradually extended to other projects — for example, in combination with test management or risk assessment.

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Conclusion: Automation does not replace requirements engineering — it enables it in the first place

Automation and AI are not a way out of requirements management. They are the next logical step in making it more efficient, objective, and transparent.

But anyone who believes that this solves all problems has not yet understood the core of the issue.

The biggest challenges in requirements engineering do not lie in capturing data, but in understanding, deciding, and communicating. These tasks remain human.

AI and automation create the conditions for teams to focus on precisely that — and for requirements management to become what it should be: the bridge between ideas, technology, and successful implementation.

In short: Automation is not a substitute for thinking — it is an invitation to finally do it again.