AI-powered requirements management in Polarion with Garantis

Requirements management has always been about more than writing requirements. It is about keeping engineering knowledge understandable, reviewable, traceable and useful throughout the lifecycle of a product or system.

That is also why AI becomes especially interesting in this area. When AI is embedded directly into a requirements management tool, it does not need to be a separate chatbot on the side. It can become part of the actual engineering workflow: reviewing requirements, improving wording, checking quality, generating related artifacts and helping teams keep documentation up to date.

Recently I have been working together with Garantis on AI-powered applications for Polarion ALM, one of the established requirements and lifecycle management platforms used in regulated and engineering-heavy environments.

Garantis describes the solution here: AI Plugin for Polarion ALM

AI belongs inside the requirements workflow

Many AI experiments in engineering organizations start outside the system of record. A user copies a requirement into a generic AI tool, asks for feedback, copies the result back and tries to remember what changed.

That may be useful for experimentation, but it is not enough for serious requirements work.

In real requirements management, teams need:

• traceability
• version history
• review workflows
• access control
• auditability
• domain-specific quality rules
• alignment with standards and best practices

This is why embedding AI inside Polarion is so powerful. The AI can work where the requirements already live.

From text generation to requirement quality

The most valuable AI use cases are not only about generating more text. In requirements management, the higher-value use cases are often about improving the quality of existing content.

Examples include:

• rewriting unclear requirements into a more precise form
• checking requirements against INCOSE-style quality criteria
• validating requirement structure such as EARS patterns
• detecting ambiguity, missing context or weak acceptance criteria
• suggesting requirement decomposition
• generating test cases, use cases or acceptance criteria
• identifying duplicate or overlapping requirements with semantic similarity

This changes the role of AI. It is not just a content generator. It becomes a reviewer, sparring partner and quality assistant for engineering teams.

Always up-to-date and reviewed documentation

One of the persistent problems in requirements management is documentation drift. The specification is updated, but the related test cases are not. A requirement changes, but the acceptance criteria remain outdated. A review comment is resolved, but the surrounding documentation is not harmonized.

AI can help reduce this gap.

When requirement analysis, generation and review are available inside the tool, it becomes much easier to keep related documentation synchronized. Teams can continuously ask:

• Is this requirement still clear?
• Are the acceptance criteria still aligned?
• Are related test cases missing?
• Does this item duplicate another requirement?
• Has the requirement changed in a way that should trigger review?

This is not only efficient. It opens a different way of working: documentation can become something that is continuously reviewed and improved, not only cleaned up before a milestone or audit.

Human in the loop — at the right level

For regulated environments, the goal is not to let AI make uncontrolled engineering decisions. The goal is to make expert review faster, more systematic and better documented.

With applications like the Garantis AI Plugin for Polarion, the human can remain in the loop at the level required by regulations, quality systems or internal best practices.

In some cases, AI may only provide suggestions. In others, it may generate a draft that a requirements engineer reviews and approves. For more mature workflows, AI-generated checks can become part of a formal review or quality gate.

The important point is that the level of automation can be designed. AI does not have to mean loss of control. Used well, it can mean more transparency and better control.

Enterprise AI needs governance

Another important aspect of the Garantis approach is that AI is treated as a platform capability, not as isolated prompt usage.

The Polarion AI solution includes ideas such as:

• centralized AI control and governance
• reusable prompt libraries
• module-based capabilities for analysis, generation, similarity and integration
• Azure OpenAI, OpenAI API and local model options
• usage monitoring and cost visibility
• project-level configuration and access control

This is exactly the direction enterprise AI needs to take. The question is no longer only “which model should we use?” The more important question is: how do we make AI usage consistent, secure, governable and useful inside real engineering processes?

My work with Garantis and Reqtech

This cooperation also connects closely with the AI concept work I have been doing with Garantis and Reqtech around practical AI for requirements engineering.

My focus has been on turning AI from a generic assistant into a domain-specific capability for requirements management and quality improvement. That means working on concepts such as requirement review, ambiguity detection, acceptance criteria generation, test case generation, semantic similarity, human approval flows and enterprise deployment models.

The common theme is simple: AI should not sit outside the engineering process. It should support the process in a controlled way, inside the tools where engineers, analysts, testers and product teams already work.

For tools like Polarion, this is a very natural next step.

Closing thought

AI in requirements management is not just about faster writing. It is about better requirements, better reviews, better traceability and documentation that stays closer to reality.

That is why I see this as one of the most interesting new development areas in requirements and lifecycle management tools.

When AI is embedded into the tool, governed properly and kept human-in-the-loop, it can help teams move from static documentation toward continuously improved engineering knowledge.