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AI can write requirements. But can you trust them?

In many engineering teams, AI is already part of the workflow. Engineers are using tools like Copilot and ChatGPT to draft requirements, generate test cases, and accelerate documentation, often without a formal rollout or defined process.

The appeal is obvious. Output is faster, more complete, and easier to produce.

But as AI accelerates generation, evaluation does not keep pace. Review processes built for human authoring speeds start to break down, and the bottleneck shifts.

Authoring is no longer the constraint. Evaluation is.

Generative AI and structured evaluation serve distinct roles. One produces. The other judges.

But neither works in isolation. Both depend on a clear, structured understanding of the engineering knowledge they operate on. Without that, outputs may look correct, but cannot be reliably assessed or trusted.

Organizations are already seeing the impact: requirements that pass early review but fail in testing, risk introduced into regulatory submissions, and systems that don’t fully meet their intended purpose.

In this recording, you will learn:

• Why requirements quality is fundamentally a governance and evaluation problem, not just an authoring challenge
• How separating generation and structured evaluation preserves integrity in AI-assisted workflows
• How teams are introducing milestone-based quality checkpoints to surface risk earlier in the lifecycle
• Practical approaches for detecting misalignment between intent and implementation before it reaches downstream teams
• How requirements quality data is being used to improve visibility across projects and programs

Why watch?

The teams getting ahead aren’t avoiding AI. They are adapting to a shift where speed is no longer the constraint. The challenge is ensuring evaluation can keep pace with generation, so AI can be used safely and consistently at scale.