Requirements Quality AI: Enhance Your Workflows

Host Katie Huckett's picture alongside the topic of this webinar on The Collapse of Requirements Quality Under System Complexity – How AI Can Help

This blog recaps a section of our webinar, to watch the entire presentation, visit The Collapse of Requirements Quality Under System Complexity – How AI Can Help

Transforming Requirements Engineering with AI to Enhance Clarity, Consistency, and Scalability

As systems grow more complex, traditional processes struggle to keep up, ultimately impacting requirements quality.  AI can assist in processing the sheer volume of data, enhancing clarity, consistency, and scalability across workflows.

Join Katie Huckett, Product Line Manager for Advisor/AI at Jama Software, for an exclusive webinar exploring how AI is becoming an essential cognitive amplifier in requirements engineering. Discover how AI is redefining the way teams detect ambiguity, surface hidden conflicts, and maintain alignment at scale.

What You’ll Learn:

Understand why requirements quality is declining under modern system complexity.
Learn the hidden costs of poor requirements and why traditional practices fall short.
Discover how AI amplifies cognitive processing and improves requirements quality.
Explore practical steps for adopting AI in your engineering workflows.
Gain insights into the future of requirements engineering with AI.

The video below is a preview of this webinar, click HERE to watch it in its entirety

WEBINAR TRANSCRIPT PREVIEW

The Collapse of Requirements Quality Under System Complexity – How AI Can Help

Katie Huckett: Welcome, and thanks for joining. Today we’re going to talk about something many engineering organizations are experiencing, but rarely say out loud. Requirements quality is collapsing under the weight of modern system complexity. This session isn’t about tools, features, or automation for automation’s sake. It’s about why this problem exists, why traditional fixes are no longer sufficient, and why AI is becoming a necessity rather than a nice to have in requirements of engineering.

My name is Katie, and I lead product strategy focused on AI-driven capabilities and requirements management. I spend most of my time working with engineering teams in highly regulated complex industries, aerospace and defense, automotive, medical devices, and other systems where requirements quality is not optional. What I’m sharing today is based on what those teams are actually struggling with in practice, not theory.

Here’s how we’ll spend our time together. We’ll start looking at why requirements quality is breaking down despite increased process maturity. We’ll talk about the hidden costs of complexity and why traditional approaches no longer scale. Then we’ll look at how AI changes what’s possible, not as a replacement for engineers, but as a cognitive amplifier. And finally, we’ll discuss what this shift means for engineering organizations moving forward. We’ll have a brief Q&A portion before we conclude today. Let’s dive in.

Here’s the paradox we’re living in. Requirements practices are more mature than they’ve ever been. Teams have invested heavily in process, tooling, standards, and governance, and yet many organizations are seeing more rework, more late stage surprises, and more friction between teams than before. What’s important here is that this isn’t happening because teams stopped caring about quality. It’s happening because the nature of the systems we’re building has changed faster than the way we manage requirements. In other words, the rules of the game changed, but most practices did not.

Modern products are no longer confined to a single domain. A single system now routinely spans software behavior, physical components, data flows, safety constraints, regulatory requirements, and operational considerations. All of these elements evolve together, often on different timelines and often with different teams responsible for each part. As systems scale and change in parallel, the number of relationships between requirements increases dramatically, not linearly. And yet, many traditional approaches still assume that these relationships can be reasoned through manually during periodic reviews or checkpoints. The challenge isn’t capability or commitment. It’s that the structure of the work itself has fundamentally changed.

RELATED: Jama Connect Advisor™ Datasheet

Huckett: Before we go further, I want to ground this discussion in your experience. We’re going to launch a poll. Please take a moment to answer honestly. What is the biggest contributor to requirements quality issues in your organization?

Looks like we have the results in. In nearly every organization I work with, the answer is rarely just one of these. These challenges stack on top of each other, and that compounding effect is exactly what overwhelms traditional requirements practice.

Traditional requirements practices were built for a world where change was slower, and systems were more predictable. Reviews happened at defined milestones. Documents were relatively stable. Dependencies were fewer and easier to reason about. Today, however, requirements are changing continuously, often across teams working in parallel. When you apply periodic document-centric review models to this environment, gaps are almost inevitable. The process itself isn’t wrong. It’s just being asked to operate outside the conditions it was designed for.

It’s important to say this clearly. This is not a lack of skill problem. It’s not a lack of effort problem. It’s not a lack of accountability problem. It’s a structural mismatch between human cognitive limits and the complexity of modern systems.

One of the most dangerous things about requirements quality issues is that they rarely fail loudly. A single ambiguous requirement doesn’t stop a project. It quietly creates multiple interpretations. Those interpretations propagate into design decisions, test cases, and validation activities. By the time the issue is discovered, multiple teams have already invested time and effort based on different assumptions. And at that point, the cost isn’t just fixing the requirement. It’s undoing everything that was built on top of it.

RELATED: Buyer’s Guide: How to Select the Right Requirements Management and Traceability Solution

Huckett: Let’s do another quick poll. Where do requirements quality issues most often surface too late in your lifecycle?

Some interesting results here. Wherever this shows up in your lifecycle, the pattern is consistent. Humans don’t see the issue until it’s already costly. That’s not a vigilance problem, that’s a visibility problem. When quality issues surface, the instinctive response is to add more safeguards. That means more reviews, more sign-offs, more documentation. The problem is that these measures increase effort without increasing visibility. Teams end up spending more time checking artifacts, but not necessarily improving quality or alignment. In highly complex systems, quality doesn’t improve by adding friction. It improves by improving signal.

This is where AI fundamentally changes the equation. AI doesn’t get tired. It doesn’t lose focus. It doesn’t skip over sections because a document is long or familiar. It can continuously scan requirements, compare them, and look for patterns or anomalies across the entire system. That doesn’t replace human expertise. It supports it by ensuring that engineers are spending their time where judgment actually matters. In that sense, AI becomes part of the engineering infrastructure rather than a separate tool.

TO WATCH THE ENTIRE WEBINAR, VISIT:
The Collapse of Requirements Quality Under System Complexity – How AI Can Help

Author
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Katie Huckett

Senior Product Manager at Jama Software

One of the things I love about product management and customer experience is the craft of it. Finally, a discipline that allows me to draw from every aspect of my eclectically diverse skill sets and interests.

With 14+ years of Enterprise SaaS experience, I have come across many complex feature requests and use cases. Never one to back down from a good challenge, I enjoy pushing the limits of what is possible while ensuring the big picture aligns with the overall product vision and strategy.

Experienced in:
• Developing product vision, strategy, and goals
• Develop and validate personas
• Creating and maintaining product roadmap
• Gathering product requirements (from user research, market research, user feedback, internal stakeholders, etc.)
• Creating and maintaining product backlog including prioritization
• Leading cross-functional teams across multiple office locations
• Presenting to stakeholders and obtaining buy-in
• Technical, analytical, and strategic thinking
• Managing product lifecycle from concept to end-of-life
• Product marketing to both customers and end-users

Specialties: User Experience, Customer Experience, User Research, Market Research, User Stories, Graphic Design, Data Analysis, Enterprise SaaS, User Champion, Product Lifecycle, Product Strategy, Goal Creation and Tracking, Technical Communication, Value Proposition, Product Marketing, Product Positioning, Training, Product Demonstration, QA Testing, Client Engagement, Business Analysis, Go-to-Market Strategy, Customer Segmentation Analysis, Sales Engineering, Customer Success, Agile Software Development, SCRUM, Value Selling

Tools and Languages: PhotoShop, InDesign, Illustrator, CRM, CMS, Aha!, Trello, InVision, Camtasia, Google Analytics, Zendesk, Sketchbook, SharePoint, Eloqua, Jira, Slack, Scrivner, HTML (some), Markdown

[Webinar Recap] The Collapse of Requirements Quality Under System Complexity – How AI Can Help​