
This blog previews a section of our webinar on AI-Assisted Engineering. To watch the entire presentation, visit, AI-Assisted Engineering: Shaping the Future of Requirements Management
[Webinar Recap] AI-Assisted Engineering: How Data, Governance, and Trust Are Shaping the Future of Requirements Management
AI is transforming how engineering teams write, verify, and manage requirements. Across consumer electronics, industrial manufacturing, and energy, teams are leveraging it today to innovate faster, strengthen quality, and enhance compliance—from generating draft requirements and identifying risks to automating verification and ensuring governance.
In this session, Patrick Garman, Manager of Solutions & Consulting at Jama Software, explores how AI-assisted and autonomous engineering are shaping the future of product development and what it means for your organization.
What You’ll Learn:
- AI-assisted authoring to generate structured, high-quality requirements
- Maintaining end-to-end visibility across regulations, requirements, models, and tests
- Predictive analytics to detect conflicts, gaps, and compliance risks early
- Autonomous verification with suggested test coverage and verification methods
- Building governance and ethical practices into AI-enabled engineering workflows
- Leverage AI to accelerate innovation while maintaining compliance and safety across consumer electronics (Right to Repair, ESPR) and industrial manufacturing (ISO 13849, IEC 61508)
- How Jama Connect® supports smarter decision-making and accelerates innovation
BELOW IS A PREVIEW OF THIS WEBINAR. WATCH THE ENTIRE PRESENTATION HERE
TRANSCRIPT PREVIEW
Patrick Garman: Hi, and welcome, and thank you for joining us today to talk about AI-assisted engineering. My name is Patrick Garman, and I’m the Solutions Manager for Industrial and Consumer Electronics here at Jama Software. I’m here today to talk about the rise of AI in requirements management, what’s coming and what’s already here, and what do you need to know to prepare. And I’m also going to be joined by Katie Huckett, our product line manager for Jama Connect Advisor™ and AI for a chat about Jama Software’s point of view on AI and to answer some common questions. We all know AI has moved from theory to practice, and you don’t have to imagine tools that can, for example, analyze requirements against INCOSE rules and flag where they are out of compliance because Jama Connect Advisor already does this. But what if in addition to analyzing your requirements, AI could actually draft requirements and structured syntax, automatically generate verification plans, and keep traceability intact across the development lifecycle.
Maybe even take stakeholder text or regulatory language and translate it directly into models or requirements artifacts. And looking ahead, predictive AI, using AI to highlight gaps, surface risks, or conflicts between requirements before they turn into costly defects downstream. And again, all of this is either already happening or will be available very soon. Let’s think about AI-assisted requirements authoring, models that automatically generate draft requirements, and structured syntax like EARS that saves hours of work for your engineers because you are going to start with a set of draft requirements that can be based and pulled from your existing IP, your previous development efforts to kickstart new product development. Think about autonomous verification planning, using AI to suggest verification methods. Even write test cases with detailed test steps and coverage analysis directly linked to requirements. For example, our test case intelligence tool takes requirement statements and generates context-specific test cases with detailed test ups and links these automatically to requirements for traceability. And thinking of traceability, what about contextual traceability?
RELATED: Jama Connect Advisor™
Garman: Think about an AI tool that can match requirements semantically, helping you uncover impacts you might not otherwise have discovered until later in the development cycle. Or consider maybe importing a large set of requirements from an external store and having Jama Connect suggest where there might be links to existing requirements, drastically reducing what is currently a very manual and, well, very important, often tedious process. Using natural language to construct models, so think about being able to translate stakeholder inputs or regulatory text into SysML or requirements artifacts automatically. This is already happening to write software code. You think of Git’s Copilot, where you can enter a plain language prompt for a function or code script and it will generate the code for you.
And again, taking that further to predictive analytics. So again, using AI to predict requirements conflicts or uncover requirements conflicts, uncover gaps, surface risks before they become downstream defects. Now let’s take a minute and talk about how is this going to impact consumer electronics and industrial manufacturing companies? Well, for consumer electronics, this is going to mean faster innovation cycles. Requirements can be captured, written, and connected more quickly so products get to market faster. AI can also monitor regulations like right to repair and ESPR and flag when requirements need updating. Finally, AI lets us mine customer reviews and support tickets for insights that automatically generate or update product requirements.
In industrial manufacturing, the benefits are really in handling complexity. AI can help decompose large systems of systems projects like commercial elevator systems, industrial robots into consistent requirement hierarchies. It can automate the generation of safety compliance artifacts for standards like ISO 13849 or IEC 61508. And by feeding back IOT and sensor data, AI can also help us to write predictive maintenance and after sales requirements. Ultimately, for requirements management, it means faster authoring and verification, proactive compliance tracking, better use of real-world data, and competitive differentiation through speed and quality.
RELATED: Manage by Exception: Data-Driven Practices to Improve Product, Systems, and Software Quality
Garman: So this all sounds great. And the good news is that, again, this is all either available today or will be very soon. So what do you need to know to be ready? Well, when we think about preparing for AI-assisted and autonomous engineering, the first thing to recognize is that data readiness is absolutely key. We all know the saying, garbage in, garbage out, and it’s maybe never been more applicable. AI is really only as good as the information that it has to work with. So that means our requirements, our traceability links and test evidence need to be structured, high-quality, and consistent. And companies that invest in clean, well-governed data models, the kind that are supported by Jama Connect, will be in the best position to take advantage. Second, and I can’t stress this enough, it’s important to keep in mind that AI is not a replacement for engineers.
It is an augmentation. Think of it like a copilot. It can draft requirements, review, and analyze gaps, but accountability still sits with the engineering team. There must be human overview and review of these requirements. And that brings us to governance and trust. So we need validation workflows to make sure that no AI-suggested requirement ever bypasses human review, and audit trails will need to clearly capture what was generated by AI versus what was authored or approved by humans. And we’re going to need new KPIs to measure the impact. For example, what percentage of requirements are drafted by AI versus humans? How much time did we save in verification planning? Are defect rates dropping because we are catching missed requirements earlier? And finally, there is a strategic advantage to being an early adopter.









