Rethinking Requirements: How AI and the Digital Thread Are Transforming Product Development

Summary

Requirements management is often treated as a box-checking exercise. It’s seen as necessary, but not strategic. However, in today’s complex, regulated and fast-paced engineering environments, this mindset is no longer sustainable. Requirements are the backbone of successful product development. They define what must be built, under what constraints and why it matters. When managed effectively, they serve as a connective tissue linking design intent, regulatory compliance and system behavior. When mismanaged, they introduce confusion, trigger rework and increase risk across the product development lifecycle.
 
The landscape is changing fast. Innovations in AI, the growing adoption of model-based systems engineering (MBSE) and heightened focus on a consistent, organization-wide digital thread are reshaping how organizations approach requirements. To keep pace, it’s time to move beyond static documents and embrace a modern, data-centric approach. This means treating requirements as dynamic, living assets.
 

Creating a digital backbone

Historically, requirements have been authored and stored in word processors, spreadsheets or PDF documents. These formats may be easy to create, but are very difficult to trace, reuse, or validate at scale. As a result, it’s nearly impossible to answer critical questions like: Which design decisions were made based on this requirement? What test cases validate it? Has it been impacted by a regulatory change?
 
A modern approach treats requirements as structured, queryable data. In a connected system, they are enriched with metadata, linked to downstream activities and aligned with both business and engineering goals. This data-centric view enables true traceability. It connects initial stakeholder input to design, testing and regulatory compliance.
 
Beyond improving traceability, this structure enables automation. Requirements can be verified against models, checked for duplication or ambiguity and used to generate or validate test cases. When a requirement changes, the system can automatically notify stakeholders of downstream impacts. These capabilities are essential in fast-paced, high-risk industries such as aerospace, automotive, energy or medical devices.
 
This digital backbone forms the foundation of the broader digital thread. It connects product information across domains and throughout the lifecycle. Requirements no longer live in isolation; they can become dynamic, contextualized inputs that drive decisions and validate outcomes.
 

Optimizing AI use

AI can play a transformative role in modernizing requirements management when applied thoughtfully. The goal isn’t to “AI everything.” Instead, it’s to identify opportunities where automation, prediction or language understanding can reduce effort and improve quality.
 
There are already clear use cases where AI is adding value. It can assist in drafting initial requirements, breaking down regulatory text into structured formats, or analyzing large volumes of stakeholder input. Natural language processing helps identify vague language, enforce writing standards and classify requirements by type or domain. Machine learning algorithms can identify patterns in historical defect data to flag potentially risky requirements.
 
AI must be deployed responsibly–especially when it comes to handling sensitive product and customer information. Unrestricted data may be safe for broader use. But sensitive data should only be accessible to individuals with the proper access controls in place. Confidential information should never be made available to uncontrolled AI models.
 
Ultimately, AI should be viewed as an enabler, not a replacement for expert human judgment. The most effective systems will combine AI’s speed and scale with human oversight and domain expertise.
 

Enabling interoperability

One of the biggest challenges in requirements management is fragmentation. Different teams use different tools, formats and vocabulary. This leads to silos, miscommunication and duplication of effort. These challenges are amplified in large, distributed organizations or multi-vendor programs.
 
Industry standards play a critical role in breaking down silos by providing common frameworks for structuring and exchanging information. When teams align on open, supported protocols, it becomes easier to connect requirements across design, verification, safety and compliance–even when using different tools or working across domains.
 
Beyond technical interoperability, semantic alignment is just as important. Requirements written by different teams may use similar terms to mean different things–or different terms to mean the same thing. This makes automation difficult and increases the risk of misinterpretation.
 
A robust requirements framework includes shared vocabulary and ontologies that define what key terms mean and how they relate to one another. This semantic layer provides the groundwork for AI, automation and traceability–reducing the risk of errors. It also enhances collaboration across engineering, regulatory and business stakeholders. It reinforces the need to keep a human in the loop.
 
For organizations operating across multiple regulatory jurisdictions, semantic consistency also helps streamline compliance. When requirements are traceable to specific regulations and interpreted consistently across teams, the risk of audit issues or non-compliance drops dramatically.
 

Taking a phased approach

While the benefits of modern requirements management are compelling, the path to get there can feel overwhelming. Most organizations rely on legacy tools, scattered documents and complex processes in place. A rip-and-replace approach is rarely realistic.
 
Instead, the best approach is to start small. Begin by identifying where requirements live today, how they are structured, who uses them and what pain points exist. Then define a target state that aligns with business goals. This might mean improving regulatory readiness, accelerating product cycles, or reducing risk.
 
From there, adopt a phased plan. Focus first on high-impact, achievable use cases. For example, you might begin by structuring regulatory requirements into a shared database, linking them to engineering requirements and setting up basic traceability. Or you might automate the review process for new requirements using AI-powered consistency checks.
 
Delivering early wins builds momentum, earns stakeholder trust and helps refine your approach before expanding to broader use cases. Over time, you can extend the digital thread, integrate additional tools and evolve toward a fully data-driven, AI-enabled strategy.
 
It’s also important to build the right team. Successful transformation requires close collaboration between engineering, compliance, IT and AI experts. Each brings essential insights. Having them involved from the start strengthens alignment and long-term success.
 

Requirements are a strategic asset

Too often, requirements are seen as a necessary evil. Static documents that must be created and reviewed before the “real work” begins. But in a modern product development organization, requirements are strategic assets. They encapsulate intent, drive design, enable validation and document compliance. When structured properly, they can also serve as inputs to simulations, digital twins and predictive analytics.
 
By embedding requirements into the digital thread, organizations can ensure that every decision–from design to production to maintenance–is traceable to the original intent. This improves accountability, reduces rework and enables faster adaptation to change.
 
Modernizing requirements management isn’t just about better tools. It’s about adopting a new mindset. One that treats requirements not as static deliverables, but as living, evolving data that drives innovation, quality and compliance. Organizations that embrace this shift will be better equipped to manage complexity–and better positioned to seize new opportunities.