Transforming Requirements Management in the Digital Thread Era

Product requirements are everywhere. In many companies, requirements are buried in market research, scattered across engineering docs, hiding in safety protocols, and flooding in from customer support. They’re often outdated, duplicated, or sitting in someone’s inbox gathering digital dust.

A recent Aras webinar, Rethinking Requirements Management in the Age of the Digital Thread, Cloud Infrastructure, and AI, explores ways to tame this complexity. Moderated by CMO Josh Epstein, the panel brought together four experts at the intersection of digital engineering, regulation, and AI: Rob McAveney, CTO at Aras; Michael Arnold, VP of Product and Technology at engineering solutions company Accuris; Nico Wägerle, co-founder and CEO at RegTech software company Certivity; and Rik Rasor, co-CEO at AI Marketplace.

Their discussion centered on how requirements, when treated as connected data by means of the digital thread, can unlock more traceable, AI-enhanced, efficient product development.

A new model for a connected organization

In response to growing requirements from various sources, many companies have tried to standardize on a single tool to manage them. But as McAveney explained, that’s rarely realistic. It’s difficult, if not impossible, for teams like software engineering, systems engineering, service, and manufacturing to all work with the same requirements tool.

To reframe how we think about managing requirements in the digital age, McAveney recommends treating requirements as a service. Rather than forcing every team into a single tool, this approach delivers requirements in a distributed, unified way. It entails aggregating requirements from internal and external sources and converting them into a common, certified format, then making them available to engineers in the tools they already use.

The digital thread, a core pillar of modern product lifecycle management (PLM), provides the connective framework needed for requirements as a service. It enables the structuring of requirements and the maintenance of traceability, even across cloud-based sources.

“I need access to all of the requirements that are applicable to me in one place at one time. And the only realistic way to achieve that is to treat delivery of them as a service.” — Rob McAveney, CTO, Aras

In this model, requirements aren’t static documents tucked away in a drive. They’re dynamic and ready to be used, updated, and traced across the full product lifecycle.

Building a foundation for traceability and compliance

So, what does this new approach to requirements look like in practice?

In most industries, engineers expect machine-readable data. Yet the information they receive arrives in formats written for humans: PDF standards, Word-based specs, and legal text from regulatory bodies. This results in manual workarounds, where engineers highlight key passages, retype them into PLM tools, or copy and paste them into spreadsheets. This wastes time, breaks traceability, and creates blind spots when teams make decisions based on outdated or incomplete information.

Arnold explains how Accuris helps customers (typically large manufacturers in highly regulated industries) convert unstructured materials like standards and specifications into structured formats like XML or JSON. This allows the data to integrate with modern engineering systems.

This is where change management and impact analysis come into play. When requirements are connected to the digital thread, organizations can quickly detect changes, determine whether they impact their product, and confirm that the right teams have responded.

To ensure that this structured data integrates seamlessly into existing ecosystems, Accuris uses Open Services for Lifecycle Collaboration (OSLC), an increasingly adopted set of open standards that allow data to flow between tools from different vendors. By aligning with OSLC, Accuris ensures structured data stays traceable and connected across the full product lifecycle.

« As long as the data we extract is compatible from an OSLC perspective, it creates traceability and connectivity into all the other systems. » — Michael Arnold, VP of Product and Technology, Accuris

Wägerle of Certivity described a related perspective on the regulatory front. For many companies — especially those where strict laws drive compliance — regulatory information is difficult to access or scattered across unreliable sources.

Certivity works with legal experts to identify official regulatory sources to remedy this. Once sourced, the documents are parsed into a structured format and broken into smaller, machine-readable snippets. Certivity then connects these snippets inside a graph-based knowledge model, allowing users to trace document relationships. If a paragraph changes in one place, the model automatically updates linked references, maintaining live traceability between documents.

« If one paragraph changes, you have direct traceability inside the graph, and that’s how you maintain compliance across evolving regulations. » — Nico Wägerle, Co-founder and CEO, Certivity

In both cases, the goal is to turn scattered, natural language data into structured, machine-readable requirements that can move through the digital thread. Without this transformation, requirements remain disconnected from the workflows that depend on them.

Transforming requirements management with AI

AI is a valuable tool in modern requirements management, but only when the data it works with is clean and connected through the digital thread. Rasor of AI Marketplace explained that structured requirements go beyond traceability to unlock AI automation, faster analysis, and smarter decision-making.

« If you have thread-connected requirements, you unleash the power, especially when it comes to time to market. » — Rik Rasor, co-CEO, AI Marketplace

When requirements are fully linked across the development lifecycle, AI can do more than simple tasks like extracting text and identifying keywords. It can detect inconsistencies, highlight downstream impacts, and recommend mappings between conflicting requirements. But without structure, these capabilities fall apart. Rasor outlined key applications of AI in requirements management:

• Parsing unstructured documents into machine-readable formats
• Mapping inconsistent requirement styles across teams or standards
• Recommending fixes where intent is similar, but phrasing differs

Additionally, Wägerle explained how Certivity’s knowledge graphs are essential for accuracy and make it possible for AI to work with the full context. This structured foundation enables advanced AI use cases like:

• Automatically generating test cases based on specific regulations
• Comparing new and previous document versions
• Querying regulatory content based on a product’s context

The more structure and context built into requirements upfront, the more reliable and powerful AI becomes downstream.

Moving toward standardization without disruption

One of the most consistent themes across the panel was the need to support organizations at different levels of digital maturity.

Not all teams operate in model-based environments. Some still rely on SharePoint, CSVs, or semi-structured documents. To be effective, a modern requirements strategy must meet teams where they are, delivering data in whatever format they need: XML, JSON, CSV, or others.

Flexibility is key to progress. Standards and data models constantly evolve, and solutions must accommodate that evolution. Still, the long-term value comes from building toward a unified model, where every input flows into the digital thread.

With Aras Innovator®’s open, API-first architecture, organizations don’t have to overhaul everything completely. They can integrate structured data sources incrementally, linking them to product records and systems as they modernize.

How Aras supports requirements management transformation

Requirements shape every part of the product lifecycle, from concept and design to compliance and maintenance. By embedding requirements into the digital thread, organizations can shift away from document-based processes and toward actionable systems. Aras Innovator makes this possible by connecting structured data from across the organization and surfacing it wherever needed.

To learn more, hear from the experts here.