Waltham, MA—Hosted by Aras and moderated by Josh Epstein, Chief Marketing Officer at Aras, the webinar “From Strategy to Execution: How MIT Lincoln Laboratory Advances Digital Engineering” brought together leaders from MIT Lincoln Laboratory, INCOSE, and AMC Bridge to explore how organizations are connecting engineering information, improving decision-making, and accelerating innovation through digital engineering. The discussion provided practical insights into how digital threads, model-based systems engineering (MBSE), and AI-enabled technologies are helping organizations create more connected and resilient engineering environments.

The session featured Denise Fitzgerald, Group Leader at MIT Lincoln Laboratory, who shared firsthand lessons from advancing digital thread initiatives across complex research and engineering programs. Joining the discussion were David Long, Director for Strategic Integration at INCOSE, and Igor Tsinman, President and CEO at AMC Bridge, who examined how organizations can scale digital engineering practices through lifecycle connectivity, systems thinking, and emerging AI capabilities.

As manufactured products become increasingly complex, organizations across aerospace, defense, industrial manufacturing, and other engineering-intensive sectors are seeking better ways to connect requirements, models, simulations, test data, manufacturing information, and operational knowledge across the entire product lifecycle. The discussion highlighted how digital engineering initiatives are moving beyond tool adoption toward creating connected ecosystems that improve traceability, collaboration, and engineering productivity.

“We're not trying to put everything into the digital thread. We're trying to make information available in a way that helps people make better decisions,” Denise Fitzgerald, Group Leader at MIT Lincoln Laboratory.

Key Themes from the Webinar

From Models to Knowledge

While the first generation of MBSE focused primarily on models, methodologies, and tools, organizations are increasingly focused on connecting engineering knowledge across disciplines and lifecycle stages. According to David Long, Director of Strategic Integration at INCOSE, many organizations are now moving beyond isolated systems engineering practices and integrating them with digital engineering initiatives across the broader enterprise.

“Systems engineering was never intended to be a silo. It's intended to be a through-life, systemic, transdisciplinary concern," said David.

Knowledge graphs, semantic relationships, digital threads, and increasingly accessible simulation capabilities are becoming fundamental tools for managing engineering complexity and supporting more informed decision-making throughout the product lifecycle.

Digital Threads as a Business Enabler

A central theme throughout the discussion was the growing importance of the digital thread as a foundation for modern digital engineering. Moderated by Josh Epstein, the conversation highlighted how leading organizations are creating context-rich environments that connect people, processes, and information across the product lifecycle.

Panelists emphasized that successful digital thread initiatives are not about consolidating every piece of engineering data into a single repository. Instead, they focus on establishing relationships between information sources, enabling teams to access trusted, connected information that supports faster and more informed decisions.

Denise Fitzgerald, Group Leader at MIT Lincoln Laboratory, stressed that the value of a digital thread extends beyond technology itself, enabling engineers, program managers, and stakeholders to access the information they need when they need it.

“The digital thread isn't just about technology. It's fundamentally about people. Making information visible and accessible helps teams identify issues early and enables individuals to do their jobs more effectively,” said Denise.

Providing a real-world example of digital thread implementation, Denise explained how MIT Lincoln Laboratory leverages Aras as a key component of its digital engineering ecosystem. By helping connect engineering activities, business systems, and cross-functional teams, Aras supports the visibility, traceability, and information accessibility required to enable effective decision-making across the lifecycle.

“Aras manages much of the digital thread and helps connect people, tools, and business systems into a cohesive environment,” she added.

The discussion reinforced a broader industry shift toward connected engineering environments where digital threads serve as strategic business capability. By improving visibility across disciplines and enabling traceability between data sources, organizations can strengthen collaboration, reduce information silos, and improve engineering outcomes throughout the lifecycle.

The Growing Role of AI in Engineering

Artificial intelligence is creating new opportunities to improve engineering workflows by helping teams discover information, navigate complex engineering environments, preserve institutional knowledge, and collaborate more effectively across disciplines.

Denise described AI's growing potential as an engineering assistant, capable of supporting engineers throughout the development lifecycle.

“We see many opportunities for AI as an engineering assistant. It can help generate requirements, build interface models, automate aspects of verification and validation, and identify weak points in the digital thread,” she said.

Panelists agreed that the value of AI in engineering will depend heavily on its ability to operate within a connected digital environment. Access to trusted enterprise data, governed processes, and lifecycle context will be essential for enabling AI systems to deliver meaningful and reliable engineering insights.

Improving Engineering Productivity

Another recurring theme was engineering productivity. Panelists noted that engineers often spend significant time searching for information, locating expertise, and navigating disconnected systems. Digital thread initiatives can help address these challenges by making information easier to discover, understand, and reuse across teams and programs.

As organizations advance their digital maturity, the focus is increasingly shifting toward lifecycle-wide integration.

“Organizations are asking: How do we better connect our systems engineering practices to the larger engineering lifecycle and the broader enterprise?” said David Long.

Making Digital Engineering More Accessible

During the panel discussion, Igor Tsinman, President and CEO at AMC Bridge, shared perspectives on the practical implementation of digital engineering and MBSE strategies, drawing on AMC Bridge's experience helping engineering organizations adopt digital transformation initiatives, modernize engineering workflows, and improve lifecycle data connectivity.

Igor noted that the engineering software industry is undergoing a significant shift toward more open, connected, and adaptable technology ecosystems. While engineering platforms historically sought to become comprehensive standalone environments, today's digital engineering landscape increasingly depends on interoperable ecosystems where specialized tools, data sources, and business processes can work together seamlessly.

“To truly support all aspects of complex product development, tools have to be flexible and expandable,” he said.

Igor also highlighted the industry's growing emphasis on openness and interoperability. Modern engineering organizations increasingly require platforms capable of connecting data, applications, and workflows across disciplines, enabling information to flow through the digital thread rather than remain isolated within individual systems.

The rise of AI is further increasing the importance of connected enterprise data. While publicly trained models can provide general knowledge, engineering use cases require AI systems that can leverage product, process, and lifecycle information within the context of the enterprise.

“If AI is going to help businesses, models need access to information in the context of a particular enterprise,” he said.

Igor pointed to the growing importance of emerging integration approaches such as Model Context Protocol (MCP), which enable AI systems to securely interact with enterprise tools, data sources, and engineering applications. By creating a standardized way for AI assistants to access and reason over information from multiple systems, these technologies can help organizations bridge the gap between open knowledge and enterprise-specific expertise.

As digital thread initiatives mature, capabilities such as MCP have the potential to make engineering information more discoverable, improve collaboration across teams, and allow engineers to interact with complex engineering ecosystems in more intuitive ways. Combined with advances in AI, interoperability, and systems engineering methodologies, these developments could significantly broaden the adoption of digital engineering practices.

Looking ahead, Igor observed that organizations continue to operate at different levels of digital maturity and complexity. Improved technology platforms, stronger integration standards, and AI-enabled assistance will make sophisticated engineering approaches more accessible, enabling not only large enterprises but also suppliers, smaller manufacturers, and SMBs to benefit from model-based and digital engineering practices.

“Together, advances in interoperability, AI, and engineering methodologies can help make these approaches more accessible and practical across the industry,” said Igor.

Across all topics, panelists emphasized that successful digital engineering strategies should remain focused on measurable business outcomes. Whether through digital threads, MBSE, interoperability, or AI, organizations are seeking to improve productivity, accelerate decision-making, strengthen collaboration, and reduce program risk.

Key Takeaways for Engineering Organizations

Organizations pursuing digital engineering initiatives should consider:

• Focus on solving business challenges, not implementing technology for its own sake.
• Build digital threads around context, traceability, and decision support.
• Improve the discoverability of engineering information across the enterprise.
• Treat adoption and user engagement as critical success metrics.
• Establish strong data governance and interoperability foundations before scaling AI initiatives.

Watch the webinar recording to hear industry experts from MIT Lincoln Laboratory, INCOSE, AMC Bridge, and Aras discuss practical approaches to digital engineering, digital threads, MBSE, interoperability, and AI-enabled engineering workflows.

About Aras

Aras is a leading provider of product lifecycle management and digital thread solutions. Its technology enables the rapid delivery of flexible solutions built on a powerful digital thread backbone and a low-code development platform. Aras’ platform and product lifecycle management applications connect users in all disciplines and functions to critical product data and processes across the lifecycle and throughout the extended supply chain. For more information, visit www.aras.com.

About AMC Bridge

AMC Bridge is a trusted technology partner for engineering, manufacturing, and construction enterprises, whether they are actively pursuing AI-driven digital transformation or only beginning to recognize its potential. We help organizations to move beyond experimentation and achieve consistent ROI by delivering production-ready software and end-to-end solutions for their transformation journey.

We design, build, and integrate enterprise-grade software – applications, workflow extensions for CAD/PLM/BIM, data integrations, AI-enabled features – and deploy them with monitoring and lifecycle management so they remain reliable over time. Our services include assessing data readiness; preparing and unifying product and project data; and embedding AI into the workflows teams use every day. With 25+ years of industrial software expertise and deep ecosystem partnerships, including Aras, Autodesk, Bentley, Dassault Systemes, PTC, Siemens, Tech Soft 3D, and others, we empower enterprises to move confidently from experimentation to operational AI at scale. For more information, visit amcbridge.com.