Main Facts
The strategic value of 3D engineering data has transcended traditional research and development (R&D) departments, establishing itself as an enterprise-wide asset. According to recent research encompassing large-scale manufacturers, the demand for accessible, shareable, and actionable 3D engineering data is surging. Far from being a static resource utilized solely by design engineers, 3D data now serves as the foundational communication layer for the entire product value chain, powering downstream manufacturing, field services, high-fidelity digital twins, and emerging artificial intelligence (AI) initiatives.
Despite this universal need, enterprises continuously face operational friction: not everyone across the modern enterprise has seamless access to the 3D information they require. To bridge this gap, IT and engineering leaders are forced to make critical strategic decisions regarding how they source the applications that create, access, and share this data.
The findings dispel the notion of a rigid, binary "build-or-buy" paradigm. Instead, industry leaders operate within a dynamic, hybrid ecosystem. On average, manufacturing enterprises buy roughly 78% of their 3D-related software solutions while developing the remaining 22% in-house. However, this balance is not immutable; it shifts continuously based on emerging technologies, specialized operational needs, proprietary security concerns, and the rapidly accelerating influence of artificial intelligence.
Chronology of the 3D Data Evolution
To understand how enterprises arrived at today’s complex application landscape, it is necessary to examine the historical trajectory of 3D engineering data within the manufacturing sector.
- The Transition to 3D Modeling (Late 20th Century): The initial wave of digital transformation in engineering saw computer-aided design (CAD) migrate from 2D drafting tables to 3D modeling environments. While this revolutionized how products were conceptualized and simulated, 3D data remained heavily siloed within specialized engineering workgroups.
- The Siloed Era of Product Development (Early 2000s): During this period, 3D files were treated as proprietary artifacts exclusively for designers and structural analysts. Downstream departments—such as procurement, manufacturing, sales, and technical documentation—relied on translated 2D drawings or static physical prototypes, leading to costly communication gaps, design errors, and elongated time-to-market cycles.
- The Democratization and Digital Thread Push (2010s): As manufacturing supply chains globalized, organizations realized that 3D data needed to travel across the enterprise and beyond the firewall. This era marked the birth of enterprise-wide viewing tools, product lifecycle management (PLM) integrations, and early digital thread initiatives aimed at making 3D models universally accessible.
- The Emergence of Digital Twins and Smart Manufacturing (Late 2010s to 2020s): The maturation of the Internet of Things (IoT) and digital twins elevated 3D engineering data from a static design file to a living operational asset. Models were increasingly linked to real-time sensor data, requiring robust, scalable applications capable of handling massive assemblies and complex metadata.
- The Modern Hybrid and AI-Driven Era (Present Day): Today, enterprises are grappling with a dual mandate: scaling 3D data access to non-engineers while leveraging AI to automate application development and analytical decision-making. Companies no longer view build-versus-buy as an all-or-nothing proposition; instead, they deploy hybrid strategies utilizing commercial off-the-shelf (COTS) software supplemented by custom-built micro-applications.
Supporting Data and Research Insights
To quantify these trends, researchers recently interviewed leaders across 15 large manufacturing companies, capturing deep qualitative and quantitative insights into how organizations source and manage their 3D application portfolios.
1. Growth in Demand
- 100% of surveyed companies reported that the demand for 3D engineering data across their enterprise has either increased or remained steady.
- More than two-thirds (66%+) explicitly stated that the need for company-wide 3D data access is actively growing.
- Zero respondents reported a decrease in demand, underscoring the expanding footprint of 3D visualization and simulation across non-engineering functions.
2. The Build vs. Buy Split
- 78% to 80%: The average and median percentage of 3D-related solutions that organizations choose to buy (commercial off-the-shelf software).
- 20% to 22%: The average and median percentage of solutions that organizations choose to build in-house.
- Outlier variance: While most companies cluster tightly around an 80/20 or 75/25 split, the strategy varies based on organizational maturity. Some niche enterprises build up to 40% of their tools due to specialized requirements, while others rely almost entirely on commercial packages, viewing custom development as a rare exception.
3. Dynamic Shifts Over Time
The research revealed that the preference for COTS versus custom development is far from static.
- Case A (Shift to COTS): One participating enterprise noted that their historical balance was roughly 75% custom-built and 25% bought. Over time, as commercial software matured, they inverted that ratio to 25% built and 75% bought.
- Case B (Shift to Custom): Conversely, another manufacturer reported the opposite trajectory. Their reliance on custom software rose from 10% to approximately 30% because commercial solutions failed to scale with their rapidly evolving, highly specialized operational demands.
Official Responses and Industry Perspectives
Industry leaders and engineering executives participating in the research highlighted the complex trade-offs they navigate when architecting their software ecosystems.
On the Necessity of Commercial Solutions
Engineering IT directors emphasized that buying commercial software remains the default position for a reason: speed to market and lower total cost of ownership for standard workflows. As one participant noted, leveraging proven applications allows internal teams to focus on core product innovation rather than reinventing foundational visualization software.
On the Limitations of Off-the-Shelf Tools
At the same time, companies with unique or cutting-edge manufacturing processes cautioned that commercial software can occasionally present a ceiling. When standard APIs or out-of-the-box viewers cannot interpret proprietary CAD formats or support complex mathematical geometry manipulations at scale, enterprises are forced to bridge the gap with custom-coded applications.
On the Role of Artificial Intelligence
The intersection of AI and 3D data engineering emerged as a dominant theme. Executives noted that AI is transforming the software development lifecycle, making custom coding faster and more accessible. However, they also warned that while AI lowers the barrier to building user interfaces or simple data-fetching scripts, managing complex 3D CAD topologies, boundary representations (B-Reps), and rich metadata still requires deep, specialized domain expertise.
Implications for Enterprise IT and Engineering Leadership
The convergence of escalating 3D data demand, hybrid sourcing models, and rapid AI advancement carries profound strategic implications for enterprise leaders. Navigating this landscape successfully requires a nuanced, multi-faceted approach.
1. Adopt a Needs-Based Hybrid Strategy
Organizations must abandon rigid dogmas regarding whether to build or buy. The optimal strategy treats commercial software and custom development as complementary forces. When determining which path to take, leaders must look beyond initial acquisition costs and implementation timelines to evaluate:
- The Total Cost of Scaling: Can the solution scale efficiently as the user base expands across the enterprise and supply chain?
- Intellectual Property (IP) Protection: Does building or buying expose sensitive engineering data or proprietary algorithms to external vulnerabilities?
- Maintenance Overhead: Who will shoulder the long-term burden of updating custom code when underlying CAD kernels or operating systems change?
2. Never Start from Scratch
The specialized mathematics, complex file structures, and rich metadata inherent in 3D CAD and simulation files mean that developing a 3D visualization or data-exchange pipeline from the ground up is prohibitively expensive and resource-intensive.
Whether an enterprise decides to build a custom tool or purchase a commercial platform, they should leverage existing components, software development kits (SDKs), and robust application programming interfaces (APIs). Utilizing pre-built building blocks ensures that developers—including less experienced staff empowered by AI-assisted coding tools—do not waste valuable time reinventing geometric interpretation engines.
3. Partner with Trusted Domain Experts
To maintain data integrity across the digital thread, companies must collaborate with trusted technology partners. When purchasing commercial applications or utilizing development components, organizations should demand solutions that accurately interpret multi-format CAD and simulation data without corrupting geometry. If custom-creating 3D data, applications must generate files precisely as original design software providers intended, preserving the fidelity required for downstream manufacturing and analysis.
4. Prepare for Continuous Evolution
Finally, engineering and IT leaders must recognize that the rapid acceleration of artificial intelligence will continue to reshape the software landscape. The metrics and formulas that govern today’s "build vs. buy" decisions will inevitably shift. Organizations that cultivate flexibility, maintain a hybrid operational mindset, and invest in robust underlying data components will be best positioned to meet the ever-expanding demand for 3D engineering intelligence.
