01 Growing Adoption of Cloud-Native Engineering Platforms Engineering organizations are increasingly moving design, product-development, and data-management workflows toward cloud-native platforms. Unlike traditional file-based engineering systems, cloud-native solutions allow teams to work from continuously updated product information while reducing manual file transfers and dependence on complex on-premises infrastructure. This approach is particularly valuable for geographically distributed engineering teams that require faster collaboration, version control, and access to current design information. In February 2026, PTC introduced a fully cloud-native Model-Based Definition capability within its Onshape CAD and PDM platform. The solution allows engineers to embed manufacturing information such as geometric dimensions, tolerances, weld symbols, and datums directly into live 3D models, creating a continuously updated product definition for design, manufacturing, and quality teams. The transition toward browser-based and cloud-native engineering environments is becoming one of the important engineering software market trends as manufacturers seek more connected product-development workflows.
02 Increasing Integration of Digital Twins and Physics-Based Simulation Digital twins are becoming more deeply integrated into engineering workflows as manufacturers seek to evaluate products, factories, and production processes before making physical changes. By combining real-time operational information with 3D models, simulation, and artificial intelligence, engineering teams can identify potential problems, compare design alternatives, optimize layouts, and validate investments virtually. In January 2026, Siemens introduced Digital Twin Composer, combining Siemens digital-twin technology with NVIDIA Omniverse libraries and industrial AI. PepsiCo used the technology across selected U.S. manufacturing and warehouse operations, where initial deployment delivered a 20% increase in throughput, identified up to 90% of potential issues before physical modifications, and supported estimated capital-expenditure reductions of 10% to 15%. The growing use of digital twins is expanding engineering software from conventional design applications toward continuous simulation, validation, and optimization of physical systems.
01 Growing Demand for Faster Product Development and Virtual Validation Manufacturers are under increasing pressure to shorten product-development cycles while managing greater product complexity and reducing expensive physical prototyping. Engineering software allows teams to create digital models, perform multiphysics analysis, evaluate design alternatives, and identify potential failures before physical products are manufactured. These capabilities are particularly valuable in automotive, aerospace, electronics, energy, and industrial equipment applications where design changes can otherwise become expensive later in development.AI-assisted automation is further improving virtual engineering by accelerating geometry preparation, simulation setup, optimization, and results analysis. These capabilities are strengthening engineering software market demand as companies seek to bring products to market faster while maintaining engineering accuracy.Reducing physical prototyping requirements while identifying design problems earlier is supporting engineering software market growth across increasingly complex manufacturing industries.
01 Complex Integration with Legacy Engineering and Enterprise Systems Many engineering organizations continue to operate combinations of older CAD, CAE, PLM, ERP, manufacturing, and internally developed software. Engineering information accumulated over many years may also exist in different formats and databases. Migrating these datasets or connecting them with modern platforms can require substantial integration work, employee training, workflow redesign, and software customization.These requirements increase implementation costs and can delay modernization projects, particularly for large manufacturers with extensive legacy engineering environments. Integration complexity can therefore restrict the engineering software market size by making organizations hesitant to replace established systems despite the potential productivity benefits.Improving compatibility between modern engineering platforms and established enterprise systems remains important for reducing migration barriers.
01 Expansion of Engineering Software for Software-Defined Products Products such as vehicles, industrial machinery, aerospace systems, medical equipment, and connected devices increasingly combine mechanical components with electronics, embedded software, sensors, and intelligent controls. This transition is creating opportunities for engineering platforms capable of supporting model-based systems engineering, embedded software development, requirements management, verification, and multidisciplinary product development within connected workflows.Software-defined products require engineering teams to validate interactions between physical components and embedded software before deployment. This creates opportunities for vendors to expand beyond traditional CAD and simulation into integrated systems-engineering and software-verification environments.Expansion into software-defined engineering provides opportunities for vendors to increase engineering software market share as physical and digital product development become increasingly interconnected.
01 Managing Multidisciplinary Engineering Complexity and Data Scale Modern products increasingly combine mechanical structures, electronics, embedded software, thermal systems, advanced materials, sensors, connectivity, and control systems. Engineering teams must understand how changes in one discipline affect the performance of others while simultaneously managing growing quantities of simulation, test, design, and operational data.High-fidelity simulations can also require substantial computing resources, particularly when engineers perform multiphysics analysis or evaluate thousands of possible design configurations. Bringing these disciplines together without compromising accuracy, usability, performance, or traceability remains a major challenge for the engineering software industry.As products become more multidisciplinary and software-intensive, engineering software providers must balance computational performance, engineering accuracy, data management, and ease of use while supporting increasingly complex development environments.
In July 2026, Siemens announced plans to acquire Defacto Technologies, an electronic design automation software specialist. The acquisition is expected to strengthen Siemens’ chip-design capabilities by bringing more design, verification, and implementation processes into a connected engineering environment.
In July 2026, Dassault Systèmes expanded its 3DEXPERIENCE platform with AI-powered Virtual Companions, including LEO for engineering applications. The new capability helps engineers work more efficiently across design and simulation tasks while making complex technical information easier to access and use.
In July 2026, Babcock International and Dassault Systèmes formed a collaboration to strengthen digital engineering across Babcock’s operations. Through the 3DEXPERIENCE platform, engineering teams can work with connected data, simulation tools, and product information, supporting better collaboration throughout complex project lifecycles.
In May 2026, Autodesk agreed to acquire MaintainX for around USD 3.6 billion in cash. The deal is designed to bring engineering and operational workflows closer together, allowing manufacturers to better connect product design and production activities with equipment maintenance and day-to-day operations.
In May 2026, Bentley Systems introduced OpenFlows 2026 with new AI-assisted capabilities for water infrastructure engineering. The release includes Bentley Copilot for hydraulic modeling and improved digital-twin integration, helping engineers analyze infrastructure systems, manage project information, and make design decisions more efficiently.