Design & Engineering Software Adoption Report 2026

A 150-page report on the design and engineering software market, with a focus on the adoption of MBSE, shift-left, cloud, AI, and digital threads.

Questions answered

• What is Design & Engineering (D&E), and which software categories does it span (MCAD/ECAD, simulation/CAE, PLM, SE/ALM, cloud platforms)?
• Why and how is D&E changing due to new customer demands?
• What is systems engineering, what challenges are associated with it, and how does MBSE address them?
• What is shift-left engineering, and what are its adoption trends, benefits, and challenges?
• How is cloud being adopted in D&E, which workloads are migrating, and which will stay on-premises?
• What is the role of AI in D&E, including generative and agentic AI? And how far does engineering trust lag adoption?
• What digital thread trends are shaping D&E, what is their level of integration, and what challenges persist?
• Who are the leading vendors across D&E categories, what positions do they hold, and what recent developments have they introduced?
• How does adoption differ by industry and company size?
• What is the survey base and methodology behind the findings?
• What are the 5 things to watch in D&E through 2027?

Companies mentioned

A selection of companies mentioned in the report.

• AWS
• Accenture
• Altair
• Ansys
• Atlassian
• Autodesk
• Cadence
• Dassault Systemes
• GitHub
• Google
• Hexagon
• IBM
• MathWorks
• Microsoft
• NVIDIA
• Oracle
• PTC
• SAP
• Siemens
• Synopsys

About the report

Product design and engineering processes are shifting from linear workflows to integrated systems model frameworks. The digitalization of hardware products, ongoing post-shipment software updates, and embedded intelligence correlate with the need for manufacturers to align mechanical, electrical, and software domains across the product lifecycle.

The Design & Engineering Software Adoption Report 2026 provides a structured analysis of software adoption across the product development lifecycle. It examines the market across 5 primary software categories: Computer-Aided Design (CAD), Computer-Aided Engineering (CAE), Product Lifecycle Management (PLM), Software Development Lifecycle (SDLC)/Application Lifecycle Management (ALM), and D&E Cloud Platforms.

The research relies on a survey of hardware manufacturers conducted in May 2025 and June 2025, supplemented by in-depth interviews with industry experts. The respondent sample comprises OEMs and component suppliers with over 1,000 employees across 16 countries in North America, Europe, and Asia-Pacific.

Report at a glance

• Adoption report: Details the adoption of MBSE, shift-left engineering, cloud platforms, AI, and digital threads across 148 pages.
• Stakeholder insights: Comprises data from 120 hardware manufacturers, including engineering decision-makers, R&D leaders, and IT architects across 16 countries.
• Market forecasts: Outlines market size and competitive leadership data across CAD, CAE, PLM, and Cloud Platform Services through 2032 (SDLC is not sized quantitatively)
• Strategic outlook: Identifies 5 critical watch items in engineering software through 2027.

Key topics of the report

• Introduction to design & engineering: Details the technical backbone of the product development lifecycle and examines key strategic outcomes: differentiation, speed, innovation, and customer-centricity. It outlines the transition from traditional linear workflows to systems engineering and MBSE.
• Market & competitive landscape: Details the expansion of the overall D&E software market and its projected growth trajectory. Outlines market share distribution across categories, identifying category leaders such as Dassault Systemes, Ansys, PTC, and Microsoft.
• Systems engineering & MBSE adoption: Analyzes the transition from document-based systems engineering to model-based methods. Highlights how organizations coordinate software sprint cycles with hardware milestones, while a smaller portion directly connect software lifecycle tools like Atlassian Jira or GitLab to PLM systems. Foremost among integration obstacles is a lack of shared systems for traceability.
• Shift-left engineering adoption: Examines the adoption of simulation-driven design at concept stages, noting its broad institutionalization among manufacturers. Correlates shift-left practices with notable R&D domain benefits in mechanical and electrical design, while highlighting a lack of shared simulation models as a primary implementation challenge.
• Cloud adoption: Analyzes toolchain deployment models across cloud, hybrid, and on-premises environments. Source-code management leads cloud adoption, whereas ECAD/PCB design trails behind. Demonstrates that data security and compliance remain top priorities when evaluating cloud migration, with intellectual property protection emerging as a primary concern.
• AI adoption: Details expected value and current trust levels for generative AI and agentic AI tools. While there is a widespread expectation for AI to be embedded into CAD, PLM, and ALM platforms, high trust in AI outputs remains limited, particularly for LLM-generated documentation and AI-based part design. Identifies potential orchestrators for AI-powered engineering workflows, led predominantly by traditional CAx/PLM vendors.
• Digital thread adoption: Evaluates data continuity across engineering disciplines, finding the strongest integration between MCAD and simulation, and the weakest between ECAD and MCAD. Parallel to this, findings highlight ongoing bill of materials (BOM) misalignment across PLM, ERP, and MES systems.
• Market outlook: Identifies critical strategic themes for the near future, analyzing AI trust verification, platform orchestration competition, hybrid cloud deployment, common data model adoption, and engineering workforce skill requirements.

A data-driven foundation for key business functions

• Strategy & corporate development: Inform strategic planning and portfolio decisions with market projections through 2032, vendor consolidation analyses, and evaluation of incumbent versus hyperscaler orchestration platforms.
• Product management & marketing: Guide product roadmaps and positioning using adoption data on generative AI, shift-left simulation toolchains, and domain-specific integration requirements.
• Sales leaders & account managers: Identify buyer pain points and qualify account readiness using data on BOM misalignment rates, PLM-ALM traceability gaps, and workload-specific cloud migration preferences.
• R&D & engineering leadership: Benchmark internal engineering workflows against industry standards for software-hardware synchronization, digital thread maturity, and MBSE implementation.
• Engineering IT & toolchain architects: Direct technical architecture priorities with insights into cross-domain data federation, cloud deployment security concerns, and AI orchestration preferences.