설계 및 엔지니어링 소프트웨어 도입(2026년)

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.

Table of Contents

Design & Engineering Adoption Report 2026

1. Executive summary

2. Introduction

• Introduction: Chapter overview
• Starting point: Design and engineering serves as the technical and creative backbone of the product development lifecycle
• Why does D&E matter?…. It helps manufacturers achieve strategic objectives
• But what gets built has fundamentally changed in recent years
• These shifts place new demands on D&E teams
• Traditional D&E cannot meet these demands so it is giving way to SE and MBSE as new paradigms
• This report is structured along key topics that make MBSE a reality
• The report is based on a survey of hardware manufacturers and in-depth interviews with experts in the D&E domain

3. Background: D&E software market & competitive landscape

• Background: D&E software market & competitive landscape: Chapter overview
• Market definition & segmentation
• Design & engineering software market forecasts by software category
• Market shares by D&E software category
• Competitive dynamics: Industry has been consolidating lately while new AI-native challengers are emerging
• Siemens D&E priorities
• Dassault Systemes D&E priorities
• Usage share: Which vendors survey respondents use
• Usage share: Where the most-used vendors are strong
• Design software company profile 1: Dassault Systemes
• Design software company profile 2: Autodesk
• Design software company profile 3: Siemens
• Design software company profile 4: PTC
• Simulation software company profile 1: Ansys (acquired by Synopsys)
• Simulation software company profile 2: MathWorks
• Simulation software company profile 3: Siemens
• Simulation software company profile 4: Altair (acquired by Siemens)
• PLM software company profile 1: PTC
• PLM software company profile 2: Siemens
• PLM software company profile 3: SAP
• PLM software company profile 4: Dassault Systemes
• PLM software company profile 5: Autodesk
• Software development lifecycle (SDLC) platforms company profile 1: Atlassian
• Software development lifecycle (SDLC) platforms company profile 2: Github
• Software development lifecycle (SDLC) platforms company profile 3: Gitlab
• Cloud platform company profile 1: Microsoft
• Cloud platform company profile 2: AWS
• Cloud platform company profile 3: Google Cloud

4. Systems engineering & MBSE adoption

• Systems engineering & MBSE: Chapter overview
• Fundamentals of SE and MBSE
• MBSE: Key features of MBSE
• Software and hardware development alignment (2 parts)
• Root causes responsible of low software-hardware collaboration
• Other challenges in SE (2 parts)
• Challenge 1
• How manufacturers are using MBSE: Example-Siemens Healthineers
• MBSE outlook: The shift from human-driven to AI-augmented MBSE

5. Shift-left engineering adoption

• Shift-left engineering: Chapter overview
• Shift-left engineering: Overview
• Adoption of shift-left practices (2 parts)
• Benefits of shift-left practices (2 parts)
• Shift-left enables early engineering input sharing (2 parts)
• Maturity in shift-left practices (2 parts)
• Challenges in shift-left practices (2 parts)
• Shift-left engineering’s application across vendors and adopters

6. Cloud adoptionn

• Cloud adoption: Chapter overview
• Cloud deployment across D&E toolchain components (2 parts)
• Cloud adoption outlook for D&E workloads (2 parts)
• Current cloud adoption for D&E workloads: By region/industry/company size
• Example: Siemens’ cloud and SaaS strategy
• Considerations for cloud migration (2 parts)
• Data security & compliance is a critical consideration for cloud migration
• Cloud migration concerns (2 parts)
• The engineering workloads that are unlikely to migrate to the cloud

7. AI adoption

• AI Adoption: Chapter overview
• Role of AI in D&E (2 parts)
• AI’s added value in D&E (2 parts)
• Trust in AI tools (2 parts)
• GenAI in D&E (2 parts)
• GenAI adoption by D&E vendors: Example – PTC and Siemens
• Agentic AI in D&E (2 parts)
• Agentic AI tools in D&E workflows: Example – Cadence and Ansys
• Agentic AI’s added value in D&E (2 parts)
• Trust in agentic AI (2 parts)
• Agentic AI orchestration in D&E (2 parts)
• Agentic AI example: AI agents are taking on orchestration roles across D&E systems
• Hyperscalers enter the discussion as D&E orchestrators

8. Digital thread adoption

• Digital thread adoption: Chapter overview
• What is a digital thread?
• Adoption of digital thread (2 parts)
• Barriers to digital thread adoption
• Key challenges in digital threads (2 parts)
• Solutions for digital thread challenges
• Adoption of digital thread tools and practices (2 parts)
• Barriers to adopting digital thread tools and practices
• Cloud and API adoption in digital threads (2 parts)
• Digital threads across vendor tools (2 parts)
• Digital thread offerings: Examples – Siemens and AWS

9. Outlook

• What is next for design & engineering software?
• Trend 1
• Trend 2
• Trend 3
• Trend 4
• Trend 5 (3 parts)

10. Research methodology

• About this report
• Complete list of survey questions (2 parts)
• Respondent sampling overview (3 parts)

11. About IoT Analytics

• About IoT Analytics
• Other publications by IoT Analytics
• Information and contact