디지털 트윈 시뮬레이션 및 시나리오 모델링 분야 생성형 AI : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the generative AI in digital twin simulation and scenario modeling market size is expected to increase from USD 6.23 billion in 2025 to USD 9.80 billion in 2026 and reach USD 32.45 billion by 2031, growing at a CAGR of 27.06% over 2026-2031.

This report is Segmented by Component (Software, and Services), Deployment Mode (Cloud, On-Premises, and More), Enterprise Size (Large Enterprises, and Small and Medium Enterprises), Application (Predictive Maintenance, Optimization, and More), End User (Manufacturing, Automotive, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Generative AI In Digital Twin Simulation and Scenario Modeling Market Trends and Insights

Rapid Enterprise Adoption of Generative AI Capabilities Across Industrial Platforms

The generative AI in digital twin simulation and scenario modeling market is moving faster because industrial software vendors are adding generative models to simulation environments that enterprises already use at scale. This change reduces the time needed to test design options and operating scenarios, making simulation useful to a wider group of teams beyond specialist engineers. Siemens stated in January 2026 that Digital Twin Composer helped PepsiCo reach near-100% design validation, improve throughput by 20%, and reduce capital expenditure by 10-15% in early deployments. That shift makes proprietary operating data more valuable because the quality of AI-generated scenarios depends on the depth and relevance of the data that trains and updates the models. As a result, the generative AI in digital twin simulation and scenario modeling market is rewarding vendors that can combine physics models, industrial data, and high-performance computing into a single offering.

Rising Demand for Scalable, Scenario-Based Decision-Making and Risk Simulation Tools

The generative AI in digital twin simulation and scenario modeling market is also benefiting from the need to evaluate supply chain disruptions, maintenance risks, energy cost swings, and production tradeoffs far more often than static simulation tools can support. Companies now want digital twins that do not just reflect current conditions, but also generate plausible operating paths and compare them against business objectives. Airbus stated in April 2025 that its digital twin environment is used by more than 50,000 engineers to predict wear, optimize maintenance schedules, and reduce unplanned downtime across the aircraft lifecycle. Dassault Systemes and Airbus also extended their strategic partnership in April 2025 to deploy the 3DEXPERIENCE platform across more than 20,000 users for future civil and military aircraft and helicopter programs. This is widening the role of the generative AI in digital twin simulation and scenario modeling market beyond engineering teams and into broader planning, risk, and operational decision functions.

High Model Validation Costs and Technical Accuracy Barriers

The generative AI in digital twin simulation and scenario modeling market still faces a major barrier in proving that AI-generated models reflect physical reality across a wide range of conditions. High-quality validation requires sensor data, physics checks, repeated testing, and review against real operating behavior, which keeps cost and time high even when model generation becomes faster. A 2025 peer-reviewed study in the Journal of Intelligent Manufacturing found that validation remained the main bottleneck to broader enterprise adoption even when generative AI accelerated digital twin design cycles. This creates a difficult trade-off: firms that skip validation face operational and liability risks, while firms that perform full checks can struggle to justify the return on investment at scale. Because of that, the generative AI in digital twin simulation and scenario modeling market favors vendors that can automate more of the checking process and support regulated use cases with stronger evidence.

Other drivers and restraints analyzed in the detailed report include:

1. Cloud-Native Digital Transformation and Platform-as-a-Service Deployment Models
2. Need for Accelerated Product Development and Compressed Time-to-Market
3. Fragmented Data Architecture and Cross-System Integration Gaps

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software held 65.43% of the generative AI in digital twin simulation and scenario modeling market share in 2025, which reflected the central role of simulation engines, AI orchestration layers, and lifecycle data management tools in enterprise deployments. Software remains the revenue anchor because customers usually start with the core platform before expanding into higher-value scenario libraries, model management, and workflow integration. As more assets, production lines, and design histories move into the same environment, the underlying platform becomes harder to replace and more useful over time. This dynamic keeps software in a leading position across the generative AI in digital twin simulation and scenario modeling market, even as the mix of value is changing.

Services are projected to grow at a 27.62% CAGR through 2031, which shows how quickly buyers are asking for help with fine-tuning, ongoing model operations, and system-level integration. The shift is important because competitive advantage is moving away from one-time deployment work toward continuous model improvement in live operating settings. PTC reinforced that direction in June 2026 with PTC Orbit, a cloud-native asset intelligence solution that connected PLM, ERP, CRM, IoT, EAM, and FSM systems into a unified AI-powered asset record. That kind of release shows why the generative AI in digital twin simulation and scenario modeling market is creating more room for recurring service revenue, because customers need support after deployment, not just during initial installation.

Cloud accounted for a 62.45% share in 2025, reflecting enterprise preference for scalable GPU access, lower upfront costs, and easier links to AI model-serving environments. The cloud model fits well with generative simulation because usage often occurs in bursts when teams run large batches of scenarios, compare design alternatives, or test multiple operating conditions in parallel. It also reduces the need for organizations to build and maintain their own high-end simulation infrastructure. That cost and access advantage keeps cloud at the center of the generative AI in digital twin simulation and scenario modeling market as adoption widens across industries.

Hybrid is the fastest-growing deployment mode, with a 27.92% CAGR for 2026-2031, because many users need real-time inference near physical assets while still relying on the cloud for large-scale model training and rendering. Siemens and NVIDIA expanded their partnership in January 2026 to build an Industrial AI Operating System that combined GPU acceleration with simulation and inference across industrial environments. This model is especially relevant when firms face latency constraints, data residency rules, or site-level control requirements. For that reason, the generative AI in digital twin simulation and scenario modeling market is likely to keep cloud as the largest mode while hybrid becomes the preferred architecture for more complex industrial operations.

Complete Report Scope:

• By Component
  • Software
  • Services
• By Deployment Mode
  • Cloud
  • On-Premises
  • Hybrid
• By Enterprise Size
  • Small and Medium Enterprises
  • Large Enterprises
• By Application
  • Predictive Maintenance
  • Design and Simulation
  • Scenario/"What-If" Modeling
  • Optimization
• By End User
  • Manufacturing
  • Automotive
  • Aerospace and Defense
  • Energy and Utilities
  • Healthcare and Life Sciences
  • Telecommunications and IT
  • Smart Cities and Infrastructure
  • Retail and E-Commerce
  • Other End Users
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Russia
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Rest of Asia-Pacific
  • Middle East
    • Saudi Arabia
    • United Arab Emirates
    • Turkey
    • Rest of Middle East
  • Africa
    • South Africa
    • Egypt
    • Rest of Africa

Geography Analysis

North America held 36.78% of the generative AI in digital twin simulation and scenario modeling in 2025, making it the largest regional contributor. The region benefits from strong enterprise software adoption, deep access to GPU and cloud infrastructure, and close ties between industrial software vendors and large corporate users. The United States remains the center of this position, supported by active development work around Siemens, NVIDIA, PTC, and other platform ecosystems. Siemens and NVIDIA stated in January 2026 that customers such as PepsiCo were already using combined capabilities tied to the Industrial AI Operating System direction, which reflects how quickly production-linked use cases are moving into real deployments.

Asia-Pacific is projected to expand at a 28.34% CAGR through 2031, which makes it the fastest-growing regional segment in the generative AI in digital twin simulation and scenario modeling market. China, Japan, South Korea, and India form the core of this growth corridor, each with different strengths in manufacturing, engineering talent, or technology services. China is pushing industrial digitalization across major manufacturing sectors, while Japan is using advanced simulation and automation to offset labor constraints and protect export competitiveness. South Korea brings strong use cases in semiconductors and shipbuilding, where process quality and scenario testing carry high value. India is emerging as an important services-layer base for the generative AI in digital twin simulation and scenario modeling market, because its IT services ecosystem can support managed simulation delivery for global clients.

Europe held a significant share in 2025, led by Germany, France, the United Kingdom, Italy, and Spain, where precision manufacturing and process industries continue to support demand for advanced simulation environments. The region is also shaping deployment choices through GDPR and EU AI Act compliance needs, which makes hybrid and sovereign cloud models more relevant than in some other markets. Schneider Electric, AVEVA, and ETAP joined the Alliance for OpenUSD in November 2025, which signaled a clear push toward interoperability standards across the European industrial software base. South America remains earlier in adoption, with Brazil showing the clearest demand path through oil and gas and agricultural equipment manufacturing. The Middle East and Africa are still less penetrated, but Saudi Arabia, the UAE, Turkey, South Africa, and Egypt are building longer-term demand through infrastructure, energy, and industrial modernization programs, which keeps the generative AI in digital twin simulation and scenario modeling market relevant in these regions even from a smaller base.

1. Siemens AG
2. NVIDIA Corporation
3. Microsoft Corporation
4. PTC Inc.
5. Dassault Systemes SE
6. ANSYS, Inc.
7. IBM Corporation
8. SAP SE
9. Autodesk, Inc.
10. Amazon Web Services, Inc.
11. Rockwell Automation, Inc.
12. Schneider Electric SE
13. Bentley Systems, Incorporated
14. Hexagon AB
15. General Electric Company
16. Oracle Corporation
17. Honeywell International Inc.
18. Emerson Electric Co.
19. Altair Engineering Inc.
20. AVEVA Group Limited

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Rapid Enterprise Adoption of AI-Enhanced Simulation Workflows
  • 4.2.2 Rising Demand for Scenario Testing Across Complex Systems
  • 4.2.3 Cloud-Native Digital Twin Platforms Lowering Deployment Friction
  • 4.2.4 Need for Faster Product, Process, and Asset Decision Cycles
  • 4.2.5 Twin Data Exhaust from IoT, PLM, and Operational Systems
  • 4.2.6 Growth in Autonomous Operations and Closed-Loop Optimization
• 4.3 Market Restraints
  • 4.3.1 High Model Validation Burden for Safety-Critical Use Cases
  • 4.3.2 Fragmented Data Architecture and Poor Interoperability
  • 4.3.3 Rising Compute Cost for High-Fidelity Generative Simulation
  • 4.3.4 Governance Gaps Around Traceability, IP, and AI Accountability
• 4.4 Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Bargaining Power of Suppliers
  • 4.7.2 Bargaining Power of Buyers
  • 4.7.3 Threat of New Entrants
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Industry Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Component
  • 5.1.1 Software
  • 5.1.2 Services
• 5.2 By Deployment Mode
  • 5.2.1 Cloud
  • 5.2.2 On-Premises
  • 5.2.3 Hybrid
• 5.3 By Enterprise Size
  • 5.3.1 Small and Medium Enterprises
  • 5.3.2 Large Enterprises
• 5.4 By Application
  • 5.4.1 Predictive Maintenance
  • 5.4.2 Design and Simulation
  • 5.4.3 Scenario/"What-If" Modeling
  • 5.4.4 Optimization
• 5.5 By End User
  • 5.5.1 Manufacturing
  • 5.5.2 Automotive
  • 5.5.3 Aerospace and Defense
  • 5.5.4 Energy and Utilities
  • 5.5.5 Healthcare and Life Sciences
  • 5.5.6 Telecommunications and IT
  • 5.5.7 Smart Cities and Infrastructure
  • 5.5.8 Retail and E-Commerce
  • 5.5.9 Other End Users
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
    • 5.6.1.3 Mexico
  • 5.6.2 South America
    • 5.6.2.1 Brazil
    • 5.6.2.2 Argentina
    • 5.6.2.3 Rest of South America
  • 5.6.3 Europe
    • 5.6.3.1 Germany
    • 5.6.3.2 United Kingdom
    • 5.6.3.3 France
    • 5.6.3.4 Italy
    • 5.6.3.5 Spain
    • 5.6.3.6 Russia
    • 5.6.3.7 Rest of Europe
  • 5.6.4 Asia-Pacific
    • 5.6.4.1 China
    • 5.6.4.2 Japan
    • 5.6.4.3 India
    • 5.6.4.4 South Korea
    • 5.6.4.5 Australia
    • 5.6.4.6 Rest of Asia-Pacific
  • 5.6.5 Middle East
    • 5.6.5.1 Saudi Arabia
    • 5.6.5.2 United Arab Emirates
    • 5.6.5.3 Turkey
    • 5.6.5.4 Rest of Middle East
  • 5.6.6 Africa
    • 5.6.6.1 South Africa
    • 5.6.6.2 Egypt
    • 5.6.6.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Initiatives and Partnerships
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 Siemens AG
  • 6.4.2 NVIDIA Corporation
  • 6.4.3 Microsoft Corporation
  • 6.4.4 PTC Inc.
  • 6.4.5 Dassault Systemes SE
  • 6.4.6 ANSYS, Inc.
  • 6.4.7 IBM Corporation
  • 6.4.8 SAP SE
  • 6.4.9 Autodesk, Inc.
  • 6.4.10 Amazon Web Services, Inc.
  • 6.4.11 Rockwell Automation, Inc.
  • 6.4.12 Schneider Electric SE
  • 6.4.13 Bentley Systems, Incorporated
  • 6.4.14 Hexagon AB
  • 6.4.15 General Electric Company
  • 6.4.16 Oracle Corporation
  • 6.4.17 Honeywell International Inc.
  • 6.4.18 Emerson Electric Co.
  • 6.4.19 Altair Engineering Inc.
  • 6.4.20 AVEVA Group Limited

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment