Global Agentic AI in Digital Engineering Market Size, Share, Statistics Analysis Report By Technology (Generative AI for Engineering Design, Digital Twins & AI-Driven Simulations, AI in Robotics & Automation, Explainable AI (XAI) for Engineering, Others), By Deployment Model (On-Premise, Cloud-Based), By Industry Vertical (Automotive & Aerospace, Energy & Utilities, Construction & Civil Engineering, Electronics & Semiconductor, Healthcare & Medical Devices, Others), By Application (Product Design & Development, Predictive Engineering Analytics, Process Automation & Workflow Optimization, AI-Augmented Simulation & Testing, Intelligent Infrastructure & Smart Manufacturing, Others), Region and Companies - Industry Segment Outlook, Market Assessment, Competition Scenario, Trends and Forecast 2025-2034

Report Overview

The Global Agentic AI in Digital Engineering Market size is expected to be worth around USD 378.5 Billion By 2034, from USD 3.4 Billion in 2024, growing at a CAGR of 60.20% during the forecast period from 2025 to 2034. In 2024, North America dominated the market with over 39.4% share, generating USD 1.3 billion in revenue.

Agentic AI in Digital Engineering refers to AI systems that can autonomously make decisions and take actions without human intervention in digital engineering processes. This subset of AI focuses on creating digital systems and solutions that can improve engineering processes, design, and operations through intelligent automation and data-driven decision-making.

The growth of the agentic AI in digital engineering market is fueled by several key factors. The rising complexity of engineering projects demands more efficient and precise tools, which agentic AI provides through task automation and design optimization. Moreover, industries like automotive, aerospace, and construction require faster product development cycles, driving the demand for AI systems that can quickly analyze and adapt to new data.

Additionally, the integration of IoT devices and smart sensors in the industrial sector generates vast amounts of data that Agentic AI can utilize to improve decision-making and operational efficiencies. Finally, ongoing advancements in AI and machine learning technologies continue to expand the capabilities and applications of Agentic AI, making it an increasingly vital tool in digital engineering.

Agentic AI’s popularity in digital engineering is increasing as industries recognize its potential to enhance relevance and efficiency in a rapidly changing technological landscape. This is reflected in the growing number of partnerships between engineering firms and AI providers, focused on co-developing solutions tailored to specific industry needs and challenges.

There are significant opportunities in developing tailored agentic AI solutions for specific industries, enhancing effectiveness and offering a competitive edge. Additionally, expanding these solutions to small and medium-sized enterprises (SMEs) presents potential, as they often have limited access to advanced digital engineering technologies.

This market expansion is fueled by the global push for digital transformation, encouraging businesses to innovate and improve operations with digital technologies. As industries increasingly rely on data-driven decision-making, agentic AI’s role in digital engineering will become more central, reshaping existing markets and creating new ones.

Key Takeaways

• The Global Agentic AI in Digital Engineering Market size is expected to reach USD 378.5 Billion by 2034, up from USD 3.4 Billion in 2024, growing at a CAGR of 60.20% during the forecast period from 2025 to 2034.
• In 2024, the Generative AI for Engineering Design segment held a dominant position, capturing more than 45.7% of the market share.
• The On-Premise segment also held a leading market position in 2024, capturing more than 56.6% of the market share in digital engineering.
• The Automotive & Aerospace segment commanded a dominant position in the market in 2024, with more than 38.9% of the total market share.
• The Product Design & Development segment held a dominant position in the market in 2024, capturing more than 35.5% of the total market share.
• North America held a dominant position in the market in 2024, capturing more than 39.4% of the total market share, generating revenues amounting to USD 1.3 billion.
• In 2024, the market for Agentic AI in Digital Engineering in the United States was estimated at USD 1.95 billion, with rapid expansion expected at a CAGR of 58.4%.

U.S. Market Size

In 2024, the market for Agentic AI in Digital Engineering in the United States was estimated at USD 1.95 billion. This sector is experiencing a rapid expansion, with a projected compound annual growth rate (CAGR) of 58.4%.

The remarkable growth in the U.S. Agentic AI market is primarily driven by the increasing demand for digital transformation across various industries including healthcare, automotive, and manufacturing. As organizations continue to integrate advanced digital solutions to optimize operations and enhance decision-making, agentic AI plays a critical role by providing more proactive and context-aware automation and analytics.

The adoption of agentic AI is driven by supportive government policies and significant investments from both public and private sectors. These investments fuel research and development, accelerating AI advancements. With its human-like interaction and enhanced autonomy, agentic AI is poised to transform digital engineering, offering substantial economic benefits and boosting global competitiveness.

In 2024, North America held a dominant position in the Agentic AI in Digital Engineering market, capturing more than 39.4% of the total market share, with revenues amounting to USD 1.3 billion. This leadership can be attributed to several factors, including North America’s robust technology infrastructure, high investment in artificial intelligence research and development, and the presence of major industry players.

The region benefits from a well-established ecosystem of technology companies, academic institutions, and government agencies that are actively promoting AI innovation. Additionally, North American businesses are among the early adopters of digital engineering solutions, with a strong focus on automation, data analytics, and process optimization.

The rapid adoption of agentic AI technologies is further fueled by substantial government initiatives in the United States, such as funding for AI research, innovation hubs, and strategic partnerships between public and private sectors. The presence of major global tech companies like Google, Microsoft, and IBM has also significantly contributed to the development and deployment of advanced AI technologies.

Moreover, the region benefits from a favorable business environment, characterized by a high degree of digital transformation across key industries such as aerospace, automotive, and manufacturing. These sectors are increasingly integrating agentic AI to enhance productivity, reduce costs, and improve product innovation.

Technology Analysis

In 2024, the Generative AI for Engineering Design segment held a dominant market position, capturing more than 45.7% of the market share. This leadership role stems from the transformative potential of generative design in engineering.

This capability accelerates design, reduces material waste, and provides innovative solutions beyond traditional methods. As industries aim to enhance design workflows and product quality, the demand for generative AI solutions grows, making it the most influential segment in the Agentic AI in Digital Engineering market.

The growing adoption of Generative AI for Engineering Design is further driven by its ability to handle complex design challenges in industries such as aerospace, automotive, and construction. These sectors require high-performance and lightweight materials, which can be effectively optimized through AI algorithms.

Another key factor driving this segment’s dominance is the growing integration of cloud-based AI platforms. Cloud computing allows organizations to scale generative AI tools without significant upfront infrastructure investments. This enhanced accessibility and affordability make generative AI appealing to both SMEs and large corporations.

Deployment Model Analysis

In 2024, the On-Premise segment held a dominant market position, capturing more than a 56.6% share in the digital engineering market. This significant market share can be attributed to the growing preference for on-site data control and security, especially within industries where confidential and sensitive information is paramount.

The leadership of the On-Premise segment is driven by its high customization capabilities. On-premise deployments allow companies to tailor AI solutions to their specific needs without the limitations of cloud environments. This flexibility in adapting and optimizing AI tools is crucial for larger enterprises in complex, high-stakes industries.

Concerns about internet connectivity and potential downtimes also drive the preference for on-premise deployments. For organizations in areas with limited internet access, on-premise solutions ensure operational continuity, offering greater reliability compared to cloud-based alternatives.

The ongoing investment in infrastructure by large enterprises continues to support the dominance of on-premise deployments. As more organizations build or upgrade their internal IT systems, they are increasingly inclined to adopt on-premise AI solutions that can seamlessly integrate with their existing infrastructure.

Industry Vertical Analysis

In 2024, the Automotive & Aerospace segment held a dominant position in the Agentic AI in Digital Engineering market, capturing more than 38.9% of the total market share. This dominance can be attributed to the critical role agentic AI plays in enhancing design, manufacturing, and maintenance processes within these high-precision industries.

Both the automotive and aerospace sectors are characterized by their complex engineering requirements, where AI-driven solutions help optimize vehicle and aircraft design, improve safety protocols, and streamline production processes. As a result, these industries are among the leading adopters of AI technologies for digital engineering.

The automotive industry is rapidly adopting agentic AI to support autonomous vehicle development, boost manufacturing automation, and enhance predictive maintenance. With a focus on increased efficiency and improved customer experiences, automotive companies are using AI across vehicle design, production, and after-sales services.

The aerospace industry greatly benefits from agentic AI by designing more efficient and reliable systems for both commercial and military applications. AI technologies help optimize aerodynamics, predict component wear, and improve safety. As aerospace systems grow more complex, agentic AI improves design validation, predictive maintenance, and simulation, boosting safety and reducing operational costs.

Application Analysis

In 2024, the Product Design & Development segment held a dominant position in the Agentic AI in Digital Engineering market, capturing more than 35.5% of the total market share. This dominance can be attributed to the increasing need for enhanced product innovation, faster design cycles, and improved design accuracy across multiple industries.

AI-driven technologies such as generative design, parametric modeling, and machine learning algorithms are revolutionizing how products are conceptualized, tested, and brought to market. The ability of agentic AI to optimize designs, predict performance, and streamline iterations significantly contributes to its central role in the product design and development phase.

One of the key drivers behind this segment’s growth is the rising demand for custom, high-performance products in industries like automotive, aerospace, consumer electronics, and healthcare. AI tools enable engineers and designers to quickly evaluate numerous design options, accelerating the decision-making process and reducing time-to-market.

Agentic AI in product design simplifies the creation of complex products, especially in aerospace and electronics. It aids in advanced materials research and the design of innovative components by analyzing large datasets to identify the best materials, components, and configurations, ultimately boosting performance and efficiency.

Key Market Segments

By Technology

• Generative AI for Engineering Design
• Digital Twins & AI-Driven Simulations
• AI in Robotics & Automation
• Explainable AI (XAI) for Engineering
• Others

By Deployment Model

• On-Premise
• Cloud-Based

By Industry Vertical

• Automotive & Aerospace
• Energy & Utilities
• Construction & Civil Engineering
• Electronics & Semiconductor
• Healthcare & Medical Devices
• Others

By Application

• Product Design & Development
• Predictive Engineering Analytics
• Process Automation & Workflow Optimization
• AI-Augmented Simulation & Testing
• Intelligent Infrastructure & Smart Manufacturing
• Others

Key Regions and Countries

• North America
  • US
  • Canada
• Europe
  • Germany
  • France
  • The UK
  • Spain
  • Italy
  • Rest of Europe
• Asia Pacific
  • China
  • Japan
  • South Korea
  • India
  • Australia
  • Singapore
  • Rest of Asia Pacific
• Latin America
  • Brazil
  • Mexico
  • Rest of Latin America
• Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE
  • Rest of MEA

Driver

Growing Demand for Automation and Efficiency in Digital Engineering

One of the primary drivers for the integration of agentic AI in digital engineering is the increasing demand for automation and operational efficiency. Digital engineering has become a cornerstone of modern industries, from aerospace to automotive, where design processes, testing, and simulation require immense amounts of time and resources. Agentic AI, capable of learning and making decisions autonomously, enhances this efficiency by automating repetitive tasks and optimizing complex processes.

By performing predictive analysis, identifying inefficiencies, and automating decision-making processes, agentic AI accelerates development cycles and reduces human error, ultimately leading to faster product deliveries. As businesses face increased pressure to innovate quickly while maintaining precision and quality, the adoption of agentic AI is expected to increase, providing organizations with a competitive edge by allowing them to achieve cost reduction and faster time-to-market.

Restraint

High Implementation Costs and Technical Complexity

Despite its potential, one of the key restraints hindering the widespread adoption of agentic AI in digital engineering is the high implementation cost and technical complexity. Integrating agentic AI into existing systems requires substantial investment in infrastructure, software, and specialized talent.

Many companies, especially small to medium-sized enterprises (SMEs), may find these initial expenditures prohibitive, thus limiting their ability to implement these advanced solutions. Additionally, the complexity of AI systems, particularly in adapting them to highly specialized engineering workflows, presents a significant challenge. Ensuring that AI algorithms are accurately aligned with engineering design specifications often requires highly customized solutions, which can be time-consuming and costly.

Opportunity

Enhanced Innovation through AI-Driven Design

A significant opportunity presented by the integration of agentic AI in digital engineering is the potential for enhanced innovation in design processes. AI-powered systems are capable of generating novel solutions that humans may not immediately consider, driving creativity in the development of new products and systems. By leveraging vast datasets, AI can analyze patterns and predict outcomes more efficiently than traditional methods, thus enabling engineers to explore a wider array of design possibilities.

This opens the door to breakthroughs in industries such as architecture, automotive engineering, and aerospace, where novel designs can improve performance, sustainability, and safety. Instead of relying solely on human intuition and experience, AI can suggest designs based on vast engineering databases, providing alternatives that are both innovative and highly optimized. By embracing these AI-driven opportunities, companies can not only reduce material waste and costs but also accelerate their innovation cycles, offering new, optimized products to the market faster.

Challenge

Data Privacy and Security Concerns

A significant challenge posed by agentic AI in digital engineering is the ongoing issue of data privacy and security. Vulnerabilities in the AI systems themselves or in the data-sharing infrastructure can expose critical information to malicious actors. Moreover, the use of third-party AI providers adds another layer of complexity in ensuring data confidentiality and compliance with industry standards.

In industries like automotive or aerospace, where safety-critical systems are designed, any breach or misuse of data could have serious implications, potentially leading to product recalls or safety failures. Additionally, as AI systems become more autonomous, tracking and controlling their actions and decisions may pose challenges in ensuring that these systems operate within regulatory boundaries. Consequently, businesses must invest significantly in cybersecurity measures and data protection protocols to mitigate these risks, which may require substantial resources and ongoing monitoring.

Emerging Trends

The integration of agentic AI in digital engineering is reshaping the way industries approach design, optimization, and automation. Agentic AI’s greater autonomy allows it to make decisions and take actions with minimal human intervention, making it valuable in fields like manufacturing, construction, and product development, where precision and efficiency are essential.

One notable trend is the use of agentic AI for generative design, where AI-driven algorithms create optimized solutions based on set parameters. This approach not only accelerates the design process but also leads to innovative, cost-effective solutions.

Another emerging trend is the role of agentic AI in predictive maintenance. By analyzing real-time data from sensors embedded in machinery, AI systems can detect potential failures before they occur, enabling proactive maintenance. This significantly reduces downtime and operational costs, making it particularly valuable in industries like aerospace and automotive manufacturing.

Business Benefits

• Enhanced Efficiency: By automating complex workflows, agentic AI reduces manual effort, leading to faster processes and fewer errors. For instance, AI-driven tools can autonomously troubleshoot and fix software issues, allowing engineers to focus more on development tasks.
• Improved Decision-Making: Agentic AI analyzes vast amounts of data in real-time, providing actionable insights that help businesses make informed decisions swiftly. This capability is crucial for adapting to market changes and staying competitive.
• Strengthened Cybersecurity: Agentic AI can monitor network traffic, detect anomalies, and respond to threats in real-time, bolstering an organization’s security posture and protecting sensitive data.
• Continuous Learning and Adaptation: These AI systems learn from each interaction, improving their performance over time. This continuous learning enables businesses to stay ahead by adapting to new challenges and opportunities.
• Innovation and Competitive Advantage: Implementing agentic AI fosters innovation by automating routine tasks, allowing teams to focus on creative and strategic initiatives. This shift enhances productivity and provides a competitive edge in the market.

Key Player Analysis

Major players in the Agentic AI space are leading the way by developing innovative technologies that enable organizations to leverage AI for optimizing their operations.

Microsoft Corporation is a significant player in the Agentic AI field, with a strong focus on cloud computing and AI services through its Azure platform. The company provides cutting-edge AI solutions that help digital engineering firms automate complex processes and gain insights from data in real time.

Nvidia Corporation is renowned for its graphics processing units (GPUs), but the company has also become a significant player in the AI and deep learning space, including Agentic AI in digital engineering. Nvidia’s GPUs are widely used to accelerate AI workloads, making them ideal for training and deploying autonomous systems.

IBM has long been a leader in AI and cognitive computing, and its role in Agentic AI for digital engineering is no exception. Through its IBM Watson platform, the company offers AI-driven analytics and automation tools that help industries improve productivity and innovation. IBM’s focus on creating adaptive and intelligent systems for real-time decision-making sets it apart in the digital engineering space.

Top Key Players in the Market

• Google LLC
• OpenAI
• Microsoft Corporation
• Nvidia Corporation
• International Business Machines Corporation
• Siemens
• Autodesk, Inc.
• Dassault Systèmes
• ANSYS, Inc.
• PTC Inc.
• Boston Dynamics, Inc.
• Others

Top Opportunities Awaiting for Players

• Polyfunctional Robotics: The deployment of polyfunctional robots in engineering environments offers significant improvements in operational efficiency and flexibility. These robots are capable of performing diverse tasks, adapting to changing production needs, and improving human-robot collaboration, which can lead to a faster return on investment and operational excellence in manufacturing and logistics​.
• Hyper-Personalization in Engineering: AI-driven personalization is revolutionizing product and process design, leading to substantial market share gains for companies that adopt this strategy. The ability to tailor engineering solutions to specific customer requirements enhances client satisfaction and drives innovation​.
• No-Code and Low-Code Platforms: These platforms are democratizing the development of engineering solutions, enabling rapid deployment and fostering agile engineering practices. By simplifying the creation of cloud-based solutions, they empower more organizations to participate in digital transformation, thus broadening the engineering landscape​.
• AI-Enhanced Complex System Control: AI is redefining the field of system control through the creation of more accurate models that combine data-driven insights with traditional engineering principles. This enhances the control and efficiency of complex systems, particularly in sectors like aerospace and automotive, where precision and adaptability are crucial​.
• Generative AI in Creative and Design Processes: The application of generative AI is transforming content creation within engineering, from automating routine tasks to generating innovative design solutions. This technology not only speeds up the design process but also pushes the boundaries of creativity, allowing for more rapid prototyping and iterative design​.

Recent Developments

• In February 2025, PagerDuty, a global leader in digital operations management, is set to introduce Agentic AI in its Spring 25 release. This new functionality will enhance the PagerDuty Operations Cloud, empowering enterprises to autonomously tackle mission-critical issues with Site Reliability Engineer (SRE) capabilities.
• In January 2024, Google LLC announced the launch of a new cloud-based AI solution tailored for digital engineering. This platform integrates machine learning models with simulation and optimization tools, providing engineers with faster design iterations. It uses Google’s Vertex AI platform to offer AI-powered insights across various industries, including manufacturing and automotive.

Report Scope