Generative Artificial Intelligence (AI) in Engineering Market Report 2025

This Generative Artificial Intelligence (AI) in Engineering market report delivers an in-depth analysis of the market’s key characteristics, including size, growth potential, and segmentation. It provides a detailed breakdown of the market across major regions and leading countries, highlighting historical data and future growth projections. The report also examines the competitive landscape, market share insights, emerging trends, and strategic developments shaping the market.

The generative artificial intelligence (AI) in engineering market size has grown exponentially in recent years. It will grow from $0.97 billion in 2024 to $1.34 billion in 2025 at a compound annual growth rate (CAGR) of 38%. The growth in the historic period can be attributed to increasing computational power, increasing focus on ethical considerations, increasing deep learning, increasing investment from venture capital firms, and increasing awareness of ethical implications.

The generative artificial intelligence (AI) in engineering market size is expected to see exponential growth in the next few years. It will grow to $4.84 billion in 2029 at a compound annual growth rate (CAGR) of 37.8%. The growth in the forecast period can be attributed to the growth of educational programs, the development of standards and regulations, the development of fundamental algorithms, educational and research institutions, and growing cross-disciplinary collaboration. Major trends in the forecast period include integration with simulation and testing, advanced automated engineering workflow, integration of virtual prototyping, customizable AI models, and development of smart engineering systems.

The increasing focus on automation is anticipated to drive the growth of generative artificial intelligence (AI) in the engineering market moving forward. Automation involves the use of technologies like machinery, software, or robotics to perform tasks or processes with minimal human intervention. This growing emphasis on automation stems from the need to enhance efficiency, reduce costs, maintain consistent quality, improve safety, and achieve a competitive advantage in an increasingly globalized and data-driven environment. Generative AI contributes to automation in engineering by designing and optimizing complex systems, generating innovative solutions, and automating repetitive tasks, which accelerates the development process and enhances overall efficiency. For example, in April 2024, a report from the International Federation of Robotics, a Germany-based NGO, indicated that U.S. automotive sales rose by 1% in 2023, coinciding with the installation of 14,678 robots, following a 47% increase in 2022 when 14,472 units were installed. Furthermore, car and component manufacturers accounted for 33% of all industrial robot installations in the U.S. Therefore, the growing emphasis on automation is driving the expansion of generative artificial intelligence in the engineering market.

Leading companies in the generative AI in engineering market are developing advanced solutions, such as AI-enabled platforms, to enhance engineering and design processes. An example is Cognizant Technology Solutions Corporation, a US-based IT company, which launched Cognizant Flowsource in February 2024. This generative AI-powered platform aims to revolutionize enterprise software engineering by integrating all stages of the software development lifecycle. It provides cross-functional engineering teams with tools and digital resources to improve efficiency in delivering high-quality code, while also enhancing control and transparency throughout the process.

In April 2024, Ernst & Young Global Limited, a UK-based services organization, acquired Nuvalence LLC for an undisclosed amount. This acquisition is expected to help clients leverage generative AI to transform core business functions and explore new AI-powered business models. Nuvalence LLC, a US-based developer of digital platforms, is involved in generative AI in engineering.

Major companies operating in the generative artificial intelligence in engineering market are Google LLC, General Electric Company, International Business Machines Corporation (IBM), Honeywell International Inc., SAP SE, ABB Ltd., NVIDIA Corporation, Dassault Systèmes SE, Hexagon AB, Autodesk Inc., Siemens Digital Industries Software Inc., ANSYS Inc., PTC Inc., Bentley Systems Incorporated, Altair Engineering Inc., 3D Systems Corporation, C3.ai Inc., ESI Group, Anthropic Inc., LeewayHertz Technologies Inc., Plex Systems Inc., nTopology Inc., Markovate Inc.

North America was the largest region in the generative artificial intelligence in engineering market in 2024. The regions covered in the generative artificial intelligence in engineering market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in the generative artificial intelligence in engineering market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

Generative artificial intelligence in engineering involves using advanced AI algorithms to autonomously create, optimize, and refine complex engineering designs and solutions by simulating different scenarios and outcomes. This approach enhances design processes, boosts product innovation, optimizes structural integrity, and speeds up the development of efficient and cost-effective engineering solutions.

Key tools and platforms in generative artificial intelligence for engineering include software tools, cloud-based platforms, and application programming interfaces (APIs). Software tools are crucial applications that offer engineers and designers flexible, accessible, and comprehensive functionalities, allowing them to handle tasks from conceptual design to simulation and quality control. These tools support various stages of design and manufacturing, including early-stage conceptual design, detailed design, prototyping, simulation, manufacturing process optimization, and quality control. Generative AI technologies are applied across numerous domains such as design optimization, product development, materials engineering, and structural analysis, serving a wide range of industries including automotive, aerospace, manufacturing, energy, and construction.

The generative artificial intelligence in engineering market research report is one of a series of new reports that provides generative artificial intelligence in engineering market statistics, including generative artificial intelligence in engineering industry global market size, regional shares, competitors with a generative artificial intelligence in engineering market share, detailed generative artificial intelligence in engineering market segments, market trends and opportunities, and any further data you may need to thrive in the generative artificial intelligence in engineering industry. This generative artificial intelligence in engineering market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

The generative artificial intelligence in engineering market includes revenues earned by entities by providing services such as consulting, custom software development, AI-enhanced design solutions, training and support, and data analysis and insights. The market value includes the value of related goods sold by the service provider or included within the service offering. Only goods and services traded between entities or sold to end consumers are included.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD, unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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