Generative AI in Engineering Market By Design and Manufacturing Stages (Early-stage Conceptual Design, Detailed Design, Prototyping, Simulation, Manufacturing Process Optimization, and Quality Control), By Tools and Platforms, By Application, By Industry Vertical, By Region and Companies - Industry Segment Outlook, Market Assessment, Competition Scenario, Trends, and Forecast 2023-2032

Report Overview

The Global Generative AI in Engineering Market size is expected to be worth around USD 10,085 Mn by 2032 from USD 503 Mn in 2022, growing at a CAGR of 36% during the forecast period from 2023 to 2032.

The surge in demand for advanced technologies and the rise in utilization in different verticals of industries are some of the main driving factors for generative AI in engineering.

Generative AI in engineering is defined as the application of artificial intelligence methods, especially generative AI, to solve different engineering risks. Unlike old AI techniques that depend on predefined rules and labeled data, generative AI has the potential to develop new information and outputs based on structures it learns from present data.

In engineering, such technology enables the automatic generation of designs, simulations, and enhancements for complex systems, starting from mechanical elements to architectural structures. By using generative AI, engineers can discover a wider design space, explore novel solutions, and accelerate new innovative methods.

It encourages engineers to handle design problems with greater efficacy and creativity, leading to more robust and innovative engineering solutions. Moreover, generative AI changes engineering methods by optimizing human expertise with computational intelligence, paving the way for advancements all across the different engineering domains.

According to Study in April 2023, more than ½ of the respondents mentioned that generative AI is presently used in their software engineering departments. 55% of the respondents suggested that their software engineering department is presently using generative AI. Almost 23% are not using generative AI in their software engineering department, while 22% said that some teams of employees are also using it independently.

According to Mckinsey in June 2023, it highlights that in May 2023, Anthropic’s generative AI, Claude, was able to process 100,000 tokens of text, equal to about 75,000 words in a minute, the length of the average novel, as compared with roughly 9,000 tokens when it was introduced in March 2023.

In May 2023, Google announced several new features powered by generative AI, including Search Generative Experience and a new LLM called PaLM 2 that will power its Bard chatbot, among other Google products.

Generative AI in engineering provides innovative design exploration by enabling the discovery of unconventional solutions. It fastens iterative design methods by minimizing the time to market for products and systems.

Moreover, it improves creativity by producing multiple design substitutes, which leads to more innovative and enhanced engineering solutions. The demand for generative AI in engineering will increase due to its requirements in the various engineering sectors, which will help in market expansion in the coming years.

Key Takeaways

• Market Value: The Global Generative AI in Engineering Market is set to reach USD 10,085 million by 2032, exhibiting a remarkable CAGR of 36% from 2023 to 2032.
• Dominant Segments:
  • Design and Manufacturing Stages Analysis: Early-stage Conceptual Design leads with 27% market share, emphasizing the pivotal role of Generative AI in accelerating innovation and exploration in product development.
  • Tools and Platforms Analysis: Software Tools dominate with a 40% share, offering comprehensive capabilities and user-friendly interfaces to engineers.
  • Application Analysis: Design Optimization emerges as the dominant application, revolutionizing engineering processes by enabling the identification of optimal solutions.
  • Industry Vertical Analysis: The Automotive sector leads, leveraging Generative AI for design optimization and material engineering. Aerospace, Manufacturing, Energy, Construction, and Other Industries also significantly benefit from AI-driven innovations.
• Regional Dynamics: North America dominates with a 40% market share, driven by advanced research institutions and progressive AI policies. Europe holds approximately 25% market share, supported by strong research infrastructure and regulatory frameworks.
• Key Players: Major players such as Autodesk, Inc., ANSYS, Altair Engineering India Private Limited, Siemens PLM Software, and Dassault Systemes lead the market, driving innovation and adoption of Generative AI technologies in engineering.
• Analyst Viewpoint: Analysts anticipate significant growth opportunities in Generative AI in Engineering, fueled by increasing demand for innovation, efficiency, and sustainability across various industry verticals.
• Growth Opportunities: Opportunities lie in further development of AI-driven tools and platforms, expanding applications in emerging sectors, fostering cross-industry collaborations, and capitalizing on regional growth potentials through strategic partnerships and investments in research and development.

Driving Factors

Design Optimization and Iteration Drives Market Growth

Generative AI in engineering significantly boosts market growth by enhancing design optimization and iteration processes. This technology empowers engineers to explore extensive design possibilities and assess multiple performance parameters efficiently. As a result, the product development cycle is expedited, leading to faster time-to-market and superior product performance. The impact is quantifiable; for instance, Autodesk's generative design software showcases the ability to evaluate thousands of design options, driving innovation and efficiency in product development.

This not only cuts down development time by up to 50% but also reduces material costs by an average of 20%. The synergy between AI-driven design capabilities and traditional engineering processes fosters an environment where innovation flourishes, directly contributing to market expansion. This collaboration between human expertise and algorithmic precision creates a powerful catalyst for growth in the engineering sector.

Customization and Personalization Enhances Market Opportunities

The introduction of Generative AI into engineering opens up new avenues for customization and personalization, meeting a wide array of customer demands. By analyzing individual user requirements and feedback, AI algorithms can create customized solutions that significantly enhance user experience and satisfaction. Adidas, for example, leverages generative design to produce customized midsoles for its Futurecraft 4D shoes, achieving a notable increase in performance and comfort for athletes.

This level of personalization has led to a surge in customer loyalty and brand differentiation, paving the way for new market opportunities. The capability to offer personalized products has shown to increase customer willingness to pay by up to 20%, demonstrating a clear path to higher profitability and market share for companies investing in Generative AI technologies. As industries continue to recognize the value of customization, the demand for Generative AI solutions is expected to rise, further driving market growth.

Resource Efficiency and Sustainability Promotes Market Expansion

Generative AI plays a pivotal role in promoting resource efficiency and sustainability within the engineering sector, aligning with global efforts to combat climate change and promote a circular economy. By optimizing material use and minimizing waste through smarter design algorithms, companies can significantly reduce their environmental impact. This is not only beneficial for the planet but also resonates with the growing consumer demand for sustainable products.

For example, incorporating environmental constraints into AI-driven design processes can lead to a reduction in material usage by up to 25%, directly contributing to lower CO2 emissions and less waste production. This sustainable approach to design and manufacturing not only meets regulatory requirements and environmental standards but also opens up new markets focused on green technologies and sustainable products. The emphasis on sustainability is becoming a significant competitive advantage, encouraging further investment in Generative AI technologies to drive market growth.

Restraining Factors

Data Quality and Availability Restrains Market Growth

The challenge of securing high-quality and sufficient data acts as a significant brake on the advancement of Generative AI in engineering. AI algorithms need vast amounts of accurate data to produce dependable designs. However, the quest for such data often encounters obstacles, notably in specialized fields where information may be scarce or guarded.

For industries like aerospace, where data on flight performance and material characteristics are closely held, the potential of Generative AI is markedly constrained. The scarcity of accessible, quality data leads to AI models that may not fully capture the complexity of real-world scenarios, thereby limiting their utility and applicability. This bottleneck not only slows innovation but also imposes additional costs and delays in model development and deployment, stifling market growth in sectors where Generative AI could have a transformative impact.

Interpretability and Trustworthiness Limits Market Expansion

The opacity of Generative AI systems and the difficulty in deciphering their decision-making processes pose significant barriers to their acceptance, particularly in sectors where safety and reliability are paramount. The "black box" nature of many AI algorithms can undermine confidence among engineers and regulatory bodies, who must fully understand and trust these systems before implementing them in critical applications.

For example, in the automotive industry, where the safety of vehicle designs cannot be compromised, the inability to interpret or validate AI-generated solutions transparently can prevent their adoption. Regulatory requirements for clear documentation and rigorous validation further complicate the deployment of Generative AI, as meeting these standards with opaque algorithms is challenging. This lack of transparency and trust reduces the willingness of industries to integrate AI into their design processes, hampering market growth and innovation in areas that could benefit significantly from AI-driven optimization and creativity.

Design and Manufacturing Stages Analysis

Early-Stage Conceptual Design Dominates Sector with 27% Share

In the fast-evolving field of Generative AI in Engineering, the market is distinctly segmented based on the design and manufacturing stages. Among these, Early-stage Conceptual Design emerges as the dominant sub-segment, accounting for 27% of the market focus. This prominence can be attributed to the foundational role that conceptual design plays in the engineering process.

It's where the broad strokes of innovation are painted, leveraging Generative AI to explore a wider array of possibilities with greater efficiency than traditional methods. The ability of Generative AI to rapidly generate, assess, and refine design concepts based on predefined criteria and constraints significantly accelerates the early stages of product development. This acceleration not only reduces time-to-market but also fosters a culture of innovation, as engineers are free to explore more creative solutions without the usual time and cost penalties.

The detailed design, prototyping, simulation, manufacturing process optimization, and quality control segments, while not as dominant, play critical roles in integrating and refining the concepts developed during the conceptual phase. Detailed Design further refines these concepts into actionable designs, incorporating specific technical and material considerations.

Prototyping, empowered by Generative AI, allows for rapid physical or digital model creation, providing immediate feedback on design viability and performance. Simulation uses advanced algorithms to predict how designs perform under various conditions, identifying potential improvements or the necessity for redesigns. Manufacturing Process Optimization utilizes Generative AI to streamline production methods, reducing costs and improving efficiency. Lastly, Quality Control benefits from AI's ability to predict and identify defects, ensuring the final product meets the required standards.

Tools and Platforms Analysis

Software Tools Command 40% Dominance in Generative AI Market for Engineering

In the Generative AI in Engineering, tools and platforms serve as the backbone for deploying innovative solutions. Software Tools, as a market segment, command the lion's share with a 40% dominance. This predominance is grounded in the flexibility, accessibility, and comprehensive capabilities that software tools provide to engineers and designers. They are the primary enablers of Generative AI, offering a wide array of functionalities from conceptual design to simulation and quality control.

The significant investment in developing intuitive, powerful software tools reflects the industry's recognition of their critical role in harnessing the full potential of Generative AI. These tools are designed to be user-friendly, making advanced AI capabilities accessible to professionals without requiring deep expertise in machine learning or data science. Moreover, the integration of these software tools with existing engineering workflows has been pivotal in their adoption, allowing for seamless transition and immediate productivity gains.

Despite the dominance of Software Tools, other segments like Cloud-based Platforms and Application Programming Interfaces (APIs) play foundational roles in the ecosystem's growth and development. Cloud-based platforms offer scalability, collaboration, and high computational power, enabling engineers to access Generative AI tools and resources remotely and collaborate in real-time across the globe.

Application Analysis

Design Optimization Leads Generative AI Applications in Engineering Sector

In the Generative AI in Engineering market, Design Optimization stands out as the dominant application, accounting for a significant portion of the industry's focus. This prominence is attributed to the fundamental role that design optimization plays in enhancing efficiency, performance, and innovation within engineering processes.

Generative AI revolutionizes how engineers approach design challenges by enabling the exploration of a vast design space to identify optimal solutions that meet predefined criteria, such as weight, strength, cost, and environmental impact. This application not only accelerates the design process but also leads to more innovative and sustainable engineering solutions.

Other applications, including Product Development, Materials Engineering, Structural Analysis, and Other Applications, contribute to the growth and diversification of the market. Product Development benefits from Generative AI through the rapid prototyping and testing of new concepts, significantly reducing time-to-market.

Materials Engineering leverages AI to discover new materials and optimize material combinations for specific applications, enhancing product performance and sustainability. Structural Analysis, another key application, utilizes AI to predict and optimize the structural integrity of designs under various conditions, improving safety and reliability. The collective growth in these areas underscores the broad applicability and potential of Generative AI to revolutionize multiple facets of engineering.

Industry Vertical Analysis

Automotive Industry Leads Generative AI Adoption in Engineering Sector

The Automotive sector emerges as the leading industry vertical in the Generative AI in Engineering market. The automotive industry's complex design and manufacturing processes, combined with the constant push for innovation and efficiency, make it an ideal domain for the application of Generative AI technologies. Generative AI is used extensively for design optimization, reducing vehicle weight while maintaining strength, which improves fuel efficiency and performance. Additionally, AI-driven materials engineering aids in the development of new, lighter, and more durable materials for vehicle construction.

The Aerospace, Manufacturing, Energy, and Construction sectors, along with Other Industries, also significantly benefit from Generative AI applications. In Aerospace, Generative AI is crucial for designing more efficient aircraft structures and optimizing fuel consumption, directly impacting sustainability and operational costs. The Manufacturing sector utilizes AI for optimizing production processes, enhancing product quality, and reducing waste.

In the Energy sector, Generative AI facilitates the design of more efficient renewable energy systems, such as wind turbine blades optimized for maximum power generation. Construction benefits from AI through the design of structures that are both aesthetically pleasing and structurally sound, optimizing material usage and minimizing environmental impact. Across these industries, Generative AI acts as a catalyst for innovation, driving advancements that not only enhance competitiveness and efficiency but also contribute to sustainability and safety.

Key Market Segments

Based on the Design and Manufacturing Stages

• Early-stage Conceptual Design
• Detailed Design
• Prototyping
• Simulation
• Manufacturing Process Optimization
• Quality Control

Based on Tools and Platforms

• Software Tools
• Cloud-based Platform
• Application Programming Interfaces (APIs)

Based on Application

• Design Optimization
• Product Development
• Materials Engineering
• Structural Analysis
• Other Applications

Based on Industry Vertical

• Automotive
• Aerospace
• Manufacturing
• Energy
• Construction
• Other Industries

Growth Opportunities

Integration with Additive Manufacturing Offers Growth Opportunity

The fusion of Generative AI with additive manufacturing marks a significant leap forward in engineering, especially beneficial for sectors like aerospace, automotive, and electronics. This integration fosters the creation of complex, lightweight structures previously unattainable through traditional manufacturing techniques. Generative AI's role in this context is to refine designs specifically for additive processes, slashing material waste and elevating product efficacy.

Notably, aviation giants such as GE Aviation and Boeing have harnessed generative design to engineer aircraft components that are both lighter and more fuel-efficient, showcasing tangible benefits like reduced operational costs and enhanced performance. This synergy not only paves the way for groundbreaking design and manufacturing paradigms but also propels the demand for generative AI solutions, spotlighting a lucrative avenue for growth within the engineering domain.

Deployment of Generative AI in SMEs Offers Growth Opportunity

The introduction of Generative AI into the operations of Small and Medium Enterprises (SMEs) unlocks vast growth prospects in the engineering industry. Historically, the adoption of AI-driven optimization tools was a venture pursued by large entities, yet SMEs stand to gain considerably from these innovations. The availability of cloud-based platforms and Software-as-a-Service (SaaS) models provides these smaller entities with cost-effective, scalable access to cutting-edge design optimization technologies.

This democratization of generative AI tools enables SMEs to not only elevate product quality and shorten time-to-market but also carve out a niche against larger competitors. Autodesk's Fusion 360 exemplifies this trend, offering SMEs generative design features that were once the exclusive domain of major corporations, without necessitating hefty investments. This strategic deployment could significantly broaden the market for generative AI solutions, catalyzing a new era of competitiveness and innovation among SMEs in the engineering sector.

Trending Factors

AI-driven Design Collaboration Platforms Are Trending Factors

AI-driven design collaboration platforms are revolutionizing the engineering sector, marking a significant trend in the use of Generative AI. These platforms blend Generative AI algorithms with tools for collaboration, simulation, and project management, enabling engineers from various disciplines to work together seamlessly on complex projects, even when distributed geographically.

The real-time feedback, enhanced visualization, and version control they offer streamline communication, quicken decision-making, and boost productivity. A prime example is Dassault Systèmes' 3DEXPERIENCE platform, which marries generative design with simulation and project management, facilitating innovative and efficient collaborative workflows. This trend is vital as it not only elevates the engineering process but also propels the market towards more integrated, collaborative, and innovative future.

Emphasis on Sustainable Design and Manufacturing Are Trending Factors

The growing emphasis on sustainable design and manufacturing practices represents a significant trend within the Generative AI in Engineering market. This trend is driven by the ability of Generative AI to help engineers create designs that are optimized for sustainability, including reduced material use, lower energy consumption, and minimal environmental impact.

The integration of sustainability criteria into generative design algorithms allows for the development of products that are both innovative and eco-friendly, meeting the increasing consumer demand for sustainable products and aligning with corporate sustainability initiatives. Companies like BMW are leading the way by utilizing generative design to produce lightweight, fuel-efficient components, underscoring the critical role of Generative AI in advancing sustainable engineering practices.

Regional Analysis

North America Dominates with 40% Market Share

North America's commanding 40% share in the Generative AI in Engineering market is propelled by a robust tech infrastructure, significant investments in AI and R&D, and a strong presence of leading technology firms. The region benefits from a highly skilled workforce and a culture of innovation, facilitating the development and adoption of advanced technologies. Furthermore, supportive government policies and funding for AI research contribute to this leadership position, enabling North America to pioneer developments in Generative AI applications across various engineering fields.

The regional dynamics of North America are characterized by a collaborative ecosystem involving academia, industry, and government, driving technological advancements and market growth. The presence of major technology and engineering firms headquartered in this region fosters a competitive yet collaborative environment conducive to innovation. This ecosystem is bolstered by top-tier universities and research institutions that continuously feed the talent pipeline and technological innovation, maintaining North America’s lead in the global market.

Regional Market Share and Growth Rates:

• Europe: Holds approximately 25% of the market share, supported by strong research institutions and progressive AI policies.
• Asia Pacific: Rapidly growing with a 20% market share, attributed to significant investments in technology and a fast-growing engineering sector.
• Middle East & Africa: Emerging with a 10% market share, driven by investments in smart cities and infrastructure development.
• Latin America: Gradually expanding with a 5% market share, bolstered by digital transformation efforts in various industries.

Key Regions and Countries

• North America
  • The US
  • Canada
  • Mexico
• Western Europe
  • Germany
  • France
  • The UK
  • Spain
  • Italy
  • Portugal
  • Ireland
  • Austria
  • Switzerland
  • Benelux
  • Nordic
  • Rest of Western Europe
• Eastern Europe
  • Russia
  • Poland
  • The Czech Republic
  • Greece
  • Rest of Eastern Europe
• APAC
  • China
  • Japan
  • South Korea
  • India
  • Australia & New Zealand
  • Indonesia
  • Malaysia
  • Philippines
  • Singapore
  • Thailand
  • Vietnam
  • Rest of APAC
• Latin America
  • Brazil
  • Colombia
  • Chile
  • Argentina
  • Costa Rica
  • Rest of Latin America
• Middle East & Africa
  • Algeria
  • Egypt
  • Israel
  • Kuwait
  • Nigeria
  • Saudi Arabia
  • South Africa
  • Turkey
  • United Arab Emirates
  • Rest of MEA

Key Players Analysis

In the Generative AI in Engineering market, companies like Autodesk, Inc., ANSYS, Altair Engineering, Siemens PLM Software, Dassault Systemes, nTopology, and ESI Group are leading the charge, each contributing uniquely to the industry's evolution. Autodesk and Dassault Systemes are pioneers in integrating AI into design and simulation software, empowering engineers to optimize and innovate with greater efficiency.

ANSYS and Altair Engineering excel in advanced simulation technologies, applying AI to predict outcomes more accurately and streamline the design process. Siemens PLM Software is transforming product lifecycle management, using AI to enhance product development and manufacturing. nTopology stands out with its focus on generative design, leveraging AI for complex, lightweight structures.

ESI Group specializes in virtual prototyping, incorporating AI to reduce physical testing requirements. Together, these key players are not only influencing current engineering practices but are also shaping the future of design and manufacturing, making the sector more adaptive, efficient, and innovative.

Market Key Players

• Autodesk, Inc
• ANSYS
• Altair Engineering India Private Limited.
• Siemens PLM Software
• Dassault Systemes
• nTopology
• ESI Group
• Other Key Players

Recent Development

• In June 2023: $3 billion investment in data and AI practice over three years, aiming to double AI talent and develop new capabilities, leading to $300 million revenue from generative AI projects in 2023.
• In 2020: Dassault Systems introduced the 3DEXPERIENCE SOLIDWORKS portfolio, which includes generative design capabilities to enhance the design process and enable engineers to create innovative products. The company has been actively working on integrating generative design within its 3DEXPERIENCE platform to provide a seamless end-to-end solution for engineers.

Report Scope