Generative AI in Product Design Market By Deployment Model (Cloud-based and On-premises), By Application, By End-User, By Region and Companies - Industry Segment Outlook, Market Assessment, Competition Scenario, Trends, and Forecast 2023-2032

Report Overview

Generative AI in Product Design Market size is expected to be worth around USD 615.6 Mn by 2032 from USD 31.6 Mn in 2022, growing at a CAGR of 35.6% during the forecast period from 2023 to 2032.

The global generative AI in product design market has been witnessing substantial growth. Factors such as the increasing adoption of AI technologies in various industries, the need for faster and more innovative product development, and the demand for optimized product performance are driving the market growth. The market is projected to continue expanding as more companies recognize the benefits of generative AI in enhancing their product design processes.

Generative AI in product design can assist in various stages of the design process, from ideation and concept generation to optimization, validation, and customization. It can help designers explore different design alternatives, optimize designs for specific performance metrics, and validate designs through virtual simulations.

Driving factors

Automation and Efficiency

Generative AI enables the automation of various aspects of the product design process. It can quickly generate multiple design options, iterate through different configurations, and optimize designs based on specified criteria. This automation leads to increased efficiency in the design process, allowing designers to explore a broader range of possibilities in less time.

Cost Reduction and Resource Efficiency

Generative AI can help in reducing manufacturing costs and optimize resource utilization. It can minimize material waste and streamline production processes by generating efficient designs. Additionally, the automation and speed offered by generative AI can reduce time-to-market and development costs, making product design more cost-effective.

Increased Design Complexity

Product design has become increasingly complex due to evolving customer demands, technological integration, and sustainability considerations. Generative AI helps address this complexity by automating and optimizing the design process, enabling designers to handle intricate design requirements more efficiently.

Restraining Factors

Limited Data Availability and Quality

Generative AI algorithms rely on large, diverse datasets to generate accurate and meaningful design variations. However, obtaining such datasets can be challenging, especially in industries involving proprietary or sensitive data. Limited data availability or poor data quality can limit the effectiveness of generative AI in product design, reducing its potential impact.

Lack of Domain Expertise

Generative AI algorithms require a deep understanding of the domain and design principles to generate relevant and viable design alternatives. Design expertise is necessary to define constraints, evaluate design outputs, and ensure that the generated designs meet functional and aesthetic requirements. The shortage of skilled professionals who can effectively work with generative AI tools can hinder its widespread adoption.

Integration Challenges

Integrating generative AI tools into existing design workflows and software platforms can be challenging. Compatibility issues, learning curves, and the need for additional infrastructure or computational resources may pose implementation challenges. This can slow the adoption of generative AI in organizations reluctant to disrupt their existing design processes or invest in new technologies.

Covid-19 Impact on Generative AI in Product Design Market

The COVID-19 pandemic has significantly impacted various industries, including the product design sector. The pandemic accelerated the digital transformation of various industries. As companies sought ways to streamline their operations and reduce reliance on physical processes, generative AI in product design emerged as a valuable tool. It enables rapid prototyping, simulation, and optimization, saving the product development lifecycle time and costs. Generative AI in product design offers automated and data-driven solutions, enabling faster iterations and improved efficiency. As companies sought ways to optimize their operations during the pandemic, generative AI tools provided opportunities for faster prototyping, simulation, and optimization of product designs remotely. The pandemic caused disruptions in global supply chains, affecting the availability of hardware components and software tools required for generative AI in product design. This may have slowed some companies' adoption and implementation of generative AI technologies.

By Deployment Model Analysis

Cloud-based is dominated in Deployment Model Segment in Generative AI Product Design In 2022.

The cloud-based deployment model predominantly dominated the generative AI in the product design market. Cloud-based deployment offers several advantages: scalability, flexibility, accessibility, and cost-effectiveness. It allows companies to access generative AI tools and resources remotely through cloud platforms. Cloud-based deployment provides the ability to leverage the power of distributed computing, enabling faster processing and analysis of large datasets.

It also facilitates collaboration among geographically dispersed teams, as multiple users can simultaneously access and work on design projects. The on-premises deployment model is still relevant; it typically requires significant upfront investment in hardware, software licenses, and IT infrastructure. Large enterprises commonly adopt this model with specific security or compliance requirements that necessitate maintaining data and processes within their infrastructure.

By Applications Analysis

Design Exploration and Ideation will be a significant share of the Application Segment in Generative AI Product Design in 2022.

The Design Exploration and Ideation category dominated the application of generative AI in product design. Design Exploration and Ideation involve generative AI algorithms to generate and explore various design options and concepts. This application allows designers and engineers to quickly iterate through multiple design alternatives, facilitating creativity and innovation in product development.

This application uses generative AI to optimize product designs and simulate their performance under various conditions. Generative AI algorithms can analyze multiple design parameters, constraints, and objectives to identify optimal solutions and simulate how the designs perform in real-world scenarios. This helps in improving product performance, reducing costs, and enhancing efficiency.

Generative AI enables product customization and personalization based on customer preferences and requirements. By leveraging customer data and preferences, generative AI algorithms can generate customized product designs, allowing companies to offer unique and tailored products to their customers at scale.

By End-User

Automotive Manufacturers dominated in End-User Segment in Generative AI Product Design in 2022.

The automotive industry has been at the forefront of adopting generative AI in product design. Automotive manufacturers leverage generative AI algorithms to optimize vehicle designs, improve aerodynamics, enhance fuel efficiency, and develop lightweight components. Generative AI enables rapid exploration of design alternatives and simulation of various scenarios, helping automotive manufacturers accelerate product development and achieve performance targets.

The healthcare industry has also seen significant adoption of generative AI in product design. Medical device manufacturers use generative AI to design and optimize medical devices, prosthetics, and implants. Generative AI assists in creating patient-specific designs, optimizing device performance, and simulating device behavior in different scenarios.

It enables the customization and personalization of medical devices. Construction and architecture firms have started exploring the potential of generative AI in product design. Generative AI algorithms can aid in the design of complex architectural structures, optimize building layouts, and simulate structural performance.

Key Market Segments

Based On Deployment Model

• Cloud-based
• On-premises

Based On Application

• Design Exploration and Ideation
• Design Optimization and Simulation
• Customization and Personalization
• Other Applications

Based On End-User

• Automotive Manufacturers
• Healthcare and Medical Device Manufacturers
• Contraction and Architecture Firms
• Other End-Users

Growth Opportunity

The adoption of cloud-based solutions in generative AI design tools is expected to grow. Cloud-based deployment offers scalability, accessibility, & collaboration capabilities, making it easier for companies to access and leverage generative AI tools without significant infrastructure investments. The flexibility and cost-effectiveness of cloud-based solutions make them attractive for businesses of all sizes.

Integrating generative AI in Product design with manufacturing processes can optimize production efficiency. By analyzing design parameters and manufacturing constraints, generative AI algorithms can generate optimized manufacturing and construction plans, streamline production workflows, & improve overall manufacturing efficiency. Manufacturers can benefit from reduced production costs, improved quality control, & enhanced productivity.

Latest Trends

Generative design, powered by AI algorithms, is gaining traction in product design. It allows designers to input design parameters and constraints and generates multiple design alternatives automatically. The need for faster iteration, optimization, and exploration of design possibilities drives this trend.

AI integration with existing design tools and software is becoming more prevalent. Generative AI capabilities are embedded into popular design software, enabling designers to seamlessly leverage AI algorithms within their familiar design environments. This integration enhances productivity and streamlines the design process.

Cloud-based deployment of generative AI tools is on the rise. Cloud platforms provide scalability, flexibility, and accessibility, allowing design teams to work on projects simultaneously from different locations. Cloud-based solutions also offer computational resources required for complex generative AI algorithms.

Regional Analysis

North America Accounted for the Largest Share of the Generative AI Product Design Market in 2022.

North America is expected to be a significant market for generative AI in product design, driven by technological advancements, a strong presence of key technology companies, and a focus on innovation. The region has a robust ecosystem of AI startups and research institutions, contributing to developing and adopting generative AI technologies. Industries such as automotive, aerospace, and consumer goods are expected to drive demand for generative AI in product design in this region.

Europe is also anticipated to be a prominent market for generative AI in product design. The region has a solid manufacturing base, particularly in the automotive and aerospace sectors. There is an increasing emphasis on sustainability and energy efficiency, which aligns with the optimization capabilities offered by generative AI. European countries have been investing in AI research and development, fostering the growth of generative AI technologies in product design.

Asia Pacific is experiencing rapid growth in the generative AI in the product design market, which has a significant demand for advanced design optimization and customization. The increasing adoption of AI technologies in various industries and a large consumer market drive the growth of generative AI in product design in this region.

Key Regions and Countries

North America

• 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

Market Share & Key Players Analysis

Autodesk offers generative design tools that integrate with its popular CAD software. The company provides generative design capabilities for various industries, including automotive, aerospace, and architecture. Dassault Systèmes offers generative design solutions through its CATIA and SOLIDWORKS software suites. The company's generative design tools enable users to optimize designs based on desired performance criteria.

ANSYS offers generative design tools as part of its simulation and analysis software suite. Their solutions enable designers to optimize designs for structural integrity, fluid dynamics, and other performance criteria.

Top Key Players in Generative AI in Product Design Market

• Autodesk Inc.
• Dassault Systemes SE
• Siemens PLM Software
• Ansys Inc.
• Altair Engineering Inc.
• Unity Technologies
• PTC Inc.
• Granta Design
• Other Key Players

Recent Development

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