How Demand for 3D Models Is Changing in the Age of AI and Automation

The 3D modeling market is experiencing a period of unprecedented growth, driven by impressive financial performance and widespread adoption across various sectors.

Key Industries Driving Demand
The demand for 3D models spans a broad range of industries, far beyond traditional applications. Architecture, Engineering, and Construction (AEC) is a primary driver, dominating end-use segments with a 22.2% market share in 2024. 3D mapping and modeling are essential for identifying issues before construction begins, helping save time and costs. They are indispensable for architectural visualization, virtual walkthroughs, and Building Information Modeling (BIM).

Media and entertainment is a broad category that includes film (realistic character and environment creation, visual effects, motion capture), gaming (designing landscapes, characters, props), and animation. The rapidly growing gaming industry, especially in the Asia-Pacific region, is a major force behind the demand for high-quality 3D assets.

The healthcare and life sciences sector is expected to show the highest CAGR, using 3D models for medical imaging, surgical planning, simulation, as well as the development of prosthetics and implants. In the automotive industry, 3D technologies are used for product design, detailed performance testing, aerodynamics, and safety features—alongside an increasing use in interactive in-car displays.

In manufacturing and engineering, 3D models are critical for product development, rapid prototyping, plant layout optimization, defense simulation, and the creation of precise tools and equipment. The growth of 3D printing, which relies heavily on 3D modeling, is further fueling demand in manufacturing for fast and efficient prototyping.

AI and Automation: Catalysts of Unprecedented Transformation

Artificial intelligence and automation are fundamentally reshaping the landscape of 3D modeling, bringing unprecedented efficiency, accessibility, enhanced quality, and scalability.

Unmatched Efficiency and Speed

Traditional 3D modeling is known for being labor-intensive and time-consuming, often requiring advanced manual skills. Creating complex models with intricate details can take hours, days, or even weeks. AI algorithms are revolutionizing this process by generating complex and innovative 3D models with minimal human input, enabling designers and artists to explore a broader range of creative possibilities without being limited by time or technical expertise.

AI automates many of the tedious and repetitive tasks that traditionally consume a large portion of an artist’s time, such as sculpting fine details, generating realistic textures, UV unwrapping, retopology, and rigging. For example, AI-enhanced workflows can reduce rigging time by up to 40% compared to manual methods. This automation dramatically reduces production bottlenecks and accelerates prototyping, allowing for faster iterations and design improvements. Product development cycles that once took weeks can now be completed in a matter of days.

Multiple sources consistently highlight the shift from “weeks to days,” “lightning-fast” performance, and “shorter turnaround times.” This isn’t just a convenience or marginal improvement—it’s a critical business imperative. In highly competitive markets such as e-commerce, gaming, and product development, speed enables businesses to capitalize on emerging trends, respond rapidly to changing consumer demands, and maintain a significant competitive edge. Thus, the true business value of AI is evolving from being a purely cost-saving tool (though that remains an advantage) to becoming a driver of fast, flexible, and iterative 3D content development cycles. This shift transforms demand from slow, bespoke production to rapid, iterative creation and deployment.

Improved Quality and Realism

AI is also significantly enhancing the realism and accuracy of 3D models. Machine learning algorithms trained on massive datasets—including millions of images, videos, and patterns from real-world environments—enable the generation of highly realistic models that accurately replicate natural environments, lighting effects, materials, and textures.

Advanced rendering methods such as physically based rendering (PBR) are being widely adopted, and AI-driven texture synthesis makes the creation of photorealistic materials much more efficient and accessible. Improvements in rendering software, often accelerated by AI, allow for more realistic and detailed visualizations with enhanced lighting, shading, and texture effects. AI-generated models can achieve high precision even for complex shapes and produce high-quality meshes suitable for demanding applications like physical simulations.

While tools like 3DFY.ai confidently claim their AI-generated models are “on par with human modelers,” and AI often shows gains in realism, a notable study presents a more nuanced picture. It found a “general preference for human-made models” (41.7% of votes compared to 35.3% for AI in one sample), citing superior design consistency and visual coherence. However, the same study also noted that AI models outperformed human ones in specific categories (e.g., fox and table models). Additionally, a seasoned 3D artist observed that AI excels at predictable content but struggles with “control over intermediate steps” and incorporating client feedback—crucial in professional workflows.

This indicates a bifurcated demand for 3D models. AI is rapidly closing the gap in technical accuracy and baseline realism, especially for predictable or mass-produced assets where speed and volume are critical. However, for complex, nuanced, or artistically-driven projects where “design consistency” and “visual coherence” (requiring deep creative intent, narrative integration, and iterative refinement) are paramount, human creativity and oversight still hold a distinct advantage.

The market is evolving to require both:

• Mass-produced, “good enough” assets for broad applications (e.g., e-commerce, basic virtual environments), which AI can generate efficiently, and
  
• Unique, high-fidelity, narrative-driven assets, which still demand significant human involvement and craftsmanship.
  

Scalability and Customization

AI-generated 3D content radically optimizes workflows by reducing the need for intensive manual labor. This makes it an ideal solution for achieving mass customization across industries such as automotive, fashion, and e-commerce. Businesses using generative AI can now create hundreds of design variations from a single base model with minimal added cost, enabling unprecedented product diversification and personalization.

Specialized platforms like Sloyd are emerging to deliver customizable, game-ready assets that can be quickly adapted to specific project needs. Beyond mass production, AI also enables highly personalized model creation. For example, in healthcare, AI can analyze patient-specific data (such as bone structure or skin texture) to generate perfectly tailored prosthetics or implants.

Traditional 3D modeling is inherently bespoke, making each asset complex and labor-intensive to produce. AI’s ability to “generate hundreds of design variations at minimal additional cost” and “mass-produce 3D content” fundamentally changes the economic model of content creation. This transformation is exemplified by solutions like Tangiblee in e-commerce, which delivers scalable virtual try-on (VTO) capabilities by processing images in seconds—significantly reducing costs for retailers and boosting conversion rates by up to 9%.

This trend signals a profound shift in the nature of demand for 3D models: from a focus on individually crafted, high-cost assets to an urgent need for scalable, customizable, and often personalized 3D content. AI is the technology enabling this shift, unlocking entirely new business models and market opportunities in sectors such as e-commerce, digital fashion, and personalized healthcare—where mass customization was once economically unfeasible.

New Frontiers: Where 3D Models Meet AI-Driven Innovation

The convergence of 3D modeling and artificial intelligence is opening up new application areas, transforming how we interact with digital content.

Immersive Experiences: The Integration of AR/VR

Augmented Reality (AR) and Virtual Reality (VR) technologies are driving a new wave of 3D modeling use cases, particularly transforming industries like retail, architecture, and professional training simulations. Businesses are increasingly leveraging these immersive technologies to allow customers to “place” products in real-world environments (e.g., AR try-ons for clothing, furniture, accessories) or “walk through” virtual spaces (e.g., VR building previews) before making purchases or commitments.

The synergy between 3D modeling and AR/VR is elevating visualization from static imagery to deeply immersive and interactive environments. VR and AR technologies provide rich settings for engaging with 3D assets, fueling growing demand for highly realistic and interactive visualizations. Importantly, AI systems integrated with VR and AR enhance these experiences by making them more realistic, personalized, and responsive—pushing the boundaries of digital immersion.

The consistent focus across sources on “immersive,” “interactive,” and “personalized” experiences signals a fundamental shift in both consumer and business expectations. The emphasis is no longer just on visual representation but on experiencing products, spaces, and simulations in dynamic, engaging ways. AI plays a critical role in rapidly generating, optimizing, and adapting 3D content to power these real-time interactive environments, driving demand for 3D models that are not only visually appealing but also functional, adaptable, and capable of supporting dynamic scenarios—fueling innovation in customer engagement and training.

The Rise of Digital Twins

Digital twins are sophisticated virtual replicas of real-world objects, systems, or environments that integrate real-time data with advanced simulation tools. The digital twin market is projected to grow explosively—from approximately $10.3 billion in 2023 to $73.5 billion by 2032—driven by applications in manufacturing, healthcare, and urban planning.

Their use cases are highly diverse, spanning real estate, travel and hospitality, AEC (architecture, engineering, construction), facility management, insurance and disaster recovery, and public infrastructure. AI-powered digital twins enable transformative capabilities:

• Smart Cities: Improve energy efficiency by up to 20% and optimize traffic flow through detailed scenario simulations.
  
• Manufacturing: Enhance product design and identify defects throughout the product lifecycle.
  
• Predictive Maintenance: Simulate aircraft component wear to proactively plan servicing, reducing machinery failures by up to 70% and cutting maintenance costs by 25%.
  
• Healthcare: Model patient-specific digital twins to simulate treatment plans and forecast outcomes, and optimize hospital layouts for better patient care.
  
• Automotive: Facilitate virtual prototyping of vehicles for simulating aerodynamics, engine performance, and durability—minimizing reliance on costly physical prototypes.
  

Companies like Matterport use AI (including its Cortex AI engine) to automate many aspects of digital twin creation, such as facial blurring, optimal scan image selection, precise measurement generation, and object recognition within spaces. Generative AI plays a key role in producing realistic data for simulating complex scenarios within digital twins.

Digital twins fundamentally elevate 3D models from mere visualization tools to operational assets for real-time analysis, predictive modeling, and optimization. The strong forecasted growth of this market reflects a surging demand for functional applications of 3D technology. AI’s role in automating the creation of digital twins and enabling their predictive capabilities unlocks their full potential. This marks a strategic shift in the demand for 3D models—from static design assets to dynamic components of advanced business intelligence and operational performance. AI doesn’t just create static models—it transforms them into intelligent, actionable digital systems, fueling demand for high-precision, real-world, data-integrated 3D representations that can support critical decision-making.

Building the Metaverse

3D models are not just important—they are foundational building blocks for creating immersive and interactive environments in the emerging Metaverse. In this context, AI is rapidly becoming the “central intelligence system” of the Metaverse, acting as the architect, orchestrator, and operator that makes these virtual worlds dynamic, personalized, and functional for both business and entertainment.

AI tools are indispensable for the rapid generation and iteration of the massive volume of 3D models, textures, and avatar designs required by the Metaverse. They also automate the cost-effective creation of diverse environment assets, which is crucial for building rich, varied virtual worlds. Advanced AI algorithms are increasingly capable of complex tasks such as generating realistic non-player characters (NPCs) and performing real-time language translation, enhancing the realism and interactivity of virtual experiences.

The Metaverse concept demands an unprecedented scale, variety, and dynamism of 3D assets. Manually creating this content is infeasible at such scale. AI’s ability to “quickly generate and iterate 3D models, textures, and avatar designs” and “automate economically scalable content creation” directly addresses this massive need. Thus, demand for 3D models in the Metaverse is inherently demand for scalable, AI-generated content. AI is not merely a tool for generating individual assets—it is becoming the essential engine for constructing the entire digital infrastructure of future virtual economies, social spaces, and interactive experiences.

Cloud-Based Workflows

Cloud-based 3D modeling solutions are gaining momentum, significantly improving collaboration and accessibility across the industry. The benefits are substantial: faster project delivery times, scalable storage solutions for large 3D files, and enhanced collaboration across geographically distributed teams. Cloud platforms also provide secure sharing and coordination with various ecosystem stakeholders. This large-scale shift is largely driven by the rise of remote work culture and the need for distributed teams to collaborate seamlessly.

The accelerated move to cloud workflows is a direct and necessary response to the increasing complexity of modern 3D projects and the global nature of production teams. It directly addresses long-standing “collaboration pain points” by enabling “seamless teamwork” that was previously difficult to achieve. This trend complements and enhances AI’s capabilities, offering the infrastructure needed for distributed teams to effectively use AI-generated assets and automated workflows. As a result, there is growing demand for 3D models that are not only easy to generate and customize but also easy to share, manage, and integrate into highly collaborative, global production pipelines—reflecting a more interconnected and agile industry.

Conclusion

Demand for 3D models is experiencing exponential growth, supported by robust market forecasts and expanding use across diverse sectors. From traditional drivers like architecture, engineering, and construction to emerging applications in healthcare, retail, and navigation, 3D models are becoming foundational to the digital economy. This growing demand is fueled not only by the need for more 3D content but also by the imperative to produce it more efficiently, accessibly, and at scale.

Artificial intelligence and automation are the key catalysts of this transformation. They are revolutionizing 3D model creation with unprecedented speed and efficiency—reducing design cycles from weeks to days. This allows businesses to respond rapidly to market trends and secure competitive advantages. Moreover, AI is democratizing 3D modeling, making it accessible to a broader audience regardless of technical expertise through intuitive “text-to-3D” and “image-to-3D” tools. This expands the creator base and helps meet the massive demand for content. AI is not just accelerating production—it is redefining what 3D modeling can achieve