If you want to read a more general introduction into the topic about how AI impacts rendering tasks, I recommend reading this article.
In today’s dynamic architectural landscape, the integration of artificial intelligence renderings is fundamentally transforming how architects plan, iterate, and communicate their designs. Traditionally, architects have relied on a combination of technical CAD drawings and manual visualization methods to convey their concepts to various stakeholders—clients, city administrations, citizens, and contractors. These traditional methods, while effective to a degree, often result in significant information asymmetries. Stakeholders frequently struggle to fully comprehend the intricacies of technical plans, leading to misunderstandings, mismatched expectations, and, ultimately, dissatisfaction with the final outcomes.
To address these communication gaps, architects have increasingly turned to advanced visualization tools like Twinmotion, Enscape, Lumion, and others. These tools translate complex plans into more accessible and comprehensible visual formats. While these tools offer a solution to enhance understanding and engagement, the process of creating high-quality visualizations is not as straightforward or cost-effective as it might appear.
You can find a comprehensive list of rendering tools as well as fundamental explanations about rendering in this article.
A comprehensive study by Pelicad involving 250 qualitative interviews revealed that architects spend, on average, 8 hours creating visualizations. This indication varies between 3 and 32 hours. Despite the availability of advanced tools, the complexity and time-intensive nature of the rendering process remain a barrier for many. Costs associated with outsourcing rendering-tasks can vary dramatically, with some architects reporting expenses up to €1,500 per image for basic perspectives, and others indicating expenditures exceeding €7,200 per image for high-end visuals required for marketing purposes. This financial and temporal investment is a significant consideration for architects, who must balance the need for effective communication with the constraints of their budgets and timelines.
Given the significant time and cost investment required to produce traditional renderings, many architects opt to visualize only as much as necessary to convince stakeholders. According to Pelicad's interviews, 82% of architects indicated a desire for the capability to create more visuals at a lower cost, which would enhance their communication with stakeholders. The remaining 18% either preferred limited use of visuals to manage expectations or did not provide specific responses.
The financial burden of generating multiple high-quality visuals also impacts public tender offers, where city administrations often demand one to four visuals per application for use in public relations efforts. Given the uncertainty of winning these competitions, architects typically limit their visualizations to the minimum required, despite a desire for more variety in their presentations. In these contexts, AI could provide a significant advantage by allowing architects to produce a broader range of visuals more quickly and affordably, potentially increasing their chances of success in competitive scenarios.
Worthy to mention is that there are substantial differences between how architects communicate their projects towards stakeholders. In Germany, architects mostly use renderings to receive feedback from clients. However, architects from Turkey, Sweden, the US, and the Netherlands reported that they also use real-time renderings engines to guide clients through the whole project instead of simply showing renderings. The difference seems to stem from differences in the level of openness to receive feedback. Whereas German architects show only as much as needed to minimize the amount of iterations necessary, other architects prefer receiving as detailed feedback as possible to reduce information asymmetries later in the execution phase.
Artificial intelligence renderings is a new way to create visuals from planning data that aims at drastically reducing the time to derive understandable imagery for stakeholders. Leveraging stable diffusion networks, AI offers the potential to convert complex architectural plans into high-quality visuals more quickly and at a lower cost. This capability has generated substantial interest within the architectural community, with many professionals hopeful that AI can streamline their workflows and enhance their creative processes. Indeed, 80% of architects surveyed by Pelicad reported experimenting with or regularly using AI tools to augment their work. Despite this optimism, the current state of AI rendering technology is not without its challenges.
While AI tools show immense promise, they often fall short of accurately representing the nuances of architects' CAD inputs. According to Pelicad's survey, 98% of architects using AI renderings have experienced issues where crucial details from their CAD designs were altered or lost in the AI-generated outputs. This disconnect between the architects' precise plans and the AI's rendered visuals poses a significant hurdle, limiting the practical application of AI in rendering to early concept stages or for generating creative inspiration rather than precise, client-ready visuals.
Mostly, the artificial intelligence rendering tools struggle to understand and reproduce the complex geometries within CAD plans as well as metadata such as materials or surrounding information. As soon as architects work with plans with a high level of detail, generative networks often suffer from hallucinations that lead to either a disappearance of elements and/or the creation of completely new elements that are often not wanted. This results in repetitive workflows in which the AI generates inaccurate content that needs to be adjusted by the architect over and over again. As most artificial intelligence renderings tools also struggle to keep output elements steady while changing minor details, the rendering process often needs to be repeated, offering no real cost advantage. Another problem is that AI render-tools struggle to keep information about atmosphere, materialization or surroundings equal, while changing the perspective. This often leads to inaccuracies between the visuals for the same project.
We dove deeper into the challenges of AI in this article.
For a deep-dive into potential use cases for AI in the architectural workflow (not just for visualization) you can read this article.
Despite these challenges, AI's potential in architectural rendering is undeniable. Some architects have started utilizing AI models like Midjourney and Veras to enhance creativity during the initial stages of design or to transform sketches into polished renderings. Veras for instance has also already launched a plugin for Revit that allows direct plan-to-visualization translations to streamline the integration of AI tools in everyday workflow. However, the challenges for accurate visuals from artificial intelligence renderings
still persists.
The ability to process and accurately render 3D CAD or Building Information Modeling (BIM) files remains a critical gap in current AI capabilities.
Addressing this gap is a primary focus for companies like Pelicad. The approach involves developing AI rendering solutions that allow architects to control generative networks more precisely by using 3D geometries and BIM metadata. This advancement promises to bridge the gap between AI's capabilities and architects' requirements, offering a more accurate and controllable way to generate high-quality renderings directly from detailed CAD and BIM files.
Imagine a future where artificial intelligence renderings tools achieve the necessary accuracy to perfectly render architectural designs. In this likely scenario, architects would only need to follow a simple three-step process to create a rendering:
This streamlined approach of artificial intelligence renderings would drastically reduce the average rendering time compared to the current process, resulting in a transformative impact on architects' workflows. However, creating visuals faster does not only have an impact on time management and cost efficiency but the way how architects plan and communicate their project on a much deeper level.
In the early design stages, some architects already use available artificial intelligence rendering tools to turn first sketches into useful visualizations. These visuals often vary from the initial input quite drastically. By doing so, architects receive creative inspiration for their projects in terms of the chosen design and choice of materials.
Future artificial intelligence rendering tools will have a sophisticated understanding of the limitations and dependencies within BIM plans. Enhanced by specific architectural knowledge and environmental data, these tools will generate more specific outputs that are already in 3D and ready to be edited. Moreover, design ideas could offer preliminary assessments for ESG compliance, financial ROI, and regulatory adherence, providing valuable insights much earlier in the planning process. Forma by Autodesk is already a good example of how architects can leverage AI in early design phases to derive impact-analyses about their projects.
During the planning stage, architects will experience significant improvements in stakeholder communication. Enhanced artificial intelligence renderings tools will enable better citizen participation in real estate projects by providing more understandable information, facilitating more precise and actionable feedback. Architects can focus more on planning and execution activities rather than the cumbersome task of turning their plans into virtual realities for stakeholder communication. As the speed to create visualizations will drastically decrease, architects will be able to communicate more details to stakeholders and receive feedback without having to learn rendering skills. This leads to a democratization of visualization as well as the general understanding of planning data for stakeholders without planning expertise.
AI will also streamline specific processes within architectural visualization. Many architects currently struggle to accurately recreate their chosen materials for individual projects, often resorting to manual adjustments in tools like Photoshop. Advanced AI will enable architects to simply take a picture of their material and apply it automatically to the selected area, requiring only minor adjustments for dimensions.
Furthermore, architects will benefit from improved internal collaboration. As technical aspects can be visualized and communicated more effectively through AI, all changes will be visible from a holistic perspective, enhancing teamwork and project coherence.
The integration of artificial intelligence in architectural rendering heralds a transformative shift in how architects plan, iterate, and communicate their designs. AI’s role extends beyond merely enhancing existing workflows—it represents a fundamental change that promises to redefine the entire architectural process. Despite the current limitations, AI rendering technology is poised to bring unparalleled efficiencies and creative opportunities that could revolutionize the industry.
AI’s most significant impact lies in its ability to revolutionize communication between architects and stakeholders. Traditional methods, reliant on technical CAD drawings and manual visualizations, often lead to information asymmetries. Stakeholders, lacking technical expertise, struggle to fully understand complex plans, resulting in misaligned expectations and potential dissatisfaction with the final project. AI-rendered visuals bridge this gap effectively by converting intricate architectural data into highly realistic, easily comprehensible images.
These AI-generated visuals facilitate better understanding and engagement, ensuring that all parties, from clients to city administrations, are more accurately aligned with the project's vision. This enhanced communication is crucial, particularly in urban planning and public tender scenarios, where the ability to present clear and compelling visuals can significantly influence project approvals and stakeholder buy-in.
Traditional rendering processes are both time-consuming and costly, often involving substantial investments in manual labor and financial resources. AI has the potential to streamline these processes by automating the creation of high-quality renderings, drastically reducing the time and cost associated with producing visuals. This efficiency gain is particularly advantageous in competitive contexts such as public tenders, where quick and cost-effective production of multiple visuals can enhance an architect’s chances of winning projects.
By lowering the barriers to creating sophisticated renderings, AI democratizes the visualization process. Architects of varying scales and budgets can leverage AI to produce polished visuals, ensuring that high-quality renderings are no longer a luxury but a standard practice accessible to all.
AI’s impact extends beyond practical efficiencies to significantly enhancing creativity. By swiftly converting initial sketches and early design concepts into refined visualizations, AI allows architects to explore and experiment with various design ideas more freely. This capability encourages a more iterative and exploratory design process, fostering innovation and potentially leading to more refined architectural solutions.
Future AI tools, with improved capabilities for handling complex geometries and BIM metadata, will further integrate into the design process, providing preliminary assessments of environmental impact, financial feasibility, and regulatory compliance. These insights will guide architects from the earliest planning stages, enhancing the quality and feasibility of their designs.
Despite its promise, AI rendering technology still faces challenges, particularly in accurately translating detailed CAD data into renderings. Current AI tools often misinterpret or omit critical design elements, limiting their use to early concept stages or creative inspiration rather than precise, client-ready visuals. Companies like Pelicad are working to overcome these issues by developing AI solutions that better integrate with 3D geometries and BIM metadata, aiming to provide more accurate and controllable rendering outputs.
The future of AI in architectural rendering envisions a streamlined, efficient process where creating a high-fidelity rendering is as simple as uploading a CAD file, adjusting settings, and letting AI handle the rest. This transformation will free architects to focus on creativity and problem-solving, enhance stakeholder communication, and democratize the visualization process. As AI continues to evolve, it will not only serve as a powerful tool for rendering but also as an integral partner in the architectural design process, driving innovation and excellence across the industry.
