AI has entered architectural practice at speed, but its role is uneven, and its true impact is often obscured by hype. Some firms are embracing new tools, others are cautious, and clients themselves are starting to experiment in ways that shift the architect’s role.
This article aims to cut through speculation and reveal what AI is actually doing in practice today in terms of reshaping workflows, influencing design culture, and altering client relationships.
Editor’s note: This article is based on the original Chaos white paper: AI in architecture: trends,
hidden risks, and what comes next.
Key findings
- AI is reshaping architectural practice gradually, not in a single leap. Beneath the hype, adoption continues to be defined by contracts, deliverables, and regulatory frameworks.
- Clients are entering the design process with AI-generated concepts. This shift is pushing architects to demonstrate their value through authored design, contextual reasoning, and informed decision-making.
- Human judgment remains the defining multiplier. Without professional oversight, AI’s polished outputs risk being misaligned, incomplete, or misleading.
- Efficiency is emerging not from speed, but from the removal of entire steps within the workflow. The most meaningful gains come when AI eliminates redundant translation layers between concept, documentation, and delivery.
- Architects are becoming increasingly aware of hidden risks. Concerns now extend beyond data privacy to include authorship, bias, overtrust, and homogenization.
- Responsible-use frameworks are becoming essential. Firms are building AI literacy, data governance, and review protocols to ensure technology supports design integrity rather than undermining it.
- Beyond image generation, AI’s next phase is expected to emphasize analytical partnership. Instead of functioning as isolated tools, AI will be embedded within core authoring environments, maintaining live data connections that support evaluation, compliance, and performance monitoring throughout the project lifecycle.
How AI is changing AEC
For now, AI’s impact on the AEC industry is best described as gradual rather than revolutionary. Its influence is emerging unevenly across firms and workflows, but it is already beginning to shift how architects and clients interact and how projects are being designed. The nature of that shift is still taking shape, raising new questions about roles, responsibilities, and the value architects bring to the table. The following sections examine the most visible trends shaping this transition and their implications for practice.
Clients are curious, and some are already experimenting
Our interviews with leading practitioners suggest that most clients today recognize AI is relevant, even if they lack clarity on how to use it. Architects frequently report questions from clients who feel they “should” be engaging with AI but do not know where to begin. This curiosity marks a willingness to explore, often coupled with a fear of missing out.
At the same time, some clients have gone further, using tools like Midjourney to generate concept images or massings themselves. These outputs are sometimes crude, but they can be persuasive enough to communicate their vision for the project. In many cases, developers have shared AI-generated images with architects, asking them to design something similar. This trend underscores a shift where AI is no longer just inside the architect’s studio, but in the hands of clients who are shaping design conversations in new ways.
This is changing the economics of early-stage design, particularly in concept visualization and interior work, which are the most exposed to rapid iteration. Firms are already responding by bringing more of this imagery work back in-house from third-party visualization studios, both to maintain authorship and to keep control over the narrative.
The practice implications of client-led AI exploration
This client experimentation is beginning to alter the dynamics of architectural practice. On the positive side, it enables faster, more visual exchanges at the earliest stages of a project, opening up new opportunities for collaboration. But the same trend also risks narrowing the architect’s role to refining or executing a vision that has already been set elsewhere.
Industry experts consistently warn that as clients become more adept with AI, architects must demonstrate added value beyond image production. That value increasingly lies in areas where professional expertise cannot be substituted by automated outputs, such as authored design, strategic storytelling, and the integration of real-world constraints.
Architects’ risk of being reduced to implementers
“Without understanding AI, the architect risks being reduced to a technician rather than a designer. The race to the bottom on pricing for visuals has already begun – you can now get a rendering for 15$. Top firms will still command a premium because of their brand and authored designs, but for many in the middle, the pressure to stand out will be intense.” – Kostika Lala, Founding Partner at Flashcube Labs
The most significant risk is that architects are reduced to implementers rather than originators. When clients arrive with AI-generated concepts, the pressure on fees for visualization intensifies, and the architect’s contribution can appear secondary. Without clear differentiation, firms may find themselves competing directly with inexpensive, client-generated outputs.
To avoid this, architects must reassert their position as authors of design intent. Their role is not merely to produce images but to curate and guide decisions: embedding feasibility, performance, and coherence into every option, and ensuring that AI-generated visions can be translated into buildable architecture.
Storytelling and framing are now essential skills
With AI capable of generating a flood of options, the challenge for architects is no longer scarcity but abundance. Projects involving multiple stakeholders are particularly vulnerable to “decision overload” if every AI-generated variation is treated as a viable path.
In this environment, success depends on disciplined framing. Architects who filter and present outputs carefully, showing the right level of detail at the right moment, curating options to avoid distraction, and embedding design intent into every image are best positioned to maintain authority in the design process. Storytelling is becoming as critical as technical expertise, ensuring that AI outputs advance a coherent vision rather than scatter attention.
Beyond generative AI
While most public discussions of AI in architecture focus on generative imagery, its broader influence is already reshaping other parts of the workflow. Many of the most practical gains are taking place behind the scenes, in areas such as analysis, coordination, and data-driven decision-making.
Tools that automate routine authoring tasks or integrate rule-based checking are changing where architects spend their time. Within core authoring platforms, AI can now evaluate compliance, accessibility, energy performance, and material quantities directly against project data. These capabilities extend the architect’s ability to test ideas with greater rigor and speed.
AI is also helping firms learn from their own work by extracting insights from past projects, identifying patterns in construction performance, and supporting documentation and visualization management.
In practice, this indicates that AI’s lasting impact may come from creating intelligent feedback loops that help architects understand, evaluate, and refine their designs across every stage of the process.
Humans and AI in practice: new divisions of design labor
“AI is most effective when paired with experienced practitioners. Human and computer together can make experts sharper, while unguided use can leave less experienced staff lost. This reinforces the architect’s role as guide and translator rather than mere tool operator.” – Charles Portelli, Senior Associate and Digital Innovation Strategist at Perkins & Will
Through the interviews conducted, one point was clear: AI is not replacing designers. Instead, it is beginning to streamline repetitive steps such as documentation and visualization setup, while focusing design conversations on the decisions that truly shape a project. The themes below reveal how AI and human expertise are reshaping different stages of the design process.
From playfulness to shared understanding
While much discussion of AI focuses on efficiency, practitioners point to something less obvious but equally important: fun. Clients experimenting with AI, running a rendering through a model during a meeting, for example, create moments of curiosity that enliven collaboration. For architects too, these quick experiments lower the stakes of early exploration, opening space for “what if” questions that might otherwise feel too speculative.
Making design feel less rigid can open creative dialogue and lead to unexpected outcomes. More importantly, it fosters faster, more intuitive conversations about aesthetic intent, which clients often struggle to express in words.
By experimenting with prompts, metaphors, and visual references supported by AI tools, clients and architects can align on taste and direction earlier in the process. This shared visual language clarifies expectations, strengthens trust, and builds confidence that the client’s vision is understood.
Removing steps from the workflow
Interviewees highlighted that the most significant gains come when AI removes entire steps from the process. Emerging examples hint at a near-future ability to move directly from sketches to usable geometry or to link parametric models with real-time visualization.
From the technology side, similar progress is happening in visualization. As noted by Dan Ring and Vladimir Koylazov, AI now automates routine setup tasks like populating scenes or adjusting materials, compressing multi-step operations into concise actions while preserving full creative control. This keeps designers focused on intent rather than tool management.
These advances reduce translation layers between concept, documentation, and review, shifting effort from re-modeling toward decision-making. The same principle extends to the authoring environment itself, where embedding AI directly into design tools is expected to automate much of the drafting and coordination work. Looking ahead, AI assistants could maintain live project data, propagate updates automatically, and flag issues across the project lifecycle. As these systems mature, the distance between concept and delivery will continue to shrink, giving architects more space to concentrate on design intent and quality.
Right details at the right time
When reviewing a project, one of the main challenges is keeping clients focused on the information that matters for the current stage of work. Interviewees observed that visuals with too much polish too early often shift attention away from core questions of form, layout, or performance toward surface qualities that are not yet relevant.
AI-supported visualization can modulate the level of detail to match each design phase. Early outputs may appear as abstract massing or program diagrams, while later stages can include refined materials and lighting for presentation purposes. The same tools can also generate composite views that balance simplified and detailed elements, directing attention to specific aspects of the design under discussion. In practice, this helps maintain clarity across reviews and supports decisions in a logical sequence.
Iteration with purpose
“Unlimited iterations? That’s not necessarily helping. We’ve lost the original purpose of iteration, which was to evoke an emotional reaction and get direction, not to just cast a net and see what you catch.” – Charles Portelli, Senior Associate and Digital Innovation Strategist at Perkins & Will
Interviewees cautioned that generating large numbers of near-identical renderings risks diluting direction and obscuring weak ideas. More effective use of AI involves purposeful iteration: exploring meaningful differences tied to a clear design question, then stopping once the question has been addressed.
In this sense, AI’s value lies not in multiplying options, but in reducing the time it takes to find the right ones. By compressing iteration cycles, it helps teams reach clarity earlier and focus their energy on the substantive design work that follows.
Moreover, loosening control in early phases can open space for novel combinations of ideas, sites, and typologies that would be impractical to explore manually. When guided appropriately, this exploratory phase can serve as a source of original direction, with AI broadening the option set without displacing professional judgment.
AI’s ability to adopt a designer’s style as a new form of creative support
Perhaps the most surprising development is how quickly AI can learn a designer’s style. With the right references, it produces outputs that reflect a studio’s voice almost immediately, acting like an apprentice who has been absorbing lessons for years. This stylistic fluency accelerates the creative process: instead of spending days establishing a baseline look, teams can focus on testing variations and advancing new ideas.
This capability turns AI into a genuine creative partner that can expand rather than dilute identity. But it also places responsibility on architects to maintain authorship, ensuring that the “apprentice” strengthens the voice of the practice rather than flattening it into sameness. When used carefully, it can shorten the distance between intention and exploration, giving more time for innovation and refinement.
Hidden risks of using AI in architecture and how to avoid them
“I’d push for more transparency from AI vendors — how the models are trained, what data they’re using, what biases might be in there. Right now, there’s a lot of black-box behavior, and that’s risky when we’re using these tools to make design decisions that have real-world impacts.” – Charles Portelli, Senior Associate and Digital Innovation Strategist at Perkins & Will
AI is proving to be a powerful accelerator, but interviewees stressed that it also introduces risks capable of undermining trust, reducing creative diversity, and complicating delivery. Several of these concerns appeared across different interviews and sources. The following section summarizes the most common themes and outlines practical measures identified by participants to help mitigate them within professional practice.
Data boundaries and client privacy
A well-known concern across the industry is how public AI models handle user data. Many of these systems retain or even learn from user inputs, creating a direct conflict with architectural practice. Project files often contain both client and firm intellectual property or sensitive design information, and uploading them to public systems risks uncontrolled reuse or exposure.
In some cases, contracts now explicitly prohibit routing data through external AI tools, making model choice and data routing legally defined rather than discretionary technical decisions. Charles Portelli emphasized the importance of internal protocols and staff training to prevent the use of public models for proprietary data, along with clear disclosure from AI vendors on how their models are trained and governed.
Homogenization and erosion of authorship
AI tools trained on limited architectural references tend to generate outputs that converge around familiar styles and precedents. Instead of broadening creative options, they can narrow them, leading to repetitive results and a gradual drift toward homogenization. When both firms and clients rely on the same general-purpose models, outputs begin to converge stylistically, flattening into a common visual language. Kostika Lala warned that this dynamic could dilute the distinct identity of practices, turning AI into a homogenizing force rather than a creative amplifier.
To counter this, creative direction should remain human-led. Architects can mitigate sameness by curating their own training data, using project-specific references, and guiding AI through well-framed prompts that clarify intent and context. Several interviewees noted that the value of AI lies not in the quantity of iterations but in how purposefully those iterations are shaped and reviewed.
Automation bias: the risk of overtrust
AI outputs were described as particularly prone to overtrust because they often appear convincing, arriving as polished images, fluent text, or plausible data, even when their underlying assumptions are incomplete or incorrect. Under deadline pressure, this surface credibility can allow unchecked results to slip into briefs and deliverables. The danger lies less in obvious flaws and more in the tendency to overtrust content that looks reliable but bypasses traditional review standards.
Vladimir Koylazov and Dan Ring stressed that verifying such outputs requires transparency about provenance, meaning clarity on which datasets, versions, and regional contexts an AI system relies on. Without this visibility, architects risk accepting results built on outdated or irrelevant information.
Firms can reduce this risk by favoring tools that disclose data sources and versioning, and by maintaining internal review steps to confirm that AI-generated outputs align with project context and intent. Practical measures include maintaining brief compliance checklists, cross-referencing AI outputs with sketches or references, and scheduling human review points at major project milestones to ensure that AI assists rather than replaces design judgment.
Security and integration gaps
Security and integration gaps remain among the most persistent challenges in practice. Connecting AI tools to firm databases or project files can create vulnerabilities if access controls, user permissions, or isolated environments are not properly configured. Even when systems are secure, many AI outputs cannot yet be transferred directly into core authoring environments such as BIM or modeling software. As a result, teams often need to rebuild parts of the work manually, which limits efficiency and introduces room for error.
In the absence of full interoperability, firms are adopting partial workarounds. Some use AI features built directly into their existing platforms, while others rely on export formats or plugins that allow limited data exchange between tools. These solutions reduce friction but do not yet provide a seamless bridge between AI generation and production. For now, the most reliable approach is to establish controlled environments, clear governance, and well-documented workflows so that AI outputs can be recreated accurately and integrated with minimal rework once technical compatibility improves.
The speed trap
“Another worry is the temptation to go too fast. You can generate a hundred designs in a day, but without time to reflect, you’re just making noise. Speed can be the enemy of quality.” – Kostika Lala, Founding Partner at Flashcube Labs
The ability to generate hundreds of design variations in a single day highlights AI’s efficiency, but without clear criteria for evaluation, speed risks producing noise instead of insight.
Rapid iteration can encourage superficial decision-making, where selection favors surface appeal rather than deeper alignment with intent. In such cases of surface-level satisfaction, designers might feel that progress has been made, even though critical questions remain unanswered.
To counter this tendency, deliberate pauses between AI-assisted iterations were described as essential for maintaining design integrity. Each cycle should be evaluated for its alignment with context, intent, and performance, rather than visual appeal alone. Within that framework, speed becomes valuable only when it is tied to purpose.
Responsible use of AI in practice
The risks outlined earlier are already shaping how firms apply AI in daily work. Adoption is moving quickly, but policies, contracts, and workflows are being adjusted to define what is acceptable and where the limits lie. In many cases, governance now matters as much as technical capability.
To mitigate these risks, firms and clients are beginning to establish clear parameters for AI use as part of project setup. This often involves defining these parameters within project contracts and BIM execution plans, specifying what content can be shared with which tools, documenting retention and data-handling policies, and recognizing that outputs inevitably reflect the scope of the training data set. If the source material is limited, the design options produced will be limited as well.
Skills architects will need for AI-enabled practice
“I think critical thinking and design intent will be even more important. If AI is doing more of the heavy lifting in production, your value as an architect will be in asking the right questions, setting the right goals, and knowing when something’s wrong.” – Charles Portelli, Senior Associate and Digital Innovation Strategist at Perkins & Will
As AI automates increasing portions of production, the skills that define architectural practice shift toward the qualities that machines cannot replicate. Our insights underscore that success in an AI-enabled environment depends less on technical tool operation and more on judgment, framing, and communication. Four areas in particular stand out.
What to expect in the near future
“We should be honest about what AI can and can’t do, and focus on building tools that actually improve creativity and quality, not just speed. And that means involving designers directly in development — not just as beta testers, but as co-creators of the tool.” – Kostika Lala, Founding Partner at Flashcube Labs
To understand where AI may take architectural practice next, we asked the interviewed industry experts how they imagine workflows evolving over the next decade. Their answers point to a common trajectory: less friction between concept and delivery, with AI moving from isolated “assistive widgets” to continuous project infrastructure.
Article From: www.chaos.com