Where to start with AI & Generative design?
Lately, AI and generative design tools have been reshaping architectural practice, and here at Arcol, we’re deeply engaged in these developments. We touched on this evolution in our previous post, The Arcol Roadmap: Part 2, but let’s dive deeper into what this means for firms today.
The Future of Architecture Design Technology
A question we get a lot from architects is: should we be using AI or generative design in our process?
We believe that both AI and generative design have an important role to play in the future of our industry and also for our product. But we feel strongly that architects shouldn’t just use tech for tech’s sake. At the end of the day, there are two main questions you and your team should be asking yourselves when it comes to these technologies:
Are they saving us time / making our process more efficient?
Are they helping us win business?
Before we dive further into these questions, let’s take a minute to review what the difference is between AI – specifically machine learning (ML) – and generative design.
Understanding AI vs Generative Design in Architecture
"You can think of machine learning as a pattern finder and generative design as a creator [...] While machine learning is used to analyze and predict, generative design creates and generates." - Generative Design Primer
With ML, you typically start with a large dataset. The computer then uses machine learning algorithms to find patterns, form relationships, create rules, and predict new outcomes.
Generative design is a subset of computational design where designers define initial parameters and quantifiable objectives. The computer then uses genetic algorithms to generate hundreds or thousands of options, evaluates them against the designer’s objectives, iteratively improves them based on previous results, and ranks them by how well they meet the original goals.
With this in mind, let’s come back to the two questions above: how do you save time and win business?
We believe the key is to narrow in on specific use cases for AI and generative design that leverage your own competitive advantage in the industry – your secret sauce. This starts with your internal firm data. Managed strategically, this data can unlock significant potential for these technologies to supercharge your workflows and generate real value for your firm.
Arcol’s multiplayer generative design helps teams explore floor plan options together, automatically creating unit layouts and corridors to your requirements – turning your design knowledge into actionable solutions.
Leveraging Your Firm’s Data for Design Innovation
Every architecture firm has a recipe for success. Internal data is a record of your firm’s knowledge, processes, decisions, and outcomes – years of experience and learnings that make up your recipe. You wouldn’t want to use someone else’s recipe if you don’t know how or why they’ve come up with it. So, in the age of AI, your job is to translate your recipe into the language AI models speak: data.
The challenge is that your recipe is not as straightforward as making grilled cheese – it’s got a lot more steps and nuance, from how you design facades, to how you create and manage drawing sets, to how you bill for projects. That internal, historical data needs to be cleaned and structured in a way that enables you to capitalize on new technologies like generative design and AI and take your recipe to the next level of innovation.
Imagine being able to analyze all your past facade designs and automatically understand which combinations of materials, patterns, and openings performed best for different building types and orientations, helping you make smarter design decisions on future projects. This is the kind of innovation that becomes possible when your recipe – your firm’s unique approach – is translated into structured data.
So how can you do this in a way that drives real efficiency, productivity, and success for your practice?
Advanced Parametric Design: Building a Framework for Success
The key lies in advanced parametric design systems that go beyond basic 3D modeling – platforms that can capture not just geometry, but the entire modeling history and relationships between design elements, making them powerful tools for standardizing and structuring diverse architectural data.
This kind of modern software architecture could transform the typically messy, heterogeneous data across a firm’s project archive – whether projects originate in Rhino, Revit, AutoCAD, or other platforms – into structured, meaningful datasets that capture not just form, but design intent, performance metrics, and success patterns. This standardized data can then become the foundation for both AI training and generative design applications.
In practice, this creates a powerful feedback loop: as architects work on new projects using these advanced tools, they can benefit from AI assistance informed by their firm’s collective experience – suggesting initial parameters, optimizing constraints, and generating design alternatives. Such systems can accelerate routine tasks, validate design decisions against historical performance data and building codes, and help architects focus on what they’re best at: being creative and solving complex spatial problems for their clients. This approach will make firms more competitive by enabling rapid iteration, data-driven decision making, and the clear demonstration of value to clients through proven performance metrics and optimized solutions.
From Design Data to Competitive Advantage
The result is a framework that not only makes individual architects more efficient but also helps firms systematically learn from and improve upon their past work. This creates a compelling competitive advantage: the ability to combine the creative expertise of architects with the analytical power of AI, all while building upon a foundation of real-world project success.
As the AEC industry continues to evolve, firms that can effectively implement these kinds of systems will be ahead of the curve when it comes to delivering better projects more efficiently and winning more business in an increasingly competitive market.
But you don’t need to wait until you have perfectly structured data to start this process! This is where we see Arcol becoming a valuable facilitator – we see the potential to help firms move up the spectrum by naturally capturing more structured data as a part of the typical design process, rather than requiring a separate data cleaning and structuring effort. Our parametric modeling approach inherently creates more structured data, making it easier to leverage sophisticated AI and generative design capabilities over time.
So, when it comes to the question of using AI or generative design, we don’t see the answer as one or the other, but why not both!? As long as you and your firm are tackling head on the problem of cleaning and structuring internal data today, you’ll have the flexibility and autonomy to leverage that data in innovative and impactful ways in the future.
If your firm is thinking about leveraging AI and generative design, we’d love to hear about your challenges and share how we can help. Reach out here.
Photo by Christian Perner on Unsplash