Driving Design Excellence Through Computational Design
Ethan Atherton AIA Architect
As an architect serving on LS3P’s Digital Practice Team, Ethan Atherton has been an early adopter and driving force in the firm’s computational design capabilities. He’s also had his ear to the ground through participation in industry events such as the recent Advancing Computational Building Design conference and speaking engagements for BIMxT and Aspire. Benchmarking computational design practices around the industry has allowed Atherton to gather insights about what works, what doesn’t work, and what might be next.
A New Mindset
From Atherton’s vantage point, computational design isn’t simply a tech upgrade; it’s a fundamental rethinking of the way we approach problem solving in practice. For this reason, long-term success depends on aligning computational methods with project realities, established workflows, and leadership priorities. “Computational design succeeds when it’s built around how people actually work, not some idealized workflow, or one size fits all approach,” he explains. “The value comes from organic uptake and empowerment as we demonstrate what computational
design can do to elevate our work. It’s not about learning new software, but about internalizing new ways of thinking.”
Understanding computational design as a cultural and operational shift rather than a new set of tools will strengthen project design and delivery across the industry, at least for the firms who are ready to make a shift in mindset. Technology alone does not transform practice; people and processes do.
Understanding Organizational Models
Firms tend to develop computational capacity through one of three models, according to Atherton’s benchmarking. Some rely on embedded generalists who distribute computational thinking within project teams. Others build centralized expert groups dedicated to computation or data. Increasingly, hybrid structures are becoming common, weaving computational skill into practice areas while still maintaining specialized support.
He emphasizes that firm size is not the determining factor for success. Instead, success depends on cultural maturity, leadership
engagement, and persistent investment in research and development.
What does success look like?
According to Atherton, success looks like satisfaction. “It’s satisfaction in knowing that our decisions have been validated, or that we’ve found a less monotonous, or easier way to do our jobs. It’s finding new ways to think of how things might be connected,” he says. “If our computational solutions are creating more headaches than satisfaction, we’re doing it wrong.”
“Computational maturity is not a one-time initiative,” he says. “The firms that make the transformation stick will be the ones that start small, document and communicate wins and discoveries, and build shared skills and collective momentum incrementally.”
AI as a Colleague (but not as a boss)
Human-Centered Change as the Foundation
People are at the core of this mindset, not computers. According to Atherton, computational design must respond to how teams genuinely work rather than forcing idealized or rigid workflows. Culture is emergent, shaped collectively by behavior and shared values. Top-down implementation strategies tend to overlook the lived experiences of designers. A more sustainable adoption occurs when teams are empowered to experiment, learn, and integrate new methods organically.
While artificial intelligence is a powerful tool to augment architectural workflows, it’s certainly not a replacement for human expertise. AI can help designers by elevating institutional knowledge and streamlining repetitive tasks, but the critical ingredients of intuition, critique, and design judgment remain firmly in human hands. Key questions for practice leaders include: Which tasks benefit from automation, and which require a human touch? How can AI serve as a catalyst for insight rather than a shortcut? AI-assisted critique may be an avenue worth exploring, along with technical automation where feasible and knowledge surfacing across complex organizations.
Knowledge, Training, and the Quiet Bottleneck
In terms of widespread adoption of computational design strategies, Atherton notes that resource awareness can be a bigger barrier than interest. Especially within large or complex organizations, teams might not know where resources live, how to access them, or who supports them. Project-based learning, which pairs computational partners directly with project teams so that learning emerges in real contexts, is a viable solution. As project managers buy in to computational design applications, they can raise awareness and become advocates for transforming these technologies from isolated specialties into fully integrated capabilities.
Reframing Innovation
Many design firms are occupying the early- to mid-development stages of computational maturity. While pockets of expertise exist across teams or offices, fully distributed capability is still emerging for most firms. For Atherton, innovation in computational design is not about novelty for its own sake. It is the ability to cross barriers where
others stop, creating a pathway between experimentation and project delivery. “When firms build internal capacity for exploration, they strengthen external credibility with clients, consultants, and community partners,” he says. “Computational design becomes a strategic mechanism for aligning design excellence with long-term organizational value.”
Shaping the Future Architectural Process
Looking ahead, the future of architectural practice will be defined not by specific technologies, but by how human judgement and ingenuity are deliberately integrated. Practice becomes more resilient when teams are resourceful in how they enhance collaboration, expand authorship, and investigate (often very impactful) decisions. Firms that align computation with culture, strategy, and learning will be the ones best positioned to adapt as technology continues to evolve.
