How AI Is Quietly Revolutionising Architecture - Without Replacing Architects

The future of architecture isn't about choosing between human creativity and artificial intelligence, it's about orchestrating both.

When Breffney Green noticed his colleagues at Henry J. Lyons Architects secretly using ChatGPT on their screens, he knew something had to change. This wasn't rebellion, it was a signal. His team was hungry for innovation, but the 167-year-old firm lacked the framework to embrace it.

Now, as the firm's head of AI and design innovation, Green has turned that underground curiosity into a full-scale transformation. His journey reveals something crucial: AI isn't threatening architecture's soul. It's amplifying it.

The Shadow AI Problem

Green spotted the pattern early - architects discreetly experimenting with AI tools, uncertain if they were breaking some unspoken rule. The fear was palpable: Would these tools devalue our expertise?

But he saw a different story. Rather than resist, he chose to channel that underground energy into something systematic. His first move? Give everyone legitimate access and create clear guidelines. No more shadow work. No more fear-based hesitation.

The Breakthrough: Making Messy Voices Clear

Green's initial challenge was quintessentially architectural: multi-voice project meetings that generated unusable transcripts. Overlapping conversations, varying accents, background noise from construction sites, traditional transcription tools couldn't handle it.

Using tools like ElevenLabs, his team identified individual voices, cleaned up audio, and created searchable, speaker-attributed transcripts. This transformed how the firm documented stakeholder input and maintained compliance records.

The impact was immediate. Skeptics became believers. Project managers saved hours. Reports became richer. And leadership finally saw AI as an asset, not a threat.

From Transcription to Transformation

With credibility established, Green tackled compliance documentation, where architects spend countless hours cross-referencing designs against building codes and regulations.

His team built a custom "policy auditor" that scans draft reports, extracts relevant policies, maps each section against compliance requirements, and flags gaps before they become problems.

The result? Faster reviews, greater accuracy, and freedom for architects to focus on designing inspired spaces.

Preparing the Next Generation

Green champions integrating AI literacy directly into architecture curricula, not as an afterthought, but as a core competency.

His firm recently hired a graduate who developed a computer vision model that reads drawings and conducts quality assurance checks automatically. This wasn't a nice-to-have skill; it was exactly what the practice needed.

"Future architects will blend design skills with data analysis, coding, and AI integration," Green argues. The message to universities: update or become irrelevant.

The Bias Blind Spot

Green isn't starry-eyed about AI. He shares a cautionary tale: an image generation AI, asked to portray "Australian women," defaulted to stereotypical representations based on its training data.

For architects creating public spaces that shape social dynamics, such biases aren't just problematic, they're potentially harmful.

His approach: advocate for diverse datasets, audit outputs regularly, establish ethical frameworks, and never treat AI as infallible.

"We create environments that influence how people live," Green reminds his team. "We can't afford to perpetuate stereotypes through lazy tool use."

The Cultural Shift

Technology adoption isn't really about technology. It's about people.

Green describes his approach as "herding cats" gently guiding teams toward incremental AI adoption, celebrating small wins, building confidence through pilot projects.

"This is a cultural shift," he emphasizes. "It requires openness, continuous learning, and proactive governance, not just technical implementation."

The strategy works because it respects what architects already do well while expanding their capabilities.

What This Means for You

If you're leading an architecture firm: Start small. Pick one tangible problem eg: transcription, compliance checking, data organization. Solve it with AI. Celebrate the win. Build momentum.

If you're an architect: Get curious. Experiment with AI tools. Question their outputs. Understand their limitations. Develop the critical literacy that will define your career.

If you're in architecture education: Redesign curricula now. Blend design with data science. Prepare students for a profession that looks nothing like the one you trained for.

The transformation is already underway. The only question is whether you'll shape it—or be shaped by it.

Watch the full conversation with Breffney Green here for deeper insights into AI implementation and the future of architectural practice.

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