ULI Australia [26 June 2025] — If there was any lingering doubt that artificial intelligence (AI) and data science are fundamentally altering the landscape of property and urban development, ULI Melbourne’s recent event, “AI & Data-Driven Strategies for Smarter Development,” dispelled it with characteristic clarity and candour. A packed house of industry leaders and curious innovators gathered to hear how the next decade will not only reshape how we design and deliver the built environment—but challenge the very assumptions that have governed practices in the real estate and property industry for a century.

Reframing the AI Promise

Krishna Duddumpudi, Co-founder and Director at Differential, opened the evening with a keynote that managed to be both briskly abstract and grounded in real-world lessons. Duddumpudi’s central theme was the perennial “time, cost, quality” triangle—familiar to anyone who has delivered a building, yet rarely optimised all at once. AI, he argued, represents a once-in-a-generation opportunity to finally break the zero-sum nature of this trinity. Through computational design, architects can compress timeframes, control costs, and even expand the horizons of quality by exploring thousands of options that would be unthinkable by hand.

Yet Duddumpudi was quick to temper the hype. Today’s AI tools, while powerful, often frustrate with unpredictability and tedious workflows. He described the current model—where design logic is stacked sequentially—as “linear”, a process that crumbles the moment a late-stage change is made. What if, Duddumpudi asked, we could reimagine design with “parametric fields logic”, allowing for parallel exploration of options and real-time feedback from consultants, clients, and engineers alike? Rather than shuffling endlessly between static design iterations, teams could explore vast solution spaces, interrogate the trade-offs, and converge faster on the optimum. The promise, he concluded, is not just in speed, but in true collaboration—where data, logic, and creativity can coexist and thrive.

If the keynote set the stage, the panel session brought the conversation firmly into the realm of “how”. Moderator Anna Broughton (Bridge42) guided a robust discussion among three leaders shaping AI’s practical applications in planning and development: Norion Ubechel Place Intelligence ®, Piotr Klushinsky (Differential), and Gerhana Waty (Hansen Partnership).

Turning Cellphone Signals into Urban Insight

Norion Uberchel of Place Intelligence showcased how “big data” is finally becoming accessible and useful to practitioners at every scale. Using cellphone and GPS signals, Uberchel’s models can generate detailed foot traffic heat maps—offering an evidence-based view of walkability, activity, and demand across urban precincts. This data, distilled and summarised using AI-powered language models, enables developers and planners to move through early feasibility with unprecedented speed and confidence. “The goal,” Uberchel explained, “is to free up human expertise for the creative and complex, not for the drudgery of fact-finding.”

Design Experience Meets Generative AI

Piotr Klushinsky, also of Differential, illustrated how AI is translating years of architectural know-how into tools that democratise creativity. By building custom models for design exploration—such as adjusting tower heights to balance views, rents, and construction costs in real time—Klushinsky’s team can provide clients with visual, interactive scenarios. “No more waiting days for a redesign,” he noted. “We can iterate on the fly, integrating feedback live with consultants and clients.” The result is not only efficiency, but a more inclusive design process—one where human expertise guides the AI, and not the other way round.

Scaling Feasibility Studies for Victoria’s Housing Targets

Gerhana Waty of Hansen Partnership brought a crucial perspective on the limitations and strengths of AI in planning. Her firm was tasked with testing the feasibility of government controls across 30,000 sites in Victoria—an undertaking that would be nearly impossible without machine assistance. Waty emphasised that while AI excels at running through permutations and flagging technical feasibilities, it remains dependent on careful human judgment to interpret context, policy nuance, and local values. “AI isn’t replacing the role of the architect or planner,” she remarked. “It’s amplifying what we can do—provided we ask the right questions and set the right parameters.”

AI and the Future of Development: Collaboration, Not Competition

Across the panel, a clear consensus emerged: the narrative that AI is a threat to human expertise is not only misleading—it’s counterproductive. Instead, the most exciting opportunities are found where machine intelligence and human insight are woven together. AI can automate the routine, surface hidden insights, and unlock new pathways—but it is people who frame the problems, interpret the grey areas, and ultimately make the critical calls.

From streamlining planning processes and scenario testing for policy conflicts, to ensuring data is structured and inclusive, the industry’s new imperative is collaboration—between disciplines, between data sets, and between humans and machines.

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