When Korean property technology chief executive Sungho Park addressed participants at Singapore Management University (SMU), he began with a data point that captured the room’s attention: “About 37 per cent of real estate tasks can already be automated.”
That automation, he added, is no longer happening in the distant future of the industry – it is unfolding now, inside cooling systems, security cameras, lighting controls and investment models.
“Artificial intelligence (AI) is no longer optional,” said Park, who leads Reable, a climate tech company specialising in AI-driven energy optimisation. “It’s becoming essential for efficiency and sustainability.”
Park’s remarks, delivered at the Ho Bee Professorship in Sustainability Management Luminary Speaker Series 2025 held last month in Singapore, framed a real estate sector that is digitalising faster than many residents realise.
Office towers that once relied on human technicians now respond to occupancy patterns in real time; lifts generate maintenance predictions before breaking down; and investor dashboards model rental demand weeks ahead of market shifts.
In Seoul and Busan, Park said, digital twins simulate building shadows, traffic rerouting and flood risks before a permit is issued, while drones track construction progress and spot safety hazards long before a human inspector would.
“Digital twins, predictive maintenance and energy optimisation – these are not conceptual anymore. They are being deployed today,” he said.
In Korea, such systems have helped clients cut operating costs by 10–20 per cent and increase rental revenue by up to 15 per cent.
Singapore is moving in the same direction. JTC’s Punggol Digital District, for instance, is building a district-wide digital backbone, known as the Open Digital Platform, that links building systems, energy flows and mobility data in real time. The aim is to enable more efficient cooling, smarter grid management and integrated planning across the entire precinct.
Ho Bee Land’s developments in one-north – JTC’s R&D and digital economy hub – are also part of this shift. At the recently completed biomedical hub, Elementum, smart-building systems optimise energy use through real-time monitoring of cooling, air quality and occupancy. This paves the way for future AI-driven collaborations in mobility, biomedical research, and sustainable urban solutions.
Singapore’s conundrum
Buildings account for more than a fifth of Singapore’s emissions and air-conditioning alone reportedly consumes around 50 per cent of a commercial building’s electricity. This makes optimisation an obvious target under the Singapore Green Plan 2030, and a core focus of the National AI Strategy 2.0 launched last year.
The city has already spent a decade developing Virtual Singapore, a national digital twin used to model urban mobility, emergency response, land-use changes and climate risks. Government agencies are also trialling AI to manage district cooling, predict infrastructure stress points and reduce lifecycle emissions from new builds.
Park suggested that Singapore’s highly centralised data environment gives it “a structural advantage” over many countries where information is scattered across incompatible systems.
But his presentation also surfaced the uncomfortable question that sits beneath Singapore’s AI push: the technology itself is extremely energy-hungry.
“AI systems require a large amount of computing power,” Park said. “One big AI model can emit as much carbon as several cars over their lifetime.”
Globally, electricity use from data centres and AI is rising sharply, with the International Energy Agency warning demand could more than double over the coming years under current growth rates.
Singapore is already grappling with this trend. Earlier government studies estimated data centres used more than 7 per cent of Singapore’s electricity, a share widely expected to rise as AI workloads expand. The surge forced the government to halt approvals for new facilities in 2019 before reopening them under strict efficiency rules.
As industries digitise further, each new layer of automation – from building management platforms to city-level simulation engines – requires more processing power and cooling capacity. The risk, Park noted, is that poorly planned AI adoption could cancel out the very emissions savings it aims to deliver.
Dr Hao Liang, Associate Professor of Finance at SMU and Ho Bee Professor of Sustainability Management (left) and Sungho Park, CEO of Reable (right) speaking at the Ho Bee Professorship in Sustainability Management Luminary Speaker Series at Singapore Management University. Image: Eco-Business
Beyond energy concerns, the deeper bottleneck is governance.
“Real estate data is usually spread across legacy systems,” he said. “Without proper integration, AI cannot provide accurate intelligence.”
Even where data is available, machine-learning models trained on historical lending, valuation or tenant-screening patterns can reproduce existing biases unless continuously monitored.
Park argued that developers and property owners need clear internal structures – including audit committees, model-review processes and standardised data protocols – to prevent errors from propagating through automated systems.
He also warned that AI cannot be effective without a workforce trained to interpret its recommendations.
“Technology alone does not deliver value. Humans and AI must work together,” he said.
These challenges are not unique to Singapore, but the city-state sits at a crossroads. Asia Pacific has become the world’s fastest-growing market for AI in real estate, with cities from Tokyo to Bangalore rolling out automated planning and building-operations tools.
At the same time, Singapore’s limited land, high cooling demand and growing digital-infrastructure load mean its margin for error is smaller than that of larger economies.
Policymakers increasingly recognise that AI’s role in meeting Singapore’s sustainability goals depends on parallel progress in data architecture, energy systems and regulation.
Conversations taking place across the sector, including at the Ho Bee Professorship in Sustainability Luminary Speaker Series 2025, reflect a growing effort to examine those pressures and constraints.
For developers and investors, attention has shifted from the promise of automation to the speed and responsibility with which it should be deployed.
“AI is shaping the market today,” Park said. “But the biggest changes are still ahead.”
What comes next, industry observers say, will hinge on whether the underlying systems can scale – from energy supply and data standards to workforce capabilities. For Singapore, a compact city balancing digital expansion with strict climate goals, the challenge is not simply adopting AI, but building the foundations that allow it to deliver genuine sustainability gains.
