AI in real estate: Will it speed up construction and cut maintenance costs for buyers?

AI can cut construction timelines by nearly half and reduce maintenance costs by optimising design, basements and parking, say real estate experts

Artificial intelligence is emerging as a powerful lever for cost optimisation in Indian real estate, with experts saying it has the potential to reduce overall construction timelines by 40–50% and significantly lower the long-term common area maintenance (CAM) burden for homeowners.

AI is emerging as a key cost lever in Indian real estate, with experts saying it can cut construction timelines by 40–50% and reduce long-term maintenance costs. (Representational Image) (Unsplash Photo )

They say that by analysing historical project data, material logistics, labour productivity, and site sequencing, AI-driven tools can predict bottlenecks before they occur and recommend real-time workflow adjustments. This predictive capability allows firms to streamline on-site operations, reduce idle time, and shorten overall build cycles.

In some large projects, AI-assisted planning and execution have cut completion timelines by nearly half, lowering financial risk and improving delivery certainty in markets where delays traditionally inflate costs and erode buyer confidence.

“In one 2 million sq ft development, we completed the project in nearly half the conventional timeline, about three years,” Jay Shah, founder, Kaizen AI, said. “As construction time reduces, financial and execution risks also decline.”

Developers say that AI also helps optimise resources available at construction sites.

“Another use case is optimisation of design, wherein planning of common spaces and material requirements can be done using AI. There are further use cases, which will help in reducing the construction timelines as well as bring in better efficiencies to all processes during the timeline of the project,” Praveer Shrivastava, senior executive and vice president, Residential, Prestige Group, said.

“AI is adding value in the design phase of real estate projects, whether residential or commercial. Earlier, project planning and modifications were time-consuming and often led to delays and cost escalations. Today, AI-powered tools enable faster design iterations, precise planning, predictive analysis, and real-time visualisation. Developers can now simulate layouts, assess structural efficiencies, optimise space utilisation, and virtually visualise the finished product long before construction begins. This not only reduces errors but also improves decision-making and cost control,” points out Vikas Bhasin, Managing Director at NCR-based Saya Group.

Impact on redevelopment projects

Experts point out that AI-led approaches are increasingly relevant in Indian cities, particularly for redevelopment projects where families temporarily move out of their homes and are waiting to return.

“As buildings become taller and urban density increases, construction timelines naturally lengthen. By improving planning efficiency and making faster construction technologies financially feasible, optimisation can help developers complete projects sooner and allow occupants to move in earlier,” Shah said.

AI can help reduce the burden on common area maintenance

Real estate experts believe the real impact of artificial intelligence in real estate lies on the buyer side, particularly in reducing long-term maintenance burdens rather than just cutting upfront construction costs.

“Most optimisation today is happening from a lifecycle maintenance perspective,” Shah said. “In one of our recent projects, we were able to eliminate six basement levels entirely. Basements require continuous mechanical ventilation systems that pump in fresh air and expel stale air. Over the life of a project, that becomes a high recurring cost for maintaining such areas.”

As urban development becomes denser and high-rises become more complex, common-area maintenance (CAM) costs have been rising steadily. While developers typically bear these expenses until handover, the financial burden eventually shifts to housing societies.

“Mechanical parking is another example,” Shah noted. “The machinery required to operate these systems is expensive to run and maintain. From a long-term perspective, the impact on residents is substantial.”

Typically, parking consumes between 400 and 700 sq ft per car, including circulation areas and service infrastructure. By using AI-based modelling, Shah said the company reduced this requirement by 20–25% across multiple projects, thereby lowering maintenance costs.