Artificial intelligence is no longer confined to smart home features or digital marketing dashboards in India’s luxury real estate sector. Developers are increasingly embedding AI across planning, construction, procurement, and demand forecasting, signalling a shift from technology as an add-on to a strategic backbone in the luxury segment.
“Artificial intelligence is no longer just a back-end efficiency tool in real estate; it is beginning to influence what developers build, where they build it, and how quickly projects move from land acquisition to launch,” Shivam Agarwal, vice president, strategic growth at Sattva Group, told Hindustan Times Real Estate.
How are developers adopting AI in luxury real estate?
In Mumbai’s luxury corridors, from Worli’s high-rises to emerging communities across the Mumbai Metropolitan Region (MMR), developers are deploying AI-enabled analytics to decode enquiry behaviour, transaction trends and absorption cycles.
“Buyers today value relevance over volume. Instead of browsing endless listings, they want personalised insights that align with their lifestyle, budget and location priorities. Data-enabled tools that analyse preferences and market patterns help developers respond precisely to these expectations and improve the quality of buyer interactions. Research shows that this practice improves engagement by up to 40–50% in luxury property search experiences,” Ashish Raheja, Managing Director and CEO of Raheja Universal, told Hindustan Times Real Estate.
Saurabh Runwal of Runwal Realty said AI is increasingly embedded across the value chain. “Technology is no longer a support function. It is becoming a strategic driver of growth and operational resilience,” he noted.
“AI-enabled analytics now help decode enquiry behaviour, transaction trends and absorption cycles across these corridors, allowing developers to calibrate unit configurations, amenity planning and pricing strategies with far greater precision,” Runwal said.
Using AI for efficient construction
Shrivastava said AI enables data-driven decision-making that targets both efficiency gains and shorter timelines across the construction phase.
“ We are using AI in construction methodology in several use cases, for eg. For labour requirements at a particular site, in line with the construction progress and other parameters. It will help in optimising the resources deployed at a particular site,” he said.
One application involves modelling labour requirements in line with project progress and site-specific parameters, helping optimise workforce deployment. Other use cases include AI-assisted design optimisation for common areas and more accurate material requirement planning, reducing excess procurement and rework, developers point out.
Raheja said that AI-driven planning tools analyse past project data, labour productivity trends and procurement cycles to create predictive schedules that minimise delays. Real-time monitoring systems help identify bottlenecks early and improve coordination among consultants, contractors and suppliers.
In cities like Bengaluru and Mumbai, where land costs are high, regulatory layers are complex, and timelines are closely scrutinised, predictive and data-led execution improves delivery certainty while maintaining quality standards, he said.
Runwal, however, emphasised that integration is key. “When embedded into core operations rather than adopted superficially, intelligent systems improve efficiency without compromising quality,” he said.
