India's real estate AI pipeline in numbers: how platforms deploy automation across construction, valuation and tenant management

AI adoption in Indian real estate jumped from less than 5% to 91% in two years

The velocity of artificial intelligence integration across India's corporate real estate sector has few parallels in emerging markets. According to the JLL Global Technology Survey 2025, cited by GRI Institute, AI adoption in Indian corporate real estate surged to 91% in 2025, up from less than 5% in 2023. That trajectory, compressed into roughly 24 months, reflects a structural transformation rather than a cyclical technology upgrade.

The acceleration is inseparable from capital flows. India's real estate equity inflows reached USD 30.7 billion between 2024 and Q1 2026, marking an 88% increase from 2022–2023 levels, according to data compiled by GRI Institute and Business Standard. Institutional investment in Indian real estate reached USD 1.7 billion in the first quarter of 2026 alone, according to GRI Hub News. These capital commitments carry with them rigorous expectations around digital reporting, algorithmic deal matching, and automated asset monitoring, expectations that are accelerating AI deployment across every layer of the real estate value chain.

The broader market trajectory reinforces the stakes. India's real estate market is projected to grow from USD 650 billion in 2025 to USD 5.8 trillion by 2047, according to a FICCI-KPMG joint report. The proptech segment specifically is projected to grow from USD 1.31 billion in 2025 to USD 3.82 billion by 2034, per GRI Institute data. For institutional operators, AI deployment is the connective tissue between these capital projections and operational execution.

How is AI being deployed in construction technology across India?

Construction remains one of the most capital-intensive and risk-laden segments of the real estate value chain, making it a primary target for automation. Two categories of technology are gaining traction among institutional operators in India: advanced formwork and shuttering systems that reduce construction timelines, and digital infrastructure platforms that integrate AI-driven monitoring across large-scale logistics and industrial portfolios.

MSLG Projects exemplifies the first category. Recognized for its deployment of advanced Mivan shuttering in construction, according to GRI Hub News reporting from May 2026, the company represents a dynamic shift toward advanced construction tech. Mivan shuttering, an aluminium formwork system, enables rapid, high-precision construction of structural elements. When paired with AI-driven quality control systems, such as computer vision for surface defect detection and real-time progress tracking, these technologies compress project timelines and reduce cost overruns, two metrics that institutional investors monitor closely.

The second category is visible in the logistics and industrial segment. Logos India, now operating independently as LŌ-GOI in India, is integrating digital infrastructure, automation, and AI into its logistics projects, according to Groupe IDEC and LŌ-GOI disclosures from November 2025. For logistics platforms managing millions of square feet across multiple geographies, AI integration extends to predictive maintenance, energy optimization, and automated warehouse configuration, all of which affect net operating income and, consequently, asset valuations.

AI-driven construction monitoring is becoming a prerequisite for institutional capital allocation, as investors demand real-time digital visibility into project execution and cost performance.

India will add nearly USD 906 billion worth of new housing stock by 2034, according to the FICCI-KPMG joint report. The scale of that pipeline makes construction automation a structural imperative. Developers and operators without robust AI-enabled reporting infrastructure face a widening competitiveness gap.

What role do automated valuation models play in institutional real estate investment?

Automated valuation models represent one of the most consequential applications of AI in real estate capital markets. AVMs use machine learning algorithms to estimate property values by analyzing transaction data, comparable sales, rental yields, location analytics, and macroeconomic indicators. For institutional funds and REITs, AVMs provide speed, consistency, and scalability advantages over traditional appraisal methods.

While specific quantitative deployment rates for AVMs by institutional operators in India remain limited in publicly available data, the regulatory environment is creating strong incentives for adoption. The RBI Project Finance Directions 2025, currently active, establish a regulatory framework that demands granular digital data from developers and project sponsors. This framework effectively widens the gap between digitally equipped developers and those without robust reporting infrastructure, incentivizing the kind of structured data collection that feeds AVM algorithms.

The regulatory architecture in India is evolving to reward AI-ready operators, as frameworks like the RBI Project Finance Directions 2025 and the DPDPA demand the granular digital data that powers automated valuation and risk assessment.

The Digital Personal Data Protection Act, also active, reinforces this trajectory by mandating strict data governance in real estate transactions. For AVM providers, DPDPA compliance shapes how property data is collected, stored, and processed, creating both constraints and competitive moats for platforms that build compliant data pipelines from the outset.

Institutional investors participating in GRI Institute meetings have consistently flagged the need for standardized valuation methodologies that can scale across diverse Indian micro-markets. AVMs address this need directly, but their effectiveness depends on the quality and depth of underlying transaction data, an area where India's digitization of land records and property registrations remains uneven.

Tenant management AI: scaling across office and co-working portfolios

The third pillar of AI deployment in Indian real estate targets tenant and property management. Across co-working spaces, grade-A office portfolios, and mixed-use developments, AI-powered platforms are being deployed to optimize lease administration, predict tenant churn, manage energy consumption, and automate facility maintenance workflows.

The commercial office segment is a natural fit for tenant management AI given its data density. Large institutional portfolios generate continuous streams of occupancy data, HVAC performance metrics, access control logs, and tenant service requests, all of which can be processed by machine learning models to identify optimization opportunities. Predictive analytics can flag tenants at risk of non-renewal months before lease expiry, giving asset managers time to adjust terms or identify replacement tenants.

Exact vendor market share for tenant and property management AI platforms in India is not yet comprehensively mapped in public data. The competitive landscape remains fragmented, with a mix of global proptech platforms, Indian startups, and in-house solutions developed by large institutional operators. As the proptech market scales toward USD 3.82 billion by 2034, consolidation in this segment is likely.

India's proptech market, projected to nearly triple from USD 1.31 billion in 2025 to USD 3.82 billion by 2034, will be shaped disproportionately by platforms that integrate AI into tenant management and operational workflows at scale.

The capital-technology feedback loop

The relationship between institutional capital and AI adoption in Indian real estate operates as a reinforcing cycle. The USD 30.7 billion in equity inflows between 2024 and Q1 2026 demands digital transparency, algorithmic efficiency, and data-driven asset management. Operators that deploy AI across construction, valuation, and tenant management attract more institutional capital, which funds further technology investment.

This feedback loop is visible in the profiles of companies attracting institutional attention. LŌ-GOI's integration of AI and digital infrastructure across its logistics portfolio, and MSLG Projects' adoption of advanced construction technology, are practical demonstrations of how technology deployment positions operators for institutional capital access.

For market participants evaluating the 2026–2028 deployment pipeline, several indicators deserve monitoring. First, the depth and speed of AVM integration by Indian REITs will signal how quickly algorithmic valuation displaces traditional appraisal for institutional portfolios. Second, the pace at which construction monitoring AI moves from large logistics and commercial projects into the residential segment will determine whether the USD 906 billion housing pipeline benefits from the same technology advantages. Third, the maturation of tenant management AI platforms, and potential consolidation in that vendor ecosystem, will reshape how office and co-working operators compete for institutional tenants.

The 91% adoption figure captures a moment in time. The operational question for 2026–2028 is how deep that adoption runs, whether it extends beyond pilot programs and dashboard analytics into the core infrastructure of how buildings are built, valued, and managed. The institutions and operators that answer that question with verifiable, data-rich deployment pipelines will define the next phase of India's real estate transformation.

GRI Institute continues to convene institutional leaders across these themes, providing a platform where capital allocators, developers, and technology operators examine the practical deployment challenges and investment implications of AI integration in real estate.