India's AI Revolution in Corporate Real Estate: A Reality Check
Artificial Intelligence (AI) has rapidly become a critical tool for India’s Corporate Real Estate (CRE) teams, with 91% of companies now piloting AI in various office and workplace use cases. This is a significant jump from under 5% in 2023. However, despite the widespread adoption, only 5% of organizations have achieved most of their AI objectives, indicating a significant gap between AI activity and business outcomes.
88% of organizations admit that at least three of their existing technology systems are failing to deliver expected results, which is a major roadblock in the path to AI success. This issue is further compounded by the fact that 93% of senior executives now rank cutting workplace real estate costs as a top strategic priority, pushing AI from an experimental phase to a boardroom demand.
The findings are part of JLL’s latest report, “India’s AI Revolution in Corporate Real Estate: Executive playbook for workplace transformation,” based on the JLL Global Real Estate Technology Survey 2025. The report highlights a sector at a critical juncture—racing to adopt AI to cut costs and optimize space but struggling to convert pilots into measurable business outcomes.
Ajit Kumar, Managing Director (Work Dynamics Accounts) West Asia, JLL, emphasizes the urgency of this transition: “India has reached a tipping point where AI is becoming standard in workplace decision-making, surging from under 5% adoption in 2023 to 91% in 2025, an 18-fold increase in just two years. But the real story is not how many pilots exist; it is how many deliver results. With 56% of organizations achieving only 2-3 objectives and 26% achieving zero objectives to a large extent, while just 5% succeed across 4-5 objectives, the next phase will be unforgiving. Teams that can prove savings and performance will scale fast, and the rest will stall.”
The AI reality check reveals that while most organizations are experimenting with AI, translating these experiments into meaningful outcomes is proving to be a significant challenge. The vast majority of companies find themselves caught between ambitious AI initiatives and limited tangible results. This implementation challenge underscores the critical gap between experimentation and execution, highlighting the difficulty organizations face in moving beyond pilot programs to achieve systematic, scalable AI integration that delivers measurable business value across their operations.
The importance of this issue is further magnified by the fact that global companies are increasingly reviewing India office strategies and hybrid work policies. The ability to use AI to optimize space and cut costs has become a boardroom issue. By 2030, it is expected that 33% of workplace real estate heads will report directly to Chief Technology Officers (CTOs), up from just 16% today. This reflects how offices are increasingly being run like technology-enabled platforms rather than static assets.
With 93% of senior executives now ranking cost reduction through smarter office space decisions as a top objective tied to business strategy, the pressure on workplace teams to deliver measurable AI outcomes has never been higher. Indian companies are not using AI for small tasks; they are targeting boardroom-level transformation with strategic precision. The top three AI priorities include portfolio optimization (59%), energy management (54%), and real estate data-related workflows (49%).
However, the report also identifies a stark reality behind India’s AI execution gap: outdated technology infrastructure is systematically undermining AI initiatives. An overwhelming 88% of organizations report that they are not getting expected results from at least three existing technology systems, severely limiting data quality and integration capabilities that AI depends on. This infrastructure crisis is compounded by strategic gaps, with 57% of organizations admitting they have no clearly defined AI strategy in place to guide implementation, leading to scattered pilots rather than coordinated roadmaps with measurable business targets. Workforce and talent gaps further constrain progress, with 29% citing talent shortages in technology strategy that slow execution.
Dr. Samantak Das, Chief Economist and Head of Research and REIS, India, JLL, explains the need for infrastructure upgrades: “Many organizations are trying to run modern AI on old, disconnected systems and then wondering why results are slow. The data tells the story, 93% of organizations are now allocating budgets to upgrade outdated systems, with 58% classifying infrastructure upgrades as a strategic priority. AI needs clean, connected data and secure platforms, which is why upgrades are no longer ‘IT housekeeping’, they are the cost of entry. If you want AI that saves money and improves performance, you first must fix the basics.”
The report outlines three key actions that can help organizations move beyond the pilot phase and achieve meaningful, scalable results. Successful organizations thoroughly map existing tools, processes, and data before scaling AI, ensuring they build on solid foundations rather than broken systems. They define success upfront by setting clear, measurable targets linked to business goals such as cost savings, energy reduction, space utilization, and employee experience. Most critically, they establish cross-functional teams between workplace, IT, HR, and Finance from day one, as workplace AI affects every function. Companies that follow this disciplined approach are positioning themselves to move beyond the 91% adoption rate and into the small group achieving meaningful, scalable results that transform how business gets done.
