Artificial Intelligence (AI) has moved from “interesting” to “urgent” for India’s Corporate Real Estate (CRE) teams, the teams that manage office space, workplace operations and location decisions, with 91% of companies now piloting AI in office and workplace use cases, up from under 5% in 2023. What is most important to note, however, is that despite rapid adoption, only 5% of organizations say they have achieved most of their AI objectives so far, highlighting a sharp gap between AI activity and business outcomes. Eighty-eight per cent of organizations admit at least three of their existing technology systems are failing to deliver, creating a broken foundation that is undermining AI investments across corporate India.

The findings were 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 India findings reveal a sector at a critical inflection point – racing to adopt AI to cut costs and optimize space but struggling to convert pilots into measurable business outcomes. The JLL global executive board is meeting at Bengaluru to discuss the future of AI driven transformation across CREs in India and beyond.

“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 winners will set clear targets, fix their data, and upgrade old tools so AI can deliver outcomes, not just activity,” said Ajit Kumar, Managing Director (Work Dynamics Accounts) West Asia, JLL

The AI reality check: explosive adoption, limited results

Most organizations are struggling to translate AI experimentation into meaningful outcomes. Despite widespread adoption, the execution reality tells a different story, the vast majority of companies find themselves caught between ambitious AI initiatives and limited tangible results. This implementation challenge reveals a critical gap between experimentation and execution, highlighting the significant difficulty organizations face in moving beyond pilot programs to achieve systematic, scalable AI integration that delivers measurable business value across their operations.

Simply put, while AI is being tried widely across Indian workplaces, scaling it into day-to-day operations that save money or improve performance is proving tough and will take more time. The gap between widespread adoption and successful execution suggests that while organizations recognize AI’s potential, they’re still learning how to implement it effectively on a scale.

Why this matters now

As global companies review 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 skip AI quick wins, target boardroom-level transformation

The report reveals that Indian companies are not using AI for small tasks, they are going after boardroom outcomes with strategic precision. The top three AI priorities demonstrate this ambitious approach, with portfolio optimization leading at 59% of organizations, where companies are using AI to right-size office space and leases, reduce waste, and cut costs. Energy management follows closely at 54% of organizations, focusing on reducing energy use and bills to improve building performance and support sustainability goals. Real estate data-related workflows round out the top three at 49% of organizations, where teams are fixing messy real estate data so leaders can take faster, more confident decisions.

AI projects hit 88% system failure rate as Indian companies confront infrastructure reality

The report identifies a stark reality behind India’s AI execution gap: outdated technology infrastructure is systematically undermining AI initiatives. An overwhelming 88% of organizations report 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.

“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,” said Dr Samantak Das, Chief Economist and Head of Research and REIS, India, JLL

Action needed:

The report outlines three actions that consistently separate companies from those stuck in pilots. 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 as an 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 because workplace AI affects every function. Companies that follow this disciplined approach are positioning themselves to move beyond the 91% adoption rate into the small group, achieving meaningful, scalable results that transform how business gets done.