The commercial real estate (CRE) sector is at the cusp of a digital transformation, and artificial intelligence (AI) is at the heart of this evolution. Traditionally reliant on established relationships and legacy systems, the industry is rapidly adopting data-driven precision. AI is reshaping how market trends are assessed, assets managed, and transactions optimised. JLL forecasts AI and generative AI as among the top three technologies poised to impact real estate, projecting a market surge to $988.59 billion by 2029. This growth is driven by AI’s potential to revolutionise various aspects of the sector, from market forecasting and improved client engagement to smart building solutions, unlocking unprecedented efficiencies. Commercial real estate right from the inception of a development is end to end digital and AI transformation.
The use of drones for land surveying, profiling the terrain, the soil, and related complexities, and using technological tools to masterplan and build development plan consisting of office, retail, hospitality, etc., are commonly known. Employing infrastructure services leading to BIM (Building Information Modelling) and Digital Twins, uploading files for digital approval followed by live feed broadcast during construction by project management team to eventually transitioning to asset managers, who use technology and digital framework and AI portfolio management to enhance shareholders’ value, speak of the transformation.
Yet, as AI reshapes the CRE landscape, its adoption is not without complexities. While the technology unlocks unprecedented efficiencies, it also brings challenges that demand careful navigation. The real question is not whether AI will revolutionise commercial real estate — rather, how industry leaders can harness its power without losing sight of the nuances that make real estate a fundamentally human business. Let us understand the implications in depth.
Predictive intelligence redefining investment strategy
Machine learning algorithms can process historical data, economic indicators, and market sentiment to generate highly accurate forecasts, allowing investors to anticipate demand surges, pinpoint high-growth locations, and mitigate risks. This shift from retrospective analysis to forward-looking intelligence is empowering investors to make informed, high-yield decisions, whether in commercial office spaces, retail hubs, or emerging real estate corridors. For instance, Knight Frank is leveraging AI models to predict real estate price movements in Mumbai with an accuracy of more than 95%, underscoring AI’s ability to refine investment strategies. Commercial REITS, publicly traded now for a few years and more getting listed this year, now have very sophisticated global investors, investment managers who are already well-advanced in analytics and portfolio management.
Next-Gen leasing and tenant analytics
Commercial leasing strategies are evolving from generic to hyper-personalised approaches, driven by AI’s ability to analyse tenant behaviour, space utilisation, foot traffic, and demographic data. This intelligence enables property owners to tailor lease terms, optimise pricing models, and predict tenant needs with greater precision. AI-powered platforms can dynamically adjust rental rates based on market conditions, offering real-time pricing recommendations that maximise occupancy rates.
Smart workspace apps are improvising tenant experiences by emphasising convenience, productivity, and personalisation. These platforms unify fragmented systems into an intuitive interface, offering seamless access to amenities and services within office parks, along with real-time updates and curated offers. For instance, occupiers can book amenities, pre-order food from on-campus F&B outlets, access their workplace and parking slots effortlessly, receive updates on exclusive events, and even provide feedback — all through a single, integrated application.
Accelerating due diligence, asset management and risk assessment
AI is transforming due diligence and risk assessment by automating document analysis, detecting financial inconsistencies, and flagging potential compliance risks. AI-powered systems can rapidly analyse contracts, extract key terms, cross-check lease agreements, and identify anomalies in financial statements, significantly reducing processing time. Asset management is value creation especially for portfolio managers. Commercial real estate is high capex business. MEP (mechanical, electrical, plumbing) services require upgrades and proactive management to drive sustainability and efficiencies. Any breakdown of infrastructure can cause business losses to occupiers. AI is used through master command centre to analyse, monitor and take preventive measures. We now also invest in BIM, Digital Twins, Asset Management Software with AI enabling decision making. Additionally, AI-driven risk models evaluate geopolitical, economic, and environmental factors to provide a comprehensive risk assessment, helping investors and lenders make faster, more secure, and well-informed decisions.
Transforming real estate marketing and sales
Real estate marketing and sales have evolved from simple listings to sophisticated, data-driven storytelling experiences. AI is leading this transformation by delivering hyper-personalised customer experiences and automating lead generation. AI-powered chatbots handle initial customer inquiries, guiding potential tenants through tailored property recommendations based on their preferences and online behaviour. Advanced AI models like Gen AI analyse engagement data, allowing marketing teams to fine-tune advertising campaigns, optimise digital listings, and predict which prospects are most likely to convert. Immersive virtual tours further enhance buyer engagement, enabling remote property exploration with realistic walkthroughs.
The cost of adoption
Implementing AI requires a significant financial commitment. For mid-sized firms, the capital required to build proprietary AI capabilities may be challenging, forcing them to rely on expensive third-party solutions — often at a competitive disadvantage. Moreover, ongoing maintenance, upgrades, and cybersecurity measures add to the financial burden, making AI adoption a complex, long-term strategic decision. AI’s potential is only as strong as the data it processes, and in CRE, this can pose a serious challenge. Information is available across registries, broker reports and lease agreements that might lead to inaccuracies and privacy concerns. If AI models are fed incomplete, outdated, or biased data, their predictions become unreliable, impacting everything from investment decisions to tenant analytics.
What builders can do
Prioritise data integrity by ensuring AI-driven insights are rooted in reliable, high-quality datasets.
Invest in hybrid intelligence by leveraging AI for analytics while retaining human judgment in decision-making.
Stay ahead of regulatory shifts by navigating the evolving landscape of AI governance and data protection laws.
Foster a culture of digital adaptability by upskilling professionals to work alongside AI rather than against it.
The data paradox
AI’s potential is only as strong as the data it processes, and in CRE, this can pose a serious challenge. Information is often available across registries, broker reports and lease agreements that might lead to inaccuracies and privacy concerns. If AI models are fed incomplete, outdated, or biased data, their predictions become unreliable, impacting everything from investment decisions to tenant analytics.
The human element: Can AI replace experience? As per OpenAI, around 80% of jobs are exposed to AI disruption. However, despite AI’s ability to crunch numbers and detect patterns, CRE remains a relationship-driven industry where human judgment, intuition, and negotiation skills hold irreplaceable value. AI can recommend an optimal lease price, but it cannot establish trust with a high-stakes investor or instinctively gauge a client’s concerns. Real estate decisions often involve nuanced, situational factors that algorithms may overlook, making it crucial for professionals to strike a balance between data-backed insights and on-ground expertise.
Ethical and privacy minefields: As AI adoption accelerates, concerns around data privacy, algorithmic bias, and regulatory compliance are coming to the forefront. Tenant analytics can reveal sensitive behavioural patterns, raising ethical questions about informed consent and potential discrimination in leasing decisions. Moreover, automated decision-making in financing may inadvertently reinforce systemic biases if historical data reflects inequities. As governments tighten data protection regulations and demand greater transparency, CRE players must proactively implement responsible AI frameworks — ensuring fairness, security, and compliance in all AI-driven operations.
Striking the right balance: AI is neither a fix-everything nor a passing trend — it is an irreversible shift in the way CRE sector operates. The key lies in strategic adoption, where AI complements human expertise rather than replacing it. Forward-thinking firms are investing in AI not as a substitute for real estate acumen, but as a tool to augment decision-making, optimise operations, and mitigate risks.
The writer is MD and CEO, Tata Realty and Infrastructure Ltd.
Published – March 14, 2025 04:32 pm IST
