How will real estate and construction evolve after AGI?

Introduction

Today's AI systems (ChatGPT, Gemini, Claude and the like) can already be said to hold more knowledge than the typical human expert. Once that capability is generalized further into AGI (Artificial General Intelligence), people's lives are expected to change dramatically.
This article lays out what kinds of changes we can expect in the real estate and construction sectors.

References

A great deal has been written about AGI, but we have limited our sources to highly credible literature from authors such as the following

• Leading AI developers: OpenAI, Google, etc.
• Leading consulting firms: McKinsey & Company, BCG, etc.
• Leading securities and investment firms: Goldman Sachs, Sequoia Capital, etc.
• Leading research institutions: Harvard University, Stanford University, etc.
• Leading academic journals: Nature, Science, etc.
• International organizations: World Economic Forum, OECD, etc.

The analysis below draws on the following sources. For full details, please refer to the original publications.

• Autodesk. (2025). 2025 State of Design & Make. Autodesk, Inc.
• Bengio, Y., Mindermann, S., Privitera, D., Besiroglu, T., Bommasani, R., Casper, S., ... & Zeng, Y. (2025). International AI Safety Report (No. DSIT 2025/001). Department for Science, Innovation and Technology. https://www.gov.uk/government/publications/international-ai-safety-report-2025
• Deloitte. (2026). TMT Predictions.
• Deloitte. (2026). Operating Models for Commercial Real Estate Companies in the age of AI.
• Deloitte. (2026). Next-Generation Office Strategies.
• Deloitte. (2026). Social Value and Technology in New UK Communities: Insights and Opportunities.
• Deloitte. (2026). Outsourcing in Real Estate: People first.
• Deloitte. (2026). Future-proof Strategic Options for Developers.
• Goldman Sachs. (2026). AI and the Gigawatt Ceiling.
• International Energy Agency (IEA). (2025). World Energy Outlook 2025.
• JLL. (2024). Case studies: AI creating value for real estate.
• Gujral, V. (2024). McKinsey & Company.
• Wolkomir, A., & Kapoor, A. (2026). McKinsey & Company.
• Morgan Stanley. (2025). How AI Is Reshaping Real Estate.
• Scott, B. (2025). NAIOP.
• Procore. (2025). The Future State of Construction.
• PwC, & Urban Land Institute (ULI). (2026). Emerging Trends in Real Estate: Global Outlook 2026.
• Salas, M., Singh, A., Pignataro, C., & Pal, L. (2026). AI-powered open-source infrastructure for accelerating materials discovery and advanced manufacturing. Communications Materials, 7(65). https://doi.org/10.1038/s43246-026-01105-0
• Suleyman, M., & Bhaskar, M. (2023). The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma. Crown.
• Susskind, D. (2020). A World Without Work: Technology, Automation, and How We Should Respond. Metropolitan Books.
• World Economic Forum. (2025). The Future of Jobs Report 2025.

Changes in Workflows and Operations

1. Investment & Asset Management

• More sophisticated, data-driven decision-making: AGI-powered predictive analytics will sharpen forecasts of rental income, vacancy rates and capital expenditure, taking portfolio management to a new level. At the same time, everything from market screening to identifying risks in contracts and technical reports during due diligence will be automated, with transaction cycle times expected to shrink by 20–30% — a dramatic acceleration.
• A shift in the human role: Most of the time currently spent on middle- and back-office tasks such as aggregating data and producing routine reports will be taken over by AI. Human work will consequently shift toward activities that demand high-level judgment and empathy: handling exceptions, making strategic capital-allocation decisions, and building client relationships.

2. Development, Design & Construction

• Generative design and design automation: In the early stages of architectural design (design optioneering), AI will generate thousands of design alternatives that satisfy a project's requirements and constraints in a matter of minutes. Cost, regulatory compliance and constructability can be built into the design process from the very start, and construction industry professionals themselves point to this kind of design optimization as "the area where AI will have the greatest impact over the next five years."
• Optimizing construction through BIM and digital twins: Real-time data integration and digital twins make it possible to detect design clashes and run simulations before construction begins, dramatically cutting the time lost to rework and delays — which currently accounts for roughly 28% of a project.
• Site automation and improved safety: Autonomous layout robots, remotely operated cranes and 3D printing technologies will take over or support tasks that are dull, dirty and dangerous (the "3Ds"). In addition, image-recognition AI and similar tools will enable safety risks on site to be monitored, reported and predicted in real time, delivering major gains in both safety and efficiency.

3. Leasing & Property/Facility Management

• Better customer experiences and round-the-clock engagement: AI agents and virtual assistants will automatically handle tenant inquiries and schedule viewings 24 hours a day. Automatically generated virtual staging and marketing content tailored to the preferences of target audiences will also lift conversion rates.
• Predictive maintenance and smart buildings: Combining AI with IoT sensors and equipment data enables "predictive maintenance," which detects impending failures in HVAC (heating, ventilation and air conditioning) and other systems before they occur. Beyond cutting maintenance costs by 10–20%, this makes a significant contribution to decarbonization (net-zero) targets by optimizing energy consumption.
• Automating back-office work such as contract management: Using natural language processing (NLP), AI can read lease agreements and similar documents instantly, automatically flagging deviations from standard clauses and their implications for revenue and risk — substantially easing the burden on legal and administrative teams.

4. Fundamental Transformation of Operating & Business Models

• End-to-end workflow transformation through agentic AI: The industry will move beyond single-task AI assistance toward agentic AI — autonomous agents that pull data across systems, make plans and execute them on their own. Rather than isolated improvements to individual tasks, this delivers holistic optimization of cross-departmental processes and feeds directly into higher NOI (net operating income).
• From "asset management" to "product management": The real estate players that win will break away from the traditional model of managing physical assets and adopt a business model built on "product management" — using data to continuously deliver the best possible spatial experience to tenants.
• Demand for AI infrastructure and "Space as a Service": As demand for AI training and inference explodes, so will demand for data centers. In particular, to meet the need for low-latency, real-time processing (inference), vacant basements, parking structures and shuttered retail units in urban areas will be converted into "edge data centers," creating new sources of real estate value and new revenue models.
• Building data foundations and transforming talent models: For advanced AGI and agents to function, siloed data must be integrated and cleaned. At the same time, companies will succeed or fail depending on how quickly they can drive organizational and cultural change — defining new roles such as AI product managers and data specialists, and reskilling their workforce.

Changes in Real Estate Investment and Asset Management

1. Smarter, Faster Investment Decisions and Transaction Processes

• Data-driven deal sourcing: With AI rapidly processing and analyzing vast volumes of structured and unstructured data (market trends, satellite imagery and more), market screening and the identification of attractive investment opportunities will become automated.
• Refined forecasting and simulation: Machine learning will enable far more sophisticated simulations of rental income, vacancy rates and capital expenditure (CAPEX), along with rigorous stress testing against risks such as interest-rate swings — making optimal portfolio management possible.
• More efficient due diligence: Using natural language processing (NLP), AI will review contracts, technical reports and environmental assessments instantly and detect risks automatically. Transaction cycle times are expected to shrink by 20–30% as a result.

2. A Paradigm Shift from Asset Management to "Product Management"

• End-to-end workflow automation: Agentic AI will handle tasks such as analyzing lease agreements, responding to tenant inquiries and automatically arranging maintenance around the clock, without human intervention.
• Elevating the tenant experience as a "product": Real estate will be reframed not as a static "financial asset" but as a "product" that continuously delivers an outstanding experience to tenants. The asset managers who win will evolve into "product managers," using AI to anticipate tenant needs and deliver hyper-personalized spaces and services.
• Predictive maintenance and higher NOI: "Predictive maintenance" — combining AI with IoT sensors and computer vision — will prevent equipment failures before they happen. Across large portfolios, this is expected to lift net operating income (NOI) by 1–3% and cut maintenance costs by 10–20%.

3. The Rise of New Asset Classes and a Redefinition of Real Estate Value

• Demand for AI infrastructure and "edge data centers": The explosive spread of AI will drive a surge in real estate demand for data centers as well as power and communications infrastructure. In particular, converting urban basements, parking structures and vacant retail space into "edge data centers" — which perform low-latency inference (real-time processing) close to users — is attracting attention as a new opportunity for value creation.
• Changing revenue models: In buildings with embedded AI infrastructure, the traditional rent model based on floor area (square meters) may give way to revenue models based on computing capacity, power supply capacity and connectivity.
• Rising value of land: Some forecasts suggest that while advances in robotics and AI will eventually drive construction and labor costs down dramatically, the relative value of land — a physically finite asset — will rise substantially.

4. Full Automation of Finance and Back-Office Functions, and Improved Profitability

• Sharp reductions in back-office work: Middle- and back-office tasks such as invoice processing, service charge reconciliation, cash-flow forecasting and the preparation of complex financial reports will be automated by AI. Finance department costs are projected to fall by 20–40% as a result.
• Efficiency gains across the real estate industry: According to estimates by Morgan Stanley, AI adoption is expected to generate roughly $34 billion in efficiency gains across the real estate industry by 2030, with labor cost savings and productivity improvements anticipated particularly in brokerage and service functions.

5. The Evolving Human Role and Data Governance

• Focusing on work only humans can do: As AI takes over data aggregation and routine report writing, the human role will shift decisively toward handling exceptions, making high-level capital-allocation decisions, and building relationships with tenants where empathy and trust are essential — the "moments that matter."
• Clean data foundations and governance become mandatory: For AI systems to operate correctly, organizations need an integrated data foundation that eliminates internal silos. Data governance that prevents AI from malfunctioning on the basis of inaccurate data will come to directly determine a company's competitiveness (its ability to generate alpha).
• Tokenization and new capital flows: As AI and blockchain technologies advance, the "tokenization" of real estate (fractional securitization) will spread, potentially bringing new capital into the real estate market — not only from institutional investors but also from wealthy individuals and others — and dramatically increasing liquidity.

Changes in Design and Construction Processes

1. Reinventing the Design Process

• The spread of generative design and design optioneering AI will have an outsized impact on the earliest stages of design. In a survey of construction professionals, "design optioneering" (the exploration and optimization of design alternatives) ranked as the area where AI is expected to have the greatest impact over the next five years. Using generative AI algorithms, engineers and architects will be able to generate and explore thousands of layout options that satisfy a given set of requirements in a matter of minutes, dramatically boosting design productivity.
• Up-front engineering and more sophisticated BIM In the traditional process, architects produced drawings first and only then did engineers and contractors assess whether they could actually be built, resulting in a great deal of rework. With AGI, an AI trained on past project data and building codes will automatically calculate engineering constraints and costs and run compliance checks at the very start of design. This tightens the link between design and construction through BIM (Building Information Modeling), and pre-construction simulation on a digital twin will drastically cut waste.
• Optimizing for sustainability Roughly 80% of a product's or building's environmental impact is said to be determined by decisions made at the design stage. AI will optimize airflow and space within buildings, prevent material waste, and assess environmental impact across the entire life cycle, making it a powerful driver of sustainable design.

2. A More Sophisticated Approach to Preconstruction and Project Management

• Data-driven scheduling and supply chain optimization Construction projects have long wasted enormous amounts of time hunting for data and fixing problems caused by rework. With AI and machine learning, actionable insights can be extracted from unstructured data, timelines updated automatically, and critical questions answered in real time. Analyzing past models to forecast future needs also prevents material shortages and oversupply, streamlining ordering and scheduling.
• Automated contract and document review and risk management Beyond progress monitoring and schedule management, AI will substantially improve data-intensive tasks such as the automated review of contracts and project documents, cost control, and risk management. The result is a redesign of the entire project-operations workflow, from procurement to schedule optimization.

3. Automation and Hybrid Work on the Construction Site

• Robots take over the "3D" jobs On construction sites, automation will steadily take over the work that is Dull, Dirty, and Dangerous (the "3D" tasks). Automated layout robots, for example, can print drawings directly onto the floor with high precision, sparing workers the physical strain of kneeling to mark out lines by hand while preventing errors and clashes with other trades before they happen. Remotely operated tower cranes that analyze a range of data and suggest optimizations are another example of the collaborative human-machine environment that will take shape.
• Real-time monitoring of progress, quality, and safety Site data gathered by IoT devices, drones, smartphones, and the like will be used to build a "visual twin," a visual replica of the site. AI agents operating inside this virtual space will automatically monitor quality, progress, and safety risks at a scale no human could match. This has already been demonstrated in practice: in one advanced, multi-AI process, an AI analyzes a photo of unsafe scaffolding on site, calculates the hazard against OSHA (Occupational Safety and Health Administration) standards, and a legal AI then automatically drafts a corrective notice to the subcontractor.
• Hybrid decision-making by humans and AI Even once AGI is widespread, humans will not disappear from the job site. Complex decisions will shift to a "hybrid model" in which experienced people verify and act on the predictive analytics and data-driven recommendations AI provides. Site workers will move from purely physical labor into more sophisticated roles, such as directing robots and putting AI-generated data to work.

How Smart Cities and Sustainability Will Change

1. Smarter Buildings and Predictive Maintenance Through Autonomous AI

• Management tasks once handled by people will be highly automated through the integration of IoT sensors and AI. Facility management will be automated by mining equipment data and other sources, and AI control of HVAC systems has been shown to deliver energy savings of up to 59% along with major reductions in CO2 emissions.
• Agentic AI—systems that judge and act autonomously—will go further, redesigning the entire workflow from triaging repair tickets to dispatching technicians and closing out the issue, dramatically improving both operational efficiency and response times.

2. Advancing Sustainable Design with Digital Twins and Generative AI

• In the Design and Make sector, AI has become the single greatest enabler for hitting sustainability targets.
• By simulating the effects of renovations and energy consumption in advance on an AI-powered "digital twin," deep energy retrofits of existing buildings (large-scale renovations that improve energy efficiency by 50% or more) can be planned far more effectively and realistically.
• Generative design, meanwhile, makes it possible to generate and evaluate thousands of design options in minutes while minimizing environmental impact and material waste, contributing to substantial reductions in carbon footprint.

3. Valuation That Emphasizes "NEBs" (Non-Energy Benefits) and Improved Well-Being

• Sustainability assessment in real estate is shifting from simply "cutting utility bills" (energy savings) to a comprehensive evaluation of NEBs (Non-Energy Benefits), including occupant health, higher knowledge-worker productivity, and stronger disaster resilience.
• Smart building systems monitor not only energy use but also occupant activity patterns in real time. This not only enhances tenant well-being; it also directly lifts net operating income (NOI) and property value by qualifying the building as a prime asset that meets ESG criteria.

4. Automated Demand Response Through Grid Integration

• Smart buildings will evolve from passive consumers of energy into active systems that help stabilize the power grid. Using AI, they will coordinate smart air conditioners, heat pumps, and electric vehicle (EV) charging to automatically shift peak electricity demand (demand response).
• This allows buildings to flexibly absorb fluctuations in the supply of renewable energy such as solar and wind, making a major contribution to decarbonization plans at the regional and national level.

5. The Environmental Burden of Surging AI Infrastructure and the Rise of "Urban Edge Data Centers"

• Training and running AI demands vast computing resources, and the electricity and water consumed by data centers is growing explosively. This has created a new sustainability challenge: the environmental footprint of AI itself.
• One response is the construction of "edge data centers" in urban underground spaces, vacant retail units, parking garages, and similar locations, so that AI inference can be processed close to users. These facilities are working to reduce energy waste by capturing waste heat from servers and channeling it into building heating or district heating networks.
• Carbon-neutral data centers equipped with fuel cells and water-free cooling systems are also being rolled out, as the industry searches for ways to let AI and the environment coexist.

6. Discovering New Materials for Green Construction (Materials Informatics)

• AGI and machine learning will dramatically shorten the discovery cycle for sustainable new materials used in construction and smart buildings. Integrating AI with self-driving laboratories will accelerate the development of materials that are high-performance yet biodegradable, recyclable building components, and more.
• This makes it possible to reduce the environmental impact of building materials across their entire life cycle—from design and manufacturing through disposal—in a circular economy.

How Customer Experience and Business Models Will Change

1. A Dramatic Leap in Customer and Tenant Experience

• Autonomous engagement and support 24/7: AI-powered virtual assistants and chatbots will automatically handle viewing appointments, property tours, maintenance requests, rent payments, and more around the clock. This breaks the industry out of its traditional "nine-to-five," people-dependent sales model: tenants get the support they need instantly, regardless of time or place, and customer satisfaction rises sharply.
• Hyper-personalization and experience-driven design: Generative AI will deliver virtual staging (such as virtual furniture placement) and property marketing content that is automatically and deeply personalized to specific target segments and individual preferences. Optimal locations and spatial designs will also be proposed as tailor-made solutions matched to the needs of tenant companies and retail foot-traffic trends.
• Smart buildings and optimized well-being: "Truly intelligent buildings" that integrate IoT and AI will become widespread, enabling sophisticated environmental settings centered on each individual tenant's experience. Digital platforms that monitor energy use and activity patterns to improve tenant well-being and community connection (a sense of belonging) will be built in from the moment a building is constructed.
• Redefining and elevating the "human touch": With the bulk of administrative work and routine inquiries handled by AI, human staff will be free to focus on the high-value support only people can provide—the "moments that matter," such as building relationships with tenants and handling complex, empathy-demanding situations like a water leak emergency.

2. A Fundamental Shift in Business Models

• From "asset management" to "product management": Until now, the real estate industry has focused on managing property as a physical financial asset and allocating capital appropriately. Going forward, its role will shift decisively toward a "product management" approach that reframes real estate as a "product" that continuously delivers the best possible spatial experience to tenants.
• The end of the "build and sell" model and the shift to "developer-operators": Rising interest rates, soaring construction costs, and permitting delays mean the traditional speculative "Buy-Develop-Sell" model—acquire land, develop it, sell it—no longer guarantees a profit. Going forward, the industry will pivot toward strategies that generate recurring revenue: holding properties long-term to capture rental growth, asset-light platform strategies built on joint ventures with institutional investors, and models that renovate and operate existing assets. The winners will move beyond simply developing and owning assets to running them as well, adopting a "developer-operator" model.
• Growth of "Space as a Service" and diversified revenue streams: As AI-ready infrastructure and plug-in integration of multiple systems become feasible, real estate will be delivered not as a static box but as dynamic, agile service infrastructure—opening up entirely new revenue streams for operators.
• Redefining urban real estate as AI infrastructure, including edge data centers: As AI is embedded in everyday work, demand will grow not just for large-scale training data centers but for computing capacity that performs low-latency, real-time processing (inference) close to users and businesses. This will accelerate the conversion of underused urban spaces—basements, parking garages, vacant storefronts—into "edge data centers." In turn, the industry may shift from the traditional rent-per-square-meter model to a new "performance-based" (infrastructure-provision) revenue model built on delivering compute, power, and connectivity.
• End-to-end workflow automation by agentic AI and higher NOI (net operating income): Rather than merely assisting with individual tasks, autonomous AI agents will redesign and automate entire workflows end to end—lease contracting, maintenance scheduling, financial reporting, and more. This fundamental overhaul of the operating model is expected to unlock tens to hundreds of billions of dollars in value creation and efficiency gains across the real estate and construction industry, cutting operating costs and substantially improving NOI.

Shifts in Real Estate Demand

1. Massive investment in hyperscale data centers and increasingly demanding physical requirements

• Explosive growth in investment: Investment in the data centers that power AI model training is expanding rapidly. In 2025, global data center investment is estimated to reach roughly $580 billion, surpassing total investment in oil supply. Some forecasts put capital expenditure on AI data centers by hyperscalers, cloud providers, and others at around $400–500 billion in 2026, rising to $1 trillion by 2028.
• More complex construction requirements: Next-generation AI data centers differ fundamentally from their predecessors. They demand far more complex and sophisticated physical and construction specifications: thicker floors to support heavy, high-density server racks, new cooling systems to handle enormous heat loads, and massive power supply infrastructure.

2. The rise of inference-focused "edge data centers" and the repurposing of urban real estate

• The shift toward low-latency demand: AI usage is moving from model "training" toward "inference" in day-to-day operations and real-time processing. Inference workloads are projected to account for roughly two-thirds of total compute demand by 2026, making low-latency processing close to data sources and users critical in areas such as financial trading, customer service, smart cities, and logistics optimization.
• Repurposing existing urban properties: Remote mega data centers alone cannot satisfy all AI demand. This is rapidly opening up opportunities to convert underused urban assets—basements, parking levels, vacant storefronts, aging industrial properties—into "edge data centers" located near end users. It is regarded as the single most overlooked value-creation opportunity in commercial real estate today.

3. Blurring boundaries between real estate and infrastructure, and a shift in business models

• Valuation shifts from "floor area" to "compute and power": The basis for valuing real estate is moving away from physical size—rent per square meter—toward "performance capability as digital infrastructure": available power capacity, proximity to substations, fiber network routes, and the scalability of electrical infrastructure.
• Redefining property as service infrastructure: Winning developers no longer offer a mere physical "box"; they redefine real estate as "dynamic service infrastructure" that integrates energy management and automation solutions. This gives rise to new business models that combine traditional tenant income with revenue from digital infrastructure. At the same time, the line between real estate and infrastructure as asset classes is blurring, with the two increasingly treated as a single, integrated "real assets" category.

4. Power and water bottlenecks and sustainability (environmental response)

• Infrastructure limits and worsening connection backlogs: Surging data center demand is creating a severe bottleneck in grid capacity. In parts of the United States and in Ireland, data centers already account for 10–20% or more of electricity demand; grid connection wait times have stretched to several years in some cases, and new connection requests have at times been suspended altogether. Water consumption is estimated to exceed 250 billion gallons per year by 2030.
• Environmental integration with urban infrastructure and greening: To ensure sustainability, operators are deploying fuel cells and running water-free, carbon-neutral data centers. Urban edge data centers are also pioneering new forms of environmental integration, such as capturing the large volumes of waste heat from AI servers and feeding it into building heating systems or district heating networks.

5. Transformation of real estate investment and financing

• Data centers as the top sector: Across North America, Europe, and Asia-Pacific, data centers rank as the most promising sector for both investment and development.
• The need for new financing models: Data center development often requires enormous capital—€10–15 billion for a single project—which can exceed the lending capacity of conventional banks. This is driving demand for asset-backed financing such as securitization and commercial mortgage-backed securities (CMBS). Because the risk profile also differs from that of conventional real estate—including the risk of rapid facility obsolescence as technology evolves—investors need deeper operational and technical expertise than ever before.

Shifts in the Labor Market

1. Easing severe labor shortages and automating "3D" work

The construction workforce is aging rapidly; surveys in the United States and elsewhere project that 53% of today's workers will retire by 2036. AI and robotics offer a solution to this labor crisis. Tasks that are dull, dirty, and dangerous (the "3D" jobs) will be aggressively automated and mechanized. Beyond freeing human workers from physical hazards and improving safety, this repositions AI not as a threat that takes jobs away but as a partner that augments human capabilities and dramatically raises productivity.

2. Shrinking clerical and back-office work and the replacement of cognitive tasks

 Meanwhile, middle- and back-office work in real estate investment and management—data entry, invoice processing, first-pass contract checks, report preparation—will be heavily automated by AI. The World Economic Forum (WEF) likewise lists clerical roles such as data entry clerks, receptionists, and secretaries among the fastest-declining jobs worldwide. The time this frees up, however, will be redeployed toward high-value cognitive work that only humans can do: building tenant relationships, handling exceptional problems, and making sophisticated capital allocation decisions.

3. Exploding demand for skilled trades (blue-collar workers) to build AI infrastructure 

The spread of AI itself creates enormous new demand in the labor market, driven by a surge in construction of the data centers and power and telecommunications infrastructure needed to support AI's massive computing requirements. Goldman Sachs projects that the United States alone will need roughly 500,000 new jobs by 2030 just to meet surging electricity demand. In concrete terms, employment of skilled tradespeople on site—construction workers, electricians, HVAC installers, plumbers—will grow at an unprecedented pace, forming the critical physical foundation of the AI ecosystem.

4. The birth of new roles and the shift to "hybrid decision-making" 

As AI adoption advances, demand within the real estate and construction industry for technology roles—data scientists, AI and machine learning specialists, digital transformation (DX) experts—will rise sharply. At the same time, business decision-making will move from relying on human intuition alone to a "hybrid" model that combines AI-driven predictive analytics with human experience. The human role will evolve into that of an "AI orchestrator" or "agent boss": directing and supervising multiple autonomous AI agents and making the final business calls.

5. The growing importance of soft skills and the return of the "human touch"

Precisely because technology is advancing and automating so many routine tasks, the market value of distinctly human "soft skills" will soar. Analytical thinking, flexibility, leadership, and creative thinking will be in greater demand than ever. When a major water leak hits a property in the middle of the night, for example, what tenants want is not the efficiency of an AI but the empathy and human touch of a person who eases their anxiety and clearly explains the situation. In the AI era, this capacity for relationship-building and empathy will be highly valued as a competitive differentiator for companies.

6. Continuous reskilling and cross-generational knowledge sharing (cultural transformation) 

To adapt to such rapid changes in skill requirements, companies will need to continuously provide existing employees with reskilling and upskilling for working alongside AI. "Two-way knowledge transfer"—upward knowledge transfer—will also become important within organizations: tech-savvy younger workers teach seasoned senior colleagues how to use new technologies, while veterans pass on their tacit, on-site know-how to the younger generation. Beyond simply rolling out AI tools, change management—transforming the organizational culture so that employees trust AI and actively put it to use—will play a decisive role in attracting and retaining talent in the labor market.

Conclusion

The advance of AI is set to transform the real estate and construction sector profoundly. Companies and individuals alike will need to make sound decisions grounded in an understanding of the services and roles the industry will demand in the years ahead.