AIがリアルアセット業界にどのような変革をもたらしているか

AI is becoming a strategic force in real assets, reshaping how the industry thinks about capital, operations, risk, and long-term value. What was once viewed mainly as a productivity tool is now beginning to influence how investors assess risk, manage portfolios, allocate capital, and improve building performance. For real assets, where decisions depend on large volumes of operating data, market intelligence, and human judgment, the implications are significant.

In this special publication, we have gathered perspectives from thought leaders across the APREA network to examine how AI is being applied in practice. Their insights show that the greatest value lies in combining technology with domain knowledge and disciplined execution. As adoption accelerates, the firms that use AI thoughtfully and responsibly will be better placed to strengthen their bottom line through better decisions, lower costs, and more resilient assets.

シグリッド・ジアルシタ
CEO（最高経営責任者
アプリア

各写真をクリックして、回答をお読みください。.

アニッシュ・サングヴィ

パートナー
Price Waterhouse & Co LLP

At an organisational level, how is AI changing the way your teams work and your business operates? Where are you seeing the greatest efficiencies, and how are you redesigning workflows, processes, and roles to make the most of these gains?

AI is materially changing the way we engage with our clients and our people. AI has made a paradigm shift in our operating environment. A few classic examples of how things are changing:

Research and compliance – Our research methodology has completely changed with the help of AI powered document reviews and precedent analysis – our teams now spends materially lower time in data gathering and research and significantly more time on making judgements/decisions – thus moving from information compiling to strategic advisory.
Data analytics – Information gathering and understanding data across portfolios enables mapping of risks and identification of compliance gaps far easily and with greater accuracy
Repositioning of people skills – By automating data-heavy tasks, we have liberated our talent to engage in higher-value client delivery and advisory work. This not only improves both engagement and career trajectory for our teams, but also positions us to scale advisory capacity without proportional headcount growth.

AI is rapidly reshaping investment decision-making across real assets. How is it changing the way investors identify opportunities, assess risk, and allocate capital across sectors and markets? Are you seeing AI influence cross-border investment strategies or capital flows?

Decision making is an overhauled process. Faster decision making with more accurate and deeper research with AI assisted processes is now a given. Investors can now model across vastly larger datasets with multiple scenario analyses and stress tests—exploring different probability distributions and outcomes in timeframes that were unimaginable two years ago.

How is AI transforming due diligence and investment analysis? Which areas–from underwriting and market forecasting to portfolio optimization and valuation–are delivering the greatest improvements in speed, accuracy, and investment outcomes?

A few illustrations of usage of AI in due diligences and its impact

Area	インパクト	Relevance
Document Review	Substantially quicker to review documents and fewer missed issues	Identification of non-market precedents, automated lease/covenant analysis; embedded tax clause extraction
Market Forecasting	Improved accuracy in understanding the rental and occupancy assumptions	Enables better market analysis, understanding revenue sustainability and building IRR confidence
Underwriting	Scenario modelling 3-5x faster	Stress-test efficiency across downside cases – improved investment outcomes by reducing blind spots, anchoring analysis in data and enabling investors to stress-test assumptions that might otherwise go unexamined
Valuation	AI-informed comparable selection; reduced bias	More defensible tax positions (transfer pricing, FMV for grants)

Beyond acquisitions, how is AI changing the way real assets are managed and operated? Can you share examples of how it is improving asset performance, tenant experience, predictive maintenance, or operational efficiency?

AI has been actively adopted in undertaking predictive maintenance, dynamic energy optimisation, revenue management thereby reducing operating costs while enhancing tenancy satisfaction.

With sustainability becoming an increasingly important investment consideration, how is AI helping owners and managers optimise energy consumption and meet ESG reporting requirements? Do you expect AI-enabled buildings to command a valuation or leasing premium over time?

Businesses are charging leasing premiums of about 3-7% for AI-enabled, ESG-rated assets in institutional markets, with the premium widening as tenant preferences shift and financing costs increasingly reward sustainability performance. However, premiums are not guaranteed – Tenant ESG preferences vary by geography whereas secondary markets show minimal uplift.

Looking ahead, where do you see AI creating the greatest competitive advantage for investors over the next five years? What capabilities should owners, fund managers, and developers be investing in today to remain competitive?

Like in every other business, AI backed with accurate data mining is going to be a key for investors in real estate. Investors who embed AI in their decision-making and not just for achieving operational efficiencies or cost reductions should have a marked edge over their competitors.

アニッシュ・サングヴィ
パートナー
プライス・ウォーターハウス＆カンパニーLLP

クリスティン・リー

アジア太平洋地域調査部長
ナイト・フランク

The immediate benefits of AI will come from automating process-heavy tasks such as due diligence, underwriting, document review and market analysis. However, any advantages from these productivity gains will rapidly dissipate as such tools become widely available. In my view, the longer-term competitive advantage will come from proprietary AI capabilities built on unique datasets, a contextual understanding of markets and operating experience. In much the same way that firms have historically differentiated themselves with superior investment models, research platforms or risk management frameworks, the next generation of industry leaders may be distinguished by the quality of their AI-enabled decision-making platforms.

These capabilities could materially improve an investor’s ability to identify opportunities, assess risk and allocate capital across sectors and markets. Firms that can better analyse vast volumes of structured and unstructured data, recognise emerging trends earlier and generate more precise insights will likely have an advantage in both sourcing investments and portfolio construction. This could be particularly impactful for cross-border investments, where AI can help investors evaluate opportunities across multiple geographies using a more consistent and scalable analytical framework, potentially accelerating global capital flows and increasing market transparency.

Developing these capabilities will require significant investment in data infrastructure, technology and talent. As a result, the largest global investment managers are likely to have already established a meaningful head start given their scale, resources and access to proprietary data. Many have already begun embedding AI into their research, risk management and investment processes. However, AI also has the potential to disrupt the status quo by enabling innovative firms with a differentiated vision and a strong technology culture to compete with much larger incumbents.

Just as Amazon transformed retail through its application of technology, data and operational capabilities, it is conceivable that a new generation of specialised investment managers could emerge in real assets with AI at the core of their competitive strategy. Over the next five years, some of the industry’s most significant innovations may come not only from the largest incumbents, but also from agile firms that are able to leverage AI in the investment process. Still, real estate investing is much more than just data and analytics. It is also built on relationships, negotiation, execution capabilities, regulatory considerations and an understanding of how we interact with the built environment.

In real estate, alpha is created through a combination of investment insight, disciplined capital allocation, operational excellence and successful execution over time. While AI has the potential to strengthen each of these drivers, alpha cannot be generated by technology alone. The firms most likely to generate sustainable alpha will be those that use AI to augment, rather than replace, the expertise, relationships and execution capabilities that have always underpinned successful real estate investing. While future battle lines will increasingly be drawn on the technological front, the firms that successfully combine proprietary data, advanced analytics and human judgment will be best positioned to generate differentiated insights, deploy capital more effectively, and develop a competitive edge in an increasingly tech-driven investment landscape.

クリスティン・リー
アジア太平洋地域調査部長
ナイト・フランク

ドミニク・ブラウン博士

国際研究責任者
クッシュマン・アンド・ウェイクフィールド

It is still early in the adoption of AI, and investors are at different stages of deployment, but what we see is that AI will allow investors to undertake greater scrutiny of a larger number of assets by reducing process flows.

Currently, time is one of the greatest capacity constraints for investors. This means that they must form quick, initial assessments to decide whether an asset is worth investigating further. AI will support these high-level screenings while allowing in-depth scrutiny at every step, allowing investors to make informed decisions at each stage of the investment process.

Furthermore, a greater range of data can be assessed that may not directly relate to the asset itself but could ultimately influence its performance over time. This will allow for a more detailed risk assessment scoring process (market risk, climate risk, economic risk) to better understand an asset’s strengths and weaknesses.

Capital is increasingly global, with investors needing to assess assets in multiple markets simultaneously. AI allows for set analyses to be undertaken repetitively which will allow for standardised comparison of assets to help identify the highest priority targets for investment given available capital.

Overall, AI will not remove the need for human judgment, rather, it will improve the speed and clarity of the decision-making process through areas such as greater data analysis and greater scenario modelling. This will allow for wider and faster consideration of potential assets to invest or divest.

AI is well-suited to repetitive, data-heavy tasks – the epitome of due diligence. In simple terms, this means more analysis undertaken more quickly. For example, the quality of lease abstraction by AI has improved considerably, which reduces the time taken to extract relevant clauses in each lease as well as maintaining, if not improving, the accuracy of extracted information compared to human intervention.

Over time, larger, more complex models will continue to be developed to forecast future market conditions as well as provide scenario parameters around those forecasts. This will allow for stronger upside or downside assessments at both the asset and portfolio level, which can then be used to assess concentration risks and the need to rebalance a portfolio to help maintain diversity and resilience.

Automated valuation models have been under development for some time. This will continue to improve but will still require human assessment to ensure accuracy of analysis as well as regulatory compliance.

Facilities management (FM) generates a considerable quantum of data, which renders exciting possibilities for the way buildings are operated and managed. The linking of occupational data, cleaning schedules and predictive or preventative maintenance programmes would represent one of the greatest opportunities to optimise cost efficiencies. This will allow the FM contract model to continue to evolve from personnel requirements to operational outcomes. AI is also enabling more personalised tenant engagement and self-service tools, allowing issues to be identified and resolved more proactively and improving the overall occupier experience.

The greatest competitive advantage will come from combining proprietary data, investment judgment and operational execution. Investors that build strong data governance and integration capabilities today are more likely to sustain that advantage into the future.

Dominic Brown
国際研究責任者
Cushman & Wakefield.

Doug Kang

VP, Asia Pacific Japan
Fifth Dimension

AI is not simply automating tasks. It is changing what counts as a task, and that distinction matters more in real assets than in most industries, because one asset can generate thousands of pages of documents, dozens of stakeholder reports and months of analytical cycles.

Where the gains are landing

The efficiencies are not in the use cases that get written about. They are in the dull, high-volume work that eats analyst time: lease abstraction and review, operating statement normalisation, variance commentary, rent roll reconciliation, investor reporting. That work used to absorb two to four days per asset per month from a junior analyst. Built into a properly designed AI workflow it takes minutes, with source citations and an audit trail attached. Once that happens you have to revisit how the team is staffed and what you are actually asking people to do.

Cross-document analysis is the other place the gains are large. A manager running 30 assets has historically depended on summaries prepared by property managers, and a summary loses information by design. AI can read what sits underneath it, the leases and operating statements and inspection reports and CAM reconciliations, and surface the covenant breach or the expiry risk that nobody thought to put in the summary. In practice that is where most of the surprises live.

How workflows and roles are being redesigned

The organisations getting the most out of AI have not bolted a chatbot onto an existing process. They went back to first principles and asked what their people should be doing if the extraction, the normalisation and the first-pass analysis are handled somewhere else.

The answer keeps coming back to judgment. Analysts move from gathering data to framing decisions. Asset managers spend less time compiling reports and more time acting on what the reports say. Investment professionals review a screening output instead of building the screen from scratch. None of that is a smaller job than the one it replaced, which is worth saying to any team that hears “AI” and assumes headcount reduction.

The redesign that matters most is where you put AI in the sequence. Put it at intake, reading the new lease, pulling the terms, flagging the odd clause, populating the database, and every step after that benefits. Use it to polish a report a human has already written and the gain is cosmetic.

A word on platform choice and cost

One of the more consequential decisions in front of real assets companies right now is not whether to adopt AI but which platform to adopt, and the economics there are less obvious than they look.

Ballooning AI spend is a real concern. Plenty of organisations are finding that generic platforms, built for broad use cases like coding or customer support, get expensive quickly when pointed at real assets workflows. The reason is structural. A generic platform does not know the domain. When an agent does not know what a CAM reconciliation is, or how WALE is calculated, or what the difference between a gross and a net lease does to an operating statement, it compensates by pulling in more context, taking more steps and retrying more often. Every one of those compensations burns tokens, and the cost compounds fast.

The number worth watching is the total cost of getting a specific piece of work done to the required standard, not the price per token. A platform built around the vocabulary, document types, workflows and analytical frameworks of real assets does the same job with much less computational waste. At Fifth Dimension our domain-optimised agent harness, the layer controlling how the AI pulls in context, uses data, breaks down tasks, manages handoffs and applies guardrails, produces output of the same quality as generic platforms at a fraction of the token consumption. That gap decides whether an AI programme scales or gets quietly wound back because the bills got too high.

Two things I would say to anyone evaluating platforms. Ask for total cost of task completion benchmarks rather than token prices. And be sceptical of anything claiming to serve every industry equally well. In real assets, domain depth drives both quality and cost efficiency, and most of the market has not priced that in yet.

Investment decision-making in real assets has always been information-heavy and time-constrained. AI is working on both constraints at once, widening the information set an investor can realistically process and shortening the time it takes to process it.

Opportunity identification

Deal screening changed first. Investors who relied on broker relationships and market reports to surface opportunities can now run systematic screens across much larger universes of assets. Submarket data, transaction comparables, planning approvals, tenant credit trends and macro signals go in together, and what comes out is a ranked shortlist that a research team would have needed weeks to assemble.

The more interesting capability is spotting opportunities that do not look like opportunities yet. Patterns in leasing activity, vacancy trends, infrastructure investment and demographic shift can flag a submarket approaching an inflection point before it shows up in transaction volumes or pricing. Processing the same information faster than your competitors is worth something. Processing information they are not looking at yet is worth considerably more.

Risk assessment

At asset level, AI reads a full lease file, base lease and amendments and estoppels and guaranties, and produces a structured risk register in minutes: expiries, embedded options, co-tenancy clauses, rent review mechanisms, tenant credit flags. A week of legal and analytical review now finishes before the site visit.

At portfolio level it makes concentration analysis practical for the first time. An investor can ask what aggregate exposure to a given tenant, sector, lease structure or market looks like today, and stress-test that exposure against a range of scenarios. For a diversified fund spanning several asset classes and geographies, that question used to take a fortnight to answer badly.

Capital allocation and cross-border strategies

Cross-border investment is where the impact is largest and least discussed. Information asymmetry between local and foreign investors has been one of the most persistent brakes on cross-border capital in real assets. Local investors know the market. Foreign investors either pay a premium for that knowledge or accept a higher risk premium for going without it.

AI is eroding that asymmetry. Pulling together local market data, regulatory frameworks, planning environments and transaction histories across several markets at once gives a cross-border investor a more credible analytical base than it has ever had. Local expertise still matters, because relationships, on-the-ground judgment and regulatory navigation are not going to be automated. But the information gap is narrowing, and it is narrowing faster than most allocation models assume.

Asia Pacific is the clearest case. The region’s diversity in regulatory environment, market maturity, currency dynamics and asset class depth has always made pan-Asian portfolio construction analytically demanding. Anyone who has tried to compare a Tokyo office asset with a Sydney logistics asset and a Seoul retail asset on consistent terms knows how much of that work has been manual. AI makes it more tractable, and I expect that to accelerate intra-regional capital flows over the next few years.

For member companies, the capability worth investing in is one that synthesises multi-market data coherently rather than processing it market by market. The advantage will go to organisations that can hold the whole regional picture in view at once.

How is AI transforming due diligence and investment analysis? Which areas are delivering the greatest improvements in speed, accuracy, and investment outcomes?

Due diligence is where AI has delivered the most measurable improvement, for an unsurprising reason. It is a document-heavy, time-constrained process where the volume of material is large and the cost of missing something is high.

Document review and extraction

The speed and completeness of document review has changed most. A commercial acquisition involves hundreds of documents: leases, title reports, environmental assessments, property condition reports, operating statements, service contracts, insurance certificates. A thorough human review runs to weeks and significant professional fees. AI reads the same set in hours, pulls structured data out of every document, cross-references findings and produces a prioritised list of what a person needs to look at.

It also does not get tired, does not skim and does not miss the clause on page 47 of a 200-page lease that restricts the landlord’s ability to redevelop. Completeness under time pressure is consistently better, and time pressure is the condition most due diligence actually happens under.

Underwriting and financial modelling

Underwriting speeds up because model population stops being manual. Instead of an analyst transcribing rent roll data, lease terms and operating expenses into a model, those inputs come straight out of the documents with citations attached. Transcription errors disappear, build time drops, and every assumption in the model has an auditable link back to its source.

Market forecasting is improving too, with more caveats. AI can synthesise a much wider range of leading indicators than traditional approaches, including satellite data, foot traffic, search trends and sentiment. But a model is only as good as its training data and its embedded assumptions. The teams getting the best results use AI to produce a range of scenarios and stress-test assumptions, and treat any single AI forecast as one scenario rather than a number to underwrite against.

Valuation

Valuation is the most nuanced of these. AI has improved the speed and consistency of comparable analysis, finding relevant transactions, adjusting for differences in asset characteristics and assembling a defensible comparable set much faster than manual research. Where there is enough transaction data, automated valuation models are getting genuinely good.

Complex or illiquid assets still need human judgment. The best AI-assisted valuation processes have AI doing the data assembly and the comparable work while the valuer concentrates on the judgment calls: the right cap rate for this asset in this market at this point in the cycle, the discount for a near-term expiry, the premium for a building with real ESG credentials.

Portfolio optimisation

Portfolio work is becoming continuous rather than periodic. Instead of reviewing composition quarterly or annually, an investor can monitor sector allocation, geographic concentration, expiry profile, WALE and DSCR against target parameters and get an alert when the portfolio drifts out of range. For a large diversified fund that is a real improvement in governance.

One piece of advice I would offer here: prioritise the applications that leave an auditable, citable record of what was reviewed and what was found. Regulators across the region are paying more attention to investment process governance, and being able to demonstrate that a systematic review happened, and to show what the AI found and what the human then decided, is a risk management asset in its own right.

Asset management is where AI shows up most tangibly for the widest group of stakeholders: not only investors but tenants, property managers and the surrounding community. It is also where the spread between the best and the worst organisations is widest, because operational AI needs data infrastructure as well as technology, and a lot of real assets organisations are still laying that foundation.

Asset performance and operational intelligence

The quickest operational gains come from putting AI alongside the building management system. Sensor data, occupancy patterns, weather forecasts and utility pricing analysed in real time let the AI adjust HVAC and lighting dynamically, cutting energy use without making the building uncomfortable. In commercial buildings that typically lands somewhere between 15 and 25 percent, with payback inside two to four years.

Benchmarking has also become more sophisticated. Asset managers can compare an individual asset against a peer group live, on operational metrics as well as financial ones: energy intensity, maintenance cost per square metre, tenant satisfaction, renewal rates. That makes underperformance easier to locate and, more usefully, easier to explain to an investment committee.

Tenant experience

Tenant experience is starting to differentiate. AI-powered engagement platforms handle routine service requests, answer questions about amenities and flag issues before they turn into complaints. The more advanced applications analyse how tenants actually use space, when they are in it and which services they value, then feed that into leasing strategy, amenity spend and renewal negotiations.

The commercial logic is simple. Tenants who feel well served renew more often, and renewal is almost always cheaper than re-leasing. AI that improves tenant experience is accretive to asset value, not just a service upgrade.

Predictive maintenance

Predictive maintenance is one of the more mature applications in this industry. Sensor data from lifts, chillers, pumps and electrical systems reveals the patterns that precede failure, which lets maintenance happen before the breakdown. The financial case holds up on its own: unplanned maintenance runs three to five times the cost of planned maintenance, and a failure in an occupied building adds tenant disruption and potential liability on top of the repair bill.

The organisations doing well here invested in sensor infrastructure and data pipelines first. The AI is only as good as what reaches it, and plenty of older buildings in this region need IoT work before predictive maintenance is viable at all. That is an uncomfortable conversation to have with an owner who was hoping AI would be a software purchase.

Operational efficiency in property management

At property management level, AI is taking over a large share of the routine administrative load: lease abstraction and critical date tracking, receivables monitoring, service charge reconciliation, compliance reporting, investor reporting. The quality improves along with the speed. AI-generated reports are more consistent, more complete and more timely than manually prepared ones, and they leave a record that supports internal governance and external reporting alike.

The organisations that will get the most out of operational AI are the ones treating data infrastructure as a strategic asset rather than an IT line item. Invest in the pipelines, the sensor networks and the system integrations before the applications. AI applied to poor data produces poor results, and in asset management poor results have direct financial consequences.

Sustainability is where AI’s potential in real assets lines up most clearly with both regulatory direction and investor demand, and where the gap between what is claimed and what is actually running is widest.

Energy optimisation

AI-driven energy management is the most commercially proven sustainability application in the sector. Using machine learning to adjust building systems in real time against occupancy, weather and utility pricing is well established and delivers consistent results across commercial, industrial and residential assets.

What is newer is tying that to grid-level data and renewable procurement. A building that can shift consumption in response to grid signals, drawing more power when renewable generation is high and prices are low and easing off at peak, is doing more than reducing its own carbon footprint. It becomes an active participant in the energy transition, and in some markets it earns revenue from demand response programmes.

The practical starting point for most owners is an energy audit run over existing building management system data. That usually surfaces a set of low-cost optimisation opportunities requiring no capital expenditure at all, which is the easiest business case anyone in this industry will ever have to make. The AI then keeps monitoring and finds more as conditions change.

ESG reporting

ESG reporting is an acute pain point for most real assets organisations. The regulatory picture is complicated and still moving, TCFD, SFDR, GRI, GRESB and a growing set of jurisdiction-specific requirements, and the data those frameworks want is scattered across building management systems, utility accounts, tenant surveys and third-party assessments. Assembling it by hand is slow, error-prone and expensive.

AI helps in two ways. It automates collection and normalisation across those sources, which removes most of the manual assembly and the errors that come with it. And it maintains a continuous, auditable record of performance data, so a regulatory request or an investor query can be answered quickly and with confidence.

The organisations furthest ahead treated this as a data infrastructure problem rather than a reporting problem. When the underlying data is clean, consistent and current, the reporting is easy. When it is fragmented and hand-assembled, no amount of sophistication at the reporting layer will save it.

The valuation and leasing premium question

The evidence on a premium for AI-enabled buildings is still thin, but I think the direction is clear.

On leasing, corporate tenants with their own ESG commitments are increasingly unwilling to take space in a building that cannot produce verified sustainability data. Real-time energy performance, carbon intensity and waste metrics are becoming a threshold requirement for the most creditworthy tenants rather than a point of difference. Buildings that cannot clear the threshold will find a growing pool of tenants closed to them.

On valuation, the premium for certified, AI-optimised buildings is starting to appear in transaction data across the major Asia Pacific markets, though it is still easier to see in Sydney and Tokyo than in the emerging markets. The mechanism is not complicated. Lower energy costs improve NOI, better tenant quality reduces vacancy risk, and stronger ESG credentials reduce the regulatory and stranding risk that investors are increasingly pricing into cap rates.

I expect the premium to widen over the next five years. Regulation is tightening, which raises the cost of non-compliance. Corporate tenants want verified performance, not certificates. And investors are actively hunting for assets with demonstrably lower climate risk. Owners investing in AI-enabled sustainability infrastructure now are protecting long-term asset value as much as reducing operating cost.

The next five years will separate the organisations that have embedded AI in their investment and operational processes from the ones that experimented at the margins. The gap will be visible in returns, in cost structures, in who they manage to hire and in the quality of the assets they can attract and hold.

Where the advantage will come from

The most durable advantage will come from proprietary data and the systems built on top of it. Every transaction closed, every lease signed, every operating statement analysed and every tenant interaction recorded is a data point. Organisations that have built the infrastructure to capture, normalise and learn from that data will have an analytical edge competitors cannot easily copy, because the data itself is theirs.

That is a different competitive dynamic from the one real assets has run on for decades, where advantage came from relationships, market access and balance sheet. Those still matter, and anyone who tells you otherwise has not tried to win a deal in this region. But they are increasingly being supplemented, and in places replaced, by data advantages that compound.

Speed is the second source. In a market where AI-assisted diligence turns a four-week process into four days, the fastest organisations reach opportunities the slower ones never see. That matters most in competitive auctions, where getting a credible, fully underwritten bid in early is a genuine differentiator.

Then there is operational alpha, extracting more value from the assets you already own through better operational intelligence. As acquisition yields compress and the easy financial engineering gains disappear, organisations that can demonstrably improve asset performance through AI-driven operations will have a more defensible value creation story to tell their own investors.

What to invest in now

For fund managers and investors, the priorities are data infrastructure and domain-specific platforms. Data infrastructure means the pipelines, standards and governance frameworks that let AI work across the portfolio. Platform choice means picking tools built for real assets rather than generic enterprise AI adapted after the fact.

That distinction matters more than it looks. The economics of AI in this industry turn on what practitioners call the harness, the layer wrapping the model that controls how it accesses context, uses data, breaks down tasks and applies domain guardrails. A harness built for real assets workflows, one that understands lease structures, operating statement formats, valuation methodology and regulatory frameworks, gets through the same work with far less computational waste than a generic harness pointed at the same tasks.

So the total cost of AI-assisted work in real assets is not set mainly by the price of the underlying model. It is set by how well the harness fits the workflows of the industry, and that only becomes more important as AI is applied across a whole portfolio rather than a pilot.

For asset and property managers, the priorities are sensor infrastructure, system integration and workflow redesign. Sensors enable predictive maintenance and energy optimisation. Integration makes sure data from building management systems, tenant platforms and financial systems reaches the AI without manual handling. Workflow redesign is probably the most important of the three, and the most often skipped, because AI delivers most when it sits at the front of a process rather than bolted to the end.

For developers, the priority is designing AI readiness into new assets from the start. Buildings with comprehensive sensor networks, open data architectures and integrated building management systems will be considerably cheaper to operate with AI than buildings that need retrofitting. Given how long these assets live, decisions made in design and construction now will determine operational competitiveness for decades.

The talent dimension

The organisations that win will be the ones that attract and keep people who can work with AI properly, meaning they understand its limits, interrogate its output and design the workflows that make it useful. That is a different skill set from traditional real assets expertise and it needs deliberate investment in training and hiring.

The most effective real assets professionals of the next decade will combine deep domain knowledge with the ability to use AI as an analytical partner. The domain knowledge tells them what to ask and how to judge the answer. The AI gets them there faster and more completely than was previously possible. I have yet to meet anyone in this market who is strong on both, which is probably the most interesting hiring problem the industry has right now.

Doug Kang
VP, Asia Pacific Japan
Fifth Dimension

Jiyuan Wang

Assistant to the Chairman
上海技術取引所
International Trade Center

AI is moving from a productivity tool to an operating layer for real assets. This shift is already visible in China. AI is moving rapidly from answering questions and generating content toward reasoning, decision-making and task execution, with AI agents increasingly becoming part of everyday work. China’s average daily Token usage rose from around 100 billion in early 2024 to more than 140 trillion by March 2026 — an increase of more than 1,000 times in just over two years. This reflects how rapidly AI is moving from experimentation into real business applications.

At an organisational level, the biggest change is not simply that employees can complete individual tasks faster. AI is beginning to change how work itself is organised. Routine information gathering, analysis and reporting can increasingly be delegated to AI, allowing teams to spend more time on judgement, decision-making, relationship management and execution. The next step is therefore not simply adding AI tools to existing workflows, but redesigning workflows around data and intelligence.

We are seeing this transition in our own operations. For example, our AI-enabled industrial data platform integrates enterprise, industrial and operational data to support functions such as tenant analysis, targeted business attraction and enterprise retention. Instead of relying entirely on fragmented manual research, teams can use AI to identify patterns and opportunities across a much broader information base, while people remain responsible for judgement and decisions.

The real productivity gain from AI will not come from doing the same work faster, but from redesigning how work gets done.

AI is changing investment from asset-centric analysis to ecosystem-centric capital allocation. AI is increasingly helping investors move beyond traditional financial and market indicators to analyse a much broader set of information — including industry trends, tenant and business performance, supply-chain relationships, policy environments and emerging sources of demand. This can improve how investors identify opportunities, assess risks and determine where capital is most likely to create long-term value. The shift is particularly important in real assets, where the value of an asset is closely linked to the economic activity and ecosystem surrounding it.

In China, this is becoming increasingly relevant as technology, industrial policy and capital markets become more closely connected. We are seeing AI and data-driven approaches already widely used in financial investment, particularly in quantitative investment and research, and similar capabilities are now extending into private equity and real assets. The key difference is that real assets require investors to connect financial data with physical, spatial and industrial information.

In our own investment activities, AI is helping us broaden the lens through which we assess potential assets. Rather than looking only at the building, rental income and historical operating performance, we can increasingly analyse the businesses within and around an asset, the surrounding commercial and industrial ecosystem, and the potential future demand for the location. This provides a more comprehensive basis for evaluating both opportunity and risk.

The question is gradually shifting from “What is this asset worth today?” to “What economic activity is this asset positioned to capture tomorrow?”

AI is transforming due diligence by expanding both the speed and the breadth of what investors can analyse before making an investment decision. AI is increasingly being applied across real estate investment workflows, particularly in document analysis, data extraction, market research, underwriting, scenario analysis and portfolio monitoring. The greatest near-term gains are not necessarily in replacing professional judgement, but in allowing investment teams to process far more information, identify patterns and test assumptions much faster. Real estate firms are already using AI and big data to support asset screening, portfolio analysis, market benchmarking and investment decisions.

In China, we are seeing a similar transition. AI is already widely used across financial investment, including quantitative investment and equity research, and its application is now extending into real assets. Importantly, AI does not replace the professional foundations of investment analysis. Traditional due diligence — involving financial, legal, tax, technical and market analysis, often supported by external advisers — is already designed to provide a comprehensive view of an asset.

What AI changes is the scale, speed and cost of that analysis. In our own Pre-REITs investment work, we can now use AI to process and connect a much broader range of information before and alongside traditional due diligence — from the businesses operating within an asset to the commercial and industrial ecosystem surrounding it, tenant development and market trends. This allows us to screen opportunities earlier, narrow the investment pipeline more efficiently, and enter detailed due diligence with a much better-informed starting point.

AI therefore adds another layer of intelligence to the existing investment process rather than replacing it. The greatest improvement is not that AI makes due diligence or investment analysis unnecessary; it makes our work more scalable, faster and more cost-efficient.

AI is changing not only how real assets are operated, but also what makes a real asset productive. China offers an interesting perspective on this transformation. Traditionally, land, labour and capital have been fundamental inputs to economic development. In 2020, China took an important step in its factor-market reform by explicitly recognizing technology and data as factors of production, alongside land, labour and capital. This reflects a broader shift in how value is created in the economy: technology and data are no longer simply supporting resources, but increasingly integral to production and business decisions.

As AI becomes a general-purpose technology, every industry needs to rethink how it creates and delivers value — and real assets are no exception. For real asset owners, the question is no longer simply how to operate buildings more efficiently, but how to combine physical assets with data, technology, capital and the businesses operating within them.

We are exploring this approach through our own parks and incubators. We have invested in an AI-enabled industrial data platform that integrates enterprise, industrial and operational data. Rather than simply monitoring the physical asset, the platform helps us understand the businesses within it and the ecosystem around it — including tenant development, potential relocation risks, policy opportunities, industrial-chain relationships and potential targets for targeted tenant attraction.

For example, AI can identify changes in a company’s operating trajectory that may signal future relocation risk, while also helping identify potential policy support, research partnerships or industrial-chain connections that could help retain and grow the business. On the other side, the same intelligence can identify potential tenants that fit a park’s industrial strategy and strengthen its existing ecosystem. This creates a continuous feedback loop between asset operations, enterprise services and industrial development.

The next evolution of real asset management is not simply about managing buildings more efficiently; it is about combining physical assets with new factors of production to make the economic ecosystem around them more productive.

China is moving from ESG awareness toward more systematic measurement and implementation, and AI can significantly accelerate this transition.

As sustainability requirements become increasingly integrated into corporate and investment decision-making, the challenge is no longer simply reporting ESG performance, but continuously measuring and improving it. AI is connecting data from buildings, energy systems, equipment and occupancy, enabling owners to identify inefficiencies, optimizing energy consumption and monitor environmental performance in real time.

For real assets, this could make sustainability much more operational rather than simply a reporting exercise. In the long term, I believe AI-enabled assets may command a premium — but not simply because they use AI. The premium will come from measurable improvements in energy efficiency, operating costs, tenant experience and long-term resilience. AI can turn ESG from something we report into something we continuously optimize.

Over the next five years, I believe the greatest competitive advantage will come from embedding AI into the operating and economic fabric of real assets — not simply adopting AI as another technology tool.

For owners, fund managers and developers, three capabilities will become increasingly important: high-quality data, AI-enabled decision-making, and the ability to connect physical assets with emerging technology ecosystems. Organisations will need to build reliable data foundations, develop AI capabilities within their investment and operating teams, and redesign workflows so that AI becomes part of everyday decision-making rather than a separate technology initiative.

At the same time, AI will not only change how existing real assets are operated; it will create new forms of demand for physical assets. We are already exploring this in our own portfolio. Beyond traditional incubation and leasing, we are looking at how our properties can become physical infrastructure for emerging AI industries — for example, through embodied AI data-collection facilities, data centres and computing infrastructure. In this model, the asset is not simply providing space to an AI company; the physical asset itself becomes part of the AI industry’s infrastructure and value chain.

This could also create new opportunities for REITs. AI-related demand can diversify tenant demand, support occupancy and recurring operating income, and potentially enhance distributable income over time. More importantly, it can strengthen investors’ confidence in the long-term growth potential and relevance of the underlying assets.

The winners over the next five years will not simply be the owners who use AI most effectively. They will be the ones who understand how AI is creating new forms of demand for physical assets — and position their assets ahead of that demand.

Jiyuan Wang
Assistant to Chairman
上海技術取引所
International Trade Center

マイケル・ピロ

共同最高経営責任者
インドシナ・キャピタル

At an organisational level, how is AI changing the way your teams work and your business operates?

At ICC, we approach AI through the lens of “Augmented Intelligence” — positioning AI as a productivity co-pilot that enhances human capabilities rather than replacing our team’s specialized expertise. Operating across multiple sectors including Investment Advisory, Industrial Real Estate Development, Offices, and Hospitality, we have seen the most significant productivity gains in three key areas:

Investment Advisory & Market Research: We leverage AI to scan, aggregate, and analyze macro-economic reports, newly enacted policies and regulations, and macro market trends. However, adhering strictly to internal data risk management principles, ICC applies an “AI Proposes – Human Verifies” All AI-generated data must undergo a rigorous verification process against actual market transaction records and field surveys before being finalized and packaged for clients.
Marketing & Communications: AI supports key tasks such as keyword mining, tracking notable economic events, generating content themes, and analyzing market insights and audience behavior. It also streamlines operational tasks such as workflow planning and presentation design via automated Excel/PowerPoint templates. This significantly shortens the time required to research and execute communication campaigns for both our corporate brand and underlying real estate projects.
Human Resources (HR): AI automates resume screening (CV screening), job description creation, and proactive candidate sourcing across our various project teams.

How is AI reshaping investment decision-making and cross-border capital flows?

In the real estate sector, alongside the intrinsic quality of a project, the speed and transparency of project information delivered to the market are crucial factors determining success.

Opportunity Screening & Risk Assessment: By continuously refining our prompt engineering capabilities, ICC’s advisory team can rapidly analyze complex multi-variable datasets — ranging from land location, infrastructure connectivity, and regional planning to supply chain shifts. This has enabled us to compress the pre-feasibility study cycle from 1 month down to just 1 week.
Impact on Cross-Border Strategies: When investing in Vietnam, beyond traditional direct channels or basic web searches, AI enables experts at foreign funds to swiftly locate, process, and analyze targeted real estate market data. This reduces information asymmetry, instills confidence, and accelerates decision-making for international investors.

Where do you see AI creating the greatest competitive advantage over the next five years, and what capabilities should firms invest in today?

Looking ahead over the next five years, I believe the competitive edge will not belong to firms that purchase the most expensive AI tools, but to those that possess proprietary, real-time data and the ability to seamlessly integrate AI into their actual operational workflows. Key competitive advantages will include:

Supply-Demand Forecasting: The ability to predict market shifts early (such as factory relocation trends in industrial real estate or occupancy fluctuations in office and hotel sectors) to adjust pricing strategies and operations in real time.
Enhanced Investment Underwriting:Combining AI’s big data processing power with the local know-how of experienced advisors to deliver optimal M&A structures and negotiation outcomes.

To build and sustain these advantages, the core capabilities to invest in today are:

Proprietary Data Infrastructure: AI is only as effective as the data fed into it; hence, building a clean, standardized, and proprietary database is essential.

AI-First Mindset with Critical Thinking: Encouraging personnel to master AI as a powerful assistant while maintaining a high level of critical thinking to thoroughly verify and validate all AI-generated outputs.

マイケル・ピロ
共同最高経営責任者
インドシナ・キャピタル

Michaela Hrnčířová

Client Service Specialist
ZDRインベストメンツSG VCC

Ruchi Jain

Tax Partner
EYインド

Dr. Shikha Bagai, CAIA , CA

Market Lead / MD – India
Vistra

From AI Adoption to AI Advantage: What It Means for Real Assets

The most significant shift we are seeing is not simply automation, but the augmentation of professional capability. Across real assets and investment services, highly skilled professionals have traditionally spent considerable time gathering information, reviewing documentation, reconciling data, preparing reports, and managing workflow processes. AI is helping reduce much of this administrative burden, allowing teams to redirect their time toward analysis, judgment, stakeholder engagement, and strategic decision-making.

One of the most important determinants of success is the breadth of adoption across the organisation. The wider AI is embedded into day-to-day activities, the more meaningful the impact becomes. Making AI tools accessible to employees is important, but equally critical is fostering a culture of experimentation, encouraging internal champions, and enabling self-driven adoption.

As AI usage expands, governance becomes increasingly important. Organisations must ensure that AI is adopted responsibly, supported by clear policies, safeguards, and accountability frameworks. We are placing significant emphasis on responsible AI practices and encouraging employees to understand both the opportunities and risks before deploying AI extensively.

At the workflow level, we are redesigning processes around a human-in-the-loop model. AI supports information gathering, summarisation, analysis, and recommendations, while accountability and decision-making remain firmly with experienced professionals. The objective is not to replace expertise, but to amplify it.

At current juncture, we see both ends of the paradox. We’re seeing some investors identify emerging trends earlier, assess risk more comprehensively, and evaluate investment scenarios with greater efficiency. AI is particularly effective at uncovering patterns and relationships that may not be immediately apparent through traditional analytical approaches, helping investors identify opportunities across sectors, markets, and geographies. At the same time, we continue to see traditional patterns also at play in certain pockets. Some exits remain difficult and we continue to see challenges in some parts of the investment cycles in pockets. For instance, litigation remains slow and not too impacted by AI

From a cross-border perspective, AI enables investors to compare markets more effectively by simultaneously analysing regulatory developments, demographic shifts, infrastructure investment, and macroeconomic indicators. This supports more informed capital allocation decisions and expands the opportunity set available to institutional investors.

That said, real assets remain fundamentally local. Technology can significantly enhance analysis, but local market knowledge, regulatory expertise, tenant dynamics, and commercial relationships remain essential drivers of successful investment outcomes.

Beyond acquisitions, how is AI changing the way real assets are managed and operated?

We’re again seeing mixed impact. The modern buildings and infrastructure assets can gain significantly from embedding AI. At the same time, there is a significant inventory of real assets in countries like India which is not yet in redevelopment but is ready for it. Capital is both available and scarce at the same time depending on what is being funded

Today, modern buildings and infrastructure assets generate vast quantities of operational data. AI enables owners and operators to analyse that information in real time, improving performance, lowering costs, and enhancing occupier experiences.

Predictive maintenance is one of the clearest examples. By continuously monitoring equipment performance and identifying patterns that indicate potential failures, AI can enable proactive interventions before disruptions occur. This reduces downtime, improves reliability, and lowers maintenance costs.

Sustainability is a particularly compelling application for AI because environmental performance is fundamentally a data challenge.

AI can aggregate, monitor, and analyse information relating to energy consumption, water usage, emissions, and overall building performance. This helps owners identify inefficiencies, optimise operations, and improve environmental outcomes while supporting more accurate and transparent ESG reporting.

At the same time, sustainability is not necessarily a key theme for every market. Its adoption varies at different level of asset owners and operators

Over time, I believe AI-enabled buildings are likely to benefit from a meaningful competitive advantage. Occupiers are placing increasing emphasis on operational efficiency, sustainability, wellness, and digital experiences. Buildings that can demonstrate lower operating costs, stronger environmental performance, and a superior occupier experience will naturally become more attractive to both tenants and investors.

Whether this ultimately translates into a measurable valuation premium will vary across markets.

The greatest competitive advantage will not come from simply having access to AI technology. It will come from an organisation’s ability to embed AI into decision-making, operations, and institutional knowledge in a structured, governed, and scalable way.

Dr. Shikha Bagai, CAIA , CA
Market Lead / MD – India
Vistra

Wong Hwee Lim

Chief Digital & Technology Officer
キャピタランド・インベストメント

CapitaLand Investment (CLI) views AI as an execution capability that strengthens how we analyse opportunities, operate assets, serve customers, improve enterprise workflows and prepare our workforce for AI-enabled work. CLI is embedding and scaling AI into core workflows across investment management, operating platforms and enterprise functions to support its long term growth ambitions.

CLI is now scaling AI across investment workflows, operating businesses, enterprise functions and workforce capability to support its long-term growth ambitions.

In 2025, CLI rolled out 110 AI initiatives, delivering more than S$12 million in revenue uplift and more than S$5 million in cost optimisation.

In Commercial Management, around 80% of asset reporting has been automated, generating estimated annual savings of approximately 1,300 man-hours.

AI agents are helping teams analyse financial performance, leasing activity, tenant trends, footfall, customer behaviour and operational issues more quickly and consistently.

While AI is reshaping roles and work itself, CLI’s position is that AI supports people rather than replaces them. At CLI, human judgement, accountability and decision-making remain central, while AI takes on more routine, repetitive and research-intensive tasks.

CLI’s approach is to redesign and enhance workflows so employees can focus on higher-value activities such as business decision making, relationship management, customer engagement, asset enhancement and business growth.

In the first half of 2026, more than 2,800 employees were trained on AI tools. CLI employees are also encouraged to develop governed AI agents that address recurring operational pain points.

AI enables investors to process larger volumes of information, identify opportunities more quickly and evaluate risks with greater consistency. AI is also expected to strengthen sourcing reach, underwriting discipline and risk assessment by validating assumptions against increasingly rich datasets and market intelligence. However, investment judgement, validation and final decisions remain with investment professionals.

ATLAS, CLI’s proprietary investment intelligence AI platform, supports market intelligence, property analysis, upside identification and risk assessment across the investment lifecycle.

ATLAS helps investment teams retrieve and structure market intelligence, review investment materials, analyse property and location characteristics, compare assumptions against market data and organise risk reviews. This provides teams with a stronger analytical starting point, allowing opportunities to be evaluated more rapidly while improving consistency and traceability throughout the investment process.

ATLAS is built on data from more than 10 curated data sources across over 20 countries, including Southeast Asia, China, India, Australia, Europe and USA, and draws information from more than 70,000 institutional-graded reports across the major real estate sectors including industrial, office, digital infrastructure, healthcare, residential, logistics, hospitality and retail. This breadth of coverage enables teams to draw insights across markets and sectors while supporting a more structured review of global investment opportunities.

ATLAS enables teams to organise and review large volumes of information more efficiently, and by increasing the speed, structure and consistency of investment preparation.

The greatest improvements are currently seen in research-intensive and time-sensitive activities. ATLAS helps teams evaluate opportunities faster and produce clearer, more traceable investment outputs by retrieving and structuring market intelligence, reviewing investment materials, comparing underwriting assumptions against reference market data, and identifying risks and mitigants earlier in the process. It also supports market research, investment memorandum review, assumption comparison and risk assessment.

Beyond deal analysis, CLI is leveraging AI to organise and connect over 25 years of proprietary investment data across regions and portfolios, enabling faster access to information, more effective use of institutional knowledge, and better decision-making through a shared institutional knowledge base.

The platform also strengthens underwriting by supporting data-backed validation of assumptions and helping create a more disciplined investment process. AI is actively contributing to improvements in analytical rigour, consistency and efficiency.

At CLI, AI is increasingly being embedded into the way assets are operated and managed, helping improve asset performance, customer engagement, operational efficiency and decision-making. Some of the most visible examples can be seen across retail, lodging, self-storage and intelligent building operations.

In retail, Casey, our digital assistant, provides AI-enabled customer support through the CapitaStar app and mall websites. Casey has handled more than 370,000 chat sessions, supporting customer enquiries around the clock, reducing reliance on concierge desks and call centres. Beyond improvements in service and customer satisfaction, this has enabled concierge space in some malls to be repurposed into revenue-generating retail space. AI is also supporting leasing, tenant engagement and asset reporting workflows, helping teams derive insights faster and manage assets more efficiently.

In lodging, AI is supporting revenue management through dynamic pricing across 105 properties, delivering annual revenue uplift. Ascott’s AI-powered digital concierge, Cubby, has handled more than one million guest enquiries since launch, helping members plan trips, book stays and access property information. These capabilities strengthen personalisation, enhance guest experience and service quality, and support direct demand generation and additional revenue opportunities.

CLI’s self-storage platform uses AI to support both customer experience and operational efficiency. AI agents manage enquiries, estimate storage needs, arrange viewings, facilitate instant reservations and support collections and customer service. Early results include a 60% reduction in sales cycle times, with around half of customer engagements occurring outside business hours. These capabilities have enabled some high-occupancy facilities to transition to unmanned operations while maintaining service levels and improving operating margins.

AI is becoming an important enabler of sustainable real estate by helping owners and managers move from reactive management to data-driven optimisation. At CLI, AI is being applied to analyse operational data, optimise building performance and support predictive maintenance.

Beyond customer-facing applications, AI is also being applied to how assets themselves are run. The Intelligent Building Platform supports predictive maintenance and energy optimisation across the portfolio by analysing operational data to identify equipment issues early and improve building performance. Examples include detecting inefficient chiller plant operations and sub-optimal return air temperatures, helping reduce energy consumption, lower operating costs and support sustainability objectives.

AI can also strengthen ESG reporting by improving how data is collected, validated and analysed across large portfolios. At CLI, AI pilots are being tested to potentially enhance the efficiency and accuracy of environmental data collection. As reporting requirements become more complex and investors demand greater transparency, AI can help asset managers process large volumes of environmental and operational data more consistently, enabling better measurement of energy consumption, carbon emissions and sustainability performance. This supports more informed decision-making and enhances confidence in sustainability disclosures.

Over the next five years, the greatest competitive advantage will accrue to organisations that can combine proprietary investment intelligence with AI-enabled operating platforms. For real asset managers, AI’s value will increasingly lie in identifying opportunities faster, operating assets more efficiently, serving customers more effectively and scaling platforms without a corresponding increase in cost and complexity.

At CLI, we believe competitive advantage will increasingly come from four areas:

Proprietary data and intelligence platforms:ATLAS illustrates how AI can institutionalise investment knowledge, combine proprietary data with market intelligence and support a more disciplined and scalable investment process. Organisations with access to unique investment, operating and customer data will be better positioned to continuously improve decision-making and create advantages that become increasingly difficult to replicate.
AI-enabled operating platforms.Organisations that embed AI across leasing, tenant engagement, asset operations, customer service and portfolio management will be better positioned to improve operating leverage while enhancing customer outcomes. The ability to deploy and scale AI across multiple operating platforms will become a key differentiator.
Workforce capability.Workforce readiness is becoming a strategic differentiator. Successful AI adoption requires employees who are AI-fluent and empowered to redesign workflows, automate routine activities and build practical solutions close to the business.
Governance and trust.As AI becomes more deeply embedded in investment and operating decisions, organisations that can deploy AI responsibly, maintain transparency and preserve human accountability will be better positioned to scale adoption and maintain stakeholder trust.

Ultimately, investors, owners, fund managers and developers should invest in four foundational capabilities: proprietary data foundations, AI-enabled workflows, workforce capability development and robust AI governance frameworks. Together, these capabilities will determine which organisations can scale AI effectively and create sustainable long-term value.

Wong Hwee Lim
Chief Digital & Technology Officer
キャピタランド・インベストメント

SCGCリアルティ・キャピタル

Real estate has traditionally been a highly local business. Investors rely heavily on local market data, brokers, consultants and other intermediaries to identify opportunities, understand market conditions and conduct due diligence. This dependence becomes even more pronounced when investing overseas, where differences in language, regulation, market practices and data accessibility create additional barriers.

It enables investment teams to collect and process information from a much wider range of sources, cross-check market data more efficiently, and conduct preliminary research and due diligence with less reliance on intermediaries. The efficiency gains can also translate directly into lower transaction costs. With AI improving access to information and analytical capabilities, some of these functions can increasingly be performed in-house, allowing investors to screen more markets and opportunities at a lower cost.

However, we do not believe AI will replace human investment judgement. Real assets remain highly heterogeneous and local, and investment decisions often involve factors that cannot be fully captured by data or models. AI can make the decision-making process faster and more rational, but accountability and final investment judgement must remain with people.

The real value of AI is therefore not to replace investors, but to give them better information, stronger analytical tools and more time to focus on the decisions that truly require judgement.

AI is improving the efficiency of real asset management, particularly in areas such as daily data monitoring, lease administration, maintenance inspections and operational reporting. By combining AI with property management systems, many routine tasks can be automated or made more efficient.

However, real estate management is still fundamentally a people business. Tenant relationships, leasing and day-to-day problem solving often depend on direct human interaction. This is especially important in a challenging market, where retaining existing tenants and attracting new ones often requires extra service and personal attention.

For example, after a typhoon caused flooding at one of our properties, a tenant was extremely frustrated. Our leasing manager went directly to the site and personally helped the tenant remove the water. The tenant was deeply impressed and later told us that this experience strengthened their willingness to continue leasing with us. This is difficult for AI to replicate. Ultimately, tenants often judge the quality of an asset not only by the building itself, but also by the people managing it.

Real assets are, after all, real and physical. AI can significantly empower asset management, but the human element remains difficult to replace.

Looking ahead, where do you see AI creating the greatest competitive advantage for investors over the next five years? What capabilities should owners, fund managers, and developers be investing in today to remain competitive?

Over the next five years, we believe AI will be more enabling than disruptive for real asset investors. The greatest competitive advantage will not come from simply using the latest AI tools, but from how quickly investors can understand the changes AI and broader technological innovation are bringing to industries and real assets. For investors, this means strengthening research capabilities and staying close to changes in the real economy — for example, understanding how technology is reshaping demand for energy, computing power and physical infrastructure. AI can help us process information faster, improve forecasting and make more informed capital allocation decisions. In that sense, AI enhances our investment intelligence,
but it does not replace experience and market judgement.

There is also an important distinction between public and private markets. In public markets, where information is relatively standardized and accessible, AI can play a much greater role in investment analysis. Private real asset investment, however, remains highly relationship- and resource-driven. Identifying an attractive asset is one thing; actually, gaining access to the opportunity and executing the investment is another.

Ultimately, successful real asset investing still requires patience, experience, judgement, relationships and strong asset management capabilities. AI can make investors smarter and more efficient, but staying ahead of a changing world will remain the real competitive advantage.

AIがリアルアセット業界にどのような変革をもたらしているか

アニッシュ・サングヴィ

クリスティン・リー

ドミニク・ブラウン博士

Doug Kang

Jiyuan Wang

マイケル・ピロ

Michaela Hrnčířová

Ruchi Jain

Dr. Shikha Bagai​​​​, CAIA , CA

Wong Hwee Lim

SCGCリアルティ・キャピタル

Dr. Shikha Bagai, CAIA , CA