Part One: Why is AI important? Why now? And is any of it real?

There is a mind-blowing level of hype around AI. I’m not going to add to it. My goal in this series is to present the reality - the opportunities and the risks, the blockers you’ll face, and guidance on how to move forward.

This series is written for real estate professionals, covering AI’s current and future impact on building operations, investment, and the market in general. This first piece is an overview - later papers will go into a bit more detail. It’s not for technology people, it’s for business leaders. These are things you should know.

No-one expects real estate professionals to become experts in AI, but if you want to go down the AI road, you should at least know what you might want to do with it, what questions you should ask, and how to avoid costly mistakes.

Artificial Intelligence is no longer a theoretical concept in commercial real estate (CRE). It’s growing pretty fast, and becoming part of the operational infrastructure of new(er) buildings, portfolios, and investment strategies across the global property market. But like a lot of change, it’s happening unevenly. AI is still in its infancy in CRE - the opportunity to steal a march on the competition is available, but not without risk.

As a sector, we’re starting from a lowish point

For all its history, commercial real estate (CRE) has relied heavily on manual processes, fragmented data systems, spreadsheets, and institutional experience. And most buildings still do. Property managers monitor building systems reactively. Asset managers rely on quarterly (printed) reports and historical trends. Leasing decisions often depend on intuition and broker relationships. Even advanced buildings frequently operate with disconnected systems that generate data but fail to use it intelligently.

That environment is changing. Artificial intelligence can now reshape how buildings are operated, how portfolios are analysed, how tenants interact with workplaces, and how investors evaluate assets.

The convergence of AI, smart buildings, cloud computing, IoT sensors, and advanced analytics is creating a new generation of intelligent commercial properties. And the CRE industry is entering a period where buildings will increasingly function like technology platforms rather than static physical assets. But there’s a way to go for most before they can achieve that.

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What AI actually means in commercial real estate

One of the biggest misconceptions in the market is that AI refers only to generative chat tools or robots. Most people were unaware of AI until ChatGPT appeared in 2022. But in reality, AI has been around for decades, and encompasses a broad range of technologies designed to automate decisions, analyse patterns, optimise operations, and improve predictions.

Several forms of AI are already embedded in commercial real estate operations:

•  Machine learning systems that optimise HVAC, heating, lighting, and other energy usage

•  Predictive maintenance platforms that forecast equipment failures

•  Occupancy analytics that analyse workplace usage patterns

•  Security systems using computer vision

•  Generative AI tools that automate reporting and tenant communication

•  Portfolio analyticsplatforms that identify investment risks and opportunities

Many organisations are already using AI without labelling it as such (or perhaps realising it). An office tower that automatically adjusts ventilation based on occupancy patterns is already leveraging AI-driven optimisation. A logistics facility using predictive analytics to reduce downtime is using AI. A landlord deploying smart tenant engagement apps is using AI.

The key shift is that buildings are evolving from passive structures into responsive environments capable of generating, interpreting, and acting on operational data.

Why AI adoption is accelerating (and relevant to you)

Several market forces are driving AI adoption. I don’t have space to go into too much detail here, but perhaps the four most important are:

1. Rising operational costs

Commercial property owners face increasing pressure from:

•  Energy inflation

•  Labour shortages

•  Maintenance costs

•  Insurance expenses

•  Sustainability regulations

•  And in some markets, refinancing costs

AI has the potential to deliver operational efficiency at scale. Predictive systems can reduce equipment failures, optimise staffing, lower utility consumption, enable the remote management of portfolios, and automate repetitive administrative tasks. In some ways, AI adoption is less about innovation and more about cost containment.

2. The hybrid workplace era

The post-Covid rise of hybrid work has softened demand in some markets, particularly for sub-prime assets, and has altered how office space is used. Organisations need more dynamic insights into:

•  Occupancy patterns

•  Space utilisation

•  Tenant preferences

•  Workplace demand forecasting

AI-powered analytics can enable owners - and tenants - to optimise workplaces based on actual behavioural data rather than assumptions.

3. ESG and sustainability pressures

Environmental, social, and governance (ESG) requirements are important to occupiers (for their regulatory reporting), and therefore becoming a key part of commercial real estate investment decisions. Institutional investors are increasingly including sustainability criteria in their evaluation of assets, including:

•  Carbon emissions & intensity

•  Energy efficiency

•  Indoor environmental quality

•  Climate resilience

•  Operational transparency

AI-driven systems can help owners measure and optimise building performance continuously, not only reducing costs, but making ESG reporting more accurate and actionable.

4. The explosion of building data

All buildings generate data. The smarter a building is, the more operational and real-time data it produces, from:

•  HVAC systems

•  Lighting controls

•  Access control systems

•  Occupancy sensors

•  Utility meters

•  Environmental monitoring devices

•  Smart elevators

•  Parking systems

Historically, much (or most) of this data has been underutilised. AI changes that equation by allowing owners and operators to analyse large datasets continuously, and identify and action operational improvements in real time.

AI use cases in CRE

Part two in this series will delve a little deeper into the opportunities that AI offers for the CRE sector. But for now, let’s outline perhaps the four most important.

1. Energy optimisation

Energy is often one of the largest operating expenses in commercial buildings. AI systems can optimise:

•  HVAC schedules

•  Lighting controls

•  Ventilation rates

•  Peak load management

•  Demand response participation

Rather than relying on static schedules, intelligent systems adapt dynamically to occupancy, weather conditions, and energy pricing, generating both cost savings and sustainability benefits.

2. Predictive maintenance

Traditionally, maintenance has been reactive. Equipment fails. A problem is discovered. A repair team responds. AI shifts maintenance toward prediction. Sensors monitor equipment performance continuously, identifying anomalies before failures occur. Machine learning systems analyse vibration, temperature, pressure, and runtime data to forecast maintenance needs. The benefits include:

•  Reduced downtime

•  Lower maintenance costs

•  Longer equipment life

•  Improved tenant satisfaction

•  Better operational resilience

3. Occupancy and space intelligence

AI-powered occupancy analytics are increasingly valuable in the post-pandemic workplace. Buildings can now analyse:

•  Desk utilisation

•  Meeting room usage

•  Traffic flow

•  Peak occupancy times

•  Tenant engagement patterns

This data helps organisations redesign spaces, reduce unused square footage, and improve workplace experience.

4. AI in investment and asset management

AI is also transforming financial decision-making. Investment firms increasingly use AI to:

•  Analyse market trends, down to micro scale

•  Forecast leasing demand

•  Evaluate portfolio risk

•  Monitor economic indicators

•  Automate reporting

•  Identify acquisition opportunities

While AI does not replace human judgment, it can significantly enhance analytical speed and scale.

Trustek maintains a PropTech market map that categorises suppliers (and verifies those that have proven to be good options). This will soon be launching for public access - stay tuned for updates!

Challenges slowing adoption

AI isn’t easy to adopt however - for anybody. Despite the undoubted momentum, AI adoption in CRE faces several barriers. Again, I’ll just outline five for now. In no particularly order:

1. Fragmented legacy systems

Most buildings still operate with outdated infrastructure and disconnected systems.

Integrating old equipment with modern AI platforms can be expensive and technically difficult.

2. Poor data quality

AI systems depend on accurate, structured data. Incomplete sensor deployment, inconsistent building standards, and siloed systems can reduce effectiveness and even render AI useless unless data is addressed properly. It’s also relevant to consider how much of the data that would be required is currently sitting in paper-based systems.

3. Organisational resistance

Commercial real estate has traditionally been relationship-driven and operationally conservative. Some organisations remain hesitant to trust automated decision-making. Some individuals will also be distrustful of AI, being concerned about headlines like “AI will take your job”. It’s important to make clear to everyone why AI is being deployed.

4. Multiple stakeholders

Commercial real estate assets invariably have many stakeholders. Owners, property managers, FMs, the list goes on. It is all too easy in such an environment for innovation and decision-making to be slowed or halted altogether without clear guidelines on responsibilities and incentives.

5. Talent shortages

At the outset, I noted that the CRE sector is a laggard when it comes to digital technology. This isn’t a criticism - for most of its history, there has been no need for it. But there is now. Successful AI deployment requires expertise across:

•  Project specification

•  IT project management

•  Systems infrastructure

•  Cybersecurity

•  Data architecture

•  Data science

•  Building operational technologies

Most organisations in the CRE market are in the early stage of developing these capabilities, even the largest.

The sector is moving quickly. Time to get on board.

Despite the challenges, the direction of the market is increasingly clear. The commercial real estate industry is entering a new phase where technology capabilities increasingly influence asset performance - and value.

For commercial buildings to remain competitive, they need to digitise -to become digital platforms powered by data, automation, and intelligent systems. Owners that fail to recognise this and modernise will face higher operating costs, reduced competitiveness, increased tenant turnover, longer voids, regulatory exposure, and lower asset valuations.

AI may be new and unknown to many in the real estate sector, but it is starting to redefine how buildings operate and how the market works. It’s time to get on board.

Thank you for reading! The next three pieces in this series will be:

Part Two: The biggest opportunities AI offers for building owners and investors

Part Three: The threats, risks, and ethical challenges of AI in CRE

Part Four: Cybersecurity, resilience, and the future of AI-powered buildings

Make sure to follow Trustek to be alerted when they are published.

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Whether you're just beginning your AI journey or evaluating specific use cases, Trustek can help you navigate the opportunities ahead. Book a consultation with us today.