Part Two: Where can AI create real value in Commercial Real Estate?

As noted in the first paper in this series, Artificial Intelligence (AI) in Commercial Real Estate (CRE) is not something for the future - it’s here right now, and could add considerable value to assets. But how? This paper outlines some key value-adding use cases. As of today, they fall into three main areas:

•  Reducing operating and utility costs

•  Retaining tenants (reducing voids) and maximising covenant strength

•  Improving investment decision-making

If any of these are important to you, read on!

Note: It’s important to stress again that text generators (Large Language Models like ChatGPT, etc.) are only one type of AI, and potentially the least likely to add value in a CRE setting. Machine learning, computer vision, etc. are often more applicable. An adjunct to this paper will be published later in the series, showcasing examples of buildings that have adopted these types of AI to achieve their goals.

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The competitive advantage of data-rich buildings

As in every instance of AI, data is at the heart of the issue. Good quality data enables good outcomes. Poor quality and/or incomplete data will not. This is why one of the most important shifts happening in the sector today is that data itself is becoming an important asset. Buildings that generate high-quality operational data can unlock better decision-making, improve efficiency and reduce costs, generate more accurate forecasting, enhance tenant experience, and improve sustainability performance.

AI in CRE can therefore offer a considerable competitive advantage. As buildings become more digitised and data-rich, the organisations that can interpret and act on that intelligence most effectively have an opportunity to outperform competitors across leasing, operations, sustainability, and portfolio management.

By definition then, the depth and quality of data that can be harvested and properly utilised from a building has become a key contributor to the value of that asset. There’s a growing divide between digitally mature assets and outdated, analogue properties. In the future, and the not too distant future at that, technology capabilities may rival physical location in determining competitiveness.

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AI and operational efficiency

One of the largest opportunities for AI is operational optimisation. Commercial buildings are extremely complex environments involving a wide range of M&E equipment and software platforms, including:

•  HVAC systems

•  Lighting systems

•  Access control

•  Security infrastructure

•  Water systems

•  Elevators

•  Energy management platforms, and

•  Maintenance operations

Traditionally, these systems operated independently and reactively, making the operation of a building complex and disconnected. Good middleware can coordinate many of these systems dynamically, but AI takes that approach several steps further, by exploiting machine learning models to predict needs and behaviour based on incoming real-time data and previous usage and operating patterns.

There are four key ways in which AI can impact operational efficiency (and therefore your operating cost base):

1.    Intelligent energy optimisation

2.    Predictive maintenance

3.    Tenant experience

4.    Sustainability

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1. Intelligent energy optimisation

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Energy is one of the largest controllable operating expenses in commercial buildings. With good, real-time data from equipment and sensors throughout the building, AI systems can optimise energy consumption in real time by combining and analysing operating conditions including:

•  Indoor environmental quality/comfort

•  Occupancy levels

•  Weather conditions

•  Historical usage patterns

•  And, where relevant, utility pricing

The resulting analysis means that instead of operating on static schedules, intelligent systems adapt continuously, so that:

•  HVAC systems reduce heating/cooling in underutilised areas

•  Lighting automatically adjusts when daylight increases, or occupancy reduces

•  Ventilation adjusts based on occupancy and air quality

The headline outcome is operational savings - up to 30% or more, but the sustainability improvements can move the needle as well. As carbon regulations continue to tighten, intelligent energy management is increasingly valuable as part of attracting and retaining carbon-conscious tenants.

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2. Predictive Maintenance

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Mechanical failures are expensive. Unexpected downtime can disrupt tenants, increase emergency repair costs, damage equipment, and negatively impact building reputation.

AI-driven predictive maintenance systems continuously receive data from M&E equipment such as chillers, boilers, pumps, air handling units, lifts, and electrical systems. The system analyses incoming electrical draw (and sometimes vibration) data for anomalies, comparing against historical patterns to determine when a piece of equipment needs maintenance, or need replacement.

This enables maintenance to be carried out when needed, and before issues occur, getting away from fixed schedules which may carry out expensive maintenance too often, or not often enough. The result is fewer maintenance visits, reduced costs, fewer emergency repairs, longer equipment cycles, considerably lower downtime, and improved tenant satisfaction. For large portfolios in particular, these savings can be substantial.

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3. Tenant experience

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Commercial real estate is ultimately a service business. Buildings that offer better tenant experiences usually perform better financially. AI, through tenant platforms or apps, is helping owners personalise and improve workplace environments, particularly in an era of hybrid working.

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Smart workplace experiences

Modern office tenants increasingly expect technology-enabled environments. AI-driven workplace systems can provide a range of benefits that can improve convenience and enhance occupant experience - a key element in prospective tenants’ thinking about premises. Facilities can include:

•  Frictionless mobile access

•  Smart room booking

•  Occupancy-based workspace recommendations

•  Intelligent visitor management

•  Digital concierge services

•  Personalised climate preferences

It’s worth remembering that some of these features also generate operational data and insights that become inputs to optimising a building and its energy usage.

Occupancy intelligence

As noted above, the post-Covid hybrid work era has changed office utilisation, and established a need for greater flexibility in office layouts and usage. Many organisations struggle to understand how much space they truly need, when employees actually use (or plan to use) office space, and which amenities matter most.

AI-powered occupancy analytics can help answer these questions. By analysing real-time occupancy patterns, owners and tenants are able to:

•  Reduce underutilised space

•  Improve workplace layouts

•  Enhance employee experience

•  Optimise leasingstrategies

This data and analysis is becoming increasingly important as companies reevaluate long-term office footprints. Asset owners that wish to ensure their buildings remain competitive should be seeking to understand and model options that will attract and retain tenants by providing optimal flexibility.

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4. ESG and sustainability advantages

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Environmental sustainability is becoming central to commercial real estate construction, finance, and operations. Investors, regulators, lenders, and tenants increasingly evaluate buildings based on environmental performance. In the case of tenants, office space can represent a large percentage of the carbon footprint of non-industrial companies, so low-carbon options are attractive.

Continuous sustainability optimisation

As noted above, AI systems can reduce energy usage and intensity (carbon), but AI can also help optimise water consumption, waste management, and indoor air quality/occupier comfort. As well as optimisation, AI-driven analytics will also improve measurement accuracy and reporting transparency which helps support:

•  ESG reporting

•  Sustainability certifications

•  Decarbonisation targets, and

•  Regulatory compliance

For institutional investors and tenants alike, transparent and provable reporting is becoming increasingly important.

Beyond operational efficiency, AI is also transforming CRE construction, and how commercial real estate opportunities are being evaluated and financed.

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AI in construction and development

Many developers and construction firms are deploying AI to augment project development and delivery.

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AI-assisted design

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Generative design and digital twin tools are beginning to influence architecture and engineering. These systems can generate and evaluate thousands of potential design configurations based on:

•  Energy efficiency

•  Space utilisation

•  Daylighting

•  Structuralperformance, and

•  Cost optimisation

While no-one is advocating for AI-first design and human creativity remains essential, AI will increasingly support early-stage design processes. Over time, this is likely to impact how many traditional roles in the construction sector - quantity surveyors, structural engineers, among others - need to approach their roles.

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Construction optimisation

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And during the actual process of construction, AI increasingly has a role to play, no least in activities such as:

•  Schedule forecasting

•  Cost estimation

•  Labour planning

•  Supply chain risk

•  Site safety monitoring

•  Quality control

And on-site, computer vision systems can monitor job sites and identify potential safety issues or project delays, and predictive analytics can improve procurement planning and reduce budget overruns.

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AI and investment management

AI is also making inroads into how the sector is financed, and investments are managed. Historically, underwriting and market analysis has involved manual research and subjective judgement. AI is starting to allowfirms to assimilate significantly larger amounts of information, powering models that will likely replace much of that manual work going forward.

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AI-Assisted Underwriting

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Investment firms are increasingly using AI to analyse multiple datainputs, including:

•  Lease data

•  Tenant risk

•  Market trends

•  Economic indicators

•  Demographic shifts

•  Comparable transactions

•  Operational performance

In this case, AI can be used to generate extensive predictive models, which may identify patterns that humans may overlook. This doesn’t eliminate the need for experienced investment professionals of course, but it does enhance analytical capabilities.

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Portfolio Risk Forecasting

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Additionally, AI can also help firms monitor portfolio risk more dynamically. This is particularly important in larger portfolios, and can support decision-making on individual assets. Examples include:

•  Forecasting tenant default risk

•  Monitoring market volatility

•  Identifying declining asset performance

•  Tracking climate-related exposure

•  Evaluating insurance risk trends

Portfolios, like other asset classes, are becoming more data-driven. In such an environment, properly implemented AI can provide strong support for real-time decision-making, and enhanced returns.

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The era of autonomous infrastructure may be upon us sooner than you think

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Despite the challenges involved in digitising buildings and integrating the resulting data, efforts are already in train to explore semi-autonomous building operations. In such an environment, AI will move beyond the optimisation capabilities outlined above and take the next step, automatically taking operational decisions that are currently in the human domain.

If an AI system has all the data and analytics to hand, it could automatically adjust HVAC operations, manage lighting systems, optimise energy loads, schedule required (predicted) maintenance, monitor occupancy and respond directly to operational and system anomalies. As across a range of other sectors, human teams will still provide oversight, but AI will increasingly be capable of handling routine tasks, 24x7.

In other words, AI is likely ushering in a conceptual shift in the CRE sector. It’s a fundamental change in how we consider technology in buildings. AI will not be just another software layer added to a building, it’s core to the operating infrastructure of digital buildings. Just as lifts, HVAC, and internet connectivity became standard building expectations, AI-driven operational intelligence may eventually become a baseline requirement for competitive commercial assets.

Until then, AI represents an opportunity for early adopters to gain substantial operational and financial advantages.

But - and it’s a big but -all of the opportunities outlined in this paper are dependent on underlying building technologies. Today, many owners are unaware of even what technologies are installed in their buildings, never mind whether their assets are optimised, and much of the key data required to power AI systems remains in siloed databases, or on paper.

AI is not useful in an unstructured and non-integrated environment. The key to a successful journey to enhance competitiveness and financial returns with AI is to fix the underlying systems and data first:

•  Discover what tech is already in the building

•  Plan a strategy and timeline to implement new technologies that will integrate (and often provide a financial return in themselves)

•  Remove and replace siloed systems that have inaccessible data

•  Execute smart (portfolio-wide) procurement

•  Remove obstacles to deployment and move forward

•  Monitor progress to ensure ROI is being achieved

When these steps are properly executed, you’re ready to look at what forms of AI are needed, and where and how to implement them to best effect.

Look out for part three of this series, published next week, which looks at the threats, risks and ethical challenges of AI in CRE.

Not sure where your building stands? At Trustek, we help owners and operators get a clear, actionable picture of their existing technology, identifying what's working, what's missing, and where the opportunities lie. Book a consultation with us today, and take your first step towards clarity.