Is your building ready for AI?
AI can transform building operations, space use and workplace experience – but only if your data is accurate, connected and ready to use.
Artificial intelligence is entering commercial real estate fast. For many building owners, facility managers and workplace leaders, the question is no longer whether AI will affect buildings, but whether their buildings are ready for it.
The answer depends less on the AI tool itself and more on the data behind it.
AI can help optimize space, predict maintenance needs, improve indoor conditions, reduce energy waste and support better workplace decisions. But it cannot do any of this reliably if the underlying building data is fragmented, outdated or missing context. In practice, AI-ready buildings start with AI-ready data.
AI in real estate is now moving beyond experimentation
Across industries, AI adoption has accelerated quickly, but scaling it into real business value remains uneven. McKinsey’s 2025 global AI survey reports that nearly nine out of ten respondents say their organizations use AI regularly in at least one business function, yet most organizations are still experimenting or piloting rather than realizing enterprise-wide impact.
Commercial real estate is following the same pattern. JLL’s 2025 Global Real Estate Technology Survey shows that CRE teams are focusing AI efforts on high-impact areas such as portfolio optimization, energy management and data-related workflows. These include standardizing data, detecting anomalies, integrating different data sources and automating reporting – all foundational work for deeper AI use.
What does “AI-ready building data” actually mean?
Gartner defines AI-ready data as data that is aligned with the specific AI use case, qualified for that purpose and governed appropriately. It also emphasizes that AI-ready data depends on metadata, semantics, labeling, quality, trust, diversity and lineage, and that high-quality data in a traditional sense is not automatically AI-ready.
In a building context, this means data must be more than collected: it must be understandable, connected and usable.
A truly AI-ready building data foundation may include
- Occupancy and utilization data from desks, rooms, floors and shared spaces
- Indoor air quality, temperature and environmental data
- Energy consumption and equipment performance data
- Building management system data
- Cleaning, maintenance and service request data
- Room booking and calendar data
- Floor plans, room types and capacity data
- Employee experience or workplace feedback data
The value comes when these data points are no longer isolated, because AI needs context: what the space is, how it is intended to be used, when it is used, by whom, under what conditions and with what operational impact.
Hybrid work has made static assumptions too risky
The need for better building data is especially clear in today’s hybrid workplace.
CBRE’s Nordic Office Occupier Sentiment Survey 2025 shows that Nordic office utilization remains highly dynamic: average space utilization is 46%, while peak utilization reaches 68%. Nearly half of Nordic firms expect office attendance to increase, and 59% want employees in the office three or more days per week, while only 46% are achieving that level.
This creates a planning problem: a building can look underused on average and still be overloaded on peak days. A meeting room can appear available in a booking system but remain physically empty, and a floor can feel quiet on Mondays and overcrowded on Wednesdays.
CBRE also reports that 85% of Nordic occupiers are measuring workplace effectiveness. Among those, 64% focus on employee engagement, 64% on on-site experience, 60% on space planning efficiency and 60% on financial impact.
This is exactly where AI-ready data matters, as it allows organizations to move from broad assumptions to precise, evidence-based decisions: which spaces are used, when demand peaks, where experience suffers, and how changes affect cost, sustainability and employee satisfaction.
What AI-ready building data can unlock
In this environment, workplace data should not be treated as a reporting extra, it is the intelligence layer for your buildings: one that connects physical space, digital systems and real behavior to financial and business decisions. Our framing reflects this clearly, linking workplace intelligence to lease renewals, consolidations and portfolio strategy – replacing gut-feel data with actual workplace intelligence. When every square meter is under more scrutiny, that shift matters. Because in a downturn, the organizations that perform best are rarely the ones that cut fastest. They are the ones that see more clearly, learn faster and optimize with better evidence.
1. Smarter space optimization
Many organizations are still trying to answer basic workplace questions: How much office space do we really need? Which areas are overused or underused? Are we providing the right mix of desks, meeting rooms, collaboration areas and quiet zones?
AI can support this by identifying usage patterns, forecasting demand and testing scenarios.
2. Better energy and sustainability decisions
Buildings are central to global energy and climate goals. The International Energy Agency has stated that building operations account for 30% of global final energy consumption and 26% of global energy-related emissions.
AI can help reduce waste by connecting occupancy, indoor conditions, HVAC performance and energy use. For example, ventilation, heating, cooling and lighting can be better aligned with actual demand rather than fixed schedules.
This also fits the direction of European regulation. The European Commission’s Smart Readiness Indicator aims to raise awareness of smart building technologies such as building automation and electronic monitoring of heating, ventilation, lighting and other systems. It evaluates smart-ready services across domains including heating, cooling, ventilation, lighting, electricity, monitoring and control.
3. Predictive and proactive operations
Many building operations are still reactive: something breaks, a ticket is created, a technician is sent. AI-ready data enables a more proactive model.
When equipment performance, environmental data, occupancy and maintenance history are connected, AI can help identify anomalies, predict potential failures and suggest preventive action. This is not just about reducing downtime: it’s also about improving comfort, lowering operational costs and using facility teams’ time more effectively.
4. Improved workplace experience
The office now has to earn the commute. CBRE identifies lack of vibrancy on low-attendance days, anticipating future space needs and balancing demand across the week as the top attendance-related challenges for Nordic occupiers.
AI-ready data helps workplace teams understand not only whether people come to the office, but what happens when they are there. Are the right spaces available? Are collaboration areas overcrowded? Are quiet areas actually quiet? This turns building data into experience intelligence.
The biggest barrier is fragmented data
The commercial real estate industry still struggles with fragmented data, such as information scattered across spreadsheets, documents and disconnected software systems, worsened by legacy infrastructure, lack of standardized formats, departmental silos and inconsistent data policies.
For buildings, fragmentation often looks like this:
Booking data sits in one system, and occupancy data sits in another. Energy data is handled by a separate platform, and floor plans are stored as static files. Maintenance tickets are not linked to location data, indoor air quality is measured, but not connected to user experience or utilization – and so on.
In that environment, AI cannot produce reliable insights or trustworthy decisions.
How to start making building data AI-ready?
Data readiness depends on what the organization wants to achieve. Space optimization, predictive maintenance, energy efficiency and employee experience all require different combinations of data. That said, most organizations benefit from working through the same three foundational steps.
Step 1: Audit your existing data sources
Identify which systems hold occupancy, environmental, energy, maintenance, booking and portfolio data, then assess what is accurate, what is missing and what cannot currently be connected.
Step 2: Add reliable real-time data where needed
Booking systems and badge data alone rarely show how spaces are actually used, however, sensors can provide more accurate insight into real occupancy, utilization, people flow and indoor conditions.
Step 3: Connect your data in a single usable layer
A workplace intelligence platform can bring different data sources into one interface, making data easier to visualize, compare and act on. This connected layer is what AI tools need to produce reliable outputs rather than fragmented guesses.
AI-ready buildings will be easier to adapt
For building owners and occupiers, AI-ready data makes it easier to adapt to changing attendance patterns, optimize portfolios, reduce operating costs, improve sustainability performance and create better workplace experiences.
The future of building intelligence is not only about AI. It’s also about having the right building data that is accurate, connected, contextual and governed, so that AI has something reliable to work with.
In other words: before your building can become intelligent, its data must become ready.
Interested in understanding where your building data stands today? Get in touch.