Picture this. A tenants walk into a building and asks, “What’s the air quality like on the third floor today?” Or a new member of the building staff mutters under his breath, “When was the last time this rooftop AC unit was serviced?” Then, building answers. Thanks to recent advances in large language models and natural language processing, this is no longer just a futuristic idea. We are already starting to see buildings respond in ways that could fundamentally change how the real estate industry interacts with its assets. Next stop: talking buildings
The data accessibility problem
Commercial buildings produce an enormous volume of data. That data comes from building management systems, IoT sensors, utility meters, access controls, and tenant platforms. Despite the abundance of information, much of it remains out of reach for the people who need it most. Often, these systems operate in isolation or use outdated interfaces that require specialized training. Getting a simple answer might involve pulling multiple reports or searching through dense dashboards.
The emergence of natural language interfaces offers a more intuitive way to interact with this data. Instead of struggling to learn software or digging through spreadsheets, users can ask a question in plain English and get a useful answer. This kind of access allows facility managers to query faults or performance issues in real time. Asset managers can quickly get updates on building performance across portfolios. Tenants can request information about indoor comfort or sustainability. Engineers and consultants can uncover operational trends or service histories without manual digging. Even senior executives can monitor key indicators like energy use or compliance status without relying on reports from multiple departments.
This change is not just about convenience. It allows faster decisions, reduces dependency on specialists, and opens up building intelligence to more people across an organization. The result is better-informed teams and more responsive operations.
From passive structures to active participants
As systems become more advanced, these tools will evolve beyond simply providing answers. They will begin to support decision-making. For example, a building may notify you that energy use on the second floor spiked yesterday due to a specific equipment fault and offer to create a service request. That kind of proactive communication turns a passive structure into an active participant in its own performance.
To achieve this, companies must address several challenges. The first is system integration. Many buildings rely on a mix of legacy systems and modern platforms that were never designed to work together. Creating a seamless user experience across these technologies requires careful planning and investment.
Next is access control. Not every user should be able to view or request every type of data. Role-based permissions need to be in place so that tenants, engineers, and executives each have access to information relevant to their responsibilities.
Another key issue is accuracy. Users need to trust the answers they receive. That means responses must be backed by traceable data sources and clear logic. If a system says energy usage increased by twelve percent, it should be possible to verify that number and understand the factors behind it.
Change management is also important. Even the most advanced systems can fall short if people do not know how to use them effectively. Teams need to understand how to phrase questions, how to validate responses, and how to take action based on what they learn.
The platform opportunity
There is a larger opportunity here as well. Companies that successfully merge real-time data, conversational AI, and portfolio-wide visibility can create powerful new platforms. These platforms would not only make existing systems more usable but also offer new capabilities such as cross-building comparisons, predictive maintenance, and sustainability tracking. This approach could set a new standard for how property operations are managed.
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Planit’s Hyper-Intelligent Building program is one example of what this could look like. It combines years of BIM data, IoT input, and user interaction into a single interface that helps building operators work more effectively. With conversational tools layered on top, the possibilities expand even further. Teams can ask for insights, make data-driven choices, and streamline operations all through a single entry point.
Preparing for this shift means evaluating your current infrastructure. Ask whether your data is organized and accessible. Consider whether your systems are flexible enough to accommodate new tools. Look at whether your team is ready to adopt a new way of working with building data.
It is also time to rethink how we train building staff. As more responsibilities shift toward interacting with AI-driven systems, the skills required will change. People will need to become comfortable working alongside technology that interprets and suggests rather than just displays information.
We are entering a phase in real estate where automation will be accompanied by interaction. Buildings will no longer sit quietly until something goes wrong. Instead, they will surface issues before they become problems, offer insights without being prompted, and help teams prioritize their efforts. The buildings that offer this kind of collaboration will give their owners a competitive edge.
Talking buildings are not a novelty. They are the next step in making real estate smarter, more responsive, and more human-centered. The sooner companies begin preparing for this transformation, the better equipped they will be to lead in a world where information is not just stored—it is shared, interpreted, and acted on in real time.
