Trust in AI Is Rising: But what does it take to make AI work in buildings?
Trust in AI is rising, and for good reason. AI is becoming part of everyday life and is now moving into building operations.
But buildings are not controlled lab environments. They are living systems shaped by people, infrastructure, and real consequences when something goes wrong.
This article explores what it takes to make AI work in buildings and why reliability across the full system matters more than the model alone.
Consistency is the benchmark
AI has moved from experimentation to real operational impact across many industries. In real estate, interest is growing because owners and investors want efficiency, sustainability, and better control without adding risk or complexity. But in buildings, a successful pilot is not the hard part. The real challenge is delivering reliable performance repeatedly across an entire portfolio.
Buildings differ in equipment, configuration, maintenance levels, and data quality. This variability is exactly what tests whether AI can operate in the real world. As Myrspoven’s Chief Technology Officer Niklas Jonsson explains:
“Trust isn’t something you get from a demo. It’s built over time by delivering consistent results in real operations, especially when conditions change and data isn’t perfect.”
What building operations require from AI
For AI to be useful in building operations, it must deliver measurable outcomes, not just predictions. For owners, that means better energy performance, stable indoor comfort, and support for daily decisions. For investors and partners, it must also scale reliably across entire portfolios, not just individual sites.
Niklas describes “working AI” in simple terms:
“At the most basic level, the system is working when it reduces energy use while keeping indoor comfort within agreed limits. That balance is how we know the AI is doing what it should.”
In practice, this often means smoother performance rather than extreme optimization. AI-driven buildings should show more stable temperature and CO₂ levels over time, even as outdoor conditions change.
The goal is not perfection at every moment, but a continuous and reliable trade-off between comfort and energy efficiency.
From building models to operating systems
One of the most common misconceptions about AI in buildings is that the hardest part is the model itself. In reality, the greater challenge lies in everything around it: reliable data, consistent integrations, correct configuration, and predictable behavior when conditions change.
If deployments require heavy manual work or custom tuning for each building, the solution will not scale. Trust is operational. When inputs become uncertain, the system must behave predictably, with transparency and safe fallbacks, not just high accuracy.
Without the right data, configuration, and monitoring in place, even the most advanced model will fail to deliver value in practice.
What changed in the last 12 months and why it mattered
Over the past year, the focus has shifted from “can we build it?” to “can we operate it reliably and deliver it repeatedly?”
At Myrspoven, two changes have made a big difference.
First, the core of our AI model was fully rewritten, improving control, scalability, and transparency. While largely invisible to customers, this provides a stronger foundation for consistent performance.
Second, the company invested heavily in a new data platform. Beyond optimization, the platform helps customers understand what has happened in their buildings, what is happening now, and how the system plans to operate going forward.
As Niklas notes:
“The new data platform has already proven highly effective. It offers customers valuable insights into their building operations and significantly simplifies the process of developing new data-driven products and services.”
Together, these changes shift AI from being a black-box optimizer to a transparent operational tool.
What makes AI trustworthy
Trust in AI does not come from bold claims. It comes from consistency over time, in real operations.
That is especially true in buildings, where performance is never static. Conditions change, data quality varies, and systems age. The only way to earn trust is to behave predictably through all of it.
This is also why Myrspoven prioritizes simplicity over complexity. Rather than relying on highly complex but general machine-learning models, the system uses a more specialized model that is grounded in the laws of thermodynamics. This approach allows us to understand why the AI makes the decisions it does, and sets clear boundaries for how much control the AI has.
For property owners, this reduces risk and increases predictability. For partners and investors, it builds confidence that the system can scale across asset types and continue delivering value as conditions change.
At Myrspoven, trust in AI is not something we claim. It is something we earn in daily operations.
Because in buildings, trust is not built in theory.
It is built every day.
About Myrspoven
Myrspoven AB is a pioneering force within energy optimization, dedicated to revolutionizing the way buildings harness and consume energy. With a deep commitment to sustainability, Myrspoven leverages cutting-edge AI technology and innovative solutions to create more efficient buildings, consuming less energy, as well as contributing to more sustainable and stable energy systems.