Trust in AI Is Rising. But What Does It Take to Make AI Work in Buildings?

Trust in AI is growing, and for good reason. What was once considered experimental is now becoming part of everyday life and increasingly, part of building operations.

But buildings are not controlled environments. They are complex systems shaped by people, infrastructure, weather conditions, and operational constraints. When something goes wrong, the consequences are real.

So what does it actually take to make AI work in buildings?

Consistency Is the Real Benchmark

AI has moved beyond experimentation and is delivering measurable value across industries. In real estate, interest continues to grow as owners seek greater efficiency, sustainability, and operational control without increasing complexity.

However, running a successful pilot is not the difficult part.

The real challenge is delivering reliable performance across an entire portfolio, year after year.

Every building is different. Equipment varies, data quality differs, maintenance standards fluctuate, and operational conditions constantly change. This variability is what ultimately determines whether an AI solution can succeed at scale.

“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,” says Niklas Jonsson, Chief Technology Officer at Myrspoven.

What Building Operations Require from AI

For AI to create value in buildings, it must do more than generate predictions. It needs to produce measurable outcomes.

For property owners, that means reducing energy consumption while maintaining indoor comfort. For investors and partners, it means delivering those results reliably across portfolios, not just individual sites.

As Niklas explains:

“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, success often looks less like aggressive optimization and more like stability. Well-performing buildings maintain consistent temperatures and healthy indoor environments while using less energy, even as external conditions change.

The goal is not perfection at every moment. It is achieving a reliable balance between comfort and efficiency over time.

AI Is Only Part of the Equation

One of the most common misconceptions about AI in buildings is that the model itself is the hardest part.

In reality, success depends just as much on everything surrounding the model: data quality, integrations, configuration, monitoring, and operational reliability.

An AI solution that requires extensive manual adjustments for every building will never scale effectively. When data becomes uncertain or conditions change unexpectedly, the system must remain predictable, transparent, and safe.

Without the right infrastructure and operational framework, even the most advanced AI model will struggle to deliver meaningful value.

From Building AI to Operating It at Scale

Over the past year, the conversation around AI has shifted.

The question is no longer, Can we build it?

The question is, Can we operate it reliably and deliver results repeatedly?

At Myrspoven, two developments have been particularly important.

The first was a complete rebuild of the company's core AI model, improving transparency, control, and scalability. While largely invisible to customers, it provides a stronger foundation for consistent performance.

The second was a major investment in a new data platform that gives customers a clearer understanding of what is happening across their buildings, both historically and in real time.

“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 developments help transform AI from a black-box optimization tool into a transparent part of daily building operations.

What Makes AI Trustworthy?

Trust is not built through promises. It is built through predictable performance over time.

This is particularly important in buildings, where conditions are constantly changing. Systems age, occupancy patterns shift, weather varies, and data quality is rarely perfect.

To operate successfully in this environment, AI must be understandable, reliable, and designed with clear boundaries.

That is one reason Myrspoven prioritizes simplicity over unnecessary complexity. Rather than relying solely on highly general machine-learning models, the platform uses specialized models grounded in the laws of thermodynamics. This makes it possible to understand why decisions are made and define clear limits for how the system operates.

For property owners, that means lower risk and greater predictability. For investors and partners, it creates confidence that the solution can scale across different building types while continuing to deliver value.

Trust Is Earned Through Operations

As AI becomes a larger part of the built environment, the industry's focus is shifting from innovation alone to reliability, transparency, and long-term performance.

The organizations that succeed will not necessarily be those with the most complex algorithms. They will be the ones that consistently deliver measurable outcomes in real-world conditions.

Because in buildings, trust is not built in theory.

It is built every day.