Artificial Ignorance vs. Artificial Intelligence: Is Your HVAC Data Telling the Truth?

We're all hearing it—artificial intelligence is the next big thing, right? But let me ask you this: is it really intelligence, or is the current industry state more ignorant than intelligent? In the HVAC industry, the difference between the two could mean the difference between success and failure.

The Reality of HVAC Digital Intelligence: In the age of AI, data accuracy isn't just important—it's everything. Without reliable data, your so-called "intelligent" applications are not reliable. What they are practicing isn't artificial intelligence—it's artificial ignorance.

Consider this: how many times have you seen applications labeled as "optimized by AI," only to discover that the data they're relying on is incomplete, outdated, or outright incorrect? It's like trying to construct a skyscraper on quicksand—the foundation is flawed, and everything built on it is bound to collapse.

The Industry's Big Blind Spot: The HVAC industry is getting too comfortable with this ignorance. Jumping to AI without ensuring there is high-quality data is a mistake. If your data is off, your decisions will be too. And bad data equals bad outcomes.

Today, even though the technology exists, most newly installed HVAC systems do not have digital verification that the system is operating as designed. In addition, we are not streaming performance data into a system of record database, necessary for an AI-driven world. Our PM programs still rely on technician know-how, and we aren't using scientific processes to validate operating performance. In other words, we aren't data-driven.

The Challenge We Face: As the HVAC industry moves forward, we need to ask ourselves: Are we focusing too much on the "intelligence" of our systems and not enough on capturing high-quality data? The real challenge isn't just creating smarter systems, but ensuring that these systems are working with reliable information. Only then can we truly claim to be advancing.

A Question for the Future: As we enter this AI-driven future, we should all pause and consider: Are we addressing the prerequisites to AI first - high-quality data? The technology exists today to capture performance data and use science-based algorithms to validate results. Let's move into the data-driven era first, so we are really ready for AI.