Hello readers!

On this week’s podcast, Ryan and Neil speak with Jean-Simon Venne, the CTO and co-founder of BrainBox AI, about the current state of enterprise AI adoption and how enterprises are reducing costs and driving sustainability with AI-operated smart buildings.

Let’s take a closer look at what Jean-Simon had to share with us!

ChatGPT Opened the Floodgates

Our discussion commenced with an inquiry into the adoption of AI within enterprises, which was slow until the launch of ChatGPT. This was the catalyst that ushered in a slew of new AI applications for both consumers and businesses. The motive behind these enterprise-specific applications is largely to enhance productivity at all tiers. However, amidst this innovation surge, not all applications are destined for longevity. Some will briefly shine before vanishing while others will rise to prominence and become staples in business operations.

Additionally, Jean-Simon underscored the velocity of this ongoing AI revolution. The current rate of AI's penetration surpasses that of the Internet and even the assimilation of the smartphone. This accelerated pace poses challenges in tracking and predicting the trajectory of AI.

Smart Buildings Get Smarter

Modern commercial buildings largely function based on programmed control sequences, analogous to how home thermostats work. For instance, a thermostat reacts to deviations in temperature, seeking to restore the desired setting. This reactive approach is prevalent across vast edifices like hospitals, hotels, and office towers, where systems counteract environmental changes to achieve the desired temperature and humidity levels.

The game-changer here is the infusion of deep learning, which instead of merely reacting, offers the power of prediction. By processing heaps of data, AI models can foretell temperature fluctuations in a given space, hours in advance. Equipped with this foresight, one can orchestrate the most energy-efficient responses, thereby preemptively steering the environment towards the desired state. Jean-Simon draws an analogy to spacecraft trajectory adjustments: minimal fuel consumption in anticipation of a deviation is far more efficient than a significant fuel burn to correct an already deviated course.

A Web of Protocols

Jean-Simon delineates the complexities inherent in the integration of AI in a building, highlighting the sheer diversity in control protocols for heating and cooling in commercial buildings globally. Over 700 distinct control protocols exist, and many of these have outlasted the companies that initially developed them. The prerequisite for successful integration is the compatibility of the protocol with the AI system.

Once compatibility is established, there's a phase dedicated to data extraction in a non-intrusive, read-only mode. This phase spans about five to six weeks, a duration necessitated by the need to effectively train neural networks to furnish accurate predictions.

Beyond extraction, the data undergoes extensive refinement. Cleaning, mapping, and tagging the data is paramount to the performance of the algorithms. While the process is involved, it's a one-time commitment that pays dividends down the road in the form of cost savings and emission reduction.

Giving AI the Keys to the Building

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Meet Jean-Simon Venne

Jean-Simon Venne is a co-founder and CTO of BrainBox AI. As a technology expert specializing in the fast and efficient migration of technological innovations to commercial applications, Jean-Simon has over 25 years of experience developing and implementing new technology to solve long-standing commercial issues in the fields of telecommunications, biotechnology, and energy efficiency. Prior to joining BrainBox AI, he was responsible for the successful integration of M2M technology in over 200 smart buildings across North America, Europe, and the Middle East.

Learn More About BrainBox AI

BrainBox AI uses deep learning, cloud-based computing, algorithms, and proprietary processes to support 24/7 self-operating buildings that require no human intervention and enable maximum energy efficiency. BrainBox AI enables a reduction in total energy costs of up to 25% in less than three months, with low to no CAPEX needed from property owners. It also improves occupant comfort by 60% and decreases the carbon footprint of a building by 20-40%.