Gaku Ueda, CEO of MODE, Inc. looks at the pressure of modernising building stock.
The built environment is under growing pressure to modernize its building stock, with studies indicating that updating ageing buildings could slash energy demand by 12%.
Modernization is arguably not happening fast enough. Pressures are steadily growing to increase energy efficiency as part of sustainability goals while improving operational efficiency for occupational comfort.
However, almost a quarter of buildings in Europe alone are pre-1945, and the average building in the U.S. is about 53 years old. Simply constructing new buildings that are more energy—and operationally efficient isn’t a feasible option.
That’s why many in the industry champion a no-demo approach, leaning on retrofitting to add new technologies to improve how buildings are run. Unfortunately, that comes with its challenges: complex and disconnected systems, siloed data that leads to expensive breakdowns and malfunctions, additional insights to keep track of, and more. It’s no wonder that 54% of facility managers have considered leaving their jobs, if they haven’t done so already.
The power of data to inform decisions is undeniable and cannot be undermined. Processing it is a key first step to benchmarking, building performance, and setting KPIs.
The reality is that manually sifting through a building’s vast amounts of data is simply impossible without key information slipping through the cracks. That undermined visibility cripples strategies for optimizing building performance and slashing energy consumption.
Here’s where AI comes in. Unified AI platforms are helping building managers achieve more innovative and efficient buildings, providing real-time visibility and control over their tech stacks.
Before diving into how exactly that works, it’s important to debunk an AI myth: AI is not synonymous with automation. There are widespread misconceptions that AI tools are just another way of packaging automation to layer on top of existing tech stacks, operating as a ‘black box.’
AI platforms are designed to be embedded within a building’s digital backbone, connecting otherwise siloed systems and processing data and insights to suggest actions. Unified AI platforms can be trained by data inputs from various technologies to automate workflows and processes, analyze that data, and suggest better outcomes.
That’s how these platforms streamline repetitive tasks, like processing information, generating insights, and suggesting actions accordingly. For example, an AI platform fuelled with footfall information from connected IoT devices such as sensors and cameras can indicate when lights should be switched off. Pre-authorized AI tools will automatically follow through on those data-fuelled suggestions in a split second.
To execute those functions, AI platforms must be fed accurate data. To be clear, AI deployment to power building modernization starts with strong data management. Vendors of AI platforms will ensure that data from all tech tools and across digital systems is interoperable. That means all data is formatted correctly, up to date, and doesn’t contain duplicates or errors so that it can be exchanged between tools seamlessly.
With those data-powered capabilities, unified AI platforms can significantly optimize energy efficiency. AI also keeps management teams informed about operational performance, meaning greater transparency for better results.
Let’s take a look at how AI is converting data into results. Panasonic has implemented its RE100 Solution to replace all energy consumed in its business activities with renewable energy to achieve carbon neutrality. As part of this effort, the electronics giant opened a facility—H2 KIBOU FIELD—that combines pure hydrogen fuel cells, solar cells, and storage batteries to create a self-sufficient power generation system. This three-pronged energy source was used to power Panasonic’s neighboring factory.
Given the nature of the facility’s energy production, Panasonic faced the challenge of understanding and acting on the vast amounts of data accumulated. Panasonic’s traditional system meant that only one person could access that data at a time and uncover optimal information for guiding decarbonization and energy efficiency efforts. Of course, this is highly labor-intensive, and teams quickly became overwhelmed in providing actionable and informative insights from the constant mountain of data.
Panasonic leveraged MODE’s AI agent energy management platform to solve this ongoing challenge. Through this, Panasonic was able to unify the three-pronged power generation (solar, hydrogen, and battery) data into a single intelligent system. Teams could lean on the agentic AI support to ask questions about the mixture of energy usage, while the platform also proactively detected any anomalies and flagged them accordingly.
As a result, significantly more staff in Panasonic’s facility have a much clearer understanding of operations and energy output and consumption. Additionally, the AI agent enabled Panasonic to drive operational efficiency, while addressing any fault issues proactively.
Older buildings are a piece of a country’s heritage, but that doesn’t mean we must freeze them in time. AI is transforming what we can do with historical buildings so that they’re future-ready. Technological innovations like AI and history can live and thrive together by augmenting the ‘no-demo’ approach to facilitate greater visibility and control over operational outputs and energy optimization.