Why Software Alone Won’t Solve the Climate Crisis

Insights from a Field Trip in Denmark

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Last month the entire Ento team gathered on the picturesque north-western coast of Zealand, Denmark. This retreat was not only a chance to discuss the current state of AI, but also an inspiring venue for several product discovery sessions. We explored many of the opportunities we see right now in the industry, focusing on a critical question: which innovations can deliver the most value to our customers while facilitating scalable decarbonization of the building sector?

To get to the root of this, we decided to visit one of our valued customers, the Municipality of Ballerup. Hearing from Ballerup’s energy managers about how they use the Ento platform to detect, resolve issues, and verify savings was very inspiring for me. The experience was a powerful reminder of the importance of maintaining close contact with customers, to truly understand their needs and challenges, and ensure that we develop solutions that can effectively deliver value.

Today’s post summarizes the insights I gained from meeting our users. It will be less technical and more high-level, as I aim to include more AI product development discussions in my posts — a topic I believe will interest many.

Real-world energy management

Before heading to the Ballerup town hall for the meeting, the entire Ento team participated in a company exercise. We divided into groups and used our platform to identify the most significant problems within the municipality’s building portfolio. The idea was to find the key issue that we would prioritize fixing if we were the energy managers of Ballerup. During our analysis of over 100 sites managed by the municipality, we discovered a substantial water leak at a sports facility. This leak was causing a loss of 500 liters of water per hour, translating to a potential annual financial loss of €16,000 for the municipality.

500 liters per hour seemed like a pretty concerning number. Furthermore if the leak was due to a burst pipe rather than a running shower or toilet, the building could be flooding, potentially leading to damages worth hundreds of thousands of euros. The issue had been ongoing for a few weeks already, so we headed to Ballerup with a certain sense of urgency, ready to ask: “Are you aware of this? Are you taking any measures to address it?”

Upon our arrival, the energy managers reassured us. “Yes, we are aware of it. Your system alerted us, and we are in the process of fixing it.” This highlighted an important reality of building operations for us: the resolution of such problems is neither straightforward nor immediate. It’s rarely as simple as pressing a button on a screen to magically resolve the issue. Once an alert is received, the energy managers need to first validate the results, and then get in touch with the technician or plumber responsible for the building. Next, that technician has to find an available slot to visit the site, and sometimes the issue isn’t identified on the first visit. The entire exercise powerfully demonstrated that while solving issues might appear clean and straightforward from a software perspective, real-world implementation is messy and complex when dealing with physical infrastructure.

A couple of days later, we could see on our platform that they finally got hold of a plumber and fixed the issue. An action has now been registered so that the savings can be verified, with a methodology similar to the one described in the Python tutorials from previous issues.

Two clear revelations emerged for me from this experience:

1. AI-powered assistants are the future of building energy management. Building managers juggle a multitude of tasks such as planning energy renovation strategies, managing building operations, and maintaining communication with technicians and on-the-field personnel. Managing hundreds of sites makes it impossible for them to constantly monitor data manually. This is where AI-powered assistants that can oversee all operations, identifying anomalies and potential issues in real-time, have the potential to provide substantial value. This continuous monitoring and support will allow energy managers to focus on strategic decisions rather than getting bogged down by looking at consumption data and validating energy bills. With increasing reasoning capabilities and data availability, AI assistants will be able to provide actionable insights and prioritize issues, ensuring that resources are allocated efficiently and effectively.

2. Building decarbonization requires more than just software solutions. While AI can track potential issues and suggest improvements, human validation and intervention are still necessary. The physical world presents challenges that AI cannot yet fully navigate—leaks need to be physically repaired, valves require hands-on adjustments, complex technical systems need to be installed and maintained. Fully autonomous robots handling all aspects of building management are still a very distant future. Therefore, the current and more realistic approach is to have AI-augmented building managers. These savvy professionals will leverage AI to enhance their capabilities, using data-driven insights to make informed decisions and taking prompt action when required. They will oversee the integration of renewable energy sources, manage energy efficiency projects, and ensure that buildings comply with evolving environmental regulations.

Conclusion

AI is a powerful tool that significantly enhances what energy managers can do, but it is not yet a substitute for human expertise. The future lies in the collaboration between intelligent systems and skilled professionals, working together to tackle the pressing challenges of building decarbonization and clean energy transition. The climate crisis won’t be solved by software solutions alone but by technical people proficient in using AI to enhance their abilities. Paradoxically, in a world turning increasingly digital, the ability to interact with the physical world is becoming an even more essential skill.