Mastering AI Workloads – The New ESG Challenge in the Data Centre Business

How does artificial intelligence (AI) improve energy efficiency in data centres and also support the upcoming ESG reporting? Matthew Farnell, Global Sales and Marketing Director at EkkoSense, will talk about this at the Data Center Expert Summit on 4 June. His German colleague Artur Faust, Sales Manager DACH, answers our questions in this interview:

ESG and artificial intelligence are currently two hotly debated topics in the DC industry. How do the two topics fit together, even though they don’t have much in common?

Artur Faust: At first glance, the domains of AI and ESG reporting don’t actually have much in common. However, AI can become a decisive tool for correctly aligning and ultimately achieving your own ESG goals as part of the materiality analysis. If artificial intelligence is used sensibly, it does not do away with humans, but creates full transparency and scope for faster and more precise decisions. AI can also enhance “human expertise” and thus lead to more targeted planning. That’s why we at EkkoSense like to combine these two domains, which is also reflected in our range of solutions.

With Cooling Anomaly Detection – a function that can detect anomalies in the operation of the DC cooling system before a possible device failure occurs or energy consumption increases unnecessarily – we support operators in optimising their energy efficiency parameters. Automated ESG reporting helps controllers to map the necessary reporting to support the compliance requirements of EED and CSRD guidelines in a cost- and resource-saving manner.

But you also link the topic of AI high-density consumers and ESG, how should we understand this?

Artur Faust: Existing data centres, in particular, face special challenges when customers want to use HPC servers in their AI racks, and performance shoots up at certain points. Experience has shown that this can lead to uncertainty on both sides. On the one hand, you want to ensure that this can be implemented technically without any problems and, on the other hand, these changes can be contrary to the ongoing ESG objectives of the data centre. We rely on AI-supported tools to break down upcoming changes in the necessary depth in advance and incorporate them into an ESG assessment. We achieve this using end-to-end simulations and modelling of the respective DC environments.

Many data centres already use a wide range of software tools to optimise their processes. What is the particular challenge if I want to master AI workloads as a DC operator?

Artur Faust: Hardly anyone will want to simply replace the solutions they have developed over the years. Our approach is to offer an AI layer as a “simplification tool” for existing systems, which processes and visualises the data already collected on a common platform and makes reporting easier. Data centres often use a large number of different tools that are at home in different disciplines. Site managers therefore often complain about the complexity and high administrative effort of such fragmented solutions. We have set ourselves the goal of simplifying these process and tool landscapes by connecting different systems with an AI front end, which can also be used very well in stand-alone mode and can therefore also supplement missing areas in other tools.


Mr Faust, thank you very much for the interview!

Mastering AI Workloads - The New ESG Challenge in the Data Centre Business