How to Quench AI's Thirst: Q&A with Itron's David Kushner

Recently, AI has been put on something of a pedestal in the sustainability sector. Within the last month alone, Sustainability Magazine has covered some very exciting applications AI can have in the climate fight: predicting extreme weather, incentivising recycling and cold water cleaning. The list goes on and on.

But for all its potential benefits, the surge in AI technology comes with a hidden cost — a significant increase in water consumption needed to cool its sprawling data centres. By 2027, it's expected that AI will be guzzling down approximately 6.6 billion cubic metres of water per year. That's equivalent to 2,640,000 Olympic swimming pools!

Meet David Kushner, the Global Data Management Director at Itron, a tech company guiding the way towards efficient energy and water usage. For a quarter of a century, David has steered Itron's mission to furnish the globe with smart meters, sensors and a slew of intelligent network services. These innovations arm utilities with the critical ability to monitor and control their operations more effectively.

In his pursuit of creating a resourceful planet, David orchestrates strategies that improve utility infrastructure's intelligence. His efforts significantly bolster the safety, durability and, most importantly, the sustainability of delivering vital resources like water and energy.

Dive into David’s thoughts in this Q&A, where he discusses the challenge of quenching AI's thirst without draining our planet's water resources.

Given the projected increase in water usage by AI-driven applications, what specific strategies and technologies does Itron recommend to mitigate water resource challenges?

To address the increase in water usage by AI-driven applications, Itron recommends implementing smart water management systems. This includes advanced metering infrastructure (AMI), which provides real-time data on water consumption. 

By deploying smart water meters and sensors throughout the distribution network, utilities can gain granular insights into water usage patterns, identify leaks and anomalies and proactively address potential issues. 

Additionally, advanced data analytics can be used to process the vast amounts of data generated by AMI systems. These platforms leverage machine learning algorithms to detect trends, predict demand and optimise water distribution. 

By harnessing the power of data-driven insights, utilities can take informed action to reduce waste, enhance operational efficiency and ensure the sustainable use of water resources in the face of increasing demand from AI-driven applications.

How can AI be leveraged to enhance water conservation efforts and improve the efficiency of water management practices?

AI can significantly enhance water conservation by analysing vast amounts of data to identify patterns and accurately predict demand. This enables proactive management of water resources, reduces waste and ensures efficient distribution. 

AI-driven predictive analytics can forecast water needs based on data from weather patterns, population growth and usage trends. AI algorithms can also detect anomalies, such as leaks or unauthorised usage, in real time, allowing for rapid response while minimising water loss.

Can you share any successful case studies or examples where Itron's solutions have significantly improved water conservation and management, particularly in light of the growing demands posed by AI?

Itron recently announced a collaboration with VODA.ai that is focused on leveraging AI rather than relying on guesswork to identify pipes that are at high risk of failure.

The solution, which combines Itron's detailed meter data with VODA.ai's AI engine, is designed to significantly reduce water loss associated with pipe failure and ensure more efficient water management. It also reduces costs by accurately identifying and prioritising which pipes need replacement. It uses existing utility data — GIS and historical pipe failure data — along with public data such as soil and terrain information, to predict and prevent potential pipe failures. 

This not only enhances operational efficiency for utilities but also supports sustainability by conserving water resources in the face of growing demands and challenges.

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