The vast majority of businesses are publicly committed to sustainability – but many have yet to make tangible progress in this critical area, according to Google Cloud research.
Businesses who have begun the journey are not only improving their carbon footprint, they are also becoming more efficient and innovative. These organisations are harnessing the power of data, AI and machine learning (ML) to optimise the use of resources both in IT and across their business. They’re doing so in ways that don’t just make them more sustainable, but more resilient, agile and competitive.
The 2023 Google Cloud CXO Sustainability Survey shows that while 9 in 10 organisations are talking publicly about their commitment to sustainability, only 58% are actively moving programs into implementation. Enterprises understand the value of sustainability, but many find it difficult to identify opportunities to reduce their carbon footprint or define the processes and structures needed for change.
Yet, as a recent IDC blog puts it, ‘many business leaders are looking at ways to address environmental, social and governance (ESG) issues with the greatest impact on enterprise value’.
This means ‘looking beyond the cost of building and implementing sustainability programs’ with a view to increasing ‘their operational and financial performance through sustainable transformation.’[2]
Data, AI and ML have the potential to transform supply chains at every scale. TraceMark, a solution developed by NGIS, uses Google Earth Engine, BigQuery and Vertex AI to visualise and better understand the source of raw materials, and land use change, over time. This can help environmentally aware enterprises ensure that they’re working with farmers whose practices are sustainable.
Unilever is also doing pioneering work with its own supply chain operations, working with satellite imagery and AI to build a holistic view of its sourcing and the attendant environmental impact. Using this information, it can detect deforestation and identify habitats which need protection.
What’s more, by helping organisations identify and model risks – for instance climate events or social upheavals – AI and ML can help them predict and manage disruption.
For instance, Climate Engine’s SpatiaFi platform combines massive quantities of Google Earth, climate and geospatial data with BigQuery and Vertex AI to help enterprises assess the risk from floods or wildfires, and model how this could impact their business. These insights enable institutions like the Bank of Montreal to help their clients mitigate such risks and embed sustainability within their decision-making.
Major supermarket retail chains in Europe have also leveraged data to offer consumers tailored ecommerce experiences, while reducing their operating costs by up to 40%. By applying AI to optimise their supply chains, as well as using data to predict seasonality and customer demand, they’ve improved efficiency and achieved significant reductions in food waste.
Intelligence and energy
Since 2016, Google Cloud has used deep learning technology to reduce the energy consumption and carbon emissions in its cloud data centres. Now it provides its customers with tools they can use to cut emissions associated with their own cloud use. These tools empower customers to optimise how, when and where they run their workloads to maximise cost efficiency and performance while minimising their carbon footprint.
Working closely with its AI research team, Deep Mind, Google Cloud is now using the same core tools and technology to build the Industrial Adaptive Controls platform, providing AI control of cooling systems in commercial and industrial facilities as a service. It’s a powerful example of how AI and ML, applied to operational data, can drive a more intelligent infrastructure, going beyond efficiency improvements to create innovative new services – or even transform industries.
Other companies are working with Google Cloud to reduce energy consumption. Uplight works closely with energy providers, using ML and AI to transform usage data into actionable insights that can be used to tackle peaks in energy consumption in real-time. These measures extend to customer engagement, promoting small changes in behaviour that can help them conserve energy and reduce overload on the energy grid.
Meanwhile, Kaluza, the intelligent energy platform, gathers data from electric vehicles, energy suppliers and grid operators to ensure that electric vehicles plugged into one of its chargers are charged at the lowest cost during optimal periods of the night.
Sustainability is essentially a data challenge, where AI and ML have the potential to not only reduce waste and emissions, but also make organisations more agile, adaptable and resilient. With data innovation deep in its DNA, Google Cloud is uniquely placed to help enterprises meet this challenge, and develop insights that drive innovation and transform operations.