How sustainable is AI? Let’s take a look at the bigger picture of sustainability and AI automation. 

Creating AI is energy-intensive, but technology is catching up 

Everyone knows by now that AI consumes a massive amount of energy. As a result, AI does not score very well in terms of sustainability. But did you know you can also work with sustainable data centres? Those green data centres may not be perfect yet, but they are certainly a big step towards sustainable AI. It would not be surprising if a sustainable strategy becomes increasingly important when selecting technology partners

Applying AI can yield sustainability benefits

Energy consumption is still a challenge when creating AI, but once created its usage can yield huge sustainability benefits. Besides reducing our carbon footprint, a lot remains to be done to combat pollution. The aim is ultimately to have clean water, oceans without plastics and increased biodiversity, for example. AI offers new opportunities to tackle these challenges. 

Example 1: Precision agriculture with Vision AI

A nice example: Vision AI can be used to monitor crop health and detect diseases or pests. This allows for targeted interventions, reducing the need for pesticide or herbicide use. Similar for targeted irrigation and reducing water use. What’s more, Vision AI-powered robotics in greenhouses not only help growers increase their output, but also help them cultivate high-quality crops and reduce food waste.

Example 2: Waste Management and recycling with AI

Another example of the sustainable impact of Vision AI, is the use of AI-powered cameras in waste sorting facilities. Smart cameras can identify and sort recyclables from non-recyclables more efficiently. You can use them to inspect recycled materials, ensuring that contaminants are removed from the recycling stream. 

Example 3: AI enables local production

One of the biggest opportunities to increase sustainability with AI is to increase local production. By avoiding long-distance transport altogether, manufacturing companies can drastically reduce their carbon footprint.

The shift towards domestic self-sufficiency has intensified because the covid pandemic made us all aware of the risk of supply chain disruptions. Geopolitical tensions are yet another motivator for reshoring manufacturing, especially in the US. Reshoring is even being powered by several government funded initiatives like the US Chips Act, the European Chips act, and the EU Bridge initiative.

AI is a crucial enabler in this reshoring trend. AI automation makes local manufacturing competitive by optimising processes, reducing labour costs, and overcoming the hurdle of labour shortages. With AI-driven automation, companies can now bring production back within national borders while maintaining cost-effectiveness. 

Conclusion: Assessing the sustainable impact of AI holistically 

Creating AI is energy-intensive, but more and more renewable energy technologies exist and many of AI’s applications yield strong sustainability benefits. Above all, let’s not forget that AI automation is one of our best bets to address environmental challenges by discovering new ways of working. 

Embracing sustainability in AI automation necessitates a multifaceted approach encompassing technological innovation, strategic partnerships, and policy interventions. By leveraging AI’s transformative potential, organisations can foster a more resilient and sustainable future. Collaboration and innovation will be paramount in realising the promise of AI as a force for positive change.