AI is so effective because it can address and alleviate issues like excessive energy consumption, resource depletion, and inefficiencies in worldwide operations. It does so by using swaths of data to produce valuable insights that inform and perpetuate sustainable measures. What’s more, AI can automate sustainable processes, scaling greener initiatives within and across verticals. AI can also quickly learn and adapt to changing conditions, giving humans more time to focus on more complex problems around sustainability.
As 60% of organizations have a sustainability strategy in place, and more businesses are committing to Environmental, Social, Governance (ESG) frameworks, AI has a clear role to play. The tech is not only the fuel for sustainability efforts, but it also makes financial sense as environmental savings often equate to cost savings. With AI in their toolkit then, businesses, governments, and change-makers can meet their ethical and economic responsibilities.
Here’s why AI will power sustainable development in the long term.
The AI seed has already been sewn
Many players have long adopted AI, and in doing so, are shaping baseline practices to implement and maximize AI’s potential as a driver of sustainability. The more organizations that integrate AI, the more takeaways there are about how best to apply it, scale it, and iterate for impact.
One of the most prevalent areas for AI is predictive maintenance and optimization – especially in manufacturing, where it can flag when hardware needs to be serviced or anticipate ahead of time when machinery will break. Here, AI can help companies extend the life cycle of their equipment, produce less waste, and reduce energy consumption for inefficient devices. For example, IBM has its own AI-driven “cognitive intelligence engine” which monitors, analyzes, and reports on equipment data, assigning a health score for each device.
Elsewhere, digital twins are AI replications of factory floors and allow companies to have a more holistic view of internal processes, as well as experiment with virtual changes. In this setting, manufacturers can confirm the outcomes of more sustainable alternatives before committing to them.
Meanwhile in logistics and supply chains – where greenhouse gas emissions are five times bigger than those from direct operations – AI is streamlining transport routes, shaping more accurate demand forecasting, and managing inventory. As a result, companies are seeing less material waste, lower carbon emissions, and smarter resource allocation.
AI is a scalable, sustainable solution
AI relies on computational power to function, but the technology can be scaled to support sustainable development without becoming unsustainable. How? It starts with organizations paying close attention to the data they’re working with.
Data is ultimately the nutrition for AI, and if it’s not healthy, it won’t be productive. By refining data, companies don’t waste energy on AI applications and can get more valuable insights. Businesses therefore have dedicated data and AI-oriented teams who are responsible for data quality control and reporting around sustainable impact. These individuals determine what data is worthwhile and what gets fed to AI solutions.
Energy-efficient hardware is becoming the norm too. These devices have AI algorithms built into them, so organizations don’t have to run additional software that utilizes energy. Engineers are also developing specialized AI chips for lower power consumption in edge computing. And, of course, companies can use AI to optimize their existing AI functions – for example, by bringing to light moments for downtime and only running AI resources on demand.
Beyond physical tools, government entities and organizations are collaborating to ensure safe and successful sustainable AI. For example, global cloud providers are being encouraged to redirect their energy sources to locations that provide solar and water power. Likewise, internationally recognized sustainability ratings from third parties (such as EcoVadis) uphold a standard of sustainability in business, and keep players accountable for their AI claims and goals around sustainability.
The forecast for AI is bright
AI continues to gain momentum as a sustainable force. For example, scientists have already begun using AI to trap greenhouse gasses in porous rock formations. In this process known as carbon sequestration, AI helps simulate CO2 being redirected back into the ground, selecting the right points to inject, controlling pressure buildup, and ensuring that rocks aren’t fractured in the transfer. According to research, carbon sequestration has the potential to offset the equivalent of 5-15% of global fossil fuel emissions.
Over in the transport space, AI has its footing in autonomous vehicles and the tech is being extended to drone taxis that have all-digital pilots. Traffic management systems are set to develop with AI too, as data from cameras and sensors along transport routes informs action around reducing congestion, suggests more efficient travel routes, and identifies areas for greener transport options.
AI is additionally poised to address environmental damage, not just prevent it. Rather than only tracking sustainability metrics in real-time, AI can automate steps that restore harmful effects of deforestation, climate change, and resource depletion. For instance, in aquatic habitats, AI-powered robots can monitor water quality, detect aquatic species, and even remove invasive species. In this sense, AI is a proactive (not merely reactive) facilitator of sustainability.
Much like algorithms, AI and its applications are forever evolving. So far, we’ve only seen a snippet of what AI can do, and yet the results have, on the whole, hugely benefited societies and the world. Moving forward, as the technology grows and our handling of it becomes more nuanced, the positive impact on sustainability will be felt by more people, for more time.
Are you looking to extend your teams’ AI out into, and for, the world? Speak with an intive expert and start planting the technical roots for change.