Artificial Intelligence (AI) is one technology that is getting a lot of airtime recently, particularly in the wake of OpenAI’s, Chat GPT release, which has become the fastest-growing consumer application in history. According to research by Research and Markets, AI is expected to achieve a compound annual growth rate of 52% by 2025.
How AI is used and applied to a particular field or business remains varied. And indeed, understanding how it can be adopted to help reduce carbon emissions and hence support efforts for sustainability, is still an area which must be further understood. For now, let’s take a look at what we know and understand about how AI is shaping the future of digital sobriety.
Progress in the field of AI has exploded. This doesn’t come without environmental consequences however. Computing power, combined with the environmental and physical cost of hardware, electricity and the weight of cloud computing result in substantial carbon emissions.
One study for example found that Google’s AlphaGo Zero, an AI-driven research programme created by Google’s DeepMind division, generated 96 tons of carbon dioxide over 40 days of research training. This sum is the equivalent of 1,000 hours of air travel.
What’s more, the data centres which house the facilities for cloud storage are significant generators of carbon emissions. On a global scale, the carbon footprint of data centres is estimated at more than 2% and Singapore, a recognised hub for data centres, was found to use the energy equivalent of 60,000 households in order to power all the data centres set up there.
With the known environmental consequences of AI, there is work being done to help reduce the carbon emissions related to this kind of technology.
Scientists in Singapore are for example looking to reduce the “training phase” of AIs. As AIs need to absorb significant amounts of data in order to learn from existing material and as such recreate human-like responses, understanding how to reduce this and work to limit the energy used is a crucial question being studied.
Another area that can help to make AI integration more sustainable is through the type of device that powers the technology. For example, when AI is integrated into a building to help monitor energy flows, it can be installed with lightweight mobile devices or sensors which do not rely on cloud services for data processing. This reduces the reliance on data centres, freeing up greater amounts of capacity.
Many different industries have begun to invest in AI technology. Indeed, the European Green Deal launched in 2019 highlighted the potential that AI has in contributing to a new green transition, through the controlled use of scarce resources and the optimisation of certain processes. The deal also emphasised that sustainability must be the priority behind the development of AI and in turn the forming of a digitalised society.
AI is used within farming, land and agriculture in order to help mitigate against crop issues. By combining AI with satellite images, the technology is able to detect changes in the land and any consequences of natural disasters. There is also the ability to identify crop diseases at an early stage to protect land from potential problems.
Using this technology for agriculture is a way of automating data collection and helping to protect land against environmental factors and climate related challenges. This then helps to reduce the use of water, fertilizers and pesticides, cutting down materials used and their effect on the land.
With AI technologies, renewable energy industries can now more accurately predict weather forecasts which allow costs to be cut and unnecessary carbon pollution generation to be averted. AI can also be used to manage energy storage and facilitate the integration of renewable energies.
AI when applied to the built environment can be used to manage lighting, heating and space needs more intelligently. By detecting occupancy levels, heating and lighting can automatically be turned off when not necessary. The physical environment can thus be optimised for the comfort levels of a building’s occupants and the resources used can be tailored to the quantities needed to sustain the building.
AI also has the ability to help reduce the carbon footprint of marketing and communication teams as they work to become more environmentally focused within their digital activities.
Thanks to the capabilities of AI, certain tasks can be automated which subsequently increases productivity, reduces errors and allows for new, more efficient processes to be established.
The content asset needs of companies across the world are showing no sign of slowing down. However, with companies more and more conscious of their environmental footprint, how such assets are managed and stored is an important question that many brands are asking themselves.
Using software such as Digital Asset Management (DAM) is one way that many brands are helping to manage, track and store all their assets.
AI might be an important tool for ensuring that DAM software is optimised to offer a way of reducing the emissions caused by the storage, sharing and distribution of media such as images, photos, videos and audio files.
Across large, multi-brand, multi-location and multi-team organisations, the amount of duplicates that are created presents a significant emitter of carbon emissions. With AI integrated into a DAM platform, duplicates are automatically detected and can be removed easily, freeing up essential storage and helping to reduce a company’s digital footprint.
It is inescapable that human error leads to images being wrongly tagged or data incorrectly entered into a system. With AI integration in a DAM platform, these issues no longer exist. Thanks to metatagging and facial recognition, images are intelligently and correctly tagged and statistics related to a campaign can be automatically updated.
This enhanced way of working means organisations won’t find themselves with a number of assets that have all been tagged slightly differently (think of an image of the UK which could be tagged “UK”, “U,K”, “United Kingdom”, “Great Britain”) and as such reduces the problem of the same image being uploaded multiple times.
With the ever-growing demand to have personalised, unique content across diverse platforms, brands are now having to produce a greater amount of content that is adapted for a range of different channels.
Helping to reduce the need to organise costly photoshoots, which may in turn result in carbon inducing travel and the use of resources, AI is a useful way to help generate content that doesn’t yet exist, all from within the DAM environment.
Thanks to Generative-AI, brands can use text prompts to create new images without ever having to photograph the scenario they are looking for.
What’s more, for photoshoots that have already taken place but require changing (think for example of a car model that has been shot in the desert but the brand is looking to market it within the mountains) with AI, backgrounds can be easily altered, opening up the possibility to quickly and efficiently target new markets and diversify campaign materials. This is done without having to reshoot campaigns and avoiding having to invest in any new, specific software.
The possibilities that AI is opening up for many different sectors still remain to be seen, particularly at the rate that this kind of technology is advancing at.
For marketing and communications teams, AI could prove to be a handy resource for helping to control their company’s digital footprint. From increased tracking, automated workflows and the ability to create hyper-personalised communication campaigns, AI has a lot more that it can offer.
Harnessed in the right way, AI can prove to be an essential tool for marketing and communications teams, particularly when combined with powerful software such as DAM.