AI and sustainability: how artificial intelligence is revolutionizing environmental protection
Artificial intelligence consumes enormous amounts of energy - at the same time, it opens up completely new ways of doing business sustainably. While the training of AI models pushes data centers to their limits, intelligent algorithms help companies to save resources, reduce emissions and assume social responsibility. AI and sustainability have a tense relationship: how can you exploit the potential of the technology? This article shows you practical solutions and concrete use cases for sustainable AI management in your company.
Artificial intelligence and its energy consumption
experts estimate that each AI query on ChatGPT consumes around three watt hours of electricity - ten times more than a Google search. That doesn't sound like much, but with 300 million weekly active users (source), it adds up to a huge amount.
Forecasts predict that global AI systems will consume a total of over 80 terawatt hours per year - equivalent to the electricity requirements of countries such as the Netherlands or Sweden.
Data centers already account for 4 to 5 % of global energy consumption, and experts fear that this figure will rise to up to 30 % in the coming years. But high energy consumption is not the only problem: ChatGPT requires around half a liter of water per chat to cool the servers. Training the system has already consumed around 700,000 liters of water(source).
Opportunities to make AI models more sustainable
The good news: you can reduce the energy consumption of your AI applications. Three green AI approaches have proven to be particularly effective for AI in the company:
- Use open source approaches: Instead of training your own models from scratch, use proven open source solutions. You can adapt models such as BERT (Bidirectional Encoder Representations from Transformers) or LLaMA (Large Language Model Meta AI) to your specific requirements through targeted fine-tuning - with a fraction of the original energy expenditure.
- Implement data minimalist approaches: Less is more: reduce your data sets to the essentials. Intelligent data cleansing and selection can reduce training time without compromising model quality. Techniques such as data pruning remove redundant information and speed up training considerably.
- Follow Green AI principles: Green AI prioritizes efficiency over pure performance. Choose algorithms that require less computing power, even if they are minimally less precise. Use energy-efficient hardware and optimize your model architecture for lower power consumption.
These measures not only reduce your energy costs, but also position your company as a pioneer in the responsible use of AI.
Practical examples: AI makes companies more sustainable
Artificial intelligence is conquering the corporate world - but so far mainly for economic reasons. Most AI projects focus on increasing efficiency, reducing costs and automating processes. However, a paradigm shift is emerging. The following areas of application show this: Intelligent systems can drive ecological, economic and social sustainability.
Energy efficiency in production
Imagine a production facility in which intelligent algorithms analyze production data in real time and automatically adjust temperature, pressure and machine settings.
The result: lower energy consumption with the same production output. AI-supported systems detect inefficiencies that people miss. They optimize heating and cooling systems, switch off unused machines and coordinate production processes for maximum energy efficiency.
AI in the circular economy
These two companies show what contribution AI can still make to greater sustainability:
Optimization of supply chains and logistics
DHL shows how AI is revolutionizing the last mile: The logistics group is already using intelligent systems that drive sustainability and efficiency in equal measure:
AI forecasting models analyze each individual shipment and create optimal route sequences. These take into account all the important variables for the best possible route and provide customers with precise information about the expected delivery time.
The optimized route avoids additional journeys and unnecessary stops. This makes a decisive contribution to fuel efficiency. In addition, an AI-supported journey analysis and a driving behaviour analysis are used: the system detects inefficiencies such as excessive idling times and helps drivers to optimize their fuel consumption.
Social sustainability through AI
Social sustainability means more than just fair working conditions - it includes equal access to healthcare, education and social participation. These are just a few examples:
- The development of new drugs takes many years and costs billions. AI drastically shortens this time span: algorithms analyze millions of molecule combinations in weeks instead of years and identify promising drug candidates.
- AI is already outperforming human capabilities in cancer diagnostics : Image recognition algorithms detect tumors in X-rays and CT scans earlier and more precisely. Early detection saves lives and reduces treatment costs.
- Care benefits from intelligent assistance systems. AI-supported sensors monitor vital signs around the clock and alert care staff in the event of emergencies. Robots assist with physically strenuous tasks and relieve the strain on staff. This improves the quality of care and working conditions.
- AI supports social inclusion by breaking down barriers. Speech recognition software enables people with motor impairments to operate computers and smartphones. Real-time translation systems help people with limited language skills to communicate.
Appropriateness of the use of AI
Not every sustainability challenge needs artificial intelligence. You need to carefully weigh up when it really makes sense to use it. These five criteria will help you decide:
#1: Complexity as a yardstick
AI shows its strengths in complex problems with many variables. Energy optimization in large production plants with hundreds of machines, changing production cycles and fluctuating energy prices? This is where AI pays off. Controlling a single heating system according to fixed schedules? Simpler solutions are often enough.
Rule of thumb: The more data sources, dependencies and influencing factors the problem has, the more likely AI is to justify the resource consumption. For linear correlations with few parameters, it is better to use classic optimization methods.
#2: Type of task decides
AI is particularly suitable for four types of tasks:
- Pattern recognition in large amounts of data helps to uncover hidden energy wastage in production processes.
- Forecasts based on historical data enable precise demand forecasts and prevent overproduction.
- Optimization of processes with many parameters makes supply chains, for example, more efficient.
- Automation of repetitive decisions speeds up the sorting of recycling materials.
For simple if-then rules, static calculations or one-off analyses, classic programming is often more efficient and saves resources.
#3: Desired degree of automation
Clearly define how independently your AI system should operate. Fully automated systems are suitable for non-critical, repetitive tasks such as lighting control or temperature regulation. For important business decisions, AI should act as an intelligent assistant that makes recommendations while humans make final decisions.
#4: Criticality of the system
The more critical the impact of wrong decisions, the more cautious you should be. Safety-critical systems such as emergency shutdowns or medical devices require human supervision and backup systems. Non-critical applications such as the optimization of office lighting can be fully automated.
Create a risk matrix: How likely are errors and what consequences would they have? If the risk is high, invest in elaborate protection or do without AI.
#5: Quantify sustainability benefits
Companies are using energy-intensive AI systems to improve their environmental footprint. This sounds contradictory - but it works if you approach it correctly. The most important benchmark: does the sustainability benefit exceed the ecological footprint of the AI?
Carry out a life cycle analysis and also consider indirect effects such as improved product quality or extended machine service life. The key is proportionality: an AI application that reduces the energy consumption of your production by 20% justifies its own electricity consumption many times over. It is important that you keep an eye on the net effect and avoid rebound effects.
You might also be interested in
