AI, IT and sustainability – how do they fit together?
How AI and IT can advance environmental, social and economic goals
Sustainability and digitalization are two of the most important drivers for the future viability of companies (TWIN Transformation). But how are these two areas related?
Artificial Intelligence (AI) and Information Technology (IT) open up new possibilities for achieving sustainability goals more effectively and efficiently. Through smart data analysis, process optimization and automation, resources can be conserved, emissions reduced and sustainable decisions supported, both economically and ecologically.
Find out how companies can make meaningful use of AI and IT – in the supply chain, in marketing and sales, and in the day-to-day work of sustainability managers.
Be inspired by how technology can not only optimize your processes but also make a real contribution to a more sustainable future.
Opportunities and risks with AI
The opportunities for a sustainable and economic future offered by AI and IT
AI tools such as ChatGPT, Perplexity (web search), and Canva (image creation) , Fireflies (notes), Asana (project management), Jasper (content writing), Zapier (automation), etc. can take over repetitive tasks or automate processes, saving time and resources.
AI and machine learning algorithms help companies reduce waste and emissions by analyzing data, identifying inefficiencies and optimizing processes.
They enable precise predictions, real-time adjustments and promote the circular economy by analyzing product lifecycles.
This helps to conserve resources, minimize losses and implement targeted sustainable measures.
AI-supported systems enable a comprehensive analysis of the supply chain, both upstream (supplier networks) and downstream (distribution channels, recycling), and promote sustainable practices in interactions with customers.
They help to identify weaknesses such as inefficient transportation or unsustainable material sources and develop solutions in real time.
Furthermore, companies can use AI to assess the impact of their products throughout their entire life cycle and develop new ways of reusing or recycling them, which plays a central role in the circular economy.
AI processes huge amounts of data in real time and supports data-based decisions, for example to optimize production processes or logistics chains. It identifies patterns and correlations that are difficult for humans to recognize, and thus provides a reliable basis for decision-making.
For example, companies can use AI to make predictions about demand, inventory levels or energy consumption, thus avoiding overcapacity or bottlenecks.
In addition, AI enables the integration of various data sources, such as market trends, weather data or customer behavior, to make decisions even more precise and sustainable.
AI helps to identify, organize and evaluate project data in a structured way.
It accelerates analysis, optimizes planning and provides important information for reports (e.g. CSRD).
In addition, AI makes it possible to link different data sources, such as supplier data, energy consumption or CO₂ emissions, thus creating transparency regarding sustainability measures.
Sustainability managers can thus focus more on strategic tasks instead of wasting time on manual data collection.
Challenges and disadvantages of using AI and IT
The energy consumption of training large AI models is significant. For example, training GPT-3 caused emissions of about 500 tons of CO₂, which is equivalent to the annual electricity consumption of 320 four-person households.
More recent models, such as GPT-4, require even more resources. In July 2022, just before the training of GPT-4 was completed, a data center in West Des Moines, Iowa, used about 6% of the district’s total water supply for cooling purposes.
These examples illustrate the increasing energy and resource demands of modern AI systems.
AI systems require extensive amounts of data, often including personal information. The processing of such data is subject to strict data protection regulations, such as the GDPR, to protect the rights and freedoms of natural persons.
At the same time, combining and analyzing data creates new risks, such as the unintentional disclosure of sensitive information. This is particularly problematic for inadequately secured systems that are vulnerable to cyberattacks or data leaks.
The European Union’s upcoming AI Act will additionally require companies to ensure the security and transparency of their AI systems, especially for high-risk applications. This legal framework is intended to ensure that AI systems are not only innovative but also used ethically and securely.
Greenwashing refers to the attempt to present a company or product as environmentally friendly without these claims actually being supported by concrete measures or results.
AI technologies can facilitate greenwashing if they are used superficially, for example to embellish sustainability reports without making far-reaching changes to processes.
One example is the use of AI to create marketing campaigns that emphasize sustainable values even though supply chains or production processes continue to contribute significantly to environmental pollution.
Such practices can not only damage a company’s reputation, but also have legal consequences, as misleading environmental claims may fall under the Unfair Competition Act (UWG) or the EU Misleading Advertising Directive.
Companies should ensure that their sustainability communication is transparent and backed up by verifiable measures.
The use of AI can change existing work processes and lead to a redistribution of tasks.
In some cases, activities are automated, which could make certain positions redundant.
This carries the risk of uncertainty and resistance among the workforce.
At the same time, however, AI offers the opportunity to relieve employees of repetitive tasks and free up resources for more demanding activities.
Timely communication and retraining programs are essential to actively involve employees in the change process and to reduce anxiety.
The use of AI raises fundamental ethical questions, particularly with regard to transparency, fairness and discrimination. Inadequately trained models or thoughtless use can reinforce unconscious prejudices and lead to discriminatory decisions.
Furthermore, questions of accountability arise: Who is liable if AI decisions lead to negative consequences?
Companies must develop clear guidelines and testing mechanisms to ensure that AI systems meet ethical standards and are socially acceptable. Regular audits and independent controls can help to identify and rectify potential problems at an early stage.
Conclusion
There are two sides to artificial intelligence: it offers enormous potential for making processes more efficient, sustainable and economical.
At the same time, it entails risks, such as high energy consumption, data protection issues and the danger of greenwashing.
The key is to make targeted use of the positive possibilities and to be aware of the challenges.
With a well-thought-out strategy, clear goals and responsible use, AI can not only help companies achieve their sustainability goals, but also create long-term added value – for the environment, society and business.
The benefits outweigh the risks if companies are willing to actively address risks and use AI as a tool for real innovation.
How to successfully implement AI in your organization
Targeted use of AI and IT: a path to greater sustainability
Introducing AI into a company is much more than a technical decision – it requires a well-thought-out strategy and the active involvement of all departments.
Without a clear objective, a reliable database and the necessary acceptance within the team, the potential of AI often remains untapped.
By taking the following steps, companies can ensure that the implementation of AI not only runs smoothly but is also successful in the long term.
Conclusion
The introduction of AI in companies offers enormous potential, but it requires a clear strategy and responsible implementation.
With the right preparation – from defining goals and building a reliable database to training employees – AI can not only optimize processes, but also effectively support sustainability and business goals. It is crucial that companies are aware of the legal framework, such as the GDPR and the AI Act, and involve their employees at an early stage.
AI is a powerful tool that, when used correctly, drives innovation and sustainability in equal measure. Those who seize the opportunities and keep an eye on the risks create the basis for long-term success.
Support for sustainability managers
Sustainability managers are faced with the challenge of evaluating complex data, setting goals and complying with legal requirements.
AI can help them to work more efficiently and purposefully by not only analyzing the current situation, but also by identifying potential and making progress measurable. The following approaches show how AI can be used in a targeted way to make the day-to-day work of sustainability managers easier.
Optimized reporting processes
AI helps to make the processes for creating reports more efficient by consolidating and standardizing relevant data from different sources.
Dashboards help to present the information obtained in a clear and concise way. This frees up valuable time for sustainability managers to focus on analysis and strategic planning.
Data analysis
AI tools accelerate the analysis of large amounts of data by identifying patterns and trends in areas such as energy consumption, supply chain data or CO₂ emissions.
This enables informed decision-making and accurate assessment of progress towards sustainability goals.
In addition, simulations can be used to run through various scenarios to identify potential optimization opportunities.
Define and pursue sustainability goals
AI not only helps to evaluate the current situation, but also to set clear and achievable sustainability goals.
By analyzing historical data and current trends, companies can create realistic targets based on their specific challenges.
At the same time, AI enables continuous tracking and monitoring of progress, so managers can monitor the success of their actions in real time and make adjustments as needed.
Compliance management
AI helps to efficiently monitor regulatory requirements such as CSRD or ESG guidelines.
It identifies potential risks, such as non-compliant suppliers, and suggests measures to minimize risk.
In addition, automated systems facilitate the tracking and documentation of compliance-relevant processes, enabling companies to minimize legal risks.
Sustainable, efficient, successful: AI in marketing and sales
AI offers exciting opportunities to combine sustainability and success in both marketing and sales.
Customer-centered sustainability
AI analyzes customer data and identifies environmentally friendly product alternatives that can be specifically advertised.
In addition, AI supports sales representatives and vendors in administrative tasks, such as data preparation, scheduling or proposal preparation.
This gives them more time to focus on their core competencies – personal communication and building customer relationships.
This targeted relief not only makes it possible to better position sustainable products, but also to provide customers with personalized advice and retain them in the long term.
Efficient communication
AI can significantly simplify and optimize communication with customers.
Automated chatbots take over initial customer inquiries around the clock, answer frequently asked questions and thus relieve the customer service team.
This leaves employees more time for more complex concerns. In addition, transcriptions of customer calls and video conferences can be automatically created and analyzed to efficiently document conversations and derive follow-up actions more quickly.
With AI-supported systems for prioritizing requests and virtual meeting solutions, interaction is not only made more resource-efficient, but also more productive.
Efficient campaign management for sustainable trends
Algorithms identify sustainable market needs, such as environmentally friendly products or services, at an early stage and help companies to precisely define their target groups.
These insights make it possible to manage campaigns in a targeted manner, use budgets efficiently and minimize wastage.
This ensures that marketing measures reach precisely those customers who value sustainability, while conserving resources at the same time.
Algorithms identify sustainable market needs, such as environmentally friendly products or services, at an early stage and help companies to precisely define their target groups.
These insights make it possible to manage campaigns in a targeted manner, use budgets efficiently and minimize wastage.
This ensures that marketing measures reach precisely those customers who value sustainability, while conserving resources at the same time.
Sustainable supply chains: AI brings transparency, efficiency and resilience
A sustainable supply chain is crucial to combining ecological and social responsibility with economic efficiency.
AI creates transparency, reduces risks and enables resource-saving optimization along the entire value chain. This not only makes supply chains more sustainable, but also more resilient and future-proof.
Real-time monitoring
Sensors and AI-based tools provide data on the entire production and delivery process, enabling companies to react quickly to deviations.
Real-time monitoring helps to immediately identify and rectify inefficient processes, such as delays or excessive energy consumption.
In addition, predictive analytics can be used to identify and avoid potential disruptions, such as supply bottlenecks or machine failures, at an early stage.
Risk management
With With AI, suppliers and partners can be systematically evaluated according to sustainability criteria and risks can be identified at an early stage.
Companies can specifically analyze weak points in their supply chain, such as non-sustainable sources of raw materials or problematic working conditions.
By automating risk analysis, AI saves time and enables data-driven, informed decisions to be made for a more resilient supply chain.
Optimized logistics
Algorithms analyze delivery routes, optimize transport capacities and reduce unnecessary transportation, thereby lowering costs and emissions.
AI can be used to plan logistics flows more efficiently by incorporating traffic data, weather conditions or seasonal fluctuations into the calculation.
The result: fewer empty runs, better use of means of transport and an overall more sustainable logistics strategy.
Reducing CO₂ emissions and resource consumption in the supply chain
AI-supported tools enable a precise analysis of the carbon footprint along the entire supply chain, from raw material extraction to delivery.
By identifying particularly emission-intensive processes, targeted alternatives can be developed, such as the use of regional suppliers or lower-emission transport methods.
In addition, AI helps to optimize resource requirements, for example, through intelligent planning of material flows, which minimizes overproduction or material waste.
