In the dynamic landscape of corporate responsibility, the intersection of artificial intelligence (AI) and sustainability has emerged as a catalyst for transformative change.
Traditionally, Corporate Social Responsibility (CSR) and the newer framework of Environmental, Social, and Governance (ESG) have guided businesses toward responsible practices. However, as global concerns intensify and regulations tighten, the integration of AI and deep analytics is reshaping how companies navigate the complexities of CSR and ESG.
This article explores the evolving landscape of CSR and ESG, delving into the pressing need for AI solutions in enhancing reporting, predicting future developments, and propelling sustainable practices. As we traverse the currents of a changing business climate, the role of AI in these realms becomes increasingly indispensable, offering not only solutions to current challenges but also paving the way for a more sustainable and responsible future.
What is Corporate Social Responsibility?
CSR (Corporate Social Responsibility) is traditionally broken into four categories: environmental, philanthropic, ethical, and economic responsibility. The activities in a CSR strategy could include, for example, lowering the business’s carbon footprint, corporate volunteering, improving labor practices, and engaging in charity. For example, Microsoft aims to reduce its carbon footprint and help customers do the same. Verizon has provided school children with technology to help them engage in virtual learning, thus reducing the digital divide.
ESG stands for environment, social, and governance. We can notice that environmental responsibility is clearly common with CSR. Typically, rating agencies can round up performance in these areas as a score similar to CSR (e.g. carbon footprint and emissions) but in a more measurable manner. ESG improves the valuation of the business, and more capital becomes available. Investors can use ESG as a measure of how sustainable the company is, economically and environmentally speaking.
AI/ML can support businesses in enhancing their complex analytics and automated reporting in these areas, as well as predict future developments in conjunction with existing or upcoming compliance regulations.
The Growing Importance of Sustainable Practices in Business
Amidst the dynamic landscape of global business, the year 2024 marks a pivotal moment in the commitment to sustainable practices and is set to be the year of an acceleration to enhance the consistency, accuracy and transparency in ESG reporting. Some examples of upcoming directives and legislations include:
- In Europe, the Corporate Sustainability Reporting Directive (CSRD) requires large companies to publish sustainability reports according to the European Sustainability Reporting Standards (ESRS). In total, 50,000 companies will be subject to CSRD by 2025. As a more detailed example of the CSRD assessments required, double-materiality is defined as the union (in mathematical terms, i.e., union of two sets, not intersection) of impact materiality and financial materiality. A sustainability or ESG matter has double materiality if it is material from either an impact or environmental perspective, a financial perspective, or both.
- In the UK, the Sustainability Disclosure Requirements aims to improve sustainability information for consumers, addressing greenwashing.
- In Germany, the Supply Chain Due Diligence Act (LkSG) requires companies with over 3,000 employees to observe social and environmental standards in their supply chain.
Why CSR and ESG are Important
A successful ESG Strategy can help to reduce capital costs and improve company valuation (more capital available from investors). It’s not surprising that ESG is a worthwhile investing strategy. CSR and ESG have the potential to protect company valuation as more regulations such as CSRD come into use, maintain shareholder satisfaction with board leadership and attract talents who are concerned about such topics and want to work with a purpose.
ESG should be part of a long-term strategy and not only an ad-hoc box-ticking exercise to get a certification.
Some recent trends that emphasize increased consumer and investor interest in these areas include:
- According to Reuters, capital totalling US$649 billion flowed into ESG-linked funds in 2021 and the trend is increasing.
- Typical KPIs for Environment include carbon Emissions, energy consumption, Water Usage, Waste Generation, Renewable energy adoption, biodiversity impact.
- For social impact, it could be related to diversity & inclusion, health and safety performance, community engagement, labor practices and employee satisfaction.
- Finally for Governance, the Board diversity, anti-corruption measures, ethical business practices and regulatory compliance should be monitored.
The Role of AI and Deep Analytics in CSR and ESG
ESG reporting is becoming more and more complex, with tons of data and new regulations being taken into practice each year. Companies need to understand the current situation, adapt quickly to new ones and also predict future developments.
Proper data Management and AI Models implementation can help automate certain processes, calculations and reporting, with impact extending into the following areas:
- Enhanced Data Analysis and Reporting to follow-up own ESG metrics.
- Predictive Analytics for Risk Management.
- Machine Learning can be used to determine double materiality in sustainability reporting by examining what type of information can predict environmental issues resulting from companies’ operations.
- Automated Compliance and Monitoring, supply chain transparency.
- Energy efficiency and resource management.
- Employee well-being.
- Customer and Community Management.
AI in Action for CSR and ESG
Some examples of companies who are already successfully using AI to meet their CSR and ESG objectives are:
- Körber InspectifAI helps to save costs, pharmaceuticals products (therefore less waste) as well as lives thanks to AI-powered inspection of pharmaceutical products. This will have a positive effect on ESG results.
- AI has also aided the energy sector by supporting waste reduction, allowing for more economical and sustainable use of resources with the target to get closer to net zero in the future. The complexity of the processes and huge amount of data related to the oil refineries can only be tackled with powerful models that can also help to predict future developments.
- IBM uses AI and data analytics to enhance ESG reporting and decision-making. Their AI-driven platform, the “Green Horizons” project, helps cities forecast and manage air quality, providing insights into environmental impacts.
Applications Across Various Sectors
- In Pharma, AI can be used to detect anomalies early using AI-based image recognition and therefore prevent negative impact on consumers.
- In Supply Chain, AI can also be used to evaluate if packaging can be used again instead of being thrown away every time there is a return, for example.
- IoT machine data can be streamed and analyzed using powerful models in order to improve machine efficiency (OEE) and therefore reduce energy consumption and inefficiencies.
- Carbon footprint calculations might be very complex for large manufacturing machines, therefore some extrapolation might be needed in addition to real-life data.
- In finance, companies have specialized in assessing companies ESG scores or even ‘net impact’ on society in order to provide these insights to large investors or funds that are looking for responsible investments.
How has DAIN Studios contributed to CSR and ESG goals of companies?
DAIN Studios is already ahead of the curve when it comes to aiding in companies CSR and ESG goals.
Some examples of successful projects include:
- Building Sustainability Dashboards for a large manufacturing company with different scopes emissions, enabling more visibility on areas that can be improved and focus on high impact suppliers/materials.
- Detailed Carbon footprint calculations for large machines in order to better understand the impact of each part and move towards Ecodesign (on-going project).
Looking Forward: The Future of AI in CSR and ESG
As we look to the future of AI in CSR and ESG, we can see it being used in areas such as predicting employee attrition, in the same way it is possible to predict customer churn in retail. These solutions are not yet very widely used but we can predict a usage increase in the future from HR departments.
AI-based solutions can be used to ingest and analyze complex new international or national regulations in order to make more automated current state analysis and development proposals. The focus can also be shifted more towards creating value vs. only complying to new regulations.
As a result, companies will need to prove that their CSR efforts are real and not only greenwashing. Data-driven results will be required in order to convince customers and investors that the results are real.
Potential Challenges and Opportunities
AI is evolving very fast, both leaders and employees need to acquire new skills in order to stay relevant. Companies need to stay agile, invest a lot in employee development programs and retraining. Employees will need to work together with AI Solutions in order to create the maximum value for the enterprise.
AI Governance needs to be in place in order to avoid slippage in the way AI will be used and to create transparency and trust in the models used.
There is no AI without data and bad quality data will also mean unusable AI Solutions. In some cases, if the data management is not quite in place, it is worthwhile taking a step back in order to build a solid data foundation before implementing fancy AI Solutions that will not deliver the right value due to low quality data.
There is also a need to harmonize ESG and CSR data management and reporting even more internationally in order to be able to efficiently compare the results.
By leveraging AI strategically, companies in various industries can enhance their ability to measure, manage and improve their ESG performance, contributing to sustainable and responsible business practices. AI can help to gain speed in taking new regulations into use, create more visibility and accuracy in the ESG data, especially in terms of future predictions and simulations.
Investors rely more and more heavily on detailed ESG data in order to make sustainable decisions and investments. It is not enough anymore to make generic statements on future developments, these have to be backed-up by solid data and models.
In a world where sustainable practices are paramount, the marriage of AI and corporate responsibility not only shapes the present but also illuminates a path towards a future where responsible business practices are driven by data-driven insights, fostering a world that thrives on transparency, accountability, and genuine commitment to environmental and social well-being.