Excessive use of raw materials, pollution of air & soil, melting of glaciers, rising sea levels and negative implications of climate change demonstrate that unsustainable business activities endanger the Earth’s habitability and in turn the profitability of future economic activities. For this reason, in recent years, there has been an increase in the number of regulations to ensure that companies operate in a more responsible manner. Recent developments in corporate sustainability and ESG reporting frameworks such as publication of IFRS S1 & S2 sustainability reporting standards and European Sustainability Reporting Standards (ESRS) are actually among the important indicators of this trend. Complying with these reports, which require the collection and interpretation of data across value chains in areas such as energy consumption, emissions and human rights, has posed significant challenges for companies. At this point, the rise of artificial intelligence offers important opportunities to solve these problems.




Cross-cutting ESRS 1 General Requirements
Cross-cutting ESRS 2 General Disclosures
Environment ESRS E1 Climate
Environment ESRS E2 Pollution
Environment ESRS E3 Water and marine resources
Environment ESRS E4 Biodiversity and ecosystems
Environment ESRS E5 Resource use and circular economy
Social ESRS S1 Own workforce
Social ESRS S2 Workers in the value chain
Social ESRS S3 Affected communities
Social ESRS S4 Consumers and end users
Governance ESRS G1 Business conduct

Figure – 1: Issues Covered by European Sustainability Reporting Standards (Source: European Commission)

Common problems in ESG reporting related to data    

Unlike financial information, which is prepared in a specific format, qualitative and quantitative information on ESG is often more complex due to the use of scenarios and forecasts. Considering the multiple stakeholders in the value chains, errors and omissions during manual information entries can also cause boards of directors to reach wrong conclusion about sustainability risks and opportunities. Collecting and interpreting this data using manual methods causes a huge workload for especially small and medium-sized companies with limited financial and personnel capacity. The investors’ demand of supportable and accurate ESG information has been growing, while majority of them, according to PwC, believe that most of the corporate sustainability reports contains unverified and baseless information.

Collection and interpretation of sustainability data with AI

For preparing their sustainability reports, companies need to collect up-to-date data on topics such as water consumption, waste management, anti-corruption, biodiversity and energy. AI integrated with IoT devices facilitates the collection of complex data from disparate sources and the discovery of meaningful patterns within that data.

The risk and opportunity analysis required for materiality assessment, the most critical part of sustainability reporting, is also facilitated by the opportunities offered by artificial intelligence. Collecting data quickly and comprehensively and interpreting it with the help of artificial intelligence may help companies set more rational sustainability KPIs and targets.

AI enables companies to map the risks and opportunities in their value chains. For example, AI can offer effective solutions to identify emission hotspots and identify low-emission technologies that can be used for improvement. Machine learning algorithms and natural language processing (NLP) can make sense of large amounts of dispersed information from different sources in a structured way without much effort.

ESG information can be more meaningful for investors if it can be compared with the information of other companies operating in a similar sector. Otherwise, it is difficult for investors to draw meaningful conclusions about sustainability risks and opportunities based on complex ESG data. Artificial intelligence technologies that can access different databases have the capacity to fill a major gap in this area.

Example of AI tools in corporate sustainability

It is seen that artificial intelligence tools used in the field of corporate sustainability are increasing day by day. For example, Climate TRACE can track companies’ greenhouse gas emissions in real time. Datamaran, another AI tool, analyzes companies’ data on corporate sustainability and makes recommendations on areas that can be improved. Manifest Data compares and checks the data declared by companies against the data of other companies operating in a similar sector, reporting frameworks and legal regulations.

Limitations of artificial intelligence

It is not correct to see AI as an ultimate panacea for sustainability reporting. Companies should establish efficient mechanism for protecting data privacy in using AI for data collection and interpretation. In order to ensure that the algorithm used by artificial intelligence is not biased, different sources should be used, especially in training processes. The results obtained by using artificial intelligence in ESG reporting should be carefully re-evaluated by companies’ sustainability organizations. If sustainability organizations use AI in a sloppy way just to fulfill their obligation to report quickly, the results will undermine companies’ capacity to avoid serious sustainability risks and seize big opportunities.


Current trends show that the effects of climate change will become increasingly severe so the stringency of corporate sustainability reporting regulations. The need to report across supply chains means that data needs to be collected far beyond the capacity of SMEs to manage. Taking into account the rapid progress in artificial intelligence technology, it shows that companies will benefit from the tools offered by this technology much more in the coming years in parallel with the developments in sustainability reporting frameworks. In order for these processes to be carried out in a healthy way, there is a need for experts who are well versed in sustainability reporting frameworks and artificial intelligence developers to work in a more coordinated manner to overcome the deficiencies in practice.