Is AI advancing sustainability or creating costly trade-offs we’re only beginning to understand? In this post, we dive into the reality behind AI's potential as a force for sustainability. While AI shows promise in enhancing sustainable practices and supporting business processes, it also has a significant CO₂ footprint—mainly from energy-hungry data centres—and its environmental impact will likely grow. Though AI could help achieve Sustainable Development Goals, this depends on our responsible use and governance of the technology. Without stringent oversight, AI risks reinforcing societal biases, as seen in social media algorithms that foster echo chambers. As with all innovations, AI’s promise is matched by its challenges, and only a well-balanced approach can ensure it contributes meaningfully to a sustainable future.
How can AI help connect the worlds of sustainability/ESG data, and that of company financials?
ESG is typically considered a mere cost - yet: this perception stems chiefly from a lack of integration of mutually beneficial data of these two worlds. With better approaches to cost accounting, to performance analysis, as well as using predictive analysis relate to trends, legislation and asset management, the ability of AI to integrated diverse and complex data sets may precise be the pathway to shift that needle.
Can AI help us to get to (better) grips with supply chain compliance?
Supply chains are based on fairly complex partnership networks where every link ideally must meet strict efficiency and compliance standards.
Supplier audits and legislation aim ultimately to ensure high standard, it is a not the least highly time demanding task to be successful at. AI offers the potential to support practical solutions for risk assessment, process optimization, and partner evaluation. Commercial providers are already jumping on the band wagon by providing ways to build 'digital twins' of real supply chains – hence opening them up for 'offline optimisation' - and of course highly sophisticated data analytics tools drawing from multiple disjoint data sources.
Could ESG reporting finally become less repetitive and tedious?
AI has the potential to transform ESG reporting by automating compliance tracking, integrating data from diverse and unstructured sources, and streamlining audit preparation. This opens up opportunities to free data and ESG experts from repetitive, tedious tasks. Yet, while AI offers promise, tight oversight remains essential to address challenges like data quality ('crap in is crap out') and system integration.
AI has the potential to transform corporate responsibility by handling data-heavy tasks like reporting or data and KPI management. It hence can contribute to helping companies 'being less bad'. However, its potential to support professionals and companies in driving real positive impact is still developing. This post introduces AI’s current potenntial in corporate responsibility and sustainability. In upcoming blog posts we'll explore specific applications: in sustainability reporting, supply chain management, and integrating financial considerations with sustainability impact.
How does digitalisation impact and link to corporate responsibility? This is the question we look into in this post.
Combining the two disciplines results in a range of interesting questions. For example: If humans create non-human agents (e.g. in the shape of AI): For what, towards whom are these responsible? And: are they responsible at all - or is it their creator who is?