Structured vs Unstructured Data
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.
Structured vs Unstructured Data
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.
Structured vs Unstructured Data
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.
Toshiba Robot
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?