First Step Towards Responsible Tech is Understanding the Baseline

The United Nations’ World Environment Day takes place on June 5, prompting organizations to work harder than ever on their environmental, social and governance (ESG) commitments, which even the tech giants are at last taking seriously.

The problem is that businesses generate lots of content about their operations but are overwhelmed by its sheer volume. This year 120 zettabytes of content will be generated and next year181 zettabytes. A zettabyte is a trillion gigabytes.

Hidden within this mountain of corporate information lies a significant opportunity for brands to gain insights into their ESG performance, an area where they are increasingly being held accountable. However, as 80% of the content currently exists in static documents, emails or other unstructured forms, it is a poor source for decision-supporting insight.

You Can’t do ESG Until You Know Where You Stand

The good news is that a whole spectrum of AI capabilities can unlock the value of these multiple sources of business intelligence through cloud-based content management platforms. For example, to reduce environmentally inefficient resource usage along the supply chain, AI can extract new insights into order duplications, logistics mileage and excessive energy consumption. These insights are typically scattered across purchase orders, invoices and delivery notes, and can now be easily accessed and utilized. 

Related:Convergence of AI, Sustainability in the Manufacturing Sector

This data will enable the monitoring, cross-analysis and distillation of meaningful insights from these knowledge assets, supporting or triggering the right targeted actions in any of the ESG areas organizations need to move on.

This is because AI technology is particularly effective in pattern matching and can leverage a wide range of deep learning capabilities at a massive scale. Techniques such as visual analysis, image recognition and natural language processing enable continuous scanning and detailed tagging and indexing of content, known as metadata generation. These techniques can identify and capture what is really going on in an organization’s environment or supply chain.

This will happen by extracting rich metadata for easier discovery and linking it meaningfully. Cloud-based data analytics and content retrieval and analysis platforms are also crucial. Additionally, contextual AI plays a vital role in connecting the dots between content and related metadata, capturing the context of the content and comparing or contrasting related information over time.

This process enhances the capability to identify correlations, trends, outliers and red flags or untapped ESG opportunities as needed. Imagine being able to query metadata-enabled content to distill ESG insights such as: "Show me the reality of all the environmental claims of our suppliers."

Related:AI Projects Need Intelligent Data Workflows

AI will be a crucial tool in sifting through vast amounts of data to help identify new opportunities for reducing carbon emissions and so securing future success in ESG initiatives.