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“ Especially during the front-end innovation stage, teams often had limited visibility into the sustainability implications of a product or process until much later in the development.
“ Today, AI, machine learning, agentic AI, advanced analytics and connected data platforms are helping us move from reactive decision-making to proactive planning by identifying risks, evaluating options and modeling potential outcomes earlier than ever before.” This ensures sustainability is embedded in the earliest phases of product and technology development, not added after key decisions have already been made.
Transparency in sustainability reporting A crucial part of the sustainability reporting process is transparency.
Companies, stakeholders and customers increasingly seek transparency in emissions reporting and performance data.
“ It’ s quickly becoming an expectation, not a differentiator,” Scott says.“ Customers want product carbon footprint, emissions data, responsible procurement, recycled content.
“ Sustainability programmes have to become more data-driven to provide that information, so that’ s why so many companies are investing in the infrastructure to be able to provide that transparency.
“ It’ s a foundation for stronger customer relationships at a product transactional level.” The quality and availability of data often prevents organisations from accelerating carbon transparency.
“ Carbon information is often spread across multiple systems, multiple suppliers, business functions and different geographies,” adds Jutta.
“ It’ s making it very difficult to come up with a complete and trusted view of the emissions of a company.
“ Technology can help bridge the gap by connecting sustainability information with business processes, then improving the data quality, but also providing a larger visibility and transparency across a value chain.
14 dupont. com