Environmental AI in Action: Industry Leaders Roundtable
At the Global Decarbonization Forum 2026 - New York held during Climate Week NYC, Dr. Max Rehberger of TÜV SÜD, Roberta Avelino of BAUER Group, Quentin Fouesnant of Alpine Group, and Prof. Dr. Christian Heinrich of carbmee discussed how artificial intelligence can help companies address environmental challenges, improve operational efficiency and strengthen supply chain transparency.
The roundtable explored practical applications ranging from chemical compliance and supplier traceability to product carbon footprints and procurement decisions. While AI offers significant opportunities to process complex data and accelerate workflows, the panelists emphasized that successful implementation depends on more than technology. Human expertise, organizational trust and clear accountability remain essential to turning AI’s potential into business impact.
AI in Practice: From Chemical Compliance to Procurement
For Roberta Avelino, who works in global compliance and leads AI education and cybersecurity initiatives, one of the main challenges is encouraging employees to adopt new tools. Traditional industrial organizations can be hesitant to embrace AI because of concerns about data security, cyberattacks and changes to established workflows. Her approach is to identify internal AI ambassadors, develop practical use cases and demonstrate the technology’s value with management support. In procurement, AI-supported tools can help teams combine data from different systems, compare suppliers and evaluate scenarios to identify more efficient manufacturing decisions.
Quentin Fouesnant, Environmental Director at Alpine Group, shared a practical example from apparel manufacturing. His team is piloting an AI agent to support chemical compliance by processing photographs of chemical containers and safety data sheets to identify potentially high-risk substances. The tool could help specialists prioritize their attention across a complex supply chain. As Quentin explained, AI can take on some of the heavy lifting, allowing teams to focus their efforts where they matter most.
Making Supply Chain Traceability More Scalable
For apparel manufacturers, supply chain transparency extends far beyond their own facilities. Raw materials pass through multiple suppliers and processing stages, making it difficult to obtain reliable information across the entire value chain. Regulations addressing forced labor add further pressure. Quentin explained that shipments held at customs may require extensive documentation demonstrating the origin of materials and conditions throughout production. A single case can involve around 20 to 30 documents from approximately 10 supply chain stages. Alpine Group is exploring how AI can process these documents, identify missing information and highlight traceability gaps. With millions of documents to manage each year, automating parts of this process could help teams respond more quickly to customer and regulatory requests.
The panelists acknowledged that complete end-to-end transparency remains difficult to achieve across thousands of suppliers and multiple tiers. AI can make information easier to process, but reliable results still depend on the quality and availability of the underlying data.
Product Carbon Footprints: Accuracy Alongside Efficiency
Product carbon footprints (PCFs) offered another example of AI’s potential. Despite advances in carbon accounting software and life cycle assessment (LCA) databases, generating a product assessment can still take several weeks. Databases alone cannot replace the work of collecting and connecting company- and supplier-specific information.
Dr. Max Rehberger, Head of Sustainability Services at TÜV SÜD, highlighted opportunities to use AI to automate data preparation, extract information from different sources and make assessments more scalable. However, faster calculations do not necessarily mean more accurate results. Relying exclusively on secondary data and industry averages may produce estimates without reflecting the actual environmental impact of a specific product or manufacturing process. More meaningful assessments require primary data from suppliers and operations, including information on energy consumption and production processes.
AI can help organize this information, identify gaps and support validation. But Max stressed that results must remain transparent and verifiable, particularly when they are used for certification or regulatory purposes. Human experts must retain responsibility for the final judgment. The opportunity lies in combining AI’s processing capabilities with reliable source data and appropriate verification, rather than treating automation as a substitute for evidence.
The Biggest Barrier Is Organizational
When asked what prevents companies from scaling AI and environmental initiatives faster, the panelists pointed primarily to organizational challenges.
Max identified change management as a major hurdle. Employees need opportunities to understand what AI can achieve within their own functions, rather than relying solely on centralized initiatives. Practical experimentation can help demonstrate its value and encourage adoption.
Roberta emphasized the importance of trust, communication and patience, particularly in organizations where previous cybersecurity incidents have made employees cautious about digitalization. Addressing these concerns and demonstrating how tools can be used securely are essential to building confidence.
Quentin highlighted a related issue: environmental accountability remains concentrated in sustainability departments. Unlike established functions such as quality, where responsibility is shared across manufacturing operations, environmental performance is often treated as the responsibility of a single team. In his view, companies could accelerate progress by making environmental priorities a shared responsibility across procurement, manufacturing, logistics and other business functions.
Embedding Environmental Intelligence into Business Processes
The roundtable’s central message was that technology is advancing rapidly, but organizational adoption must catch up. AI can help companies process complex data, streamline workflows and make better-informed decisions. Realizing this potential, however, requires environmental priorities to become part of everyday business processes.
From purchasing and production planning to logistics and product development, companies need to translate high-level environmental objectives into concrete responsibilities and operational decisions. As regulatory requirements increasingly intersect with costs, market access and financial performance, this integration is becoming a business necessity.
Ultimately, scaling environmental AI is not simply a question of implementing better software. It requires reliable data, human expertise and shared accountability across the organization. Companies that bring these elements together will be better positioned to turn environmental intelligence into measurable business impact.
