2026:theyearofESGregulations

The starting point

Dcycle opens this webinar with an unusual premise: "This is the story of how we discovered something we weren't looking for." After more than 25,000 annual customer conversations over five and a half years, and with data from over 600 companies in a single month, they detected a persistent paradox: "The better the data, the less impact it had."

Customer testimonials illustrated this clearly:

• "Collecting all the data is very labor-intensive and we have everything manual. Right now we have so many open fronts that I can't even stop to think about how to improve this." (Reny Picot, ILAS Poland S.A.)
• "We haven't been able to move forward with LCA because non-financial reporting and decarbonization have completely overwhelmed us."
• "Management asks me for the budget and wants to know what benefit we'll get. If it's just for prestige, they won't buy it. But if it's a legal requirement, then yes."

The diagnosis: The problem wasn't how to collect the data, but what it was really useful for. ESG teams called it "sustainability data" when it was actually company data.

The discovery: Operational Intelligence

By connecting AI agents directly to Dcycle's data platform (via MCP integration), analyses that previously took weeks could now be generated in minutes. Three discoveries were presented about a simulated large consumer company:

1. Vehicle fleet: The AI identified 23 underutilized vehicles and calculated potential cost savings, while also identifying emissions per vehicle to guide electrification.
2. Procurement management: By cross-referencing supplier and pricing data, it identified cost disparities for the same product across different suppliers (e.g., €1,000 vs €2,000), opening opportunities for renegotiation and consolidation.
3. Regulatory risk matrix: By combining procurement data with EINF risk information, it generated in 5 minutes a supplier-level matrix with CBAM exposure (€82 per tonne in one case), geographic concentration risk outside the EU and action plan.

The testimony of Blanca (real customer)

Blanca, a sustainability director at a customer company, confirmed replicating this approach on her own using Dcycle and Manus. Her conclusions were:

• The sustainability team moved from being "in a corner" to becoming a strategic source of information for management.
• Businesses need evidence in euros and concrete risks to adopt decarbonization measures, not just emissions data.
• With AI, she generated reports for the management committee in minutes, without relying on manual Excel files.
• She identified the opportunity to decouple business growth from emissions, showing understandable predictions for management aligned with SBTI.

The central message

"You are not managing ESG data for reporting. You are building one of the most strategic assets in the entire company."

Sustainability teams accumulate the largest and best context across the entire company (emissions, suppliers, logistics, waste, regulation, employees). This context is exactly what AI needs to multiply its value. Companies adopting AI with good context see up to 40% better performance according to 2024 studies, with competitive advantage estimated to be even higher following AI milestones in late 2025.