Matter

**How to turn ESG disclosures into decision-ready data, faster and with less friction - powered by AI** 

ESG data is central to investment decisions, risk management, and regulatory compliance, but the data investors rely on is often a year or more out of date by the time it reaches them. This delay undermines transition-risk pricing, net-zero/Paris alignment tracking, and timely capital allocation/stewardship decisions. 

This webinar explores why timely, reliable ESG data matters, what makes it so hard to deliver at scale, how new AI-powered approaches are changing what's possible for institutional investors.

You’ll hear about: 

*   **(Problem)** Why investors depend on timely ESG and emissions data: for pricing transition risk, tracking net-zero and Paris alignment, and meeting regulatory obligations like CSRD and SFDR 
*   **(Competitive edge)** Why ESG disclosure data is notoriously slow and messy: emissions data often lags 12–18 months behind reality, while heterogeneous formats, inconsistent units, and shifting definitions make it hard to turn raw disclosures into comparable, investor-ready datasets 
*   **(Mechanic)** How AI-powered extraction, combined with human-in-the-loop validation, is closing this gap - automating the bulk of data extraction while domain experts handle edge cases, creating a feedback loop that keeps improving quality and speed over time 
*   **(Application)** How faster, more reliable ESG data supports better regulatory compliance, reduces greenwashing risk through clearer data lineage, and supports investors to build portfolio and engagement strategies on more timely insights 
