Monitor, Report, and Accelerate ESG Performance With AI
Leverage AI to Operationalize ESG
C3 AI ESG enables companies to manage and improve their ESG (environmental, social, and governance) performance with advanced machine learning, natural language processing (NLP), and generative AI techniques. Customers can utilize C3 AI ESG to unify and store disparate ESG data as ESGbitsTM, automate reporting to standards, boost specificity and traceability of carbon emissions calculations, track stakeholder ESG priorities, and manage ESG plans with scenario analysis. The application leverages generative AI to draft reports and summarize changes in stakeholder sentiment and provides natural language search to rapidly access insights.
Benefits
Reduce
time and resources required for ESG reporting and GHG emissions calculation
Improve
accuracy and transparency of GHG emissions calculations and ESG performance data
Accelerate
ESG plans and goals in alignment with stakeholder priorities
C3 Generative AI: ESG
Rapidly find critical ESG data and track ESG initiatives
Ask questions in natural language to quickly uncover relevant ESG insights and data. Leverage generative AI to draft reports and summarize changes in stakeholder sentiment. The C3 Generative AI Product Suite is available with C3 AI ESG and as a standalone capability deployable against customer datasets and applications.
AI-Based ESG Management
Pain point
C3 AI ESG Solution
Error prone data management due to fragmented ESG data across systems
Unified ESG data across disparate enterprise systems as ESGbitsTM provides single source of truth and robust data lineage and auditability reduces compliance risk
Difficulty tracking priority ESG issues across critical stakeholders
AI-based stakeholder monitoring and generative AI summaries track perception in near real-time to surface key insights and and reduce risk
Tedious process to perform GHG emissions calculations
Automated Scope 1, 2, and 3 emissions calculation according to the GHG Protocol including NLP-based fuzzy matching to select the appropriate emission factor
Inability to track progress against enterprise ESG goals
Goal setting and real-time tracking helps achieve enterprise ESG targets and generative AI-enabled search surfaces relevant progress insights
Difficulty keeping up with rapidly evolving reporting requirements
Native support for all major ESG reporting standards and generative AI drafting streamlines reporting across frameworks
Lack of data and process to determine strategy and mitigate risks to achieving ESG goals
Plan management and optimization workflows including marginal abatement cost curve facilitate centralized planning and risk management
