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.​

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time and resources required for ESG reporting and GHG emissions calculation​


accuracy and transparency of GHG emissions calculations and ESG performance data​


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.

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Industry Recognition

2023 Winner​
ESG & ​Sustainability​

Logo Baker Hughes

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C3 AI Named 10 Innovative Vendors Advancing ESG and Sustainability Performance by Verdantix

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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

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