Climatiq’s AI-Powered Autopilot Takes The Wheel To Automate Scope 3.1 Calculations

Climatiq’s AI-Powered Autopilot Takes The Wheel To Automate Scope 3.1 Calculations

Climatiq, a Berlin-based API provider for carbon measurements, has announced the launch of Autopilot, a machine-learning-powered feature that automates large-scale Scope 3 carbon calculations, with a focus on purchased goods and services emissions. The new solution leverages AI to match unstructured text data from invoices, bills of material and ERP systems to the right emissions factors, also providing confidence levels and an audit trail. The feature is meant to reduce data mapping time and human error related to manual emission factor selection and estimates, thereby improving the overall accuracy of calculations without compromising the auditability of the data.

Autopilot is available as part of the Climatiq Calculate product, which provides carbon measurements within core enterprise software (such as Supply Chain Management, ERP, carbon and ESG management software), through API integrations. These measurements are powered by Climatiq’s database of over 70,000 built-in emission factors. Software providers such as Celonis, Cisco, Novata and Salesforce are currently using Climatiq’s technologies. In April 2024, Climatiq announced a partnership with supply chain management software provider, Kinaxis, to integrate Climatiq’s freight transport and purchased goods and services database and carbon calculations into the new Kinaxis Sustainable Supply Chain solution.

The announcement follows a general trend toward AI applications by carbon management and ESG reporting software providers. More and more vendors in this space are incorporating AI models into their products to support clients with their decarbonization journeys and drive optimization. For example, Persefoni, Sweep and Unravel Carbon have all added capabilities to map emissions factors to carbon data. In a recent Verdantix research report on AI, we highlighted this and eight other use cases relevant to carbon emissions reduction, with a plethora of vendors developing associated carbon-related products:

• Detecting anomalous data and performing estimations.
  AI can process large quantities of data and detect anomalies or gaps due to manual errors, thereby increasing data quality before data approval.
  
  
• Forecasting CO₂ emissions.
  Machine learning models can be used to perform predictive analytics on carbon data. Models can be trained on historical data, adding information on new decarbonization projects and their impact on overall emissions.
  
  
• Interacting with chatbots to gain insight and guidance.
  Carbon management software providers are starting to apply large language models (LLMs) to create chatbot-like features with conversational interfaces. For example, Sweep has developed Sweepy, a GenAI assistant that provides suggestions of materiality based benchmarks, and Persefoni has developed Copilot, which provides technical guidance on carbon accounting.
  
  

It’s only in the last year that carbon management and other providers have started incorporating AI into their product roadmaps. Sustainability-related use cases are still in their infancy, as too is internal backend expertise. Technology providers such as Climatiq offer plug-and-play solutions to bring efficiency into carbon calculations leveraging AI.

If you want to learn more about the use cases of AI for decarbonization, read Market Insight: Nine Ways AI Will Drive Decarbonization.