What are the challenges of using ai in emissions tracking

Implementing AI in emissions tracking poses several challenges. First, data quality and availability are critical, as AI models require large, high-quality datasets to function effectively. Incomplete or inaccurate data can lead to unreliable predictions and insights. Second, the complexity of AI models can make them difficult to interpret, known as the ‘black box’ problem, which can hinder stakeholder trust and adoption. Third, the integration of AI systems with existing technology infrastructure can be resource-intensive and complex. Additionally, there is a need for skilled personnel to manage AI implementations and interpret outputs accurately. The World Economic Forum notes that overcoming these challenges requires a robust governance framework to ensure data quality and ethical AI use (https://www.weforum.org/agenda/2021/01/ai-governance-framework-ethics/). Similarly, McKinsey highlights the importance of aligning AI initiatives with business strategy and ensuring cross-functional collaboration (https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/the-ai-factor-in-sustainability). Key Takeaway: AI in emissions tracking faces challenges of data quality, model complexity, and system integration.