The road to net-zero emissions is paved with data. As governments tighten regulations and businesses face increasing pressure to curb their carbon footprints, the need for precise, reliable emissions tracking has never been greater. But tracking emissions is far from simple—without accurate measurement, progress is impossible. Artificial intelligence (AI) is now stepping in to revolutionize emissions reporting and management.
Why measurement is the first step to management
You can’t manage what you can’t measure. Before organizations can make meaningful emissions reductions, they need to understand the scale of their impact. Without tracking changes in emissions levels, it’s impossible to determine if interventions are effective. Additionally, without accurately measuring impact, any improvement efforts are merely speculative.
This makes emissions tracking one of the defining challenges of the coming decades. The world’s climate response is set to reshape economies and industries—from how we travel and manufacture goods to the way we consume energy. For these changes to be effective, they need to be based on reliable data that reflects the reality of emissions trends.
The challenge of inconsistent emissions data
For businesses, emissions reporting presents two key hurdles: compliance and opportunity. On the compliance side, organizations must adhere to increasingly stringent regulations, proving not only where their emissions stand today but also how they plan to reduce them over time. This requires a deep, data-driven understanding of where emissions originate—whether from direct operations, energy consumption, or supply chains (known as scope 1, 2, and 3 emissions).
Beyond regulation, emissions data also holds financial potential. Initiatives like the U.S. Inflation Reduction Actand others around the globe offer tax incentives for companies that can effectively track and manage carbon sequestration and sustainability projects. The ability to analyze carbon data doesn’t just ensure compliance—it unlocks opportunities for businesses to gain financial benefits from their sustainability efforts.
The problem? Carbon data is often inconsistent, incomplete, or fragmented. Different units of measurement, varying calculation methods, and a lack of standardized frameworks make it difficult for organizations to generate meaningful insights. When carbon data is unreliable, decision-making suffers.
How AI is changing the game
AI is uniquely suited to tackling these challenges. By analyzing vast volumes of data, AI can uncover trends, identify inefficiencies, and highlight areas for emissions reduction.
For example, AI can pinpoint anomalies in emissions reporting, helping companies detect discrepancies or fraudulent claims. It can automate complex carbon accounting processes, ensuring accurate, real-time emissions tracking. And it can integrate disparate data sources—energy usage, transportation emissions and supply chain footprints—into a cohesive picture of an organization’s environmental impact.
AI also plays a crucial role in forecasting, enabling businesses to model different sustainability strategies and predict their impact. By running simulations, organizations can evaluate the most effective paths to reducing emissions while balancing financial and operational considerations.
Is standardization the missing piece?
However, AI is only as effective as the data it works with. Without standardized emissions data, AI models risk generating unreliable results. The issue is particularly pronounced in scope 3 emissions tracking, where companies struggle to align supplier data due to a lack of uniform measurement and reporting methods.
One solution is the adoption of standardized data models, such as the Open Footprint Data Model. These frameworks establish consistency in key data elements—naming conventions, units of measurement, and calculation methods—making emissions data more usable and AI-driven insights more accurate. Standardization reduces the need for manual data conversion, allowing companies to deploy AI solutions faster and with greater confidence.
Real-world impact: AI-powered emissions insights
Consider a multinational corporation aiming to track emissions across its supply chain. Without standardized reporting from suppliers, the company would need to manually align and convert disparate data formats before AI could analyze them. This process is time-consuming and prone to errors.
With a standardized emissions data model in place, AI can instantly process and compare supplier emissions, identifying which suppliers are making real progress in sustainability and which need to improve. This enables businesses to make data-driven decisions about procurement and partnerships, reinforcing accountability across the supply chain.
In another case, a company looking to assess its scope 1 and 2 emissions across multiple business units would face similar challenges. AI can streamline this process—provided the data is structured consistently. Standardization ensures that AI can compare emissions across different segments accurately, identifying high-emission areas and prioritizing interventions where they will have the greatest impact.
The future: AI-driven sustainability
For businesses serious about sustainability, leveraging AI for emissions tracking is no longer optional—it’s a necessity. AI’s ability to process complex data at scale enables organizations to move beyond basic compliance and towards proactive emissions reduction strategies.
But AI alone isn’t enough. Without standardized data models, even the most advanced AI solutions will struggle to provide meaningful insights. That’s why businesses must invest in both AI technology and data standardization initiatives to unlock the full potential of emissions tracking.
The climate crisis demands action, and action starts with understanding. By combining AI’s analytical power with a standardized approach to emissions data, businesses can navigate the complexities of decarbonization with clarity and confidence. The path to net zero may be challenging, but with AI as a guide, organizations can track, manage, and ultimately reduce their emissions more effectively than ever before.
Jim Hietala
Jim Hietala is VP Sustainability and Market Development at The Open Group.
