Can AI Make Carbon Capture Credible at Last?

AI tools are moving into carbon capture pilots, promising better efficiency, stronger monitoring, and more credible climate claims

7 Jan 2026

Artificial intelligence is beginning to move into the operations of carbon capture projects, as developers look for ways to reduce costs, improve monitoring and strengthen credibility with regulators and investors. While the technology is still largely confined to pilots and early commercial facilities, companies and researchers say digital tools could play a growing role as the sector tries to scale.

Across North America, carbon capture, utilisation and storage projects are experimenting with data-driven systems to manage complex processes and large volumes of information. Developers argue that traditional manual oversight is becoming less effective as facilities expand and operating conditions change more frequently.

Over the past year, several companies have said data and automation are becoming central to their long-term strategies. Carbon Engineering, for example, has discussed testing AI-driven optimisation tools at its direct air capture plants, aiming to stabilise performance as it prepares for wider deployment. Researchers note that such facilities generate thousands of data points, making automated analysis increasingly attractive.

Energy use is one of the main targets for these efforts. Power consumption remains the largest cost component of carbon capture. Studies suggest that machine learning models can analyse sensor data in near real time and support automated adjustments to equipment. Developers say this could improve capture rates while reducing energy demand, though outcomes vary by site and technology. Even modest efficiency gains could have a significant impact on long-term economics.

Digital tools are also drawing attention in carbon storage. ExxonMobil and other operators have pointed to advanced modelling and data analysis under development to better understand how carbon dioxide behaves underground. AI-assisted systems may help process large geological datasets and improve forecasts of how injected carbon moves over time. For regulators and local communities, improved monitoring is seen as important for assessing long-term safety.

Trust and verification remain central concerns. Public subsidies and carbon markets depend on accurate measurement and reporting, yet existing systems can be slow and difficult to audit. Occidental Petroleum and others have highlighted the potential of digital monitoring platforms to improve transparency, although most remain at an early stage. Analysts say AI-supported verification tools could eventually help identify anomalies sooner and lower compliance risks.

The shift reflects a broader trend in climate technology towards greater automation and accountability. Investors increasingly expect clear evidence of performance. While challenges remain, including legacy infrastructure and cybersecurity risks, developers see AI as a potential foundation for moving carbon capture from experimentation to wider deployment.