Inside the Digital Push to Cut Carbon Capture Costs

Early pilots suggest AI can cut energy demands in carbon capture, with data and digital skills emerging as key drivers of scale

4 Feb 2026

A quiet shift is taking hold in the US carbon capture industry. Artificial intelligence is no longer a distant promise. It is edging into labs and pilot plants, testing whether software can ease one of the sector’s hardest problems: how to trap carbon without burning through energy and budgets.

Carbon capture works, but it is costly. Most systems rely on solvents or membranes that demand heavy energy input to separate carbon dioxide from exhaust gases. That burden has slowed deployment, even as pressure grows to reduce emissions faster. Researchers now see AI as a way to manage the complexity that makes these systems so power hungry.

By crunching large volumes of operational and experimental data, AI models can help tune capture processes in near real time. Early pilots suggest that data-driven controls allow plants to adjust more smoothly to swings in fuel type, load, or exhaust composition. Some studies point to energy savings of up to 10%, a modest figure that could still transform project economics at scale.

AI is also reshaping research itself. Programs backed by the US Department of Energy and the National Energy Technology Laboratory are using machine learning to screen new solvents and membranes. Instead of years of trial-and-error testing, algorithms can narrow the field quickly, flagging materials with the best chance of performing well in the real world.

Beyond efficiency, digital tools are being tested for reliability and maintenance. Pilot projects are exploring whether AI can spot early signs of material fatigue, predict downtime, and extend equipment life. Commercial deployment remains limited, but the direction of travel is clear.

Challenges remain. Data quality varies widely, regulations move slowly, and cybersecurity looms as a concern. Still, the push mirrors a broader trend across the energy sector, where digital systems increasingly guide complex operations.

For carbon capture, the stakes are high. If AI can make systems cheaper, steadier, and faster to deploy, it may become less of a supporting tool and more of a deciding factor in whether the technology truly scales.