For cement producers, resilience isn’t a single objective. It’s the ability to hold performance steady (cost, output, quality, and energy intensity) even as cement prices and the labour market fluctuate.

This challenge is particularly apparent in North America, where the industry faces a paradox: demand is strong, with many plants pushing to increase throughput, yet razor-thin margins and ongoing skilled labour shortages are forcing plants to find efficiency gains wherever they can.

This balancing act isn’t unique to North America. Across Europe, Asia, and the Middle East, producers are dealing with their own concerns – energy shocks, regulatory pressure, alternative fuel complexity, carbon constraints, and intense competition. The difference is that North America’s global counterparts are increasingly turning to AI not as a “digital transformation” story, but as a practical tool for operational resilience.

So, how are global leaders using AI to make their plants more resilient?

Profit through operational stability

The most digitally advanced global producers, like Holcim, Titan and Cementos Argos, use AI as an operational layer in their production process, helping teams to make difficult trade-offs and consistently optimise for stability – because stability is profit.

In high-performing plants, “good days” are no longer the benchmark. The goal is to reduce the gap between best-case and worst-case performance.

Process variability is expensive – it drives up fuel burn, contributes to ring formations and build-ups, increases shutdown risk, and creates swings in quality and downstream costs. It also demands constant close attention from the experts on site (who are increasingly few and far between).

Using AI in closed-loop control to keep the kiln in a tighter operating window, plants can maintain targets for SHC, quality, and throughput without relying on a handful of “master operators”.

In published deployments with major producers, Carbon Re’s AI-driven process control has delivered outcomes such as ~4% overall cost reduction, and a ~33% reduction in key quality variability (e.g., C3S variation). The kind of performance that is felt in margins – not just trend lines.

Building skill resilience

It’s no secret that the industry is experiencing a widespread skills shortage. Recent findings from Beaumont Bailey indicate that 80% of cement producers are experiencing shortages. Globally, AI is being used as a force multiplier to help close this gap.

At Carbon Re, we’ve taken a different approach to how our AI learns – by introducing breakthrough training mechanisms for our models. Instead of just relying on historical data from sensors around the plant, we also convert operators’ real control decisions into usable training insights for our models. This means the know-how of your best operators – and the practical strategies that they deploy day-to-day to handle the quirks of your specific plants – is captured, preserved, and consistently applied, even as people change. It’s not about replacing people.  It’s about introducing automation to reduce the impact of staff turnover, so that fewer people can run a more stable process, more of the time.

Bridging the gap

Globally, the industry is moving toward carbon-driven operating constraints, from reducing clinker content and traditional fuel use to tightening exhaust-gas stability requirements for carbon capture.  In North America, this is more fragmented – there’s limited national policy guidance, so while some states are moving fast, others are not. This environment is actually an ideal moment for AI, because it’s a low-regret capability.

AI:

• Reduces energy and fuel costs now,
• Improves uptime immediately,
• Helps ensure that the plant is prepared to handle future constraints, such as alternative fuels, carbon emissions targets, procurement requirements, and tighter performance standards.

That’s resilience: not predicting the future but building the ability to perform through it.

In every region where cement leaders are embracing AI, the motivation looks different: carbon, cost, fuel security, talent, competitiveness. But the mechanism is the same: tighter control, fewer surprises, and performance that holds through volatility.

The next chapter for cement producers in North America will reward those who can keep costs and output stable while the world becomes less stable. Global AI leaders have already shown what that looks like. The opportunity now is to translate those lessons into a North American operating model.