Energy cost is probably the most underrated margin lever in any industrial plant. The energy efficiency with artificial intelligence (AI) can recover between 5% and 15% of that spend without touching the production process. Here’s where that margin is and how to capture it.
When finance reviews the P&L, energy spend usually shows up as a fixed cost that little can be done about. The reality is very different. In intensive sectors (steel, chemicals, cement, forging), a significant share of consumption can be optimised using the data the plant already generates every day. The challenge isn’t the technology: it’s looking at that data with the right method.
The starting point: how much energy can be optimised
Industry accounts for around 39% of final energy consumption and close to 45% of CO2 emissions, making it one of the biggest areas for improvement. And the potential is enormous: various sector analyses estimate that around 70% of the energy we consume can be optimised.
The context is adding pressure too. The International Energy Agency (IEA) has warned that AI is driving record global electricity demand, with data centre consumption on track to double by 2030. In other words: energy is going to become more expensive and more strategic, and whoever knows how to consume it better will have a competitive edge.
What AI can do on the plant floor
AI-driven energy efficiency isn’t a magic box. It’s three concrete levers that work on real processes:
1. Consumption prediction by batch, lot or shift. Anticipating how much energy each operation will require enables better planning and avoids costly peaks.
2. Setpoint optimisation informed by the physics of the process. The model recommends, for example, adjusting a furnace’s temperature based on the batch’s actual characteristics, instead of always working with conservative parameters.
3. Predictive maintenance of auxiliary equipment: compressors, pumps, heat exchangers, big hidden consumers. According to the U.S. Department of Energy (DOE), predictive maintenance can improve energy efficiency by up to 20%.
How much real margin is there? A KPMG report notes that AI-based energy management generates additional savings of between 4% and 5% even in already-optimised systems. And in industrial settings, specialised software solutions achieve savings of between 5% and 15%. On a six-figure bill, that percentage stops being a technical detail and becomes a boardroom conversation.
Without replacing your technology stack
The part that reassures operations the most: none of this requires throwing away what you already have. The AI layer connects on top of your SCADA (supervisory control and data acquisition system) and your data historian, reads what the plant already records, and returns explainable decisions to your team. It’s not a replacement; it’s a layer that’s added on top.
And explainability is key. A recommendation like “lower the setpoint to 720 degrees because this batch has 8% more moisture” is one the night-shift operator can understand and apply. A black box that just says “do this” isn’t. That’s why at DDS we always build explainable AI: the team trusts what it understands, and only what gets used actually generates savings.
From savings to revenue: Energy Savings Certificates
There’s an interesting twist here. In Spain, verified energy savings can be converted into revenue through CAEs (Energy Savings Certificates), a regulated mechanism that certifies the final energy saved by a measure. In other words: optimising doesn’t just cut the bill: it can also generate a sellable asset. The condition is always the same: without rigorous measurement there’s no provable saving and no CAE, and that’s where a well-built data layer makes the difference.
Where to start
The first step isn’t buying technology; it’s choosing the right process and defining the indicator (KPI) you want to move. At DDS we always start with a diagnosis: we identify the most expensive, most optimisable consumption, validate it against the historical record, and only then propose a model, with the estimated return laid out up front. That’s the difference between a project that stays a pilot and one that reaches the plant floor and shows up on the bill.
If your annual electricity bill runs into six figures and you want to see where your hidden margin is, we suggest a 30-minute exploratory meeting. We’ll look at your specific case and tell you, honestly, whether there’s room to move.
Sources: International Energy Agency (IEA), Energy and AI report (2025); KPMG (2026); U.S. Department of Energy; Schneider Electric.
Shall we talk about your data and AI project?
At Deduce Data Solutions we turn data into decisions for industry, with data governance and compliance from day one.
