Artificial intelligence is consuming more and more energy—yet at the same time, it helps save it. This paradox defines one of the greatest challenges of modern IT: how to reconcile the relentless growth of computing power with climate requirements and ESG reporting. The answer is not straightforward, but one thing is certain: in 2026, we can no longer afford AI that ignores environmental costs.

Energy: the new currency of data centers

When we talk about AI, we tend to focus on its capabilities—process automation, trend prediction, supply-chain optimization. Rarely do we consider what happens behind the scenes. Meanwhile, data centers convert megawatts of energy into terabytes of insights. Training a large language model can consume as much energy as an average household uses over several years.

For IT leaders, this creates a growing challenge: how to scale AI without multiplying electricity bills and CO₂ emissions. This is where the concept of “Green AI” comes in—an approach that treats energy efficiency as a core element of AI system architecture. It is not just a matter of corporate ethics, but a business calculation. In the era of CSRD reporting and ESG audits, an inefficient data center represents both reputational and financial risk.

PUE: a maturity indicator

Power Usage Effectiveness (PUE) measures the ratio of total energy consumed by a data center to the energy used by IT equipment alone. Typical facilities operate at a PUE of 1.5–2.0, while best-in-class data centers achieve values below 1.2. The difference? On an annual scale, it translates into millions in savings and thousands of tons of CO₂ avoided.

How is this achieved? Through intelligent cooling management (such as free cooling where climate conditions allow), heat recovery, dynamic workload allocation (AI helping AI become more “green”), and well-designed power architectures with next-generation UPS systems. Some organizations go even further—integrating energy storage, leveraging renewable energy sources, and scheduling workloads during periods of lower grid demand.

AI for green outcomes: optimization across every sector

That is only one side of the coin. The other is far more optimistic: AI does not just consume energy—it can save it at scale. In the energy sector, machine-learning algorithms forecast demand and optimize power distribution in real time. In manufacturing, predictive systems reduce downtime, cutting both energy and material waste. In logistics, AI optimizes transport routes, minimizing fuel consumption and CO₂ emissions.

Precision agriculture? IoT sensors combined with AI mean less water, fewer fertilizers, and lower methane emissions. Smart buildings? AI-driven HVAC systems can reduce energy consumption by 20–30%. Supply chains? Automated carbon-footprint audits and route optimization are becoming the standard, not a luxury. AI does not merely exist in a green world—it actively helps build it.

How to avoid drowning in greenwashing

The challenge is that many organizations treat “Green AI” as a marketing slogan rather than an operational strategy. Attractive dashboards show declining emissions, while in reality workloads have simply been moved to a public cloud, shifting responsibility to the provider. This is not transformation—it is avoidance.

Real change starts with an audit. How much energy does our AI actually consume? Where are the biggest losses? Are our models overtrained—a classic energy trap? Can massive, general-purpose models be replaced with smaller, specialized algorithms? In many cases, a well-trained smaller model outperforms a poorly optimized giant—and is far cheaper to run.

Strategy, not a declaration

“Green AI” is not a standalone initiative—it is a mindset applied to the entire IT infrastructure. It combines data-center energy efficiency, intelligent workload management, and conscious algorithm selection. It is about balancing computing power with environmental responsibility. Companies that understand this paradox early will gain a competitive advantage—not only in the eyes of ESG auditors, but also in the eyes of their finance teams.

Ultimately, AI is just a tool. The real question is: in whose hands, and for what purpose? If we want AI to help build a more sustainable world, we must first ensure that it is sustainable itself. Otherwise, we are powering the future with fuel from the past.

It is time for Polish IT companies to stop asking, “Can we afford this?” and start asking, “Can we afford not to?”