How the industry can balance transition commitments

Projected power demand for new data centre infrastructure in Europe could triple from 10 GW today over 35 GW by 2030, which could have widespread implications for the global energy transition.

European power operators could also see new data centre infrastructure consuming as much as 60% of peak energy demand. Across the globe, countries are increasingly turning to traditional energy sources to power new, AI-fueled infrastructure while maintaining grid stability. In Europe alone, traditional fuel sources still comprise two- thirds of the overall energy mix.

But this steady increase in power demand poses critical issues for existing energy transition goals, especially considering the E.U. isn’t currently on track to hit its 2030 targets.

To keep pace, energy companies must bridge the gap between digital readiness and decarbonisation, a challenge that demands new tools, new data standards, and new ways of proving performance.

Cutting-edge technologies and emissions accounting methods are emerging to close this gap — demonstrating it’s possible to balance energy transition commitments while powering AI innovation.

The power demand paradox

AI is an energy-intensive technology, and the data centres that house these high compute capabilities require ultra-reliable 24/7 baseload power. that renewables can’t deliver. At the same time, they’ve made corporate sustainability commitments and set ambitious climate goals they must fulfill.

Energy companies have a unique opportunity to support these dual priorities, but only by replacing outdated emissions estimates with asset-level, verifiable emissions data that reflects the complexity of real-world operations. To accurately calculate emissions intensity across energy value chains, the industry needs to shift to granular, measurement-backed emissions data.

The energy sector, including power generators, understand how to operate complex enterprises, and already have access to an array of data sources that are highly useful for understanding their carbon performance. Their transformation can accelerate with digital readiness, using platform technology to organise fragmented, siloed datasets into a contextualised, AI-ready foundation.

Context Labs: delivering the missing infrastructure for emissions intelligence

At Context Labs, we consistently solve for three common customer pain points with our Context AI Enterprise Carbon Management Platform:

Digital readiness

Fragmented, siloed data isn’t usable. Our platform ingests and contextualises data from OT, IT, and third-party systems into a cohesive, structured format ready for automation and AI deployment.

Operationalising decarbonisation

We close the latency gap between data capture and actionable insight, giving energy teams the ability to track, model, and mitigate emissions events in real-time.

Proof-of-claims for commercialisation

Our emissions intelligence platform produces auditable, tamper-proof records of carbon intensity. We also work with third-party verifiers to validate our emissions accounting methodologies all of which drives highly accurate sustainability reporting and market differentiation for our customers.

With this measurement-backed approach, energy companies and power generation operators can access high-integrity, source-level emissions intelligence to proactively inform decision-making around growing AI-driven power demands.

Accelerating responsible AI innovation — and the power demand paradox

Bringing together direct measurement, operational context, and the same rigor as financial accounting practices finally provides the end-to-end visibility energy companies need to prove the emissions intensity of their supply.

With this credible, traceable proof of performance, energy companies can build their strategic advantage and better serve downstream buyers. Context Labs is supporting customers to radically reduce the amount of time spent making sense of their emissions data, transitioning enterprises from annual retroactive reporting to near real-time quantified proof of operational performance. One Context Labs customer  is using the platform to generate proof of the low-carbon intensity power supply they are delivering to  a planned 3,200 acre data centre campus. Another is deploying Context Labs platform to quantify and certify end-to-end low carbon power supply for new AI data centre infrastructure.

These are just two examples of how measurement-backed emissions data can support next-generation data and AI infrastructure. Transforming how we approach emissions quantification also will have larger downstream impacts. It’ll allow the industry to reliably power data centres with a mix of both traditional and verified low-carbon fuel sources.

For hyperscalers, that means access to emissions insights that not only make energy costs more affordable and reliable by using traditional energy sources, but also creates transparency for sustainability claims, strengthening stakeholder trust and meeting evolving regulations in the E.U. and other markets.

It’s possible to accelerate AI innovation without compromising the energy transition. However, estimates can never drive true carbon accountability. Precise, granular emissions quantification is the only way forward to responsibly power AI — and achieve a low-carbon future.