Powering Intelligence: GIS and the Global Race to Build AI Data Centres

The global race to power AI has triggered an unprecedented boom in data centre construction, reshaping energy grids, telecommunications networks, and the digital economy itself. This surge is remodelling infrastructure demands across the telecommunications and utilities sectors – particularly in the UK, Germany, and the USA. 

The USA is experiencing rapid growth in hyperscale and colocation data centres, driven by AI workloads and cloud migrations. AI data centres are projected to push US energy demand up by 26% by 2028, requiring over $2 trillion in new energy generation resources. However, utilities are struggling to predict how much of this demand will materialise, creating uncertainty around infrastructure investment and grid planning. 

Germany is planning 43 new data centres, including both hyperscale and colocation facilities. This expansion complements its existing portfolio of 187 operational data centres, reinforcing its position as one of Europe’s leading digital infrastructure hubs. 

In September 2025, the USA announced significant investment in the UK’s AI sector through the UK–US Tech Prosperity Deal. This landmark agreement aims to deepen collaboration on AI, quantum computing, and nuclear technologies, backed by £31 billion in private investment from major US tech firms, including Microsoft, Google, NVIDIA, OpenAI, and CoreWeave. This agreement marks a pivotal moment, positioning the UK as a central hub in the global AI ecosystem.  

As a result, the UK government is planning nearly 100 new data centres by 2030. This represents a 20% increase that will solidify its position as the third-largest global data centre market after the USA and Germany. 

Reinventing Infrastructure Amid Decades of Underinvestment 

While the surge in AI data centres is driven by demand for AI processing power, it raises concerns over energy consumption, water usage, and sustainability. It also puts pressure on utilities, which must reinvent themselves to meet surging demand while balancing reliability, affordability, and environmental impact. AI workloads require high-density computing, translating into massive power draw and cooling needs. This shift is forcing utilities to rethink grid architecture, invest in renewables, and accelerate permits for new generation capacity. 

Simultaneously, the data centre boom is having a major impact on telecommunication networks. They are facing pressure to support ultra-low latency, high-bandwidth, and edge computing capabilities for AI inferencing. The surge is driving demand for fibre expansion, 5G densification, and network resilience to support real-time AI applications. AI data centres also require robust interconnectivity, pushing telcos to upgrade backbone infrastructure and peering arrangements. 

The race to power AI is more than a technical one – it has significant geopolitical, economic, and environmental ramifications. All sectors must collaborate to avoid bottlenecks and ensure scalability, while governments and regulators are being urged to streamline approvals and incentivise sustainable builds. 

However, both the telecommunications and utilities sectors have suffered from decades of underinvestment, with ageing infrastructure stifling innovation. To prepare for the AI-driven surge, they must accelerate digital transformation and modernise IT systems to keep pace. 

GIS Key to AI Infrastructure Planning 

This is where GIS (Geographic Information Systems) technology becomes a game-changer. It helps telcos and utilities modernise and optimise network design, deployment, and lifecycle management. In the race to build and deploy AI-powered data centres, GIS plays a critical role by providing spatial intelligence, operational oversight, and infrastructure optimisation, from site selection to infrastructure mapping to operational monitoring. Here is how GIS supports these developments: 

·         Site Selection and Planning 

GIS helps identify optimal locations for new data centres by analysing proximity to power grids, fibre networks, and water sources. It also evaluates environmental risks such as flood zones, seismic activity, and other natural hazards. Additionally, GIS helps organisations understand regulatory constraints and land use patterns, ensuring strategic placement that balances performance, cost, and sustainability. 

·         Infrastructure Mapping and Visibility 

Using GIS technology, real-time mapping of assets, such as power lines, substations, cooling systems, and fibre routes, allows operators to visualise dependencies and vulnerabilities across the entire ecosystem, including utility interconnects and telecommunications backbones. This spatial awareness is critical for identifying single points of failure, planning redundancies, and ensuring seamless service delivery. GIS platforms support dynamic layering of infrastructure data, enabling teams to overlay environmental factors, zoning constraints, and traffic patterns for informed decision-making.  

As AI data centres become more distributed and resource-intensive, this visibility empowers stakeholders to optimise routing, anticipate bottlenecks, and coordinate upgrades across jurisdictions and providers. 

·         Operational Monitoring 

GIS integration with IoT sensors and SCADA (Supervisory Control and Data Acquisition) systems provides continuous, real-time monitoring of critical infrastructure. This includes tracking energy consumption, cooling efficiency, and emissions, enabling operators to maintain performance and compliance.  

By visualising operational data spatially, GIS helps pinpoint inefficiencies, detect anomalies, and anticipate failures before they occur. It supports predictive analytics to forecast maintenance needs and infrastructure stress, reducing downtime and extending asset life. Automated alerts triggered by threshold breaches allow for rapid response and targeted interventions.  

GIS can also layer historical data with live telemetry to uncover trends, optimise resources, and inform upgrades, all within a unified, map-based interface. 

·         Risk Management and Resilience 

Models powered by GIS simulate climate impacts, cyber threats, and supply chain disruptions, supporting contingency planning and disaster recovery through the mapping of alternate routes and backup systems. These simulations enable utilities and telcos to identify vulnerabilities proactively and develop robust contingency plans.  

By visualising alternate routes, backup facilities, and interdependencies across networks, GIS supports faster disaster recovery, ensures service continuity, and helps prioritise investments in areas most susceptible to disruption. This spatial intelligence empowers decision-makers to balance risk, cost, and sustainability in real time. 

·         Stakeholder Collaboration 

With a diverse array of stakeholders, from utilities and telcos to regulators, developers, and local authorities, the complexity of AI data centre expansion demands more than technical oversight. Transparent reporting and strict compliance with environmental and zoning regulations are essential for responsible growth and public trust.  

GIS dashboards enable seamless cross-functional coordination, offering a unified view of infrastructure assets, regulatory boundaries, and project timelines. This spatial intelligence empowers decision-makers to align priorities, mitigate risks, and accelerate approvals while maintaining accountability across the ecosystem. 

As AI data centres become more complex and resource-intensive, GIS transforms from being viewed as a simple mapping tool into a strategic infrastructure command centre, essential for scaling responsibly and sustainably.  

The AI data centre boom is not just a technological revolution; it’s a test of how well nations can align digital ambition with physical infrastructure. GIS stands at the heart of this transformation, ensuring growth that is not only rapid but also resilient, intelligent, and sustainable.