3 Major Challenges in Operating AI-Powered Data Centres and how to Overcome Them

As AI continues to reshape industries and push the envelope of possibilities, the data centres supercharging digital transformation must evolve and scale just as swiftly.

Aug. 13, 2025

With artificial intelligence (AI) emerging as a cornerstone of digital transformation, data centres are rapidly evolving to support increasingly complex workloads and exponential data volumes. The seamless functioning of these facilities is mission-critical for effectively training, deploying, and scaling AI ecosystems across organisations.

Unlike conventional data centres designed for general-purpose computing, AI-powered data centres need to manage highly specialised workloads, including large-scale model training, real-time inference, and continuous high-volume data ingestion. The computational intensity of these tasks requires dense clusters of graphics processing units (GPUs), tensor processing units (TPUs), and custom accelerators, culminating in significant challenges in power consumption, thermal management, and system integration.

In addition to improving raw performance, AI workloads often need low latency while maximising accessibility, ease of integration, and scalability. These demands place considerable strain on data centre infrastructure, particularly in areas such as server density, network bandwidth, and data throughput. To meet these complex requirements, it is essential to upgrade compute, storage, and networking layers while optimising system architecture across the enterprise.

Alleviating Challenges in AI-Powered Data Centres

AI-powered data centres face a range of challenges related to power consumption, cooling, sustainability, scalability, and operational complexity. These stem from the intensive compute demands of AI workloads, particularly those using graphics processing units. In addition, ensuring data integrity, addressing model bias, mitigating security risks, and ensuring robust governance are pre-requisites for responsible AI adoption.

Below are three major challenges impacting AI-powered data centres, along with proven strategies to mitigate them.

Challenge 1: Scalability and Energy Efficiency

AI workloads are inherently compute-intensive, requiring specialised hardware such as GPUs and TPUs, which consume significantly more power than traditional central processing units. This leads to increased thermal output and higher cooling demands. As AI adoption scales, so does the energy footprint of data centres, making energy efficiency a critical design and operational priority.

The challenge is compounded by the need to maintain performance while managing power density and thermal constraints. Traditional air-cooling systems are often insufficient for high-density environments, and the cost of energy continues to rise. In parallel, sustainability targets and regulatory pressures are pushing data centre operators to reduce emissions and improve energy efficiency.

Suggested Mitigation Strategies:

• Deploy energy-efficient hardware: Consider using processors and accelerators designed for AI workloads that offer high performance per energy unit. This includes leveraging advanced packaging technologies and low-power silicon architectures.
• Implement advanced cooling systems: Shift from traditional air cooling to more efficient methods such as liquid immersion cooling, direct-to-chip cooling, or two-phase evaporative systems. These technologies improve thermal management in high-density environments.
• Optimise workload placement: Use AI-driven orchestration tools to dynamically allocate workloads based on thermal zones, power availability, and compute efficiency. This reduces hotspots and balances energy consumption across the data centre facilities.
• Integrate renewable energy sources: Incorporate solar, wind, or hydroelectric power into the energy mix to reduce carbon emissions and improve sustainability metrics.
• Adopt intelligent power management: Implement techniques such as dynamic voltage and frequency scaling and real-time telemetry to monitor and adjust power usage based on workload intensity.
• Use predictive analytics for energy optimisation: Leverage AI to analyse historical and real-time data to forecast energy demand, enabling proactive optimisation of cooling and power systems.

Challenge 2: Data Governance and Security

AI-powered data centres process significant volumes of sensitive data, including personally identifiable information (PII), proprietary business data, and intellectual property. The lack of transparency of many AI models, particularly large language models (LLMs), creates additional complexity in understanding how data is used, stored, and protected. The distributed nature of modern AI infrastructure, spanning on-premises and cloud platforms, and edge devices, creates several vulnerabilities pertaining to data leakage, unauthorised third-party access, and cyberattacks.

In addition, compliance with data protection regulations such as the General Data Protection Regulation (GDPR) requires transparency and control over data flows, storage locations, and access policies within AI-powered data centres. Effective AI governance must not only address IT security issues but also holistically tackle legal, ethical, and operational bottlenecks. This includes understanding how training data is sourced, how LLMs are audited, and how data ownership is maintained throughout the AI adoption lifecycle.

Suggested Mitigation Strategies:

• Implement zero-trust security architecture: Enforce strict identity verification, least-privilege access controls, and continuous monitoring to prevent unauthorised access to data and systems.
• Use end-to-end encryption: Encrypt data at rest and in transit using industry-standard protocols. Employ allowlists to restrict access to known and trusted IP addresses.
• Automate compliance monitoring: Deploy AI-driven tools to continuously assess compliance with data protection regulations such as GDPR and industry-specific standards.
• Audit training data sources: Maintain transparency over the datasets used to train AI models. Ensure that data is ethically sourced, anonymised where necessary, and compliant with privacy laws.
• Retain data ownership and control: Confirm that your organisation retains full ownership of the data at your disposal. Review your AI service provider’s data retention, logging, and sharing policies to prevent unauthorised use or exposure.
• Conduct regular audits and assessments: Engage third-party auditors or internal compliance teams to evaluate data governance practices and validate adherence to security standards.

Challenge 3: Infrastructure Complexity and Maintenance

As AI-powered data centres operate across complex environments, managing this diversity while ensuring high performance, reliability, and scalability presents significant operational challenges. AI workloads vary widely in compute, memory, and storage requirements. Training large models may involve thousands of parallel compute nodes, while inference often requires low-latency responses at the edge. Supporting these needs demands infrastructure that is both flexible and resilient.

Frequent hardware and software updates, including new accelerators, memory technologies, and data centre interconnects, must be integrated with minimal disruption. Maintenance is equally critical as hardware failures, software bugs, and misconfigurations can impact availability and system performance within AI-powered data centres.

Suggested Mitigation Strategies:

• Deploy AI-powered observability platforms: Use telemetry and machine learning to gain real-time visibility into system health, performance bottlenecks, and failure patterns. Analyse historical performance data to anticipate hardware failures and schedule maintenance proactively.
• Design modular infrastructure: Leverage composable and modular systems that allow for incremental upgrades and flexible scaling. This reduces disruption during hardware refresh cycles.
• Enable flexible storage architecture: Design storage systems that NVMe SSDs and high-bandwidth memory can dynamically scale to accommodate fluctuating data volumes. This includes support for tiered storage and object-based architecture.
• Leverage virtualisation and containerisation: Virtual machines and containers can abstract workloads from physical hardware, enabling better resource utilisation and workload portability.
• Adopt hybrid cloud strategies: Integrate on-premises and cloud environments as part of a hybrid strategy to provide elastic scalability and geographic redundancy. This is particularly important for supporting data-intensive AI applications and distributed training workloads.