The emergence of ChatGPT marks a significant milestone in AI technology, making it easily accessible to the general public. As AI continues to grow and enter the mainstream, industry leaders predict that the technology could bring about massive disruptions and innovations, akin to the internet’s introduction in the early 1990s.
Despite the much-discussed benefits of AI, concerns are growing about its potential to replace human roles, eliminate jobs, and even pose a threat to humanity if misused. While these critical questions about AI’s risks remain unanswered, the technology’s advantages in boosting efficiency and productivity are undeniable.
As AI gains momentum as a strategic imperative, its impact is growing across multiple industries, including the data centre sector. According to Gartner, half of cloud data centres will deploy advanced robots with artificial intelligence (AI) and machine learning (ML) capabilities, resulting in 30% higher operating efficiency by 2025. With this growing trend, it is worth exploring how industry leaders expect AI to affect data centre operations and performance.
AI’s influence is expanding across various industries, including the data centre sector. Gartner predicts that by 2025, half of cloud data centres will employ advanced robots with AI and machine learning (ML) capabilities, resulting in a 30% increase in operational efficiency. Given this trend, it’s worth examining how industry leaders anticipate AI affecting data centre operations and performance.
AI and greener data centres
Singapore recently introduced a new sustainability standard for data centres in tropical climates. The standard aims to help operators find the optimal operating temperature to balance energy efficiency and operational reliability, highlighting the importance of environmental sustainability in business operations.
Data centres are estimated to account for nearly 1% of global electricity consumption. This significant energy use is a pressing issue for the industry. A recent study by Veritas found that half of the respondents from Singapore are concerned that data centres contribute to 2% of global energy-related pollution emissions.
Data centres are estimated to account for nearly 1% of global electricity consumption. This significant energy use is a pressing issue for the industry. A recent study by Veritas found that half of the respondents from Singapore are concerned that data centres contribute to 2% of global energy-related pollution emissions.
Today, AI is often integrated into software running in data centres to manage power and water usage. AI-enabled tools allow operators to improve their power usage effectiveness (PUE) by identifying optimal operating conditions, collecting granular server rack temperature data without installing physical hardware, and continuously analysing sensor data to maintain those conditions. This real-time control of cooling equipment through sensors and ML reduces energy required for cooling, thereby cutting costs and carbon emissions
As organisations increasingly migrate diverse workloads to the cloud, data centre operations will inevitably grow in scale and complexity. Servers consume varying levels of energy and emit heat depending on the workloads processed. AI-enabled tools can automate workload management, distributing processing tasks more evenly across time and locations based on available resources.
Pragmatic use of AI could enable companies to save up to 40% of the power used for data centre cooling. Additionally, water usage efficiency (WUE) can benefit from consistent temperature management and real-time optimisation
Improving work efficiency and security
Traditional data centre processes are often tedious and repetitive, encompassing tasks such as capacity planning, server upgrades, maintenance, patching, and reporting. These are areas where AI and robots can significantly enhance accuracy and efficiency, reducing the need for human intervention. For instance, industrial robots can streamline the decommissioning and disposal of outdated servers and infrastructure.
To keep up with digital transformation, AI-based systems are increasingly used for monitoring and managing IT processes in data centres. Operators are adopting autonomous multi-cloud solutions that utilise AI and ML to enable flexible, autonomous data management across diverse clouds, workloads, and data sources. This automation allows data centre staff to focus on transformational activities, thereby improving overall efficiency and return on investment (ROI).
Data outages represent a significant financial risk for data centres. Ensuring both digital and physical security is a top priority. AI and ML-powered smart cameras, intrusion detection systems, and robots can enhance physical security while reducing human involvement.
With the rise in cyberattacks, AI has become an indispensable asset in secure multi-cloud data management, actively defending against threats by integrating advanced automation and AI capabilities. For example, features such as automated malware scanning, which works in tandem with AI-driven near real-time anomaly detection, enable autonomous data management. This empowers data centre operators to ensure data safety and compliance, particularly against ransomware threats. Moreover, AI can be utilised to automate the incident response process, thereby reducing the time required to address security breaches. While traditional incident response “playbooks” continue to serve a purpose, AI accelerates the planning of responsive actions. By integrating predictive technologies with AI, data centre operators can engage in proactive monitoring for both faults and security threats, taking preventative steps to stay ahead of potential risks.
The future of data centres
It is no surprise that with the unabated growth of data, the demand for data centres will remain high. With the right framework and balance in place, AI holds enormous potential to modernise data centres, achieving sustainability while enhancing operational efficiency and accuracy.
