News
Merry Christmas
18th December 2024
AI: From concept to reality
The idea of AI has ancient roots, but its formal inception began in the mid-20th century. John McCarthy coined “artificial intelligence” in 1955, and the Dartmouth Conference in 1956 marked its official birth. Despite setbacks in the 1970s and 1980s, AI research surged forward with expert systems in the 1980s and machine learning in the 1990s. The 2010s saw breakthroughs in big data and deep learning, setting the stage for today’s advancements.
Over the past year, AI’s widespread adoption across industries has driven significant progress. Although venture investments slowed in 2023, generative AI and related startups raised nearly $50 billion, up 9% from 2022. Statista forecasts that the European AI market will grow to €76.5 billion this year, continuing to expand robustly through 2030.
The rising demand for advanced facilities
The rapid integration of AI is reshaping data centres, presenting both opportunities and challenges. AI’s influence creates demand for facilities, infrastructure design, operational protocols and resource management.
AI applications, especially those involving deep learning, demand substantial computational resources – driving the need for advanced data centre facilities. This shift will lead to the development of larger, more sophisticated centres equipped with specialised hardware like GPUs and TPUs. These facilities will also require enhanced cooling and power systems to manage increased workloads and maintain efficiency.
As hyperscale AI data centres grow, they will likely be situated in regions with abundant energy resources, particularly renewable energy. Meanwhile, the rise of AI-driven services will increase the importance of Edge data centres, which are located closer to data generation points to reduce latency, resulting in a more decentralised network of facilities.
Infrastructure evolution
Data centres must adapt to support AI workloads. The structural design must accommodate heavier hardware, with floor loading capacities potentially reaching 50kN per sqm or more. AI data centres also require taller ceilings (10–12m+) to house advanced cooling systems, including liquid cooling solutions. Additionally, the electrical infrastructure must support higher power densities, with provisions for redundancy and scalability.
Operational and cost efficiencies
AI enhances operational efficiency by dynamically allocating resources based on real-time demand. This flexibility allows data centres to scale resources as needed, improving cost-effectiveness. Predictive maintenance, powered by AI, minimises downtime by forecasting potential equipment failures through sensor data analysis.
AI also strengthens data centre security. Machine learning algorithms detect anomalous network traffic patterns, identify potential threats, and respond swiftly to mitigate risks, enhancing cybersecurity.
Moreover, AI improves energy efficiency by intelligently managing cooling systems, lighting, and other power-consuming components, reducing the environmental footprint of data centres.
Targetfollow
Targetfollow Estates Limited, we recognise the transformative potential of AI in shaping the future of property asset management. As AI continues to drive demand for advanced data centres, these facilities will become increasingly valuable commodities within the proerty sector.
A data-led approach to property management is essential. AI accelerates this technological transition by capturing and managing key insights, allowing for more informed decision-making and optimised resource allocation. As we navigate this new landscape, Targetfollow is committed to leveraging AI to unlock the full potential of our property assets, ensuring we remain at the forefront of innovation and efficiency in the property market.
