The importance of digital infrastructure cannot be overstated, as it plays a crucial role in ensuring the functionality and resilience of essential systems and services that we all rely on
Artificial intelligence (AI) is revolutionising infrastructure operations, delivering improved efficiency and enhanced service offerings.
It is now a fact of life that our business and personal lives rely more and more on data, something we now accept and even take for granted. But what some people don’t know is that the continuity of data flow is related to the availability of the digital infrastructure.
Relying only on digital infrastructure without constant monitoring and management of IT systems is unlikely to be enough to avoid operational disruption and inefficiencies, which could be disastrous for businesses and end users alike.
The continuous exchange of data with critical equipment and the adoption of a monitoring system allows the identification of potential threats and anomalies that could impact business or service continuity. Identifying patterns and anomalies in the collection of large amounts of data permits faster and more accurate problem discovery, diagnosis, and resolution.
This monitoring adds an important layer of protection to the continuity and, therefore, availability of the infrastructure.
AI-enabled solutions aid operational continuity and reduce carbon footprints
As companies grapple with increasing pressure on the infrastructure, AI-enabled solutions provide a way to alleviate the burden on IT management teams, lower operating costs, and improve overall efficiency. It can be a game changer in helping with operational continuity and reducing carbon footprints.
By processing and analysing real-time data, AI enables digital services that further empower traditional preventive and reactive services. These include monitoring services, advanced incident management and condition-based maintenance.
By leveraging sophisticated algorithms, some monitoring systems can alert users about potential equipment failure and maintenance needs based on AI.
Analysing historical performance data and real-time parametric data provided by critical infrastructure makes it possible to plan for when infrastructure elements like power and cooling equipment could potentially fail, allowing for condition-based maintenance to help prevent costly breakdowns and long restoration time.
Monitoring and management systems can also help to optimise the utilisation of critical infrastructure by enabling it to be operated more efficiently, for example, by identifying stranded capacity, thus reducing energy waste and costs. This can be achieved by analysing the vast amounts of data coming from sensors, equipment and other sources and presenting them to operators and decision-makers in a more understandable and actionable format.
It can also contribute to reducing human errors by automating many decision-making processes. Combining monitoring with remote control capabilities also makes it possible to reduce the need for on-site personnel and to enhance the ability to manage the infrastructure in challenging or remote sites and locations.
Enhancing digital infrastructure with condition-based maintenance
Condition-based maintenance plays a vital role in enhancing digital infrastructure efficiency and resource management.
When AI is added into the mix, critical infrastructure monitoring/condition based maintenance can be taken to new heights. AI algorithms, when fuelled by vast amounts of data collected from monitoring systems, can provide advanced analytics. This empowers systems managers to make adjustments for physical behaviours and plan for the end-of-life of various equipment components.
AI not only aids in being prepared for potential equipment failures, maintenance needs, and environmental risks but also facilitates adaptive infrastructure optimisation.
By learning from data collected over time, AI algorithms can help to determine the most effective ways to improve IT performance and recommend adjustments to optimise the use of critical equipment. This includes identifying opportunities to reduce energy waste, enhance efficiency, and streamline operations.
The adaptive nature of AI enables these recommendations to evolve over time, aligning with the changing dynamics of the infrastructure and improving overall performance. Such insights allow operators to minimise downtime and help deliver the continuous operation of critical infrastructure, such as keeping the lights on and the internet running.
While AI-driven automation is a key component, human-AI collaboration is equally important. Monitoring and management systems should empower operators and decision-makers with actionable insights derived from AI analysis. This collaborative approach minimises the risk of errors and enhances decision-making processes. Additionally, it allows human operators to focus on strategic tasks while AI handles routine monitoring, creating a synergistic relationship that maximises operational resilience in the digital era.
Environmental considerations of digital infrastructure
Incorporating environmental considerations into digital infrastructure management involves optimising energy usage, reducing carbon footprints, and implementing eco-friendly technologies and processes.
AI in condition-based maintenance can play a pivotal role in this by identifying opportunities to enhance energy efficiency, tackle resource wastage, and contribute to the overall efficiency of operations and to the circular economy.
Environmental responsibility also extends to the energy sources powering critical infrastructure. By leveraging AI in the data lake, organisations can analyse power consumption patterns and explore opportunities to integrate alternative energy sources. This not only aligns with eco-friendly initiatives but also enhances operational resilience by diversifying the energy mix and reducing dependence on conventional power grids.
Data requirements
By its very nature, AI needs an enormous amount of data to learn and evolve, so as critical infrastructure grows, more data will be available for AI to use. At the same time, condition-based maintenance will naturally scale. This means that the more dense and varied the data population (thanks to new intelligent equipment and technologies), the better the data trending, pattern recognition, and insight learning will be.
The technology evolution driving new product design that incorporates the enablement of data communication as a critical driver will enable algorithms to scale, thanks to enhanced data breadth and quality of the data. Through algorithm evolution, the precision of the data trending can continuously improve, enabling better learning.
As technology continues to evolve, AI will undoubtedly play an even more significant role in shaping the future of the digital infrastructure.
By embracing these advancements, a landscape can be created that is more environmentally friendly, resilient and capable of meeting the growing demands of the digital age.
