Driving innovation in data centers with artificial intelligence
Artificial Intelligence (AI) has become the 'need of the hour.' It has gained the potential to become one of the most influential and transformative technologies for businesses and industries of all sizes in recent years. From automating tasks and making smart decisions to realizing the importance of data, AI is rapidly transforming businesses and customer experience. A survey by Gartner shows that 40% of Infrastructure & Operations teams will use AI-augmented automation in large enterprises by 2023, increasing IT productivity with greater agility and scalability. Additionally, the survey also highlights a 5X increase in cloud-based AI from 2019 to 2023, making AI one of the top cloud services.
In the early days, when AI was still evolving, traditional automation programs were the thing. These were designed to integrate user applications with an organization's backend systems to perform pre-defined tasks without human intervention autonomously. But over the years, the limitation to fully automate without human intervention to modify workflows has led to the decline of traditional process automation. In addition to manual tasks, there was a lack of data insights and a failure to detect and prevent errors.
With the advent of the digital landscape today, AI is now heralding the next generation of innovation by offering advanced solutions to optimize strategies, forecasts, operations, and decision-making. One of the most emerging technologies, machine learning (ML), is now an integral part of almost every industry. Further advancements in the AI domain have also allowed machine learning capabilities to be applied at an accelerated speed and produce real-time insights.
Persisting AI challenges
In my experience of working in the telecom and Data Center industry for the past so many years, I have had first-hand experience in implementing technology, which is not an easy task. I have seen business units (BUs), operating in silos, starting to implement emerging technologies like AI while others stuck to legacy systems. While it was difficult to implement full-scale technology, BUs and organizations started collaborating, coming out of their silo bubble. However, despite the rapid adoption rates and advancements in AI, persistent barriers still prevent businesses from adopting and implementing it. According to a survey by McKinsey, respondents of participating organizations reported some of the biggest challenges they faced in AI adoption and implementation.
Infusing AI into operations
While mitigating these challenges becomes quintessential for the successful implementation of AI, ensuring maximum customer delight is another critical aspect for Data Center providers like us. From a customer's point of view, a Data Center is just the tip of the iceberg - offering Uptime, Service-Level Agreements (SLAs), and more. However, the overall design and management to support the Uptime and SLAs lie beneath the surface. To ensure the highest availability standards, the design of the Data Center must be highly optimized, and resiliency must be incorporated to protect against any failures of the basic building blocks (cooling, power, IT infrastructure).
Emerging technologies like AI, enable service providers such as us to deliver improved Data Center Uptime, ensuring safety and pre-identification of component failures. Furthermore, from a Data Center management point of view, we have the option of infusing AI into operations, resulting in enhanced operational support - AIOps.
Powering data centers with AIOps
Earlier, a lot of manual intervention was required in Data Center management, the traditional process automation solutions had limited scalability and high costs. Fast forward to 2022, we see the adoption of AI rising in Data Centers as enterprises strive to deliver services to their end customers without downtime ultimately leveraging efficiency and optimized Data Center performance.
If we talk about one of the critical benefits of AIOps, it is the ability to help identify potential issues before they cause problems. For example, by analyzing log data, AIOps can detect anomalies that might indicate a pending system failure. AI also plays a crucial role in shifting workloads, which enables energy efficiency.
In addition to its ability to detect and diagnose problems, AIOps can be used to help prevent them in the first place. For example, by monitoring system utilization data, AIOps can identify underutilized resources that could be reallocated to other tasks. By doing so, AIOps can help Data Center managers optimize their operations and use their resources better.
At AdaniConneX, we work towards “Zero Harm” and “Zero Leak” as organizational goals. In line with this thought, we have started utilizing the capabilities of AI in our safety procedures. Every Data Center deployment has its distinct set of challenges, from construction to design, safety, equipment failure, and more. Using modern project management solutions, we can gain actionable insights into the security and surveillance of our Data Centers and track categories like dropped objections, work at height, and confined space monitoring, among others. We can monitor project status, delays, and KPIs and detect compliance issues in time. This way, we can increase the resilience and safety of our Data Centers by tracking a centralized dashboard for all our safety concerns.
Hype around AI technologies continues to grow
In recent years, AI based technologies have been developed at an accelerating pace. This has resulted in new AI applications being deployed in many domains. However, with the rapid expansion of AI capabilities, predicting their future development is becoming increasingly difficult.
Reports have suggested that the hype around AI technologies will continue to grow in the next few years. AI will significantly impact telecommunications and networks, along with smart Data Centers. Edge AI is another area where AI will have a significant impact. It is a piece of the puzzle that we at AdaniConneX are working on – evaluating different use cases of AI for Edge.
Edge AI helps devices become more efficient and responsive. By analyzing data locally on the device, Edge AI can make decisions faster than if the data were sent to the cloud. This can be especially important for devices that need to respond in real-time, such as autonomous vehicles or drones. We believe that by 2025, Edge AI will be used in 75% of all data collected by IoT devices and will enable the reality of Far Edge.
Jumping on the sustainability bandwagon
AI is a game-changer for Data Centers. It has helped operators like us to run the facilities more efficiently and sustainably. Furthermore, it can be used to delegate various tasks, including monitoring and managing energy usage, optimizing workloads, and detecting and diagnosing problems.
In addition to these operational benefits, AI and ML can help Data Centers become more sustainable. For example, AI-based systems can be used to monitor and report on the environmental impact of Data Centers. This information can be used to make changes that reduce the carbon footprint of Data Centers. We are already working towards making the Data Centers more sustainable by implementing a strategic ESG framework. As AI becomes more widely adopted in Data Centers, we expect to help organizations achieve their sustainability goals by making Data Centers more efficient and ESG compliant.
The future of AI is difficult to predict, but the current state of development suggests that AI will become increasingly important in a wide range of domains. As AI continues to evolve, it is bound to have a profound impact on our lives.