Why Every Business Could Soon Need Its Own Ai-Powered Data Center

New use cases for artificial intelligence (Ai) are popping up across different industries every day.

In Australia, the Ai market is projected to grow at a compound annual growth rate of 16.6% between 2024 and 2032, and could revive stagnant productivity growth in key industries such as agriculture and mining.

Ai’s raw computing need is driving demand for data centers. Depending on the speed and extent of Ai adoption, every organisation could one day require and even run its own data center, leading to questions over the industry’s ability to supply it as well as the sustainability implications of a more data intensive business environment.

One possible solution to these problems is Ai, which is being integrated into data center operations to improve efficiency, resilience, and even sustainability amid the industry’s current skills shortage.

Of course, not everything can be solved with AI. But it can be a cornerstone for sustained operational excellence that increasingly data-dependent companies should invest in.

Ai’s Impact on Data Center Operations

Artificial intelligence for IT operations (AIOps) is helping data center operators automate and maintain high-quality standards in network operations.

AIOps simplifies, expedites, and minimises errors in routine tasks. By analyzing data from a wide range of inputs, it offers a centralised overview that helps us grasp complex operations like never before.

It can also shift businesses towards proactive rather than reactive risk management. AIOps excels at filtering and analysing data to uncover patterns easily overlooked by the human eye, enabling real-time incident detection and predictive maintenance.

Better yet, all this is achievable without extensive training on various Command Line Interface (CLI) languages, saving companies time and money that can be channelled towards more strategic tasks.

Balancing Deterministic and Probabilistic Approaches

Although AIOps helps data centers predict and adapt to dynamic network conditions, it is not a universal panacea.

AIOps leverages algorithms that train on data for an answer, which means it is inherently probabilistic. But there are many tasks where certainty is paramount, such as making sure an airplane’s wheels deploy in time when landing.

This is where deterministic solutions from Intent-Based Networking (IBN) come into play. Rule-based IBN ensures reliable outcomes in situations where a probabilistic approach may introduce unacceptable risk.

For example, a system that identifies the root cause of a data center anomaly with 98 percent certainty is excellent. But being correct 98 percent of the time on a hardware configuration is unacceptable for most operators.

The key is finding a balance. A hybrid approach leveraging both AIOps and IBN enables a healthy blend of adaptable management and controlled domain context in data center operations.

Owned vs. Public Cloud Infrastructure

As businesses roll out Ai across their organisations, the question whether to use public clouds or build their own Ai data center infrastructure should be top of mind.

More enterprises are building their own Ai data centers away from the public cloud to maintain control of sensitive information and remain compliant with evolving data privacy and sovereignty regulations.

Yet many enterprises’ IT setups are hybrid, and most will continue to opt for a combination of both on-premises and public cloud solutions to manage costs.

This presents a challenge for organisations trying to seamlessly integrate both systems. These issues can be mitigated with the integration of IBN solutions, which are designed to be inherently multi-vendor for simplified management.

The Future for Ai and IBN in Data Centers

AIOps and IBN herald a new era of efficiency and automation, alleviating shortages in networking expertise by democratising management tools, reducing the importance of traditional vendor certifications, and more.

However, businesses must first assess their capacities and needs carefully to determine where Ai can add value; what mix of tools and approaches will deliver the best results; and how much ownership of data center infrastructure it can or should claim.

Only specific and strategic roadmaps can enable businesses to fully reap the benefits of the Ai era.

Bruce Bennie is VP & GM, ANZ at Juniper Networks.

Last Updated on October 22, 2024 by Bruce Bennie