The Road Ahead: 4 Ways AIOps Will Build More Resilient IT Operations

The Road Ahead: 4 Ways AIOps Will Build More Resilient IT Operations

This article is the final installment in a 4-part series on leveraging artificial intelligence and machine learning (ML) for IT operations (AIOps) to provide a more efficient, reliable, agile, cost-effective, and optimized IT infrastructure.

Just as our roads and highways evolve overtime to meet the demands of the travelers who use them, AIOps will continue to transform how organizations build, use, and manage their infrastructures. Here are four capabilities or improvements we are excited to see in the future.

Enhanced Automation and Operations

Like a valet instinctively turning on the heat before bringing your car around on a cold night, AIOps is proactive when it comes to automating routine IT tasks (based on pattern recognition) and optimizing resource allocation. Future iterations will build on more anticipatory operations (such as pulling the car around before you even ring the valet that you need it). This capability will bring more organizations closer to becoming self-healing within their IT environments. 

Watch Schneider Electric explain how LogicMonitor got them on the path to self-healing operations.

AIOps learn from automated remediation, then improve on those algorithms to expand detection of similar issues and pre-empt the problem before it happens. This ability will set organizations on a course towards self-healing: minimizing downtime, improving service reliability, and reducing MTTR so IT teams can focus on priority projects, strategic tasks, and developing innovative solutions which drive the business forward.

Cognitive Insights and Contextual Understanding

As discussed in our last article, “Key Considerations in Your Journey to AIOps,” a drawback of AIOps in 2023 is the lack of human judgment and contextual experience. Similar to how architects analyze blueprints to gain insights into a building’s design and function, in the future, AIOps is going to be able to interpret unstructured data using natural language processing (NLP), a branch of artificial intelligence that focuses on enabling computers to understand and process human language. Sentiment analysis will also become more robust, allowing for AIOps platforms to analyze more common themes in human-submitted data (such as a customer support ticket), and escalate the tickets based on patterns derived from situational awareness (in this case, as documented in the support ticket and corresponding response process).

Integration with Edge Computing and the Internet of Things (IoT) 

Specialized tools enable builders to work in remote areas or challenging environments. Likewise, the future integration of AIOps with edge computing and IoT will allow AIOps to become a specialized tool used to construct and manage distributed IT environments. By analyzing real-time data from edge devices and IoT sensors, AIOps will enable proactive monitoring, predictive maintenance, and agile decision-making at the network’s edge. This integration will accelerate the construction of resilient and scalable IT infrastructures which can handle the complexities of the distributed digital landscape.

Ethical and Responsible AIOps

Explainable AI models will be more available in the future, enabling organizations to prioritize fairness, transparency, and bias mitigation within AIOps systems. The result is improved system compliance and ethical decision-making by AIOps regardless of any biases within the analyzed data. Explainable AI models are designed to provide more of that human-like touch in regards to enhanced trust, accountability, and compliance. Just as construction companies must follow building codes and regulations to make sure a structure is safe, Explainable AI models will improve the ethical guardrails within AIOps systems.

The AIOps design and functionality of the future is within our control. Organizations which embrace AIOps will shape this new digital landscape and accelerate the development and reliability of enhanced automation, cognitive insights, integration with edge computing and IoT, and more ethical operations. 

LogicMonitor is proud to power the journey to AIOps by offering these free educational resources:

Schneider Electric consolidates monitoring tools by 83% with LogicMonitor  

AIOps and the Future of Performance Monitoring 

What Will APM Look Like in the AIOps Era? 

Monitoring and Alerting Best Practices Guide 

Comprehensive AIOps for monitoring: AIOps Use Cases for Today and Tomorrow