AIOps: A New Phase in Enterprise IT Operations

IT Operations personnel (IT Ops) have been witnessing a revolution. The forces of digital business transformation are calling for a change to traditional IT management techniques. A significant change in the current IT Ops procedures is seen and the IT ecosystems are witnessing a change in management. This change is coined as Artificial Intelligence for IT Operations (AIOps), which is a Gartner-defined platform that combines big data and artificial intelligence (AI) functionality to replace a broad range of IT Operations processes and workflows. This replacement includes event correlation and analysis, availability and performance monitoring, automation and IT service management.

AIOps and the Market Projections

AIOps as a market category has seen a huge interest and has exploded over the last 1½ to 2 years. The number of inquiries has increased exponentially so have the number of Google searches on the topic. AIOps is dubbed as the next big change as it delivers performance, improves customer experience, and provides agility to businesses via new services. Over the last few years, Businesses have been increasingly investing in AIOps solutions as a necessity to continue to drive forward their digital business transformation.

Drivers of AIOps

AIOps aims to bring about a massive change the way IT Operations are dealt by addressing to the complexity, speed and scale up challenges of digital transformation. The return on investment from AIOps is huge; and industries have woken up to this fact. The main contributors driving this trend are as follows:

1. Data Absorption: Data is on an exponential trend upwards, and leveraging this goldmine is a part of business strategies and future revenue forecasts. As the IoT takes off, IDC expects that by 2025, 163 zettabytes of data will be produced annually which is ten times the amount currently produced (16.3 zettabytes). This has dire consequences with respect to conventional technologies, which will have a tough time in keeping up with the volume of data being produced. This will consequently lead businesses to look for a more robust solution and AIOPs can be an effective solution here.

2. Improvements in IT productivity:  AIOPs can automate analysis, centralize and integrate logs from different sources to substantially reduce the number of hours needed to resolve incidents. This will make IT establishments more productive with the same resources and reduce the Mean Time to Repair (MTTR) by 45% as per industry estimates.

3. Prevention of Escalations': AIOps uses a tribal knowledge base powered to automate root-cause analysis, which can reduce the number of P1 incidents by 42%. This empowers support teams as they can handle most application and infrastructure issues without putting an escalation to DevOps or R&D departments.

4. Automation: Deploying AIOps into operations, organisations can fully automate incident responses, and can significantly reduce and sometimes even remove the human need in the resolution process while increasing the efficiency of IT.

5. Increase in Employee Productivity: Businesses can increase the overall capacity of their IT team by 18%, by introducing an AIOps solution to cover their infrastructure, applications and network monitoring capabilities. AIOps empowers IT to deliver stability and growth to the business, an essential crown jewel for the modern CIO by utilising the same team and an easier, more scalable process for operations.

6. Savings in Cost Component:  Implementing AIOps, Organizations can reduce the use of multiple on-premise solutions and associated maintenance costs in addition to saving on outsourcing costs thus saving massively on the cost component.

Looking to the Future

AIOps is a developing domain and much needs to be achieved in the years to come. As things currently stand, AIOps gives a path for partial automation making some tasks automatic, and reducing the average time taken to resolve issues. To implement a successful AIOps, the human mind will play an integral role in monitoring for issues and problem fixing when they arise.

Current AIOps systems have faced a few setbacks in understanding the relationships between applications, infrastructure, and other datasets. We expect that to change in the next two or three years. Over the years, as automation technologies evolve, monitoring will become mostly or even fully automated and the IT problems will be solved automatically and resolved before businesses can even notice that there was a problem in the first place.

Looking ahead as AIOps technologies develop and become more impactful to create efficiencies, IT businesses which will embrace to the change and adopt AIOps will gain an upper edge over their competitors who will find it tough to sustain their competitive edge with performance slowed down by the outdated and manual work.