8 predictions of AI in ITOps.

Adoption to the era of digital transformation is widespread. Most CIOs or C-executives are excited about this transformation. But a  significant impact the digitization brought was on the ITOps team. Digitization brought in newer IT infrastructure and increased complexity. For the business to cop-up with the competition, the adoption of this new infrastructure became a must. This brought in a lot of pressure on the ITOps team as they were fully responsible for implementing and maintaining this change. They need to be extra vigilant in implementing any change, as one small mistake can lead to revenue loss and can also impact the business on a large scale.
ITOps team focuses on checking if every service is up and running, there is any failure in the network if there are any performance issues of any component or application that can cause any disturbance at the end-user side, and so on. The Ops team should continuously monitor and analyze the entire IT infrastructure. Moreover, applications are getting more and more complex and often run on multi-location, multi-cloud microservice-based architecture. This, at times, can be a challenge with the shift to the era of digital technology. The team may fail to provide results accurately and may also fail to see some errors that can cause greater impact. Moreover, manually another difficulty is in handling the data. As the IT infrastructure grows, so does the data to be monitored multiples. Every structured and unstructured data needs to be monitored. This requires a lot more additional capabilities than what the current ITOps team poses.
Introducing AI for IT Operations:
The change that is needed here is the implementation of AI. For the smooth run of business, we need solutions that can identify any outliers or defects quickly. We need a solution that can automatically identify unknown problems from the huge volume, intelligently alert the team, and auto-remediate the problem. The perfect solution is the application of Artificial Intelligence to ITOps, which is known as the AIOps. The application of AI in ITOps will open up a new path in the business sector. AI enables in providing faster and better decision-making capabilities, thus improving the IT teams to make accurate decisions when any issues occur. Apart from that, AI can predict if there is a chance for failure to occur. In this way, the IT team can proactively procure the situation.
8 Prediction of AI in ITOps:
Artificial intelligence is a real game-changer for ITOps. With the application of AI, the ITOps team can completely automate most of their task. Let us look into the top 10 predictions that AI can bring into ITOps.
1. DevOps will evolve with AIOps:
The traditional implementation of the DevOps may not go well in the era of digital transformation. Another area AI establishes a change is in DevOps. With AI implementation in DevOps, application development and deployment cycles were at a much faster rate and predicting the effect of deployment on production and automatically responding to changes in how the production environment is performing. Moreover, the DevOps team can code, test, release, and cohesively monitor software automatically.
2. AI will increasingly support contextual data ingestion:
Data handling is a primary concern in every IT organization. The volume of data generated is remarkable. The need to have an efficient mechanism to handle structured and unstructured data is a primary requisite. AI can help in this process too. AI-based models are capable of ingesting large volumes and a variety of data from multiple sources. Apart from ingestion, AI-based models are capable of identifying correlations among data. Based on this correlation, the AI-based model can predict and intelligently alert any root cause of problems.
3. Event Noise can be further reduced with AIOps:
Identifying events or any abnormality from a large volume of data is another capability AI can bring. AI-based models are capable of identifying IT events efficiently by the mechanism of pattern matching. In this way, the AI models will automatically capture any correlated patterns from data such as KPI and log and automatically alert the IT operator. Effectively, by the use of an AI-based model, the number of false-positives generated can be reduced.
4. Advanced ML-based Anomaly Detection Uncovers Unknown Problems:
Unknown problems are a common scenario faced in IT operations. This brings a lot of stress to the IT team as they cannot identify what exactly went wrong. That's where the AI-based anomaly detection mechanism takes its place.AI based anomaly detection mechanism helps in automatically uncovering unknown problems termed as anomalies from a large volume of structured and unstructured data. AI will help the IT team reveal unknown problems easily and help to quickly remediate the situation before it can persist to a greater problem.
5. Monitoring will evolve and matures towards observability:
 Hanging onto traditional monitoring capability was no longer working in the era of digital transformation. Current monitoring tools are just statically configured to detect point problems. Observability is a buzzword in the current digital era. Applying AI to the concept of observability took the whole notion to a new level. The theory of observability is a derivative of control theory. The AI-based observability helps in providing a deep health assessment of your entire organization. It uses all telemetry data about applications to support their health and performance.
6. NLP-based Omni-Channel Engagement improves the SLA:
Ticket logging is an inevitable part of every IT organization. Moreover, meeting SLA requirements is a fundamental factor. IT tickets generated should be resolved effectively without getting breached. AI-based ticketing system helps in providing a more converged interface for handling out every IT ticket. The AI-based omnichannel engagement is carried out by natural language processing(NLP) and other artificial intelligence technique. The NLP system provides a better mechanism of real-time analysis of each ticket generated, which helps in automated ticket routing, prioritization of tickets. In this way, AI-based systems will provide an efficient mechanism for omnichannel engagement.
7. AIOps will change the face of IT automation:
As IT enterprise's complexity grows, this calls in need to bring in more effective solutions to overcome any situation. The implementation of AIOps will completely change the face of IT automation. Most tasks done manually by the IT team will be automated by the application of AI. AI and machine-learning technologies will discover hidden resources and threats, uncover patterns, filter the noise, and aid decision-making. IT operators can find answers to problems faster and understand how to optimize IT performance as conditions change.AI Automation can deliver significant enhancements in staff efficiency, mean-time-to-resolution metrics(MTTR), and improve service level agreement. Cognitive process automation helps in increasing business agility by processing complex structured and unstructured data. It helps the IT industry by automating almost every task done manually, which in turn helps to reduce the chance of manual errors occurring.
8. Maturing towards low-touch IT Operations:
The term low-touch ITOps might sound like a fancy term, but the reality is that this is what ITOps is ultimately evolving to. As the term, low-touch ITOps stands, almost completely automating ITOps with the implementation of AI, which is AIOps. With the concept of low-touch, every possible step of IT operations will be automated. Let's say, from the base of ingesting and monitoring data and getting inference from them to identifying any unknown problems/events through an anomaly detection mechanism and identifying root causes. Then auto-remediation or auto-fulfillment of the IT tickets generated. In this way, the capability of AI will completely evolve the entire IT operations team. 
To learn more about Algomox AIOps please visit our AIOps Platform Page.