Gartner defines AIOps as a technology that combines big data and machine learning to automate IT processes, including event correlation, anomaly detection and casualty determination.
AIOps, which is short for AI for IT Operations, signifies how data from a development environment is managed by an IT team using artificial intelligence.
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A Deeper Look at AIOps
AIOps enhances IT operations with continuous monitoring, automation and service desk tasks enriched with deep and personal insights. AIOps make data more accessible, usable and available, enabling faster resolutions to performance challenges, outages and increasing IT costs.
With AIOps, operations teams can make sense of the enormous volume of complex data generated by the sophisticated IT environments of today, thus ensuring continuity.
The Process of AIOps
AIOps tools weren’t created equal. To extract the most value out of one, an organization should deploy it centrally to gather data from all IT monitoring sources and yield a single point of reference across the organization.
Here’s how AIOps works:
The Benefits of AIOps for Startups
The primary benefit of AIOps is arming Ops teams with speed and flexibility to ensure uptime of critical services and deliver an exceptional digital experience.
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AIOps drastically improves on the Mean Time to Resolution by sifting through the abundance of noise in IT operations and correlating the data from multiple environments.
AIOps identifies root causes and proposes solutions significantly faster than humans. This helps organizations achieve previously unimaginable MTTR goals.
AIOps speeds up the detection and resolution of critical issues that lead to outages & hurt the customer experience and sales. It enables IT specialists to focus on the important and not be distracted by insignificant alerts and noise, thus improving their productivity and output.
High-quality Customer Service
More agile and adaptive processes directly improve the quality of customer service. When all internal functionality works like clockwork, customers expect a brand to show up like it always does.
This builds long-term loyalty and trust that directly impacts the bottom line. AIOps can also lead to better customer retention by that sequence. A 2% boost in customer retention equals a 10% cost reduction from a profits point of view, as per a study by Harvard Business School.
So high-quality customer service, thanks to AIOps, can impact critical metrics across the business.
Historical data from IT incidents can inform business analytics and lead to the ability to predict failures and reduce the risk of downtime, which typically costs TOP-1000 companies about $11,000 with each passing minute.
Moreover, AIOps reduces the risk of human error in managing humongous data resulting from various IT environments and the stress and fatigue it induces in concerned teams.
Reactive to Proactive to Predictive Management
An AIOps system never stops advancing its algorithms. So it keeps getting better at identifying alerts that relate to the more critical ones. By grouping such signals, AIOps can create a predictive model to let IT teams address potential anomalies before they disrupt services and cause costly outages.
Moving from reactive to proactive to predictive management can help organizations save millions of dollars and offer the same level of customer experience.
Modernized IT Operations
We keep talking about digital transformation and modernizing business processes. AIOps is how it’s done for operations and IT. Instead of having teams overwhelmed with frequent alerts, AIOps teams only receive alerts that meet certain criteria and parameters along with contextual information to diagnose issues at hand and take corrective action.
The more automated and AI-enabled your IT and operations, the more you can be sure of your business without relying on human effort, and help teams focus on strategic functions that add direct value to the company.
Challenges in AIOps
Not all AIOps implementations go as planned. Challenges such as poor-quality data and IT errors can hinder deployment. Employees may find it challenging to learn to use the tool and may resist changing the status quo.
Handing over control to an autonomous system with automated responses can concern C-suite leaders. Finally, adopting new AIOps solutions can be time-consuming.
Since AIOps’ success heavily depends on underlying data quality, getting high-quality data presents itself as a challenge.
But beyond all data challenges, there are wide skill gaps in the industry. As per a 2021 Juniper report, most respondents agreed that their organizations were struggling to expand the workforce to lead the integration with AI systems.
When talent is lacking, it comes with a high cost.
This is why we at KiwiTech make AIOps accessible to startups. Learn more about our implementation process and consultation by speaking to one of our Artificial Intelligence consultants today.