Artificial Intelligence

What Does AIOps Mean for Startups?


To understand AIOps, it helps to consider one peculiar thought: the technological systems that we humans have built have become too complex to be managed by mere humans.

Specifically, the IT operations that run businesses have galloped forward in complexity, leaving their human masters in semi-bewilderment.

It’s a great irony of human intelligence: we’ve built IT systems that are so advanced that they are, in essence, overwhelming us.

Meanwhile, the leisurely pace of data analytics is being superseded by real-time streaming data, which is far too fast for humans and requires algorithms to analyze. Then there’s the torrent of data from edge computing, the blinking universe of IoT devices, and a boundless ocean of smartphones.

Add all it together, and enterprise IT systems – under ceaseless attack by hackers, not to mention budget constraints – can only utter one word: help!

This guide will help you understand the system that comes to rescue during such challenges, AIOps.

What is AIOps?

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.

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 and outages and increasing IT costs.

“AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.”


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.

How does AIOps works?

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:

  • It combs through the data for signals – Modern IT environments generate big data. AIOps uses rule application and data selection to filter out the signals- abnormal event alerts from the noise (often 99% of the data).
  • Uses pattern discovery & inference to get to root causes – AIOps deploys industry or environment-specific algorithms to correlate and find relationships between the selected data elements across environments to get closer to the root cause of the problem and suggest a resolution.
  • Takes automated action – At its very basic, an AIOps tool can route alerts and proposed resolutions to concerned IT teams or build response teams based on the nature of the signal. It can process outputs from ML algorithms to trigger automatic system responses in real time for proactive resolution in more sophisticated use cases.
  • AIOps continually learns and improves error handling – Based on each alert and how it was resolved, ML capabilities change algorithms or build new ones to reflect new knowledge in future resolutions. So each subsequent resolution is more efficient than the previous one.

Related Reading: How Startups Can Compete in AI and ML 

Why your startup needs AIOps?

The primary benefit of AIOps is arming Ops teams with speed and flexibility to ensure the uptime of critical services and deliver an exceptional digital experience.

1. Productivity 

AIOps drastically improve 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 speed 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.

2. 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.

3. Risk Mitigation

Historical data from IT incidents can inform business analytics and lead to the ability to predict failures and reduce the risk of downtime, costing TOP-1000 companies about $11,000 per 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.

4. 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.

5. 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 specific 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.

Related Reading: 5 Strategies for Startups to Enhance Their Customer Experience

How AIOps helps your startup?

Through algorithmic analysis of IT data and Observability telemetry, AIOps helps IT Ops, DevOps, and SRE teams work smarter and faster, so they can detect digital-service issues earlier and resolve them quickly before business operations and customers are impacted.

With AIOps, Ops teams can tame the immense complexity and quantity of data generated by their modern IT environments and thus prevent outages, maintain uptime and attain continuous service assurance.

With IT at the heart of digital transformation efforts, AIOps lets organizations operate at the speed that modern business requires and deliver a stellar user experience.

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 learning to use the tool challenging and 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.

How to get started with AIOps

AIOps offers several compelling capabilities, but getting started doesn’t have to be overwhelming. Here are a few steps and assets to get you started:

1. Build a Business Case

Start by identifying areas where AIOps can help. Which KPIs do you need/want to improve? How have performance issues/outages impacted the business in the past – both financially and reputation-wise? Use this Downtime Calculator to assess how much revenue an outage could cost your business.

2. Choose an AIOps Solution

There are a lot of AIOps solutions on the market. Be sure to understand the different types of AIOps solutions (ex., Domain-Agnostic vs. Domain-Specific), the effort and time to implement, and how easy each is to use and maintain. Be sure to demo and trial them and ask for customer references. 

3. Start Small, but with a Specific Goal

As the saying goes, “Rome wasn’t built in a day.” Today’s AIOps solutions make it much easier to get started, but still require that you have a problem you are trying to solve. Start with something very specific and a couple of data sources to ingest.

How does KiwiTech help in leveraging AIOps

KiwiTech’s AIOps service offering provides predictive analytics capabilities to IT operations by leveraging big data, machine learning to analyze super large volumes of structured and unstructured data.

Our technology competencies and partnerships cuts across core infrastructure monitoring, application performance monitoring, network monitoring, log analytics, event management, and AIOps. Expertise across a best-in-breed technology stack and an understanding of business use cases for AIOps implementation are required for a successful rollout.

Since AIOps’ success heavily depends on underlying data quality, getting high-quality data presents 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 struggled 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.

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