Much like other industries, artificial intelligence (AI) is revolutionizing IT. Factors driving the push for AI in IT operations (AIOps) include data’s exponential growth and complexity, increased reliance on edge network devices, and expansion of IT environments and infrastructures across the Cloud. The need for speedy issue resolution is also a key driver of AIOps. A study from Accenture found that 43% of help desk agents routinely handle 100+ issue tickets. AIOps promises to relieve some of the burdens of managing too much data across complex systems.
A common misconception is that AI replaces IT personnel with a “robot.” Instead, AIOps provides a unique opportunity to maximize IT efficiency. AIOps collects, aggregates, and sorts through massive amounts of data to pinpoint root causes of slowdowns, network bottlenecks, and other issues. Trends and pattern analyses can guide IT teams to get ahead of potential problems.
When partnered with an experienced IT provider, AIOps is a potent means of improving customer experiences by improving efficiency, security, and support.
What is AIOps?
Gartner first created the term AIOps (short for artificial intelligence for IT operations) to describe machine learning and natural language processing deployment across IT workflows. AIOps utilizes big data, analytical tools, automation, and machine learning to:
- Collect and aggregate data – AIOps gather enormous volumes of data from various infrastructure components such as networks, security, applications, and issue tickets. As a result, they can monitor the overall health of an IT environment, granting both bird’s eye and worm’s eye views of data flow.
- Filter out signal from noise – Another vital usage revolves around pattern recognition and real-time monitoring of potential issues.
- Diagnose and report problems – AI can quickly and accurately identify issues while offering suggestions on how to remediate the problem.
- Self-heal and automate – Some AIOps systems automate responses or even correct issues without IT oversight. (While this is an attractive feature, CBTS recommends pairing AI automation tools with seasoned professionals to avoid the drawbacks discussed later in this post.)
- Predictive modeling – Through pattern recognition and machine learning, AI can identify future issues and help to prevent them.
Also read: Six ways AI will transform the future of IT recruiting
AIOps customer experience benefits
At first blush, it may seem like AIOps only benefits IT departments. However, in reality, AIOps generates a number of customer experience benefits for end users:
- Greater accessibility to networks and applications.
- Improved support and speedier MTTR.
- Greater productivity and collaboration tools.
Additionally, IT teams and their companies can:
- Reduce operational expenditures (OpEx).
- Breed better developer experience (DevEx) and reduce product time to market.
- Reduce downtime.
- Provide greater security at the network edge.
AIOps helps IT to give customers an extremely reliable experience through real-time monitoring and proactive issue management. AI also improves collaboration across the entire enterprise by de-siloing data. When every department has improved access to analytics, insights can be mined and implemented across the whole enterprise—not just in IT.
Read more: How to move your network security strategy forward with automation
Use cases for AIOps
- Security and anomaly identification: By sifting through mountains of historical data, AI can flag outlying data points that can be indicators of data breaches or other harmful events. For example, advanced threat detection can scan through massive chunks of the Internet to proactively identify malware threats.
- Root cause analysis: A lot of labor in IT is lost to treating symptomatic issues rather than rooting out the issue’s core. AIOps hunts down the root causes of a problem, recommends actions, and even sets up protections to ensure the same issues don’t happen again.
- Performance monitoring: When companies primarily relied on on-premises data centers, monitoring the performance of networks and applications was a more straightforward process. With the prevalence of cloud computing, networks, applications, and processes may be dispersed across a multi-cloud environment. AI helps IT evaluate performance across increasingly complex environments by generating automated reports on usage, storage, availability, response times, and more. For end users, AI provides better information consumption through data correlation and aggregation.
- Cloud migration: As companies transition to the Cloud, rarely can the entirety of their digital ecosystem be migrated all at once. This leads to some very complex hybrid environments that may involve interdependencies between private and public clouds, as well as third-party “as a service” providers. AI creates greater visibility across these dependencies and simplifies the process of cloud management and migration.
- DevOps: AIOps can drive DevOps by further simplifying redundant processes with automation tools and granting developers greater visibility and control without additional effort.
Learn more: Why CBTS leverages Red Hat Ansible Automation Platform for modernizing enterprises
Potential drawbacks of integrating AI
In previous decades, when your car broke down, you would take it to a mechanic who would spend several days ruling out possible issues. Now, a mechanic can simply plug in a device that communicates with the onboard computer and immediately pinpoints the problem. AIOps works similarly. IT professionals now have a much clearer path for finding and fixing the root causes of issues, but skill and experience are still needed to filter out false alarms and to “teach” the AI—which facilitates ongoing improved customer experience.
Lack of context/understanding
When AI is used to execute a vulnerability scan, the raw data may identify upwards of hundreds of thousands of vulnerabilities. Importantly, the scan cannot tell you which vulnerabilities are already protected by other security protocols, and which require attention. There may only be a small number of vulnerabilities that need your attention. A seasoned engineer can interpret these reports to understand and correlate the priorities.
With the rise of “smart” doorbells, many people have access to a camera that securely monitors their front door 24×7. However, that camera is unable to decipher potential robbers from delivery drivers. Similarly, many AI tools lack the finesse that only comes from experience. Without someone to “drive” it, AIOps can inadvertently create more noise that IT teams must sort through.
Like many students, machines that learn sometimes make the wrong connection. An AI managing a switch might pinpoint an “issue” of MDU size. If this is a self-healing AI, it might lower the MDU size globally, disrupting operations for the rest of the switch.
Only an expert can manage AI recommendations and determine when a problem should be remedied. And with expert guidance, the AI “pupil” can make more refined recommendations over time.
How to get the most out of AIOps
Gartner estimates that AIOps usage will rise to 30% this year, a 25% increase from pre-pandemic levels. Driven by increasingly complex technology estates, companies of all sizes turn to AI to gain greater visibility, mine data for insights, automate operations, and increase security. Companies also leverage AI to provide better customer experience through more dependable network access and faster issue resolution. In addition, when employees enjoy greater collaboration enabled by AIOps, they can better serve their customers.
However, poorly implemented or understood AI can have adverse effects. CBTS can build a custom suite of AIOps tools for your business to achieve the most significant results. More importantly, CBTS can help you “drive” AIOps platforms and teach them to recognize the red flags and metrics that are most vital to your organization.
Get in touch to learn more about maximizing AIOps adoption in your business to improve customer experience.