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.
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:
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:
Additionally, IT teams and their companies can:
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.
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.
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.
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.