Editorial
IT professionals are accustomed to dealing with high-stress situations, watching sensors go off while trying to figure out what’s causing a slowdown or crash. Every minute brings more tickets piling up from the help desk and more frantic phone calls from higher and higher up the org chart. You sweat it out and eventually find a solution, but you’re left wondering, “Is there a better way?”
The answer is yes. With AI.
AI can help eliminate many of the human factors that make IT so stressful. Integrating AI into your platforms can help your IT staff identify issues proactively, respond to problems quickly, reduce MTTR (“Mean Time to Repair”) and enhance service levels. It can also help team members get answers faster and keep their devices running smoothly.
Most organizations understand the promise of AI from that efficiency perspective. In fact, Gartner research on enterprise adoption found more than 60% of CIOs say AI is part of their innovation plan. But actual production deployments grew by less than 5% over the past five years, highlighting a disconnect between planning and implementation.
So, why the gap? We looked at the three key reasons organizations may want to invest more resources into deploying AI that’s purpose-built for IT use cases.
Reason 1: AI Gets the Job Done
Whether you’re using an AI-driven help desk to serve “silent sufferers” or finding other ways to leverage AI, one of the primary benefits of AI is that it just works, 24/7/365.
And the more you use it, the better it gets. As you train the model on high-quality, well-structured data, AI delivers more trustworthy outputs.
Reason 2: AI Removes the Emotion From IT
I’ve never actually thrown my laptop out the window in frustration—but I’ve certainly wanted to, many times. We all get frustrated, especially if we’re already having a bad day. Maybe someone on your IT team got a speeding ticket on their way to work or had a fight with their partner, and now they’re trying to troubleshoot a major network issue that seemingly appeared out of nowhere.
IT people aren’t perfect. They have bad days. They might have an attitude, hold a grudge, or ignore a tricky problem until the next morning. They don’t always respond the same way to different people throughout the organization. They’re human, and that’s what humans do.
But AI isn’t human, so you don’t get those messy complications. AI doesn’t hang up on the VP of Sales or panic when the CEO sends an email. AI doesn’t get stressed out when the help desk tickets keep coming. AI doesn’t miss a red flag because it’s rushing through a checklist. There is no emotion—so it stays focused and productive.
Reason 3: IT Teams Don’t Have All the Answers (but AI Might)
Being expected to solve any and all IT issues is overwhelming. Even in a small organization, there can be millions of combinations of devices, configurations, applications and networks. Very few teams are equipped with the staff to know every possible answer to every possible problem, which is why they often rely on search engines and online forums to troubleshoot and find solutions. But that takes time, patience and knowing what to search for — and which source to trust.
With AI made for IT, you can quickly find the answers to your organization’s specific problems if the model is trained on IT data such as information about device performance and user behaviors with their endpoint device. So, instead of spending hours with dozens of tabs open on your browser trying to find out why the network is slow, AI can find correlations in the data in minutes and show you, for example, that all of the affected users recently installed the same application update, which is likely causing the slowdown.
Related Article: Components of an AI Strategy
You Still Need Good Data and Human Expertise
Just because AI never has a bad day doesn’t mean it’s perfect. If you want the best results from AI, you must ensure a few key factors are in place.
The Importance of High-Quality, Well-Structured Data
A recent report from the Enterprise Strategy Group found that the availability of quality data is the top challenge organizations face when implementing AI. As the report noted, “Without good data to feed into the AI, trust can never be achieved. Without trust, full adoption can’t happen. And without full adoption, the overall goals can’t be achieved.”
When training AI models, we typically consider a variety of data characteristics, including breadth, depth, accuracy, lack of noise and structure. Each of these factors significantly impacts the quality of the data, which, in turn, affects the model's usefulness, the level of trust your employees have in AI and, ultimately, your ability to deploy AI effectively throughout your organization.
Related Article: Proprietary Generative AI Is Expensive. Enter AIaaS
Interpreting Results With Human Insight and Context
Despite its many proven benefits, AI still has hallucinations and biases. Until AI is 100% reliable (which may never happen), you need experienced, knowledgeable people to review the results, validate them and adjust the model as needed.
Context is also critical. Your AI may have access to hundreds of millions of data points, but it still doesn’t know that your marketing VP’s toddler uses his tablet at night — information that may be key to understanding why it keeps crashing.
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Many organizations also combine objective data with subjective sentiment analysis from companies like Qualtrics to measure employee sentiment and improve employee experiences.
Remember: AI Is Still a Tool
As you manage the AI adoption curve, knowing that AI is an emotionless, powerful tool can help you determine the most effective ways to integrate it throughout your organization.
The fact that AI never has a bad day doesn’t mean it’s perfect. Still, it removes many of the obstacles and limitations of working with humans — as long as you remember that you still need your people to manage it, monitor it and bring their unique experiences to the table.
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