The Benefits of Deploying AI in IT Operations
November 18, 2019

Akhilesh Tripathi
Digitate

Share this

Digital transformation reaches into every aspect of our work and personal lives, to the point that there is an automatic expectation of 24/7, anywhere availability regarding any organization with an online presence. This is a boon to consumers but a huge strain on the IT professionals who must meet that expectation in a rapidly changing environment. As much as 30% of the typical IT environment changes from year to year, forcing IT teams to reskill and stay on their toes in the midst of already-demanding jobs. This environment is ripe for artificial intelligence, so it's no surprise that IT Operations has been an early and robust adopter of AI.


IT's Redundant Task Problem

Hundreds of thousands of incidents can occur in mere minutes in today's complex, dynamic environments, generating overwhelming amounts of operations data. IT workers have to cut through this deluge to find and address problems like a credit card transaction mistakenly declined or a network crash that throws a crucial system offline. It's become nearly impossible for even the best IT teams to respond quickly and effectively.

Though these issues must be resolved, this reactive IT mode does not help the business grow. Worse, an IT worker can start to feel like the mythical Sisyphus, pushing a stone up the hill to solve one problem, only to see it roll down again when another ticket opens. Such an environment can drive even the brightest, most capable IT professionals to burn out and leave.

IT teams carry the triple burden of trying to prevent unexpected downtime — and the financial loss it entails — while improving IT efficiency and continually transforming customer experience. Doing so requires that IT workers engage in log analysis, performance optimizing, capacity planning and infrastructure scaling. While IT infrastructure is dynamic, its problems are well defined. These tasks demand finding patterns in massive data sets and are often dull and repetitive. They are perfect, then, for AI automation. AI tools can enhance both the speed and accuracy of such work, reducing stress on IT employees.

Improving Efficiency and Performance

The use of automation in IT is not new, but it typically has not scaled well in dynamic enterprise environments. Today's AI-based automation is different. IT departments using off-the-shelf AI tools are already reducing unscheduled downtime of revenue-generating systems. In fact, AI tools are helping IT operations resolve problems within minutes instead of hours and transforming customer experience for IT and the business overall.

AI can use multiple kinds of intelligence, making it autonomous, adaptive and scalable. As a recognition intelligence, it can find patterns in immense quantities of data. As a reasoning intelligence, it can tell what those patterns mean: Are they reflecting deviations in normal enterprise systems behavior that mean a system breakdown is looming or an attack from malicious sources is imminent? And as an operating intelligence, it can help manage the problem — both making recommendations for how to fix it and invoking automated, prescribed actions to fix it.

The IT environment features distinct towers of expertise. There's the database, middleware, operating systems, storage, network and so on. Each tower is staffed by people who know its area intimately but may have a limited view across the overall enterprise. AI improves how IT people see the connection between technology and the business. It can be a contextual engine that cuts across all of IT's siloed towers; it is better able to pinpoint the source of a problem than any individual in the organization. Experience shows us that the most difficult part of fixing IT issues is identifying the source of the problem.

Deploying AI in IT

AI's prominence in popular culture has created a variety of perceptions about what it can do, from panacea to paranoia. It is crucial for CIOs to have a clear sense of how and why AI is going to be applied in IT. CIOs who do not carefully define how AI will be applied risk losing control of business expectations for the technology.

CIOs can introduce AI into the IT department in a variety of ways. The greatest ROI comes from using it for business assurance, keeping revenue-generating systems running and fixing whatever problems do occur more quickly. Another effective way to get buy-in for and payoff from AI is to apply it to specific issues such as improving customer experience issues or driving IT agility.

Another benefit of AI for the IT team is that it may not be necessary to upskill current staff or hire new, hard-to-find AI talent. It doesn't hurt to have IT staff with AI skills, but vendors are building intelligence into their systems, and IT-oriented AI-as-a-Service offerings are available. From an enterprise perspective, AI-based IT should mean significantly less time putting out IT fires. That means CIOs can begin to redeploy their human capital, focusing their team more on the growth and transformation of the enterprise instead of keeping the lights on. Ultimately, that means AI will help the CIO be much more aligned with business needs.

AI offers immediate benefits to the IT department that will expand over time. It will continue to learn and be able to manage more complex tasks and issues. This will continue to free IT staff to better respond to customer needs and initiatives that drive business goals.

Akhilesh Tripathi is CEO at Digitate
Share this

The Latest

July 25, 2024

The 2024 State of the Data Center Report from CoreSite shows that although C-suite confidence in the economy remains high, a VUCA (volatile, uncertain, complex, ambiguous) environment has many business leaders proceeding with caution when it comes to their IT and data ecosystems, with an emphasis on cost control and predictability, flexibility and risk management ...

July 24, 2024

In June, New Relic published the State of Observability for Energy and Utilities Report to share insights, analysis, and data on the impact of full-stack observability software in energy and utilities organizations' service capabilities. Here are eight key takeaways from the report ...

July 23, 2024

The rapid rise of generative AI (GenAI) has caught everyone's attention, leaving many to wonder if the technology's impact will live up to the immense hype. A recent survey by Alteryx provides valuable insights into the current state of GenAI adoption, revealing a shift from inflated expectations to tangible value realization across enterprises ... Here are five key takeaways that underscore GenAI's progression from hype to real-world impact ...

July 22, 2024
A defective software update caused what some experts are calling the largest IT outage in history on Friday, July 19. The impact reverberated through multiple industries around the world ...
July 18, 2024

As software development grows more intricate, the challenge for observability engineers tasked with ensuring optimal system performance becomes more daunting. Current methodologies are struggling to keep pace, with the annual Observability Pulse surveys indicating a rise in Mean Time to Remediation (MTTR). According to this survey, only a small fraction of organizations, around 10%, achieve full observability today. Generative AI, however, promises to significantly move the needle ...

July 17, 2024

While nearly all data leaders surveyed are building generative AI applications, most don't believe their data estate is actually prepared to support them, according to the State of Reliable AI report from Monte Carlo Data ...

July 16, 2024

Enterprises are putting a lot of effort into improving the digital employee experience (DEX), which has become essential to both improving organizational performance and attracting and retaining talented workers. But to date, most efforts to deliver outstanding DEX have focused on people working with laptops, PCs, or thin clients. Employees on the frontlines, using mobile devices to handle logistics ... have been largely overlooked ...

July 15, 2024

The average customer-facing incident takes nearly three hours to resolve (175 minutes) while the estimated cost of downtime is $4,537 per minute, meaning each incident can cost nearly $794,000, according to new research from PagerDuty ...

July 12, 2024

In MEAN TIME TO INSIGHT Episode 8, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses AutoCon with the conference founders Scott Robohn and Chris Grundemann ...

July 11, 2024

Numerous vendors and service providers have recently embraced the NaaS concept, yet there is still no industry consensus on its definition or the types of networks it involves. Furthermore, providers have varied in how they define the NaaS service delivery model. I conducted research for a new report, Network as a Service: Understanding the Cloud Consumption Model in Networking, to refine the concept of NaaS and reduce buyer confusion over what it is and how it can offer value ...