How IT Departments Are Successfully Adopting Artificial Intelligence Technologies
November 15, 2018

Patricia Diaz-Hymes
Lakeside Software

Share this

We all know artificial intelligence (AI) is a hot topic — but beyond the buzzword, have you ever wondered how IT departments are actually adopting AI technologies to improve on their operations?

Because the term "AI" gets thrown around a lot, let's start with what it means. Given all the marketing hype this technology has been given, I often find myself debating with peers on what AI actually is. Understanding that many vendors have usurped some loose sense of the term in order to claim AI functionality, let's be factual in terms of what it is. As Gartner defines it, artificial intelligence is:

"Technology that appears to emulate human performance typically by learning, coming to its own conclusions, appearing to understand complex content, engaging in natural dialogs with people, enhancing human cognitive performance (also known as cognitive computing) or replacing people on execution of nonroutine tasks. Applications include autonomous vehicles, automatic speech recognition and generation and detecting novel concepts and abstractions (useful for detecting potential new risks and aiding humans quickly understand very large bodies of ever-changing information)."

46% of the companies surveyed said their IT departments are using AI

Use cases for understanding AI as technology that emulates human performance have been endless. When automation technologies started making headlines, everyone fixated on all the statistics around jobless due to automation. The Washington Post last year reported that robots would replace almost a third of the U.S. workforce by 2030. Many AI-powered technologies have garnered some bad press but IT teams are one of the first groups to experiment and more formally adopt AI technologies — not to replace humans but to complement our advancements

According to a recent study conducted by Tata Consultancy Services(TCS), 46% of the companies surveyed said their IT departments are using AI. But for what? Mostly for security intrusions, resolving user issues, automating production management and gauging internal compliance.


AI in The Real World

According to the TCS study, the highest percentage of organizations using AI is in the energy industry, with 100% of them using AI to some capacity, followed by high-tech and telecommunications.

But why are these IT teams and other departments investing in AI technologies? The study outlines six main goals that really propel AI initiatives:

■ Improving product or service quality

■ Helping customers use and get more value from their products/and or services

■ Reducing key process cycle times

■ Improving executive decisions

■ Identifying new revenue opportunities

■ Reducing costs by automating manual work

Ultimately, whether using AI for security intrusions, IT problems or otherwise, the main driver for IT in using this technology is to improve product and service quality. That is why it is no surprise that IT vendors have been developing AI solutions for IT professionals in the end-user computing space, be it for application, hardware, services management, etc.

Particularly, as it pertains to tools that monitor the digital experience, AI technologies help support this goal while also helping clear up IT's time for higher value activities.

How AI Will Transform IT Operations in 2019?

Gartner recently published its Top 10 Strategic Technology Trends for 2019 which outlines the empowered edge as a top trend noting: "Through 2028, we expect a steady increase in the embedding of sensor, storage, compute and advanced AI capabilities in edge devices."

With the introduction of AI into IT operations, IT teams continue to find new use cases for AI and its applicability to edge computing is no exception. Perhaps one of the most promising use cases in this space, in part because of its clear and strong business value proposition, is Level 0 support, an automated or self-service style where users can access help themselves without the aid of the help desk. AI technologies, used in tandem with end-user computing or digital experience monitoring tools, are now being used to deflect user-facing issues from even reaching support desks using self-healing and self-service — Level 0 support.

AI's role in Level 0 is identifying patterns in the monitored environment that can then help inform IT or automation technologies to solve a problem. The value proposition of such solutions is simple, it is a way to reduce support desk costs and free up agents' time to focus on higher value interactions, which are more complex issues and require humans.

Some components of Level 0 support are not new. Unfortunately, this level of support has not been executed successfully by many IT departments. With AI advancements, the future seems very promising for the effectiveness of self-service programs and the adoption of new automation processes that ultimately help improve quality for users.

Patricia Diaz-Hymes is Sr. Product Marketing Manager at Lakeside Software
Share this

The Latest

September 23, 2021

The Internet played a greater role than ever in supporting enterprise productivity over the past year-plus, as newly remote workers logged onto the job via residential links that, it turns out, left much to be desired in terms of enabling work ...

September 22, 2021

The world's appetite for cloud services has increased but now, more than 18 months since the beginning of the pandemic, organizations are assessing their cloud spend and trying to better understand the IT investments that were made under pressure. This is a huge challenge in and of itself, with the added complexity of embracing hybrid work ...

September 21, 2021

After a year of unprecedented challenges and change, tech pros responding to this year’s survey, IT Pro Day 2021 survey: Bring IT On from SolarWinds, report a positive perception of their roles and say they look forward to what lies ahead ...

September 20, 2021

One of the key performance indicators for IT Ops is MTTR (Mean-Time-To-Resolution). MTTR essentially measures the length of your incident management lifecycle: from detection; through assignment, triage and investigation; to remediation and resolution. IT Ops teams strive to shorten their incident management lifecycle and lower their MTTR, to meet their SLAs and maintain healthy infrastructures and services. But that's often easier said than done, with incident triage being a key factor in that challenge ...

September 16, 2021

Achieve more with less. How many of you feel that pressure — or, even worse, hear those words — trickle down from leadership? The reality is that overworked and under-resourced IT departments will only lead to chronic errors, missed deadlines and service assurance failures. After all, we're only human. So what are overburdened IT departments to do? Reduce the human factor. In a word: automate ...

September 15, 2021

On average, data innovators release twice as many products and increase employee productivity at double the rate of organizations with less mature data strategies, according to the State of Data Innovation report from Splunk ...

September 14, 2021

While 90% of respondents believe observability is important and strategic to their business — and 94% believe it to be strategic to their role — just 26% noted mature observability practices within their business, according to the 2021 Observability Forecast ...

September 13, 2021

Let's explore a few of the most prominent app success indicators and how app engineers can shift their development strategy to better meet the needs of today's app users ...

September 09, 2021

Business enterprises aiming at digital transformation or IT companies developing new software applications face challenges in developing eye-catching, robust, fast-loading, mobile-friendly, content-rich, and user-friendly software. However, with increased pressure to reduce costs and save time, business enterprises often give a short shrift to performance testing services ...

September 08, 2021

DevOps, SRE and other operations teams use observability solutions with AIOps to ingest and normalize data to get visibility into tech stacks from a centralized system, reduce noise and understand the data's context for quicker mean time to recovery (MTTR). With AI using these processes to produce actionable insights, teams are free to spend more time innovating and providing superior service assurance. Let's explore AI's role in ingestion and normalization, and then dive into correlation and deduplication too ...