AI Is Re-Inventing IT

Artificial Intelligence is starting to eat the world, one step at a time, and IT operations is no exception. Although still early in deployment, companies are taking advantage of AI and machine learning to improve tech support and manage infrastructure.

Here, natural language processing is proving to be a valuable IT tool. The technology, which fuels most customer service chatbots, is being put to us in internal IT operations, to improve tech support and user interfaces.

Credit Suisse Group AG, for example, rolled out a Chatbot last December to help process routine requests such as password resets and computer reboots.

"We were primarily a voice-only support center, which didn't enable us to have efficiencies in terms of how we handled our users queries," says Jennifer Hewit, the company's head of cognitive and digital services. Employees who called in about problems would have to wait in a telephone queue for the next available agent, she says, an approach that doesn't scale well. "So we wanted to provide a new channel into the service desk, and introduce chat for a quicker response and action to our users," she says.

Credit Suisse first started thinking about this in late 2016, chose the Amelia Chatbot system from IPSoft in early 2017, and began the installation in June. It was up and running by the end of the year.The new system serves 76,000 users in 40 countries around the world, and has allowed Credit Suisse to move some of its 80 tech support agents to higher-level support. 

With its ultimate goal of freeing up one-third of its tech support staff, Credit Suisse’s use of AI in IT underscores the impetus fueling the trend: to empower IT personnel to drive deeper business value by handing over lower-level work to machines better suited to those tasks.

Using AI to Secure and Inspire
Texas A&M University System is another organisation putting AI to work in IT, deploying Artemis, an intelligent assistant from Endgame, to help new staffers keep the university secure from cyberattacks.

"We monitor the networks for 11 universities and 7 state agencies," says Barbara Gallaway, security analyst at Texas A&M University System. Gallaway’s team includes nine full-time staff and eight part-time student workers who don't have the experience required to deal with security incidents. 

The AI system enables her staff to ask questions in plain English, helping to train them in their jobs as a side benefit. "It's on-the-job training and doing the job at the same time," Gallaway says.

Managing Infrastructure
Murphy Oil, headquartered in Arkansas, is an oil company with operations in the US, Canada and Malaysia, and 1,200 employees around the world. The company has been moving its infrastructure from traditional on-premises and colocation to cloud and SaaS models for the past year, but the biggest savings have been from adding intelligence to the management of its cloud infrastructure, says Mike Orr, IT director of digital transformation at Murphy Oil.

The cloud does allow for significant flexibility, but it can take a lot of people to adjust the workloads, and that adds up. So the company turned to an AI-powered system from Turbonomic to make recommendations about how to optimise the infrastructure. But the real payoff came once Murphy Oil grew comfortable with the system and trusted it to perform placement and sizing automatically.

Ensuring Connectivity
Ohio's North Canton school system had a different infrastructure management challenge: keeping its wireless network up across the entire campus. That includes making sure user laptops and mobile devices can connect correctly. There are about 4,400 students, 650 staff members, seven buildings, and between 6,000 and 8,000 devices total on the network, with just three people to manage the network. Last August, the district switched to Mist Systems for wireless network management, and, as an added benefit, got a new AI-powered interface.

Predictive Maintenance
Once known for its cameras, Tokyo-based Konica Minolta began using AI-powered IT infrastructure management tool ScienceLogic internally in early 2017 in support of its office and IT services business, to help predict which equipment was about to break down.

At first, the predictions were about 56 percent accurate, says Dennis Curry, the company's deputy CTO, but the system learned over time.

"Now we can predict that something will fail in the next two weeks 95 percent of the time," reducing downtime and lowering overall costs, he says. The company is adding the technology to Workplace Hub, its ScienceLogic-powered IT management platform, which should be available later this year.

Nlyte Software is also planning to offer an AI-powered predictive maintenance tool. Powered by IBM's Watson technology, Nlyte uses general information from customers to gather insights about commonly used equipment, and combines it with learning from individual customer environments.

Netherlands-based Interxion is one company already seeing savings from using machine learning to improve operations. A couple of years ago, the company, which operates 50 data centers in 13 cities around the world, began deploying data center infrastructure management (DCIM) technology EcoStruxure from Schneider Electric. But the technical capabilities of AI in DCIM is likely to expand.

"This is a whole new area, a new development for the industry, and it's powerful," she says Rhonda Ascierto, research director for datacenters and critical infrastructure at 451 Research, who mentions Eaton as another vendor in the DCIM space. 

"I think it's the beginning of a long-term evolutionary change towards integrating physical data center management with many other services. As the technology evolves, other data and services are likely to be added, including integrated workload management, energy management, staff services, and security and network management."

It's all about Point Solutions
But a general-purpose AI-powered platform for IT operations remains elusive, says Michele Goetz, an analyst at Forrester Research. 

One challenge is that AI currently needs large volumes of training data, and that is available only for particular types of problems.

In addition, systems need to be able to talk to each other better than they do today, says Shannon Kalvar, analyst at International Data Corp.

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