Scripts, Coding or Autonomous Behavior?

Artificial intelligence includes a number of approaches, some more akin to “automation,” others more like self-learning that leads to system autonomous behavior. The intended benefits can be very practical, though.

Consider the alarm resolution process. Quite often, one fault generates multiple alerts on multiple systems, even when they have the same root cause. So an applied AI capability would recognize redundant alerts and take preventative action, while suppressing alerts related to a single fault, so the alerts do not cascade, said Bhanu Singh, OpsRamp SVP.

Bhanu Singh, OpsRamp SVP

Controversial though it might be, the key ultimately is autonomous behavior. The whole point is to avoid having humans code or create scripts, said Taly Dunevich, Ayehu global business development VP.

Taly Dunevich, Ayehu VP

Also, contrary to some opinion, AI “does not require algorithms.” The systems should learn by themselves, without human intervention. Enterprise software systems these days are complex and highly dynamic; too complex for a limited number of humans to manage, said Frank Yue, KEMP Technologies solutions architect. “AI has to identify the problems, know what has to be done, and then do it,” Yue said.

Frank Yue, KEMP Technologies

There simply is too much data to make sense of.

“It doesn’t work if you only extrapolate from past events,” noted Will Houston, GAVS Technologies VP. “You have to auto-discover everything.”

“AIOps is about extracting actionable data insights and then applying those insights to information technology operations,” said Will Houston, GAVS Technologies VP. “By 2023, 30 percent of large enterprises will use AI for IT operations, where today perhaps two percent do so.”

“AI used to about rule-based systems, while machine learning is statistical,” said John Byrnes, SRI International senior computer scientist. “Both are used today.”

In a practical sense, AI use cases often are about automating existing processes such as handling trouble tickets, said Dunevich. In other cases, applied AI can be used to configure and maintain competing Wi-Fi networks, said Marcel Chenier, KodaCloud CTO.

Applied AI is diverse because it includes a range of intelligent capabilities related to autonomous infrastructure, that  “sense, think, act and learn,” said Katie Fritsch, HPE product marketing lead. Observation is what the sensors do. Learning is what the servers do when they look for patterns. Prediction is how the patterns are applied to identify abnormal behavior.

As applied to information technology operations, experts say to start small, with simple processes. “Automate the easy things first,” said Dunevich. “Eliminate the smaller problems first,” advised Houston. That might mean using AI to “keep trucks in lanes” at first, and not moving to full autonomous driving,” said Byrnes.

And people--not technology--are key parts of the journey, said Yue. “What is the incentive for the ops team if AI replaces their jobs?”

Paul Brittain, Metaswitch

“If the ops team doesn’t trust the new solution, they won’t support it,” noted Paul Brittain, Metaswitch VP.