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The emerging fields of intelligent operations and AIOps have something to offer solutions providers, whether they deliver those technologies as a service or deploy them at the customer's location.
The first task, however, is defining these technologies and the problems they aim to solve for customers. As it happens, there's some difference of opinion on the intelligent operations vs. AIOps question among the professional services and systems integration firms pursuing those areas.
That said, there is considerable agreement among partners regarding the key underlying technologies of intelligent operations and AIOps and the need for an overarching strategy to realize their full potential.
Partners define intelligent operations vs. AIOps
Partners maintain differing views on the nature of intelligent operations and AIOps, which is shorthand for AI for IT operations.
"Like anything else in technology, things get a label assigned to them," said Eric Kaplan, CTO at Ahead LLC an enterprise cloud solutions provider based in Chicago. "And you ask five different people, and often you can get five different interpretations of what something means."
For Kaplan, intelligent operations and AIOps are more a matter of both/and rather than either/or. He believes both the technologies address the need for a cohesive, automated approach for monitoring IT systems, identifying root causes and remediating problems. The technologies exist on the same continuum, in which intelligent operations tools provide help in the here and now, and AIOps represents the evolution of current capabilities.
"I think AIOps is the vision of where we want to get to," Kaplan said. "Intelligent operations is the proactive application of what is available today."
Omkar Phadnis Managing director of AI and innovation, Accenture
Omkar Phadnis, managing director of AI and innovation for Accenture operations, has a different take on intelligent operations vs. AIOps. He sees intelligent operations as the broader field in which AIOps is a component. In Accenture's schema, intelligent operations has five core elements: innovative talent, a data-driven backbone, applied intelligence, the ability to capitalize on the cloud and creation of the right relationships within a given ecosystem.
AIOps, according to Phadnis, is a subset of applied intelligence, alongside automation and analytics. "AI is a very powerful, key essential of driving intelligent operations," he said.
In addition, the umbrella category of intelligent operations includes not only IT operations, but also other business processes from human resources to supply chain and logistics, according to Accenture.
The scope of endeavor is how Raj Patil, CEO at Orion Business Innovation, a technology services firm based in Edison, N.J., distinguishes between AIOps and intelligent operations. He said intelligent operations and AIOps share the same core technologies -- automation, analytics and AI. But AIOps focuses on IT operations, while intelligent operations has broader applicability.
Intelligent operations, Patil said, cuts across various business functions. For example, intelligent operations can be used to make a call center more efficient through AI and chatbots. AIOps, meanwhile, applies AI to make IT infrastructure more manageable, efficient, reliable and secure, he added.
"The underlying technology is the same," Patil said, noting the difference lies in where and how the two approaches are used.
"They are different sides of the same coin," added Bhanu Singh, senior vice president of engineering and DevOps at OpsRamp Inc., an AIOps tool vendor that partners with managed service providers and resellers. And in keeping with Phadnis' view, Singh considers AIOps as supporting intelligent operations.
"I believe AIOps is the enabler for intelligent operations," he said.
The intelligent operations journey
Some partner executives describe customer adoption of intelligent operations as a journey. One that may include AIOps along the way.
At Accenture, that journey starts with defining a problem statement, Phadnis said. That task involves working with a client to scope out the problem that intelligent operations will address. This foundation phase, as Phadnis described it, also analyzes the as-is process the client wants to improve. This step may involve business process engineering if the customer's process is broken in some way.
The goal is to "set the right foundation well before we introduce AI or other intelligent solutions," Phadnis said.
The second phase focuses on "automating everything we can automate" to root out manual tasks and activities, he noted. And the third phase seeks to harness data to drive insights, a step that may call for predictive AI solutions or descriptive analytics.
Ahead, meanwhile, has devised a five-stage maturity model to enable customers to map their intelligent operations journeys. The model starts at stage zero, in which organizations lack visibility into their operations and reactively extinguish digital fires as they occur. In the higher levels of the model, organizations can quickly spot the root cause of problems and promptly remediate them. Along the way, intelligent operations' use of predictive analytics evolves into AIOps and prescriptive analytics, according to Ahead.
Having a strategy in place for pursuing intelligent operations and coordinating the underlying technologies for monitoring, automation and analytics is critical to the journey's success, Ahead's Kaplan said.
"It's spending the time to develop a strategy and understanding what you are trying to achieve, instead of buying the next tool that has a lot of buzz around it," he noted.