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FEATURE: AI
Achieving
successful
AIOps
integration
Ian Jansen van Rensburg,
VMware EMEA Senior Systems
Engineer, says four common
problems need to be avoided
in order to achieve successful
AIOps integration.
T
he explosion in operational data and Machine Learning
(ML) compute capacity is finally enabling AIOps:
Artificial Intelligence for Operations. But like many other
technologies, AIOps fits within a larger organisational and
systems context, and enterprises need to ensure that they
are ready for the shift. Successfully implementing an AIOps solution
requires an awareness of the potential problems associated with such
a transition. Here are four key challenges that organisations will face as
they look to adopt an AIOps solution and how to address these risks.
Challenge 1: Identifying use cases
(not just processes)
Companies that don’t identify the underlying issues they’re trying
to address with AIOps tend to utilise an incremental approach.
Each new AI and ML-related feature may seem like an easy way to
increase efficiency – replacing an existing sub-process with supervised
learning, for example.
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