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FINAL WORD
Problems to
avoid in order
to have
successful
AIOps
integration
Ian Jansen van Rensburg, VMware EMEA
Senior Systems Engineer, says you need
to avoid four common problems in order
to have a successful AIOps integration.
T
he explosion in operational data and Machine Learning
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.
However, the most meaningful results will come from rethinking
operational use cases in a top-down manner to complement
these bottom-up process improvements. AI/ML algorithms have
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