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TechRadar
TechRadar
Craig Hale

Most IT teams don't have full visibility of their IT stack, but AI is here to help

A graphic image of a cloud set in a digital background.

  • Hybrid environments, tool sprawl and poor team coordination all diminish observability
  • IT teams agree observability is key, 90% believe AI could help them
  • AI doesn't come without the usual challenges, though

New data from Solarwinds claims around three-quarters (77%) of IT teams lack the visibility they need across hybrid environments, spelling out the additional complexity involved with a mixture of on-prem and cloud.

Modern, evolving infrastructure seems to be outpacing monitoring and observability tools, and many have had to resort to buying into more solutions – however 55% say tool sprawl is now an issue, too.

And it's not just the tools that are to blame – three in four say poor coordination across teams also limits observability further.

IT teams are losing observability fast

Despite the poor state of current affairs, two-thirds (64%) say full-stack visibility is important to success, and there's a clear need for proactive protection rather than reactive 'firefighting'.

But with systems and environments showing no signs of simplification any time soon, artificial intelligence may hold the key to success. AI is already helping to reduce the noise, speeding up operations, but 90% are now confident that AI could help them improve observability too.

Some of AI's most pertinent use cases include automating incident prioritization (47%), accelerating root cause analysis (45%), predicting capacity and performance issues (45%) and reducing alert fatigue (45%). The improved efficiency could also unlock significant cost savings.

"AI should do more than reduce alerts – it should sharpen prioritisation, streamline workflows, and give teams the space to focus on strategy instead of scrambling through dashboards," SolarWinds Product Marketing Senior Director Abigail Norman wrote.

All of that said, companies are still facing major challenges when adopting AI, with security concerns coming out on top (47%). Skills gaps (42%), technology complexity (41%), employee reluctance or resistance (37%) and budget constraints (33%) are also restrictive.

Looking ahead, it's clear that hybrid infrastructure and an increasing array of tools will continue to make observability challenging, thus the need for more powerful, unified tools is clear.


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