Optimizing AI ROI from DevOps and IT Operations: The rising need for AI/LLM observability

As every organisation races to embed AI across infrastructure and application stacks, a critical question emerges: how do you actually prove — and protect — the return on that investment? IT operations, DevOps, SREs, and platform engineering teams are uniquely positioned to play a decisive role in optimising business returns and reducing risk, yet many are still absent from the AI investment conversation.

Dynatrace This blog makes the case for why that must change. Readers will learn how to frame observability’s value across five core AI ROI dimensions — productivity, customer impact, cost optimisation, innovation, and quality — and how to evaluate GenAI performance across system health, model drift, and output accuracy. While only 28% of organisations currently use AI to align observability data with business KPIs, the opportunity is clear — and this piece provides the strategic language to seize it.

7784-En-Optimizing AI ROI from DevOps and IT Operations: The rising need for AI/LLM observability

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