40% of firms deploying Artificial Intelligence to use AI observability tools

Bengaluru: By 2028, about 40 per cent of organisations deploying AI will implement dedicated AI observability tools to monitor model performance, bias, and outputs, according to Gartner.

Analysts are exploring AI and observability trends at the Gartner IT Infrastructure, Operations and Cloud Strategies Conference in Sydney this week.

“AI is everywhere, but most organisations are still figuring out how to monitor and trust these systems,” said Padraig Byrne, VP Analyst at Gartner. “That visibility gap makes scaling risky, and that’s why observability matters. Unlike traditional software, AI’s decision-making is often hidden, making it hard to explain or trust, yet errors can cause substantial financial loss, reputational damage and regulatory scrutiny," Byrne said.

Gartner defines observability as the characteristic of software and systems that enables them to be understood based on their outputs, allowing questions about their behaviour to be answered. AI observability requires dedicated tools that manage and assess the behaviour, decision-making and risks of an AI solution, such as model drift, bias and LLM logic.

“The shift to specialised AI observability tools is accelerating due to executive concern over risk management in complex AI models and agentic AI, not just for infrastructure or application health,” said Byrne. “There’s a growing need for predictive issue detection and real-time actionable insights in AI models. Failure to adopt these tools exposes organisations to significant governance risks."

According to Gartner research, AI observability also includes the ability to monitor the availability, performance and accuracy of the AI platforms beyond risk and trust, which becomes essential as enterprises increasingly rely on AI-driven outcomes for decision-making.

“Without clear, standardised model telemetry, infrastructure and operations (I&O) teams face prolonged incident resolution times for AI applications, which will require complex manual efforts to trace and debug the behaviours of opaque deep learning models,” said Byrne. “Dedicated AI observability provides the necessary mechanisms to monitor and mitigate algorithmic risk, establishing the technical foundation for widespread enterprise AI trust and adoption," he added.