Gartner: 40% of Organizations Deploying AI Will Use AI Observability to Monitor Model Performance by 2028

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner, Inc.

"AI is everywhere, but most organizations 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."

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

"The shift to specialized 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 organizations 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, standardized 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 behaviors 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."

Gartner recommends I&O leaders factor the following steps into their AI platform strategies:

1. Establish mandatory AI model monitoring policies for all production deployments, requiring continuous tracking of fairness, drift and data quality metrics.

2. Standardize monitoring frameworks across data science, MLOps and engineering teams to ensure consistency and control. This mitigates organizational silos and streamlines issue resolution.

3. Prioritize infrastructure capable of ingesting and analyzing high-volume model telemetry, focusing on specialized solutions that support distributed tracing of AI inference calls.

4. Ensure IT strategies include provisions for future monitoring of AI platform performance, detection of shadow IT activity and cost management to address these challenges as the technology matures.

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Gartner: 40% of Organizations Deploying AI Will Use AI Observability to Monitor Model Performance by 2028

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner, Inc.

"AI is everywhere, but most organizations 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."

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

"The shift to specialized 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 organizations 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, standardized 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 behaviors 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."

Gartner recommends I&O leaders factor the following steps into their AI platform strategies:

1. Establish mandatory AI model monitoring policies for all production deployments, requiring continuous tracking of fairness, drift and data quality metrics.

2. Standardize monitoring frameworks across data science, MLOps and engineering teams to ensure consistency and control. This mitigates organizational silos and streamlines issue resolution.

3. Prioritize infrastructure capable of ingesting and analyzing high-volume model telemetry, focusing on specialized solutions that support distributed tracing of AI inference calls.

4. Ensure IT strategies include provisions for future monitoring of AI platform performance, detection of shadow IT activity and cost management to address these challenges as the technology matures.

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For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

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Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

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Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

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