No corner of modern businesses is untouched by artificial intelligence. However, as use cases expand and adoption proliferates, cracks appear in technology adoption. CIOs increasingly struggle to understand what their AI systems are doing, who is using them, and how they are performing.
CIOs often worry about model drift, delays, hallucination rate, performance degradation, Shadow AI and output attenuation. Not surprisingly, the risks increase as AI systems make increasingly critical decisions and handle critical activities.
“CIOs are confident that they know how AI is being implemented within their organizations, but they typically won’t tell you how AI is actually working,” said Arnab Chakraborty, chief AI officer at Accenture.
According to Stanford HAI 2026 AI Index (Using McKinsey data) The number of organizations that rated their AI incident response as “excellent” decreased from 28% in 2024 to 18% in 2025. Meanwhile, 88% of organizations report using AI in at least one business function, but less than 10% have fully deployed AI in a single domain.
What about take-home? Observability is key as companies navigate the rapidly changing AI space. However, AI requires a fundamentally different way of thinking than traditional IT. “It’s important to think beyond traditional IT measures to understand day-to-day performance and manage risk,” said Chakraborty.
Visualizing AI performance is key
What makes AI monitoring different from traditional IT monitoring is that it is unpredictable. Metrics that support IT, such as uptime, throughput, utilization, and errors, do not capture the factors and risks associated with AI. That’s because AI is designed to be probabilistic. The same input can produce significantly different outputs.
These problems can take many shapes and forms. CIOs typically know the intended purpose of an AI system, but lack insight into accuracy, latency, user interface, cost, and risk. Model drift, agent behavior, and shadow AI issues also need to be addressed. Unfortunately, no vendor has created a tool that provides observability across all AI layers.
The root of the problem lies in the way AI works. It’s not a single model with a single output. AI is typically a stack of components such as data pipelines, underlying models, search systems, agents, and other components that all interact with humans and workflows. Agent AI brings additional risks. These include “cascading errors, integration failures, unclear accountability, and unpredictable emergent behavior when multiple agents interact between workflows,” said Ilana Golbin Blumenfeld, AI partner in charge at PwC US.
Considerations: Incorrectly tuned retrieval policies can lead to corrupted output across many downstream applications. Drift in the vector database can appear as an illusion within the chatbot. When companies chain agents together to handle long-running tasks, the number of potential problems grows faster than the tools designed to monitor the environment. “It’s not just a linear effect, it’s a compound effect,” Chakraborty points out.
Often these problems go unnoticed for weeks or months until something suddenly breaks. That’s because the level of performance degradation is invisible until it becomes visible. “If you don’t intervene early enough, you can suddenly find yourself in an undesirable situation within days,” said Grace Trinidad, research director for AI security and trust at IDC.
Trinidad said existing dashboards and security tools cannot solve the problem. Most rely on risk scores and confidence ratings that are inadequate and completely opaque to AI. In fact, two organizations can run the same model and arrive at completely different views on the same risk factors. “There is no standardization of what a risk score includes,” she says.
How is AI surveillance evolving?
You cannot govern what you cannot see. microsoft Found 73% of organizations have detected rogue AI tools in their networks, but only 28% have comprehensive monitoring or blocking capabilities in place. McKinsey’s “2026” In the AI Trust Maturity Survey, Average maturity score Organizations are rated 2.3 out of 4, with only one-third reaching maturity level 3 or higher in strategy, governance, and agent AI oversight.
“One of the biggest blind spots for organizations is that they still monitor AI the same way they monitor traditional software. They can see that their AI infrastructure is working, but they don’t understand why it produces poor or unreliable results,” Blumenfeld said. Organizations often design front-loaded uptake and risk assessment processes that don’t take into account how the AI system will actually be used or how the risks within the application will vary. “The key is to choose tools that can be integrated across a multi-cloud, multi-model, agent AI environment,” he said.
In fact, AI observability is rapidly evolving to full-stack visibility with more nuanced insights into AI behavior. In this world, telemetry data takes a backseat to things like semantic mapping and intent interpretation, continuous monitoring and auditing, role-appropriate views and controls, and tools to monitor security and regulatory requirements in a more comprehensive way. Blumenfeld said these tools need to span governance, infrastructure monitoring, and model-level visibility.
Mr. Trinidad said a robust discovery process is fundamental. It’s important to catalog the model, agent, owner, version, deployment context, and logs (preferably in an AI registry). With a clear understanding of what the system is supposed to do and what needs to change, companies can start building observability across the stack. Armed with this information, CIOs can spot data and model drift, performance degradation, hallucinations, shadow AI, and security risks before they cause problems or reputational damage.
Layered surveillance also requires automated guardrails, Chakraborty said. This means establishing appropriate thresholds for key factors such as hallucination rates, latency, bias, privacy, cost, data and model drift, regulatory compliance, and quality of output. Managing and measuring tasks also requires the right combination of tools from hyperscalers and third-party vendors.
A unified control plane (a single architectural layer that collects and displays all signals) allows managers and leaders across departments to see what really matters to them. For example, the chief risk officer looks at risk thresholds and violations, the CFO looks at cloud consumption costs and runaway costs, the chief human resources officer looks at employee impact, and engineers know the pulse of auditability and explainability. “DNA creates DNA, much like an AI’s nervous system,” Chakraborty says.
Where is AI observability headed?
“CIOs should treat AI observability as a core design principle, not something added after deployment,” Blumenfeld said. It’s also important to treat observability as a cross-functional effort that involves IT, business, risk compliance, and internal audit teams, he said. “The industry is moving beyond monitoring individual AI models to monitoring the entire ecosystem of agents, orchestration layers, data pipelines, and autonomous workflows.”
When organizations get the equation right, they can scale AI faster and more securely, manage costs as workloads grow, and generate confidential audit trails to increase customer trust. gartner prediction Investments in language model observability at scale will cover 50% of GenAI adoption by 2028, up from 15% today.
To be sure, observability is not a bolt-on item, nor does it follow the usual IT formula. This is a fundamental element that needs to be built into an AI framework. “Organizations that get this right from the beginning and invest in strengthening it will emerge as leaders in the AI era,” Chakraborty said.
