What is Data + AI Observability?

Data + AI Observability is a comprehensive approach to observing the health and reliability of your data, system, code and AI models end-to-end. It closes the loop between data inputs and AI agent outputs—ensuring trust, scale, and business impact.

The Rise of the Data + AI Trust Gap

AI adoption is accelerating, but organizational trust isn't keeping pace.

Data inputs are often incomplete, inaccurate, or delayed.

AI outputs can drift, hallucinate, or produce biased results.

Business leaders don't trust AI in production, stalling innovation and adoption.

“More than 40% of companies don't trust the outputs of their AI/ML models and more than 45% of companies cite data quality as the top obstacle to AI success”
BARC Observability for AI Innovation Study

Most teams today approach observability in silos

Data observability ensures data pipelines are accurate, complete, and timely. AI observability monitors the model performance, drift, and bias.

The problem? Looking at one without the other creates blind spots.

• If you only observe data, you might miss model behaviors like hallucinations or bias.
• If you only observe models, you risk overlooking upstream data issues that silently degrade performance.

Data + AI Observability Is Comprehensive

Data + AI Observability is the comprehensive monitoring, analysis, and understanding of data and AI systems' health, performance, and reliability to proactively detect and resolve issues across the entire lifecycle.

It’s a holistic approach that connects—

• Data inputs → quality, lineage, and pipeline reliability
• System & code → infrastructure and transformations
• AI agent models & outputs → drift, bias, and reliability in production

By closing the loop across the entire lifecycle, Data + AI Observability ensures that reliable inputs drive trustworthy outputs—and bridges the trust gap between data teams, AI teams, and business leaders.

Benefits of a Holistic Approach

Without end-to-end observability, most organizations stall at pilots. A holistic approach builds the trust and reliability needed to take AI into production at scale.

• Trust → Reliable inputs produce reliable outputs.
• Efficiency → Free up engineering time from constant firefighting.
• Risk Reduction → Stay ahead of compliance, privacy, and bias challenges.
• Innovation → Confidently scale GenAI and agentic AI initiatives.
• Collaboration → Align data, AI, and business teams around shared outcomes.

So, what's next?

Organizations like JetBlue and Nasdaq already rely on Monte Carlo to close their Data + AI trust gaps. Find out why these leaders—and many others—chose Monte Carlo.

How JetBlue Improved Internal “Data NPS” By 16 Points YoY

The Challenge:

When a data migration improved data usage, increased access brought increased scrutiny. And the trustworthiness of the data took center stage.

The Solution:

Operationalizing data + AI observability and leveraging Monte Carlo's in-app features to measure the outcomes.

Nasdaq's Journey to Reliability with Monte Carlo

The Challenge:

Nasdaq generates 6,000 reports per day across 35 services and 2,200 users. The question is—how do you make that much data reliable?

The Solution:

The team deployed Monte Carlo to monitor its entire data lake via a multi-step deployment.

"Since we're already monitoring data in our data lake with Monte Carlo, combining data and agent observability in a single platform gives us visibility into the full agent lifecycle — from the structured data to the unstructured knowledge base to the agent’s behavior — all in one place."
Travis Lawrence, Senior ML Manager, Pilot Flying J

Get started fast—scale faster.

Fast setup—even faster time to value. Connect to Monte Carlo in seconds, start monitoring out of the box and automatically scale with your environment.