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At InsightFinder AI, we hear from AI & ML teams struggling with model reliability in production. When we start talking about “observability”, there can be a disconnect. AI models face challenges that traditional observability tools can’t address. They’re two different things, right?
“Observability” is about making sure software is reliable, and it’s critical to extend that practice to AI and ML. There are fundamental differences between deterministic software and nondeterministic AI models. However, we still need effective practices to ensure trustworthiness for AI apps in production.
In this multi-part series, we unpack why we need AI-specific tooling to create reliable production AI systems, and what you need to know about AI observability.
The Sudden Race to Deploy AI
The race to adopt AI is on. Companies everywhere are eager to ship new models and unlock value, feeling the pressure to deliver results quickly. But in that rush, there’s a very real risk: sacrificing long-term trust and success for short-term wins. When AI goes wrong, it can quickly burn your company’s reputation. Organizations that achieve long-term success with AI are those that prioritize trust, transparency, and responsibility from the very beginning.
Consider the current state of trust in AI. In a McKinsey survey of the state of AI in 2024, 40 percent of respondents identified explainability as a key risk in adopting generative AI. Yet,only 17 percent said they were currently working to mitigate it. That gap between recognition and action represents a dire misalignment between AI deployment and responsible AI practices.
That’s where AI observability tools can help.
What is AI observability?
In control theory, “observability” (a term introduced by Rudolf Kalman) refers to inferring a system’s internal state by observing its external outputs. For AI systems, that’s particularly interesting because we need to expand the concept. They’re nondeterministic. Therefore, the only way to infer behavior is to observe both its inputs and outputs over time.
AI observability must examine both to give you real-time insights into how models are functioning, interacting with data, and impacting overall system reliability. For traditional deterministic software, state of the art observability tools don’t just tell you that something is broken, they also tell you why.
AI systems often operate as black boxes, making it difficult to fully understand their inner workings. While we can’t always pinpoint the exact cause of unexpected behaviors, effective observability tools must both quickly identify anomalies and also the correct contributing causal factors. Further, AI observability should empower you to detect issues proactively, resolve them quickly, prevent future occurrences, and use that to optimize future system performance.
That tight feedback loop is the action that creates alignment between AI deployment and responsible AI practices.
Trust, Transparency, and Real-World Consequences
Being first to market with an AI solution doesn’t guarantee success. AI rollbacks are becoming common for organizations that fail to implement proper safeguards. To stay competitive, your AI system must behave responsibly—the closer it is to customers; the more responsible it needs to be.
AI is everywhere, influencing decisions from credit approvals to healthcare. Yet even still, trust in these systems is fragile and it must be earned. We’ve seen teams pause high-stakes deployments because they couldn’t explain a model’s predictions. Without AI observability, these issues can quickly escalate from minor setbacks to major crises.
Case in point – Air Canada’s AI-powered chatbot gave a customer incorrect information about bereavement fare refunds. The company didn’t realize the mistake until it escalated into a legal dispute, and a tribunal ruled Air Canada liable for its AI’s output. That’s not just a PR fiasco; it’s a wake-up call for anyone deploying AI in production without robust controls.
Nor is that an isolated incident. In 2020, a widely-used healthcare AI system was found to be racially biased, allocating fewer resources to Black patients with similar health profiles as their white counterparts. Why? The model was trained on historical spending data that reflected systemic inequities instead of actual health needs. I know: how are you supposed to know, you’re asking. Sometimes you can’t. But if you were tracking inputs, outputs, weighted attributes, and the reasoning behind predictions—then you can know when that system goes live. Or, at least, you can catch and correct those failures before widespread harm occurs.
Trust begins with transparency. Engineers need to answer not just “what did the model predict?” but “why?” and “how has its behavior changed over time?”
Effective AI observability platforms offer fine-grained detection of model drift, anomaly detection, and real-time alerts to make these situations actionable. To win your AI race, you must build trust and transparency into your models by integrating observability into your workflows early. That’s the difference between rolling back your AI initiatives or becoming a success story.
The Cost of Opaque AI
An opaque AI system doesn’t just slow productivity or invite bad press, it can undermine your company’s entire value prop. Unfolding regulatory fines and churn risks in the finance industry should serve as a warning for others.
Wells Fargo faced accusations of digital redlining when their website’s Community Search Service allegedly steered prospective home buyers to neighborhoods based on race, using lifestyle descriptions that included racial stereotypes. Without proper controls in their recommendation system, the bank couldn’t even identify the discriminatory patterns in their own algorithms. The result: regulatory scrutiny, a federal lawsuit, and squandered customer trust on top of disabling the service entirely and years of reputational repair.
How are you introducing those controls into your system? With your monitoring and observability tools built for deterministic software?
Many engineering teams inherit a patchwork of monitoring tools such as Prometheus, Datadog, and Splunk; tools never designed for the complexity of modern AI systems. Infrastructure failures might get flagged, but subtle data drifts or model shifts often go unnoticed until it’s too late. Current practices like manual checks, after-the-fact investigations, waiting for failures alerts to occur before acting, and siloed indecipherable dashboards all simply can’t keep up with modern AI challenges.
In part 2, we’ll show how unified observability across data, models, and infrastructure must become adaptable as systems evolve in order to tackle these challenges.
Ethics and Bias are Critical Concerns
With AI, your understanding needs to go way beyond your systems working or not. You need to have fine-grained visibility into the precise behaviors your AI is inflicting on customers.
Bias in AI isn’t just a technical issue; it’s an ethical and reputational risk. In 2018, Amazon scrapped an AI recruiting tool after discovering it systematically downgraded resumes from women applying for technical roles. Again: biased data mirroring the company’s existing gender imbalance. Without closely observing and evaluating how those decisions were made—feature importance, data lineage, and model decisions—the bias went undetected until it was far too late.
It’s unavoidable. Human bias will show in your data in unexpected and unpredictable ways. AI observability isn’t a silver bullet. It won’t magically unbias your data. But it will help your AI & ML engineers quickly see how decisions are being made, audit the models, trace predictions to their data sources, and intervene before widespread harm is done.
AI observability is a pre-requisite for responsible AI systems. In part 3, we’ll dive deeper into handling what happens when your lab-tested data meets messy real humans.
Why AI Observability Tools Have Emerged
It’s tempting to rely on the tools you already have, but the complexity and impact of AI systems demand more. The most successful teams treat AI observability as a strategic investment made early in the development cycle, not an afterthought that gets bolted-on after they’ve deployed.
When teams can provide clear explanations for AI recommendations, they’re not just building better software; they’re building the foundation for long-term AI adoption in high-stakes environments. The question isn’t whether your organization needs comprehensive AI observability—it’s whether you can afford to deploy AI systems without it.
Where to go from here
In the next parts of this series, we’ll dive deeper into the limitations of existing tools, how to measure what actually matters in AI, and how to build the tight feedback loops necessary to quickly refine your deployed models.
Approaches like InsightFinder AI’s proactive, automated, and unified observability platform are setting a new standard for how organizations build trustworthy AI systems.
See how InsightFinder AI helps you deliver explainable, resilient, and compliant AI by registering for a free trial.
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