I was talking to a product team last week that proudly showed me their new AI dashboard. Colorful charts, real-time metrics, beautiful visualizations. They had everything—except the ability to tell me why their recommendation engine suddenly started suggesting winter coats to customers in Florida during a heatwave.
That’s when I realized we’ve been thinking about AI monitoring all wrong. We’re treating AI systems like traditional software, when they’re fundamentally different beasts. Traditional monitoring tells you what is happening. Observability tells you why it’s happening.
Let me break this down systematically. Observability in AI isn’t just about collecting more data—it’s about understanding the relationships between your model’s inputs, outputs, and the real-world context. It’s the difference between knowing your conversion rate dropped versus understanding that your model started treating “premium” customers like budget shoppers because of a data drift issue.
Remember when Microsoft’s Tay chatbot went rogue back in 2016? That wasn’t a monitoring failure—it was an observability failure. They had plenty of metrics showing the bot was active and engaging users. What they lacked was the ability to understand why the conversations were turning toxic and how the model’s learning process was being corrupted.
This brings me to something I keep emphasizing in product development: you can’t fix what you can’t understand. According to the Qgenius Golden Rules of Product Development, “only products that reduce users’ cognitive load can achieve successful adoption.” The same principle applies to AI systems—except here, we’re talking about the cognitive load on the developers and operators trying to understand their creation.
Good AI observability operates on three levels: the data plane (what’s flowing through your system), the model plane (how your AI is making decisions), and the business plane (what impact those decisions are having). Most teams focus on the first, some on the second, and almost none on the third.
I’ve seen teams spend months building sophisticated model monitoring only to discover they can’t explain why a 2% accuracy drop is costing them millions in lost revenue. They’re measuring everything except what actually matters.
The scary part? Many organizations are deploying AI systems that are effectively black boxes. They work until they don’t, and when they fail, nobody knows why. It’s like flying a plane with all the instruments except the ones that tell you why the engines are on fire.
Here’s what keeps me up at night: we’re building AI systems that make increasingly important decisions, yet we’re building them with tools designed for much simpler systems. We’re using screwdrivers when we need surgical instruments.
The companies that get this right—the ones building truly observable AI systems—aren’t just adding more monitoring tools. They’re redesigning their entire development process around explainability and transparency. They’re building systems that can tell their own story, that can explain their reasoning, that can warn you when they’re about to make a questionable decision.
So the next time someone shows you their shiny AI dashboard, ask them the tough questions: Can you explain why your model made that specific recommendation? Can you predict when it’s about to start behaving strangely? Can you trace a bad decision back to its root cause? If they can’t, they don’t have observability—they have pretty graphs.
In the race to deploy AI, are we building systems we can actually understand and trust?