For a long time, monitoring tools focused on tracking technical performance: latency, server availability, error rates… As long as the system responded correctly, the user experience was considered “satisfactory.”
But the massive arrival of artificial intelligence in digital journeys has changed everything.
Recommendation engines now influence up to 35% of e-commerce sales.
Over 60% of digital customer services already integrate conversational AIs.
Interfaces are becoming dynamic, personalized, powered by AI models that evolve continuously.
Problem: an AI doesn’t “crash” like a server. It drifts, hallucinates, confidently delivers wrong answers… and none of this triggers an alert in traditional monitoring.
Expert Insight:
In an AI-driven world, a service can be 100% technically available… while being 0% relevant to humans.
This is where a new strategic need emerges: AI-native Observability, meaning the ability to monitor the quality of intelligence itself, just like we monitor infrastructure.
Traditional monitoring relies on a simple assumption: a healthy system = a satisfied user.
But since AI has deeply integrated digital experiences, this assumption no longer holds.
Here are common situations that traditional technical monitoring fails to catch — but DEM can:
| User-side situation | Seen in traditional monitoring (technical) | Result |
|---|---|---|
| The chatbot gives an irrelevant answer | API 200 OK | Frustrating experience |
| The AI engine suggests irrelevant products | Response time within SLAs | Conversion rate drops (Ekara can detect this = Data consistency) |
| The AI “hallucinates” an answer in a support assistant | No technical error | Reputational risk |
| The AI fails to understand a specific business context | No crash | Journey abandonment (Ekara RUM can detect this) |
Today, failure is no longer visible at the server level. It shows on the user’s face when they think: “This is useless…”
A classic monitoring tool sees:
Servers available
APIs functional
Low error rate
Acceptable latency
But what it doesn’t see are the signals of a deteriorating AI experience:
Repeated reformulations in a chatbot (“That’s not what I meant”)
Fast scrolling over recommendation modules (AI suggestions ignored)
High “backtrack” rate in AI-assisted flows (Ekara RUM can detect this)
Progressive decline in engagement… with no visible system incident
| AI Degradation | Description | Impact | Visible in classic monitoring? |
|---|---|---|---|
| Model Drift | The model is no longer aligned with current reality | AI becomes less relevant | No |
| Hallucination | The AI confidently generates false information | Loss of credibility | No |
| Recommendation Bias | Repetitive, non-diversified suggestions | Experience perceived as “robotic” | No |
| UX Misalignment | The AI no longer understands user intent | Frustration and abandonment | No |
Key takeaway:
An AI incident doesn’t appear in an error log — it appears in human avoidance or rejection behavior.
AI-native Observability is the ability to measure, understand, and adjust the real performance of an AI within the user journey — beyond mere technical performance.
This requires combining three observation layers:
| Layer | What is monitored | Common tool |
|---|---|---|
| Infrastructure | CPU, network, API, availability | Classic Monitoring |
| AI Model | Drift, confidence score, statistical performance | MLOps / MLflow |
| AI User Experience | Understanding, effectiveness, implicit satisfaction | AI-Native Digital Experience Monitoring |
Expert Insight:
You can’t operate AI in real conditions if you don’t connect user signals to model signals.
Shifting to AI-native Observability requires a radical change in metric logic:
| Old Metric | New AI-native Metric |
|---|---|
| API response time | Perceived response time + AI answer effectiveness |
| Request processed | Useful / understood / clicked request |
| Server error rate | AI drift rate / user reformulation rate |
| Global click rate | Useful vs ignored AI interactions |
Drift rate ➝ Percentage of divergence between current data and training data
Confidence score ➝ Level of certainty of the model’s recommendation
“Unused” responses ➝ AI responds… but no one uses it
Number of reformulations (“Can you rephrase?”, “That’s not it.”)
Click-through rate on AI suggestions
Bypass rate: manual search after AI recommendation
Underestimated risk:
Measuring only the model’s performance without anchoring it in user behavior is like piloting a plane without looking at the cockpit.
We can build a composite score that evaluates AI relevance in real scenarios based on:
Perceived relevance (clicks, conversions)
Simplicity of interaction (few reformulations)
Absence of frustration (no abrupt backtracking)
Time saved compared to a non-augmented journey
This QAI-X could become the equivalent of SLA/SLO… but for intelligence.
Intelligent target architecture:
DEM data (real UX) + MLOps signals + business KPIs = complete AI-native Observability
Concretely:
MLOps tools monitor the model (drift, training data)
DEM tools capture real user experience (time, friction, clicks, drop-offs)
Analytics tools identify behavioral patterns (user journeys)
Today, these three worlds communicate poorly.
Tomorrow, they form a unified AI control cockpit.
Expert Insight:
The future of AI monitoring will not be driven only by engineers… but by hybrid teams: product, data science, UX, and SRE.
Fast API
No errors
Silent logs
Yet:
70% of AI suggestions are ignored
Increase in fast scrolling behavior
Drop in average basket size
Real example:
During a sales period, product data changed. The AI model, not retrained, continues to serve “classic” selections.
Result? A “functional” AI… completely disconnected from business reality.
| Captured signal | Triggered action |
|---|---|
| Drop in AI click-through rate | “AI relevance” alert |
| Data drift detected | Automatic retraining suggestion |
| Suggestions ignored + intense scrolling | Block AI recomposition + UX fallback |
We move from a passive monitoring approach to an orchestration mindset.
An AI that learns continuously from monitoring and adjusts itself to preserve user experience.
If QAI-X score < threshold ➝ Switch to stable model version
If drift detected ➝ Trigger automatic retraining
If risky content detected ➝ Immediate block + SRE alert
If AI ignored ➝ UX fallback to non-personalized mode
💡 Strategic Insight:
Monitoring will no longer be a passive dashboard, but an intelligent conductor.
Here are 7 essential questions every digital team should ask:
Can you identify AI drift?
Do you have an AI confidence indicator from a UX perspective?
Does your monitoring capture user frustration signals toward AI?
Do your product, data, and ops teams share a unified observability vision?
Can you correlate AI data with real UX data?
Have you defined an AI tolerance threshold, like an SLA?
If the AI drifts… do you have an automatic action plan?
Not monitoring AI means accepting to lose control of the experience.
We are entering an era where the user experience does not just need to “work”: it must understand, anticipate, adjust.
Companies that embrace AI-native Observability won’t just detect anomalies — they will pilot the quality of their embedded intelligence.
The next step is to connect AI signals, UX signals, and business signals into a unified cockpit.