The Silent Killer of AI Adoption: How LLM Observability Is Costing You Customers

Every customer conversation with your AI is a goldmine for revenue: gained or lost.

A product team at a B2B company shipped an AI support agent last quarter. Three months in, their metrics looked solid: 5,000 conversations, 98% uptime, average response time under 2 seconds.

Their biggest customer just told them they’re not renewing. The AI “doesn’t understand our questions.”

The product team went back through the logs. Turned out dozens of users at that account had been asking about a specific integration feature.

The AI kept pointing them to general documentation. Users would rephrase the question, get the same useless answer, try one more time, then give up. This happened hundreds of times over three months.

Every conversation completed successfully. No errors. No timeouts. The observability dashboard showed green across the board.

This could have been an upsell opportunity but instead the customer left unhappy.

The tools measure whether the system is running not whether customers are getting what they want. These are very different things.

The Problem With How Teams Monitor AI Today

Most teams use some combination of LLM observability (tracks errors, latency, tokens) and product analytics (tracks usage, engagement). Neither shows what customers actually experience inside the conversation.

LLM observability shows technical health. If the AI times out or throws errors, you’ll know. If it responds quickly with unhelpful answers, everything looks fine.

But a customer can spend 15 minutes asking the same question different ways, get nowhere, and leave frustrated and the dashboard still shows a successful session with good engagement metrics.

Product analytics are Web 2.0 oriented. They show the user had a conversation but not that they asked for help with invoice exports, got pointed to billing documentation three times, and closed the tab in frustration.

The result: teams discover problems after customers have already decided the AI doesn’t work for them. By the time usage metrics drop or renewal conversations get difficult, the damage is done.

What You Actually Need to Know

The question that matters: which customers need my attention right now?

Not in a week after the engineering finally has time to dig into the trace data.

But right now, while the engagement is happening live.

The challenge is that it’s not possible to have humans monitoring every conversation.

One solution is to run an agent on top of the conversations that monitoring for sentiment and intent signals.

Sentiment will surface scenarios like this.:

When a customer asks a question, gets an unhelpful response, rephrases it, gets another unhelpful response, and tries one more time before giving up, sentiment shows the pattern. The conversation started neutral and ended frustrated.

Current tools would show show: 4 messages exchanged, 2-second average response time, session completed.

Sentiment shows: customer tried three times to get help with the same issue and failed.

This matters more for enterprise accounts where you need to see patterns across many users. If 20 people at a major customer are all struggling with the same thing, you need to know before it becomes a renewal conversation. Account-level aggregation would roll up sentiment across everyone at that company so teams can have a clearer picture of customer health

The other thing that matters is intent: understanding what customers are trying to do.

Intent recognition shows the gap between what people ask for and what AI delivers. If users keep asking about API access and your agent provides Salesforce integration information, you have an intent problem. The AI understands the general topic (integrations) but misses what the customer actually needs: interception by the account team because the customer may have an upsell opportunity.

This surfaces which use cases work and which don’t. Your AI might handle basic questions well while completely failing at anything requiring account-specific context.

Why This Is Hard to Build

Teams try to build this themselves.

The usual path:

Install LLM observability, pipe the data to a warehouse, set up dashboards, add sentiment analysis, build account rollups, write alerting logic.

Three months later you have a stack of tools connected by fragile pipelines. Someone changes a field name and half your dashboards break.

And you still can’t answer basic questions like “which accounts had negative experiences this week” or “what are users struggling with most.”

The other issue: product teams end up depending on engineering to pull reports, which is the last thing you want your engineering team to be doing.

This creates a cycle where the people closest to customers (product, support, success) can’t see what’s happening without going through people who are heads-down building (engineering).

By the time insights make it from data to action, customers have already churned or the upsell opportunity has passed.

What Good Monitoring Looks Like

You should be able to see every AI conversation as it happens, automatically tagged with whether the customer got help. Not aggregated into metrics that hide what’s actually going on. The real conversations.

This means you can search for specific problems. “Show me conversations where customers asked about exports.” “Which accounts are showing frustrated sentiment this week.” “What are the most common questions where users ask multiple times.”

For enterprise accounts, everything rolls up automatically. You see individual conversations and account-level trends. When sentiment at a major customer starts declining, you know immediately instead of finding out during renewal talks.

The setup should take days, not months. Add some code, start seeing conversations. No multi-tool integration projects. No data pipeline maintenance.

The test: can someone on your product team answer “which customers are frustrated with our AI right now” in under 30 seconds? If not, your monitoring isn’t working.

The Result

Product teams will have more autonomy over the products they own.

Sales can act on upsell opportunities and guide your “AI pilots” to a successful outcome.

Customer success & support can provide guidance to increase adoption.

Marketing teams can build nurture programs to help users.

Executives can see dashboards whenever they need.

All without the phone tag and silos caused by data being stuck in engineering’s data warehouse.

But the customer benefits the most.

If a customer has errors or gets frustrated, they will receive help before they give up.