Your AI passed testing six weeks ago. But have you tested it since?
Most teams don’t. They monitor uptime and latency, the same metrics they’d track for any service. If the system responds fast and doesn’t crash, the dashboards stay green.
But AI doesn’t break like infrastructure breaks. It degrades. Answers become less grounded, business impact reverses, safety violations creep up, all while system metrics look fine.
By the time the gap surfaces in customer complaints or audit findings, weeks of silent degradation have passed.
The teams keeping AI reliable test three things continuously and catch problems before customers do.
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You are testing accuracy. You should be testing groundedness.
What are teams actually doing?
Teams test whether answers are correct. They measure accuracy, precision, and recall on hold-out test sets.
A model scoring 92% accuracy feels production-ready. But accuracy measures correctness, not auditability. It doesn’t tell you where that answer came from or which source supports it.
Why groundedness matters
An insurance provider deployed a policy Q&A bot for call center agents. The system achieved 95% groundedness, meaning almost all AI answers referenced official policy text.
Agents using the assistant cut average call handling time by 20% while improving the accuracy of the information they provided to customers. The high groundedness rate meant compliance could audit the answers, and customers could verify the information if questioned.
So how should you test for groundedness?
- Set a minimum citation threshold before deployment; many organizations use 95% as the gate (at least 95% of answers must contain proper source references).
- Run this check on production samples weekly. Sample 100 queries from live traffic and verify each answer includes valid citations.
- When groundedness drops below your floor, route uncertain queries to human review or pause the feature until you identify what shifted, whether it’s new query types, document corpus changes, or model drift.
You are testing performance. You should be testing business impact.
What are teams actually doing?
Monitoring tracks latency, error rates, and uptime. These metrics confirm the AI is running. But do not track if customers stop asking follow-up questions.
One insurance company watched their bot’s response time hold steady at 180ms while their repeat-contact rate climbed 12% over six weeks. The AI was working. The customers weren’t getting help.
Why business impact matters
Ask yourself: Are all customers getting what they need, or just getting fast responses that don’t resolve their issue?
Klarna deployed an AI chatbot that handled 2.3 million conversations in its first month. The system performed well technically, but the real validation came from business metrics: customer satisfaction stayed on par with human agents, repeat inquiries dropped 25%, and the initiative added $40 million in projected profit.
Those outcomes came from continuously tracking deflection rate, repeat contact rate, and customer satisfaction.
The DORA research found that higher AI adoption sometimes correlated with a 1.5% drop in delivery throughput and a 7.2% drop in stability when teams didn’t maintain testing discipline. Performance looked fine, but business outcomes reversed.
So how should you test for business impact?
- Define 2-3 outcome metrics the AI should move: deflection rate for support bots, conversion lift for recommendations, and resolution time for agents.
- Track these alongside technical metrics in your production dashboard.
- If business impact drops below baseline—say, conversion lift goes from +8% to +1%—treat it like an incident. Investigate what changed, and don’t re-enable the feature until the impact has recovered.
You are testing at launch. You should be testing continuously.
What are teams actually doing?
Before launch, teams test the model with prepared scenarios and edge cases. They set quality gates and ship when those pass. In production, monitoring shifts to infrastructure.
Teams track uptime and latency rather than re-running the same quality checks on live traffic.
Why continuous testing matters
Models degrade as conditions change. New product launches change what customers ask about. Document repositories grow, and retrieval patterns change. User behavior evolves seasonally.
The AI that performed well in March may struggle in June because the environment it operates in has changed.
Microsoft’s contact center AI required weekly reviews of first-contact resolution metrics and sampled transcripts for quality. When deflection rates dipped after a product update, teams discovered that users were asking questions the bot hadn’t been trained to answer.
They updated the knowledge base, and deflection recovered. Without continuous monitoring tied to business KPIs, that degradation would have persisted unnoticed.
So how should you test continuously?
- Run your pre-launch scenario suite on production samples weekly or monthly, depending on risk.
- Track change in both model outputs and business KPIs. Set thresholds: if groundedness drops below 90%, if deflection falls 5% points, or if safety violations exceed 1%, trigger alerts or pause the feature.
- Treat degradation like a system incident: investigate, fix, and verify before resuming.
Testing your AI continuously keeps models reliable enough to trust with customer decisions and revenue outcomes.
But running these tests manually adds operational overhead most teams don’t have capacity for.
ThoughtMesh automates continuous validation: groundedness checks, business impact tracking, and feedback loops that catch degradation before customers do. It’s how teams deploy AI faster without losing control. See how it works.
