AI failures can occur suddenly or decay gradually over time, resulting in significant reputational damage to a company.
When Klarna halved its human customer service staff in 2023 and went all-in on AI chatbots, it placed a bold bet on AI automation. That bet backfired over the next 18 months, as customers became increasingly frustrated by the poor performance, eroding their trust.
Now the company is hiring humans again, in an effort to restore what was lost. Klarna is a high-profile canary in the coal mine for what many AI systems are quietly facing: the consequences of skipping guardrails and underinvesting in continuous optimization.
AI Is Not a Set-It-and-Forget-It Solution
When AI models work in testing but fail in production, it’s often not because they were poorly built but because there was no structure for continuous maintenance.
Real-world AI performance degrades when:
- Feedback loops are slow, incomplete, or missing entirely
- Models aren’t re-trained with relevant, annotated data
- Decision-making happens in silos, without cross-functional insight
Most AI oversight tools detect failure after it impacts the customer. But by then, trust is already compromised. That’s why real-time insight into model behavior is essential to building and sustaining great experiences.
Keeping AI Aligned and Improving
Prove AI helps cross-functional teams keep AI chatbots aligned with user expectations after deployment. Our real-time observability platform gives you the visibility and control needed to stay ahead of drift and degradation.
We help you:
- Highlight opportunities for fine-tuning based on live interactions
- Support human-in-the-loop workflows to escalate, retrain, or adjust
- Create shared visibility across business leaders, ML Ops, and data science
Whether you are scaling your AI operations or just getting started, Prove AI ensures your systems improve with every interaction, not deteriorate in the dark.
Book a demo to see how Prove AI helps you keep your AI performing beyond your customers’ expectations.
