Chasing a Moving Target: The Perpetual Hunt for AI Accuracy

AI support tools are everywhere, offering instant answers powered by large language models and conversational intelligence. But behind the sleek interface and 24/7 promise lies a growing concern: these tools can’t always be trusted. Whether open-source or proprietary, LLMs hallucinate, confidently generating false or misleading information. As one MIT article rightly noted, “LLMs are so good that you hardly know they hallucinate.”

This creates a dangerous blind spot for businesses. Accuracy isn’t just a technical issue—it’s a trust issue. And the problem runs deeper when we confuse traditional automation with agentic AI.

Traditional automation tools like Zapier or UiPath follow human-created workflows. They execute rules, not make decisions. Agentic AI, on the other hand, is goal-based. You give it an outcome—say, "Why is feature engagement down?"—and it builds its plan to investigate and respond. It seems intelligent, even strategic.

And that’s the trap. Because it looks smart, teams begin to trust it like a teammate rather than a tool. Oversight becomes an afterthought. But the very freedom that makes agentic AI powerful also introduces unpredictability. Its ability to act independently doesn’t mean it understands truth, context, or consequences.

That’s why human involvement is more important than ever—not to micromanage, but to guide, verify, and correct. Without that intelligent filter in place, businesses risk not just wrong answers, but damaged trust, poor decisions, and mounting hidden costs. AI doesn’t remove the need for judgment—it increases it.

Take Klarna. They famously replaced 700 agents with an AI assistant that handled 2.3 million chats in a month. Speed was phenomenal—resolution times dropped from 11 minutes to 2. But by mid-2025, Klarna was rehiring humans. Why? Because even the best AI models can’t maintain accuracy indefinitely.

The Real Accuracy Challenge

AI tools start strong, especially on routine tasks—tracking refunds, status updates, or straightforward FAQs. But as customers push the system with nuanced questions or unexpected edge cases (Refer to the latest paper from Apple), AI’s performance starts to crack. It’s not about one spectacular failure; it’s the steady drip of small misses that erode trust over time.

• Data Drift: The real world changes faster than training data. Seasonal shifts, new products, policy changes—AI struggles to keep up.

• Feedback Loops: If AI’s mistakes go unmonitored, they get repeated. A single error can propagate into dozens of bad answers.

• Overconfidence: AI often responds confidently even when it’s wrong, making it harder for customers to know when to trust it.

• Complexity Gap: AI shines on repetitive queries but falters on complex issues that need empathy or judgment.

The Shelf-Life of AI

Every AI tool has a shelf life. What works today might (will) degrade tomorrow as customers’ needs evolve and data patterns shift. Even with retraining / reembedding et al, no model stays perfect forever. Without consistent monitoring and human oversight, even the best AI starts drifting away from real-world accuracy.

Monitoring and Long-Term Management

AI doesn’t manage itself. Keeping accuracy high means continuous monitoring, retraining, and tuning:

• Performance Dashboards: Tracking resolution times, escalation rates, and customer satisfaction helps spot accuracy drops before they become customer churn.

• Feedback Loops: Human agents reviewing and correcting AI mistakes teach the system to improve.

• Alerting: Setting thresholds to catch spikes in escalations or repeated mistakes ensures customers aren’t stuck in frustrating loops.

• Continuous Tuning: Models need to be retrained with new data, especially when the business changes—new products, new policies, or external events.

Getting the Balance Right

Accuracy isn’t a “set and forget” feature! It’s a moving target.

AI can turbocharge your support, but without robust monitoring and human collaboration, even the best system will eventually degrade, leaving customers frustrated and businesses exposed.

The takeaway? AI is a tool, not a replacement. It needs a partner, a supervisor, and a plan to keep it accountable. That’s the only way to make sure customers get the right answer, every time.

And if you need to know more, talk to us about fencing and building a watchdog on AI — we help manage and secure your AI systems through observability, red/blue teaming, and AI incident management.