AI has been hyped, overpromised, and sold like a magic wand.
No wonder so many leaders roll their eyes when yet another vendor claims their chatbot will “transform your customer experience overnight.”
The skepticism is real, and it’s not completely wrong or unfair. But the problem isn’t that AI can’t deliver in CX, it’s that too much of the AI being rolled out is shallow, generic, and poorly tested.
Let’s unpack the biggest concerns of AI in CX and talk about what actually matters.
1. AI accuracy and reliability: the elephant in the inbox
Most companies that say “AI didn’t work for us” really mean one of two things:
- The AI wasn’t trained on their data.
- The AI wasn’t continuously tested or tuned.
Large language models (LLMs) are great at sounding smart, but they’re less great at following your brand’s refund policy word-for-word or knowing the difference between a skipped subscription and a paused one.
Accuracy doesn’t happen out of the box, it happens when AI is fed with clean data, tested in real workflows, and monitored like a new team member learning the ropes.
Customers don’t hate AI, they hate bad AI.
2. The “running away from AI” trend
You’ve most likely seen or heard the stories: companies quietly pulling back on their AI investments, abandoning half-baked AI solutions that created more problems than they solved.
What’s actually happening? Leaders are realizing that AI isn’t a “set it and forget it” solution. A disconnected bot that can only parrot FAQs will frustrate customers and your support team.
When brands run away from AI, they’re not running away from the technology, they’re running away from bad implementations.
3. Generic responses = generic experiences
If you’ve ever been hit with a chatbot that greets you with “Hi, [FirstName], how can I help?” and then immediately punts you to an FAQ link, you know the pain.
Customers can smell a super-generic response from a mile away, and once they do, your AI is no longer a helpful tool, it’s a brand liability.
The fix is personalization at every step. AI should pull context from your ecommerce stack—orders, subscriptions, loyalty status—and adjust its responses accordingly.
“Hi, you’re a VIP customer. I can process your return right now or send you an exchange option” is light-years better than “Please see our return policy.”
4. Human vs AI: it’s not a cage match
The worst misconception is still that AI is here to replace humans.
AI should automate tasks, not jobs. No one wakes up excited to answer 100 “where is my order?” tickets, but AI doesn’t care, and therefore AI is perfect for that.
Humans are perfect for nuanced conversations, loyalty saves, or resolving edge cases that don’t fit a flowchart.
Think of AI as the intern—handling the grunt work—while your human agents do the strategic, empathy-driven work that keeps customers coming back.
AI isn’t coming for you. However, your competitors using AI right might be.
5. QA is everything
The difference between good AI and “AI that made the Wall Street Journal for all the wrong reasons” is testing.
AI should go through the same quality assurance you’d apply to a human agent before putting them on the front lines. That means:
- Testing workflows with real customer data.
- Running A/B experiments (skip vs. discount, refund vs. exchange).
- Monitoring containment rates and CSAT on an ongoing basis.
AI that’s tested and tuned becomes more reliable over time. AI that’s dumped into production without QA is guaranteed to disappoint.
The bottom line
Skepticism around AI in customer experience is healthy. It forces vendors (like us) to prove value, not sell hype.
The companies that succeed aren’t the ones who bought “AI in a box”, they’re the ones who:
- Integrated AI into their actual workflows.
- Personalized it with their data.
- Tested it continuously with humans in the loop.
At KODIF, we don’t expect you to “just trust the AI.” We build with transparency, we test relentlessly, and we give your team control over how automation runs.
The only thing worse than bad AI is pretending bad AI is good enough.
