Sycophancy in AI: Could People-Pleasing Chatbots Ruin Your CX?

AI assistants are changing how people search. More than that, they’re setting new expectations for brand interactions.

Among consumers who are using ChatGPT, Gemini and other mainstream tools, the most popular task is “getting answers or searching for information” (76%) according to a recent survey. Whether it’s troubleshooting a tech issue, settling a debate, or finding quick facts, mainstream tools are becoming a go-to resource for instant information.

The more routine these interactions become, the more they quietly rewrite what customers expect from AI—including your customer care chatbot.

That’s where things get interesting. On one hand, customers can show up to your AI channel expecting your chatbot to be just as agreeable and accommodating as their favorite mainstream tool. If it isn’t, disappointment follows. On the other hand, if your bot bends over backward—always validating, never pushing back—it risks coming off as untrustworthy, even inaccurate. That’s not just a technical flaw; it’s a credibility problem, and we’ll dig into why that matters.

I’ll explain how “sycophancy” in AI chatbots can shape (and sometimes sabotage) customer experience. Then I’ll list steps to tune your bots for genuine help, not just easy agreement.

What Is Sycophancy in AI? (And Why Is It an Issue for Brands?)

Sycophancy in AI is when a chatbot excessively agrees with or flatters users; it prioritizes making the user happy over providing honest or accurate responses. The AI Ethics Lab puts it this way: “Sycophancy refers to the tendency of an artificial intelligence system to flatter, agree with, or mirror a user’s views to gain approval, even when doing so compromises accuracy or honesty.”

Agreeableness gets baked into most AI models because it drives engagement—to the point where AI models are 50% more sycophantic than humans.

In the context of customer-brand interactions, AI sycophancy creates a clear authenticity issue: Bots that are “too nice” are easier to spot and less likely to be trusted, Stanford researchers found.

So how do you tune your customer care AI to address consumer expectations shaped by sycophantic mainstream tools? But without letting those same people-pleasing traits compromise your AI’s helpfulness, authenticity or accuracy in brand interactions?

The answer is in how you tune the objective bias in your brand’s AI model.

Objective Bias: Why Chatbots Act Like This

Objective bias is what’s really driving your chatbot’s behavior. Every AI reflects the goals set by its designers. If you tell your bot its job is to “maximize customer satisfaction,” (which might give it the ChatGPT-like fawning that consumers are growing accustomed to) don’t be surprised when it starts agreeing to everything the customer wants, even when it shouldn’t. That’s objective bias in AI: the system is tuned to chase whatever metric you put in front of it, whether it’s satisfaction scores, revenue generation or ticket closure rates.

If your objectives aren’t tuned for helpfulness and accuracy, you’re setting your brand up for trouble. Imagine two chatbots: one programmed for “customer satisfaction,” the other for “issue resolution.” Bot A is all about keeping the customer happy: it agrees, it flatters, it avoids conflict. Bot B is focused on solving the actual problem—even if it means pushing back or delivering an answer the customer doesn’t want to hear.

Which one builds real trust? Which one delivers lasting value?

Objective bias sounds like a technical AI detail, but it’s not. It’s strategic. If you want your chatbot to be more than a digital yes-man, you have to get the objectives right.

Concierge to Compliance Officer: The Chatbot Personality Shift

While we’re on the subject of objective bias, I want to point out a unique CX hazard that customer care chatbots present: When they suddenly go from people-pleasing to stonewalling your customers.

You’ve likely encountered this yourself. At first, the AI assistant is the consummate concierge. It’s welcoming, attentive, ready to solve your problems and make you feel heard. It’ll help you reset your modem, track an order or answer basic questions with a reassuring tone.

But try to push past the routine—ask for a refund, dispute a charge, or bring up something outside the script—and suddenly it changes hats. It becomes the compliance officer (no offense to actual compliance officers). The bot starts quoting policy and reciting legal disclaimers and canned responses: “I’m unable to assist with that request due to company policy.”

That’s how AI chatbots in customer service are programmed to handle boundaries. But it’s a jarring experience for customers, and it chips away at the sense of authenticity you’re trying to build. You can ease the transition—more on that below.

How to Tune Customer Care Chatbots for Real Helpfulness

How do you keep your AI assistants from turning into a digital yes-man? Or worse—a cold compliance officer? Here are strategies to try:

• Audit and clarify your chatbot’s objectives. If you want real help, and not just easy agreement, start by making helpfulness and accuracy the north star. Don’t let “maximize satisfaction” become code for “never say no.”
• Program the right tension among the objectives. Don’t let your AI channel be ruled by a single stakeholder’s goals. One team wants to maximize CSAT (which could foster people-pleasing), another wants to drive sales, another wants to reduce support costs, and so on. Your AI should prioritize the best long-term resolution of the customer’s issue, and then incorporate a healthy push and pull among business priorities.
• Have the bot set clear expectations with users. Let customers know what your chatbot can and can’t do. A brief welcome message that clarifies the bot’s capabilities and boundaries helps reset expectations shaped by mainstream tools. Example: “I’m here to help with account questions and troubleshooting. For billing disputes or policy exceptions, I’ll connect you to a human expert.”
• Build guardrails for escalation. Know when your AI chatbots should hand off to a human. Think of sensitive issues, complex requests or anything that risks a legalistic response—these are moments for a human-in-the-loop.
• Design escalation as a positive. Train your bot to handle boundaries gracefully. If it has to enforce a policy, it should maintain the “concierge” tone and transparency. Change the cold stonewalls (“I cannot assist with that request”) into warm transfers (“One of our customer care experts can get you the best answer for that. Shall I connect you now?”)
• Use prompt engineering and governance to balance politeness with honesty. Don’t let your bot lie just to be nice. Set clear rules for when it should push back, clarify or escalate. Governance is how you keep your AI honest. Incorporating the healthy tension among stakeholders (mentioned above) can also help with this.
• Audit for over-validation. Review transcripts for signs that your bot is agreeing or flattering when it should be clarifying, correcting or escalating. Adjust scripts and training data to reduce sycophantic patterns.
• Monitor and retrain regularly. Use analytics to spot when the bot’s agreeableness is leading to unresolved issues or customer frustration. Retrain the model to balance empathy with accuracy.

And to prepare for customers’ expectation that your chatbot should be sycophantic:

• Build your team’s awareness. Train CX staff and product managers to recognize that customers may expect instant validation and friendliness from your bot. Use this insight to design flows that acknowledge those expectations, but don’t blindly cater to them.
• Gather feedback on expectation gaps. Regularly survey customers about their chatbot experience. Ask if the bot met their expectations and where it fell short compared to mainstream AI tools. Use this data to refine both the bot’s responses and escalation triggers.

Building Trust (Beyond Agreeableness) With AI

Sycophancy in AI is a double-edged sword. On one side, it can make interactions feel smooth and pleasant; on the other, it risks undermining trust, accuracy and the authenticity that customers expect from your brand. The real challenge is to tune your customer care chatbot not to be merely agreeable, but to deliver real engagement and honest help.