These Chatbot Mistakes Could Cost You Customers

Chatbots have become a staple feature in many businesses’ customer service strategies, but they are not without their vulnerabilities. Hallucinations, bias, lack of empathy, and more, are putting a dent in customer relationships.

A landmark case involving a passenger misinformed by an airline’s chatbot shows that organizations are now answerable for what their technology says and does. AI-powered chatbots aren’t foolproof silver bullets for transforming customer service.

Instead, organizations need to practice caution when deploying chatbots, which involves keeping the human touch present and knowing how to oversee and monitor these digital tools. Failing to do so could lead to chasing customers away.

Avoiding the below mistakes not only fortifies digital-first customer service strategies but will also reshape the controversial narrative around chatbots.

Replacing the human touch

Losing sight of the human touch is one of the biggest mistakes companies make when deploying AI in customer service. AI is currently not able to replicate the emotional intelligence, empathy, or cultural touch that robust human support delivers.

According to a recent Prosper Insights & Analytics survey, 40.4% of individuals are concerned about the reliability of AI-powered chatbots, with misinformation a top concern. Organizations can’t ignore the vulnerabilities of AI bots, such as hallucinations, inaccuracies, and bias. There’s too much at stake in terms of reputation and customers.

Moreover, as a survey from Prosper Insights & Analytics demonstrates, AI chatbots are already operating within a complex customer relations environment, marked by potential resistance and mistrust. Every error can cost an organization its reputation and customers. As the Air Canada example shows, chatbots are not held culpable for giving customers misleading information—companies are.

“Think of AI as your front desk, not your full support team. If the answer isn’t clear in 10 seconds, a human should take over,” advises Mahesh Raja, Chief Growth Officer at Ness Digital Engineering. That means chatbots work best in easily resolved, repetitive, and low-stakes tasks, like tracking shipments or suggesting personalized product recommendations.

Where chatbots tend to fall short, though, is in situations where emotions are running high. A distressed customer who’s falling victim to banking fraud, for instance, isn’t going to want to deal with a chatbot to resolve their situation. Instead, they’ll want a person who can empathize, not a scripted bot.

Chatbots that exacerbate frustration in customers potentially damage brand trust and loyalty. According to a recent Prosper Insights & Analytics survey, this preference dominates in banking (85.3%) and healthcare (87.2%), two industries that rely on exceptional degrees of trust.

The research findings also show that digitally native younger generations like Gen-Z and Millennials have double the tolerance threshold for AI chatbots in sectors like eCommerce, telecom, and travel than their Baby Boomer counterparts.

Not just industry, but audience and situation, too, help organizations define where a human makes more sense versus a bot.

Pursuing a cost-first, instead of a customer-first, approach

According to Boston Consulting Group, most C-suite executives prioritize cost for AI implementation, and 90% recognize AI’s role in cutting costs over the next 18 months. However, Naveen Gattu, Global Head of Growth, Straive, urges business leaders to look beyond this: “The true strength of AI lies not just in speed or cost efficiency, but in its ability to anticipate customer needs, respond with thoughtful context, and build trust before a request is even made.”

Organizations are well-advised not to launch bots based on unproven assumptions of customer preferences. “This creates a disconnect that makes the company look out of touch,” warns Jorge Riera, CEO of Dataco, which provides data for customer-driven value. “People know when they’re being heard, and they reward brands that genuinely listen. Done well, AI enhances the brand. Done poorly, it damages trust and drives customers away.”

Experts recommend customer journey maps to better understand and anticipate customer needs. These provide an in-depth view of customer touchpoints from start to finish, enabling organizations to deeply familiarize themselves with customer motivations.

They also reveal friction points, potentially stressful situations, and objectives in each step of the customer journey. Understanding these aspects is vital for robust AI deployment that’s centered on customer needs and strengthens engagement.

Misunderstanding the metrics that matter

Organizations deploying chatbots must ensure they’re reviewing AI-generated content before it’s used and tracking KPIs. However, there are serious issues around monitoring AI chatbot performance, most of which boil down to a lack of awareness of what is worth keeping a pulse on.

It’s also challenging to accurately measure AI performance in customer service. “The more human-like an AI acts, the more difficult it is to measure how correct it is, because we humans are also variable and different, so we don’t have specific standards,” notes JD Raimondi, Head of Data Science at Making Sense.

Raimondi has addressed this measurement problem by properly measuring chatbot outputs. Business leaders must agree in advance on quantifying and classifying results and acceptable error rates. Commonly used core metrics include response accuracy, first contact resolution, and containment rates.

However, organizations must strike a balance where efficiency gains don’t compromise customer relationships. They also should explore customer satisfaction scores and measure loyalty via NPS surveys after chatbot sessions.

According to a recent Prosper Insights & Analytics survey, half of customers are concerned about privacy violations from AI using their data. Organizations need to keep on top of data security and privacy. That means monitoring and measuring security and ensuring full compliance with data laws. These steps also lower the threat of cyberattacks and legal issues that will cause irreparable damage to customer relationships.

Monitoring security involves recording how many breaches occur and tracking unauthorized access attempts. This helps organizations keep a pulse on the strength of their security frameworks while accurately gauging how vulnerable their digital infrastructure is.

It’s also important to have the right teams in place to oversee these measures. Consider looping in qualified experts, like cybersecurity and machine learning operations specialists, to ensure security frameworks stay on track.

Clearly communicating and demonstrating security and data privacy protocols to customers can help alleviate concerns around how their data is used and stored. Demonstrate these with reputable certifications like SOC 2 to build trust.

Following a customer-centric, hybrid approach to AI chatbot implementation is at the crux of driving efficiency without compromising reputation. While AI certainly has its value, it also has its vulnerabilities. Avoiding these mistakes will strengthen deployment for scalable and secure customer relations improvement.