AI-powered customer service is becoming ubiquitous, but it’s failing to deliver on its promises, leaving consumers frustrated and companies struggling to balance cost savings with customer satisfaction. The article explores the growing disconnect between corporate enthusiasm for AI chatbots and the reality of poor customer experiences.
The current state of AI customer service is problematic. Virtual assistants and chatbots often fail to understand customer problems, creating additional hurdles rather than solutions. These systems frequently redirect users through multiple automated channels before allowing access to human agents—if they allow it at all. According to a recent Gartner survey, nearly two-thirds of customers prefer that companies don’t use AI for customer service, with primary concerns including difficulty reaching real people, job displacement, and incorrect answers.
Companies are implementing AI primarily for cost reduction, viewing contact centers as cost centers rather than profit centers. The technology promises to automate tier-one support operations—handling high-volume, low-complexity questions about package tracking, account balances, and basic inquiries. DraftKings, for example, uses AI to handle millions of basic player questions that would be cost-prohibitive for human agents. However, the financial pressure is creating misaligned incentives, pushing companies to deploy AI broadly even when inappropriate.
The technology itself has significant limitations. While generative AI chatbots excel at distilling simple information, they struggle with complex, multi-tiered problems. Academic research shows that consumers evaluate service as worse when provided by bots versus humans, even when the service is identical. This emotional trust deficit stems from the perception that automation benefits companies, not customers.
Industry experts compare the current moment to building websites in 1999—everyone is experimenting, but best practices haven’t emerged. Jason Maynard from Zendesk notes that companies are “building the AI-enabled plane while flying it.” The optimistic view suggests that within five years, virtual agents will be indistinguishable from humans. However, the pessimistic scenario is that mediocre AI becomes the new normal, especially in uncompetitive industries where customers can’t easily switch providers. Michelle Schroeder from PolyAI points out that even established voice assistants like Google Home and Alexa still struggle with basic tasks after years of development, suggesting natural improvement isn’t guaranteed.
Key Quotes
It’s a lot of work, and it’s expensive to think about customer experience and design your AI in a way that’s going to be an enjoyable experience. And most companies that are thinking about cost cutting and the AI revolution are not really thinking about the customer.
Michelle Schroeder, senior vice president of marketing at PolyAI, explains why AI customer service implementations are failing. This highlights the fundamental misalignment between corporate cost-cutting objectives and customer needs.
Companies are operating in the dark, in some sense. They have this idea that this technology is going to provide them with cost savings. They don’t exactly know how to deploy it.
Michelle Kinch, assistant professor at Dartmouth’s Tuck School of Business, describes how companies are experimenting with AI without clear implementation strategies, making customers unwitting guinea pigs in the process.
When we do have that acute need to talk to a person, the chatbot becomes a hurdle.
Professor Kinch articulates the core frustration: AI systems designed to help are instead creating barriers between customers and the human assistance they need for complex problems.
They tend to view contact centers as a cost center, not as a profit center, and the only thing you want to do in a cost center is reduce cost. They’re not looking for transformative, they’re looking for incremental.
Jeff Gallino, CEO of CallMiner, explains the fundamental business mindset driving poor AI implementations—companies prioritize cost reduction over customer experience transformation.
Our Take
This article exposes a critical flaw in the current AI revolution: the disconnect between technological capability and practical deployment. Companies are treating AI as a magic cost-cutting solution rather than a tool requiring thoughtful implementation. The comparison to 1999 websites is apt but concerning—it suggests years of poor customer experiences ahead.
What’s particularly troubling is the economic coercion at play. In uncompetitive markets like health insurance and cable services, customers can’t vote with their wallets, meaning companies face no market consequences for deploying inadequate AI. This could establish a race to the bottom where mediocre AI becomes acceptable simply because it’s universal.
The article also reveals how AI hype is driving premature adoption. CFOs reading about competitors cutting jobs with AI create pressure for implementation regardless of readiness. This suggests the current AI deployment wave is driven more by FOMO and financial pressure than by genuine technological maturity or customer benefit.
Why This Matters
This story reveals a critical inflection point in AI adoption where corporate enthusiasm is colliding with consumer reality. As companies rush to implement AI for cost savings—particularly amid economic pressure and rising interest rates—they’re creating a widespread customer experience crisis that could undermine trust in AI technology more broadly.
The implications extend beyond frustrated customers. This represents a test case for AI deployment across industries: will companies prioritize customer experience or short-term cost reduction? The answer will shape AI’s long-term viability in consumer-facing applications. If companies establish a pattern of deploying inadequate AI systems, they risk creating a permanent trust deficit that could hamper future AI adoption.
For workers, the stakes are equally high. The article highlights how AI is being used to eliminate tier-one support jobs, with CFOs pressuring operations teams after reading about competitors cutting hundreds of positions. This job displacement is happening even as the technology remains inadequate, suggesting economic incentives are overriding practical considerations. The outcome of this customer service experiment will likely influence AI deployment decisions across multiple sectors.
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Source: https://www.businessinsider.com/ai-chatbots-customer-service-call-center-annoying-problems-2024-11