Apple recently published a paper questioning the reasoning capabilities of Large Language Models (LLMs). Like many who have been deep in AI for some time, I don’t believe this presents any earth-shattering new evidence. Despite some people disagreeing, most of the comments I have read border on “Well, duh.” Yet, countless people and companies will continue to dismiss this analysis and align customer-facing services with flawed AI models.
The research, led by Apple’s Mehrdad Farajtabar, suggests that LLMs are incapable of true reasoning but instead engage in complex pattern matching, challenging the belief that these AI systems can genuinely understand and solve problems like humans. It’s essential to distinguish LLMs from other advanced AI systems, such as those designed to play chess or make strategic decisions. Even in those cases, the AI is not truly “thinking” or reasoning in the human sense; instead, it is rapidly processing all possible options and selecting the most appropriate move based on probabilities. These systems are optimized for specific tasks, but their impressive results often mask the lack of genuine cognitive reasoning.
This distinction raises significant questions about the role of AI in customer experience (CX), especially for businesses that have long viewed AI as the ultimate solution for enhancing customer interactions. Many believe AI can automatically deliver personalized, empathetic experiences, but Apple’s findings suggest otherwise.
In contrast to the broader industry’s enthusiastic embrace of AI, Apple has also shifted its branding to emphasize “Apple Intelligence,” distancing itself from the overhyped narrative surrounding AI. This strategic move reflects Apple’s realistic view of AI’s limitations and highlights the growing importance of rethinking how AI should be integrated into consumer-facing applications. Another study showed that consumers don’t like anything “powered by AI.” However, as businesses increasingly rely on AI to improve customer experience, understanding the limitations of AI’s reasoning ability is vital for delivering meaningful interactions that genuinely resonate with customers.
The Flawed Assumption of AI as a One-Stop Solution for Customer Experience
AI has been heralded for years as a one-stop solution for improving CX. Businesses invested heavily in AI-driven chatbots, recommendation engines, and automated service platforms, assuming that GenAI could handle complex customer interactions and—or better than—human agents. However, the recent findings from Apple’s paper challenge this assumption by revealing that while LLMs can generate responses based on patterns from vast datasets, they cannot reason in ways necessary for understanding nuanced customer needs.
When we consider customer experience, it’s not just about delivering quick answers or solving simple problems. True customer satisfaction comes from empathy, understanding, and the ability to interpret context—areas where AI struggles in its current form. For instance, many AI-driven chatbots can answer basic inquiries but falter when faced with complex, context-dependent issues. Apple’s research reinforces this point, showing that LLMs are highly sensitive to minor changes in input. A simple modification to a customer’s query, such as changing the wording or context, can cause AI systems to respond incorrectly, highlighting the limitations of AI’s “reasoning” capabilities.
AI Reasoning vs. Customer Behavior: A Misalignment
Understanding consumer behavior requires more than recognizing patterns; it demands the capability to reason through emotions, preferences, and unique situations. Apple’s findings highlight that LLMs often fail when faced with slight modifications to familiar problems, suggesting that these systems are not honestly “thinking” but rather pattern-matching from data they have seen before. In the realm of CX, this creates a fundamental misalignment between what AI can do and what customers expect.
Customer behavior is complex and influenced by various factors, including emotions, cultural context, and personal experiences. For AI to successfully enhance CX, it needs to do more than just respond to surface-level inputs; it must understand the deeper layers of human interaction. This is where the distinction between reasoning and pattern matching becomes critical. As Apple’s research suggests, LLMs struggle with this depth of understanding, often providing inaccurate or irrelevant responses when the context shifts slightly.
For example, imagine a customer asking for support on an order issue, and the AI responds based on a pre-learned pattern rather than considering the specific details of the customer’s situation. This failure to grasp the nuance can lead to frustration and erode customer trust—two critical elements of a positive CX. When AI is unable to “think” through the problem as a human would, it falls short of delivering the kind of personalized service that customers crave.
Over-Reliance on AI: The Risk to Customer Trust and Satisfaction
The risk of over-relying on AI to manage customer experience is not just about potential service failures; it’s about the broader impact on customer trust and satisfaction. When businesses promise AI-driven customer experiences that are seamless and human-like, they set expectations that can be difficult to meet. The reality is that AI, particularly in its current state, often lacks the reasoning capabilities needed to handle complex or emotionally charged customer interactions.
Apple’s research shows that even the most advanced AI models struggle with logical reasoning, especially when faced with variations in context or input. For businesses, relying too heavily on AI without human oversight can lead to service breakdowns and dissatisfied customers. Whether it’s a chatbot that can’t resolve a unique issue or an AI recommendation system that misses the mark, these failures can damage a company’s reputation and diminish customer loyalty.
Moreover, when AI-driven systems fail, they often do so in profoundly frustrating ways for customers. Unlike human agents, who can adapt and problem-solve in real-time, AI systems are rigid, bound by their training data, and prone to making mistakes when the context shifts. This rigidity can make customers feel unheard or undervalued, leading to negative experiences that are difficult to recover from.
Redefining AI’s Role in Customer Experience
So, where does this leave AI’s role in CX? The answer lies in rethinking how businesses deploy AI. Rather than viewing AI as a replacement for human agents, companies should see it as a tool that enhances and supports human-driven CX efforts. AI can be incredibly effective when used for tasks that involve large amounts of data processing, pattern recognition, or automation of simple tasks. However, human intuition and reasoning are still irreplaceable regarding complex, context-rich interactions.
Apple’s approach provides a blueprint for how businesses can rethink their use of AI. Apple emphasizes the importance of creating intelligent systems that complement human abilities rather than trying to replace them. Lately | Neuroscience-Driven AI™ has been preaching this for years. A hybrid approach can lead to more reliable, empathetic, and compelling customer experiences, where AI handles routine tasks while humans manage the more nuanced, emotionally driven interactions.
The Future of Customer Experience: A Balance Between AI and Human Intelligence
The contrast between LLM’s lack of valid reasoning and humans’ innate ability to rapidly interpret context, emotions, and complex situations highlights the critical gap that businesses must acknowledge as they integrate AI into customer experience strategies. While AI excels at processing vast amounts of data and recognizing patterns, it lacks the cognitive flexibility that allows humans to adapt and respond in real-time to nuanced and evolving circumstances. Through rapid reasoning, humans can adjust their approach mid-conversation, empathize with a customer’s frustrations, and navigate unforeseen challenges—all of which are vital to creating a deeply satisfying customer experience. This capacity for behavioral recognition is unique to humans and presents the key challenge for businesses eager to adopt AI.
Companies relying too heavily on AI without recognizing its limitations risk alienating customers who expect more than pattern-based responses. Successful organizations will strike the right balance, leveraging AI for efficiency while ensuring human oversight remains central to handling complex, emotionally charged, or highly personalized interactions. This human touch—rooted in our unmatched ability to reason and empathize—will define the future of customer experience in the age of AI.