The Rise and Fall of Self-Checkout Kiosks: Lessons for AI Chatbots in Customer Service


Self-checkout kiosks, once hailed as a technological revolution in retail, have had their share of difficulties. These automated systems promised shorter lines, faster transactions, and increased efficiency. However, their journey has been marked by challenges, and many large retailers have reconsidered their use. In this blog post, we’ll explore the rise and fall of self-checkout kiosks, examine why some retailers have pulled back, and draw parallels with the current surge in generative AI for customer service.

The Self-Checkout Revolution

Self-checkout lanes were the initial step in transforming traditional brick-and-mortar retail settings. Customers could scan their purchases, bag them, and complete the payment without cashier assistance. It seemed like a straightforward idea, but it was only the beginning. Retailers recognized that improving the in-store checkout process was crucial for customer satisfaction and loyalty.

The Challenges

1. Technical Hurdles

Despite the promise, self-checkout kiosks faced technical challenges. Customers encountered issues such as:

  • Inaccuracies: Some kiosks struggled to recognize items correctly, leading to frustration.
  • Redundancy: Customers had to repeat information when redirected to human agents.
  • Time-Consuming: Using self-checkout sometimes took longer than expected.
  • Impersonal Experience: The lack of a personal touch left customers feeling disconnected.
  • Relevance: Conversations often lacked relevance to the actual issue.

2. Retailers Pulling Back

Major retailers across the US, including Walmart, Costco, and Wegmans, have reevaluated their self-checkout strategies. Complaints from customers and concerns about theft prompted these companies to rethink their approach123. The move away from self-checkouts signals a shift in priorities.

Generative AI in Customer Service

Now let’s connect this to the rise of generative AI in customer service. Large language models (LLMs) like ChatGPT can respond to prompts with human-like text and voice, making them ideal for chatbots. Companies are exploring how generative AI can augment customer care centers. The potential productivity gains are substantial—up to 30% to 50% or more.

The Pitfalls of AI Chatbots

However, implementing AI chatbots requires caution. Here are some lessons from the self-checkout experience:

  1. Quality Assurance (QA): Just like self-checkouts, chatbots must undergo rigorous QA. Biases and inaccuracies can harm a company’s reputation and bottom line. Automation testing tools, like Cyara’s codeless solution, can streamline the process.
  2. Human Oversight: Full-scale deployments should include some level of human oversight. Critical services may still require human agents. Transparency about chatbot limitations is essential.
  3. User Experience: Prioritize user experience. Design chatbots with simplicity and ease of use in mind. An intuitive interface and natural conversational flow enhance engagement.
  4. Set Realistic Goals: Define clear goals and objectives for your chatbot. Understand the problem it solves and its value proposition. Measure success using relevant KPIs.


Generative AI will transform customer service, but companies must tread carefully. By learning from the self-checkout saga, businesses can implement AI chatbots effectively, enhance customer engagement, and avoid driving customers away with subpar answers. The future lies in striking the right balance between automation and human touch.

Remember, just as self-checkouts evolved, so will AI chatbots. Be ready for the next wave of customer service innovation.

One thought on “The Rise and Fall of Self-Checkout Kiosks: Lessons for AI Chatbots in Customer Service

  1. Great article! Simple and Informative and maybe we can learn from self check-out. I know that I always opt for a line with a human that scans my items. That’s why I love my local grocery retailers!

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