Chatbots Under Fire: Navigating AI Pitfalls with Insights from DPD and Air Canada


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What could possibly link the French transporter DPD with Air Canada? Recently, both have experienced significant public scrutiny due to mishaps with their AI chatbots, sparking a flurry of discussion and concern about the future of AI in customer service.

DPD & Air Canada Chatbots
DPD & Source: DALL-E

The year 2023 was marked by an explosion of interest and investment in AI technologies. Innovations like ChatGPT, Twitter/X’s new bot, Google Bard (now rebranded as Gemini), Copilot, DALL-E, Midjourney, and an array of other GPT-based tools dominated headlines. These technologies painted a vivid picture of a future augmented by AI, promising advancements that could reshape industries, enhance creativity, and even provoke discussions on the ethical dimensions of artificial intelligence.

As we transitioned into 2024, the enthusiasm for AI was met with a reality check. Within the first six weeks, two high-profile incidents involving DPD and Air Canada’s chatbots made headlines, sparking debates on the readiness of companies to integrate AI into their customer service frameworks. These events raised important questions: Are we moving too fast? And what lessons can be learned from these early adopters’ experiences?

DPD and Air Canada: What happened?

DPD’s Viral Chatbot Mishap

DPD, a renowned French transporter, faced public scrutiny when its chatbot, following a customer’s prompts, began issuing responses laced with inappropriate language and criticism towards the company. This incident, stemming from a faulty system update, not only highlighted the unpredictability of AI but also the potential for such technologies to go off-script with viral consequences. The swift spread of the story on social media platforms, amassing over 800,000 views in a mere 24 hours, underscores the viral nature of AI missteps and their potential to tarnish a brand’s reputation overnight.

DPD Chatbot swears
Source: MSNews

Air Canada’s Bereavement Discount Error

In a parallel narrative, Air Canada’s chatbot informed a customer that he was eligible for a bereavement discount in contradiction to the airline’s actual policy. This misinformation, dating back to a conversation in 2022, led to a legal battle that only recently concluded, with the court ruling in the customer’s favor. This case not only highlights the legal implications of AI-driven misinformation but also stresses the importance of aligning AI functionalities with company policies and the broader responsibilities companies bear for their chatbots’ actions.

The Industry-Wide Impact

The incidents involving DPD and Air Canada’s AI chatbots highlight significant challenges and risks for businesses integrating AI into customer service. These situations have sparked concerns over the potential for caution or fear among companies considering AI, stressing the necessity for meticulous planning, thorough testing, and the creation of backup plans to manage AI’s unpredictability. The DPD case illustrates the risks of AI-driven content going awry, potentially leading to public relations issues and damage to the company’s reputation. It emphasizes the need for careful deployment of AI technologies to prevent misuse. The Air Canada incident demonstrates the legal and financial risks when technology provides inaccurate information, underscoring the accountability businesses have for their chatbots’ actions and the importance of aligning AI communications with company policies. Together, these cases advocate for a cautious, well-prepared approach to adopting AI in customer service, highlighting the need for rigorous testing and clear policies to mitigate risks.

Learning from Mistakes: Essential Insights

The DPD incident, depending on one’s perspective, can be seen as either amusing or alarming. However, the focal point of the buzz—the chatbot’s use of profanity—should not be the main concern for businesses. In essence, the chatbot was merely executing the customer’s requests. This level of responsiveness might seem ideal, yet the issue arose when the bot was prompted to deride the company, underscoring the need for protective measures to prevent such outcomes. More critical, yet overlooked by many, was the chatbot’s failure to perform its primary functions effectively. It was unable to assist the customer with locating a parcel or providing relevant delivery information, nor could it facilitate a connection with a human agent for further assistance. These shortcomings—ineffectiveness in resolving queries and obstructing direct human support—are significantly more detrimental to both the customer experience and the business.

In parallel, the Air Canada narrative is less about the chatbot’s erroneous information and more about the company’s response to the mistake. It’s a given that bots, like humans, are prone to errors. However, the issue with Air Canada was its refusal to acknowledge the chatbot’s error as its own. A more empathetic approach would have enabled the company to view the situation from the customer’s perspective, recognizing its responsibility for the chatbot’s mistake. The core problem was not just the need for better chatbot training but the company’s apparent disavowal of accountability for the chatbot’s actions.

Deploying Chatbots: A Careful Strategy

Considering all factors, the question arises: How can one introduce a chatbot without facing the pitfalls seen in recent incidents? The key lies in a thoughtful and correct deployment of chatbots. Although Generative Pre-trained Transformers (GPTs) are capable of addressing a wide range of inquiries and could, in theory, be applied across various domains, practical implementation is seldom straightforward. Just as you wouldn’t expect a newly hired expert, no matter how knowledgeable, to independently manage all customer queries without proper orientation, training, and gradual introduction to different subjects, chatbots require a similar approach. Below is a structured plan I recommend for a successful chatbot implementation.

Identify the Need

Avoid overestimating the chatbot’s capabilities. Analyze your customer interaction data to pinpoint both the most critical and the most straightforward issues to address through automation.

Educate Your Bot

GPTs often fabricate responses to fill knowledge gaps. It’s crucial to train your chatbot thoroughly, establish guidelines, and integrate it with essential tools and databases to ensure it performs effectively.

Facilitate Smooth Transitions

No chatbot is flawless, and there will be instances where a customer prefers or needs to interact with a human agent. Ensure the chatbot facilitates an easy transfer to human support, providing the agent with the conversation’s full context to avoid customer frustration over having to repeat information.

Implement Phased Testing

Before a full launch, rigorously test the chatbot internally with your team, challenging it with complex queries and scenarios. After internal validation, introduce the chatbot to a limited customer group for real-world feedback, making iterative improvements based on these insights before expanding its availability.

Manage Expectations

Finally, it’s vital to clearly communicate the chatbot’s scope to your customers, setting realistic expectations about the types of queries it can handle. This transparency prevents customer disappointment and confusion, particularly in areas the chatbot is not designed to address, such as billing inquiries if its primary function is technical support.

The Expansive Role of AI Beyond Chatbots

Remember, artificial intelligence extends far beyond the realm of chatbots. Chatbots might be the most visible application of AI, but beneath this lies a vast array of capabilities. AI plays a crucial role in enhancing and streamlining processes behind the scenes. For instance, it can intelligently route messages within an organization to the most suitable agent based on the content, optimizing internal workflows.

Moreover, AI can support agents by suggesting responses, thereby increasing their productivity and reducing the likelihood of errors without directly interacting with customers. It’s adept at analyzing customer data to predict intentions and emotions, guiding agents in their interactions or alerting supervisors to potentially challenging situations.
The potential of artificial intelligence to improve the efficiency of contact centers and other areas of business operations is immense and not limited to customer-facing chatbots.

The journey through AI’s landscape, as illustrated by the stories of DPD and Air Canada, should not deter businesses from embracing this transformative technology. These incidents, while serving as cautionary tales, underscore the immense potential of AI when implemented with foresight and diligence. They are not deterrents but valuable lessons in the careful integration, ongoing oversight, and adaptive learning necessary for harnessing AI effectively. As we continue to explore the breadth of AI’s capabilities, it’s essential to view these experiences as opportunities to refine our strategies, mitigate risks, and enhance the customer experience thoughtfully.

Julien Rio, CCXP
Julien Rio, a Certified Customer Experience Professional (CCXP) and AVP of International Marketing at RingCentral, is a renowned figure in the CX world. Julien is the founder of CX Therapy, a vlog that delves into real-world customer experiences, and the author of “Customer Experience Unearthed.” Recognized for his contributions, including being named among France’s top marketing influencers, Julien’s passion for storytelling and customer engagement marks him as a respected leader in the field who influences and inspires with a commitment to advancing customer experience.


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