There are many instances beyond collections that offer both success stories and warnings when it comes to using AI. In this blog, I’m going to look at some recent real-world cases.
Klarna’s AI assistant: a catalyst for efficiency and profitability
Klarna, a Buy Now Pay Later (BNPL) firm, has seen a transformative impact in customer services with its AI assistant, developed by OpenAI.
Integrated within the Klarna app, it performs a wide array of tasks, including multilingual customer service, managing refunds and returns, and promoting healthy financial habits.
Here are some key insights from Klarna’s recent data release:
- Global conversations: The AI assistant has been live globally for one month, engaging in 2.3 million conversations, accounting for two-thirds of Klarna’s customer service chats.
- Workload management: The AI assistant’s workload is equivalent to that of 700 full-time human agents, revolutionising Klarna’s customer support operations through its efficiency and scalability.
- Customer satisfaction: The AI assistant matches human agents in terms of customer satisfaction scores, with users appreciating its responsiveness and accuracy.
- Handling time and repeat inquiries: The AI assistant has led to a 25% reduction in repeat inquiries and reduced the average handling time to less than 2 minutes (compared to the previous 11-minute average).
- Global reach: The AI assistant is available 24/7 in 23 markets and communicates seamlessly in over 35 languages, enhancing accessibility and bridging communication gaps.
- Profitability: Klarna estimates that the AI assistant will drive a US$40 million profit improvement in 2024.
- Community engagement: Beyond customer service, the AI assistant has fostered better communication with local immigrant and expat communities across all markets, ensuring inclusivity and understanding by transcending linguistic barriers.
In essence, Klarna’s AI-powered revolution exemplifies the synergy between cutting-edge technology and customer-centric innovation.
Air Canada’s chatbot error: a lesson in liability and accuracy
In a landmark court case in Canada in February this year, Air Canada found itself in the spotlight due to its chatbot’s misleading advice.
The incident sheds light on the responsibilities companies have when deploying automated chat tools and the importance of maintaining accurate information across all channels.
Background: In 2022, Jake Moffatt, a customer seeking a bereavement fare for a last-minute trip to attend a funeral, turned to Air Canada’s chatbot for assistance. The chatbot provided specific guidance, leading Moffatt to book tickets to and from Toronto.
The problem: When Moffatt applied for the refund, Air Canada denied it, claiming that bereavement rates did not apply to completed travel. The airline pointed to the bereavement section on its website, which contradicted the chatbot’s advice.
The court ruling: Moffatt took the matter to court, seeking compensation for the fare difference. The judge ruled in his favour, ordering Air Canada to pay up. The crux of the ruling was that the chatbot’s misleading information had led Moffatt to purchase a full-price ticket.
Significance and implications: This case holds significant implications for companies utilising chatbots and other automated tools. Here’s what we can learn:
- Accuracy matters: Whether information comes from a static webpage or an interactive chatbot, it must be accurate and consistent. Users rely on this information to make informed decisions.
- Liability: Companies cannot absolve themselves of responsibility by blaming their chatbots. The court held Air Canada accountable for the chatbot’s actions, emphasising that the company’s oversight matters.
- User trust: Misleading information erodes user trust. In an era where customer experience is paramount, maintaining accurate and reliable communication is crucial.
Air Canada’s chatbot mistake serves as a wake-up call for businesses. As technology evolves, companies must ensure that their automated tools provide accurate guidance. The court’s ruling reinforces the need for transparency, accountability, and accuracy in the digital age.
These examples represent different outcomes of the same issue. In collections, we can learn from Klarna about how to effectively implement an AI-driven strategy using digital self-service, and from Air Canada about the need to be cautious in creating an AI-driven self-service bot. It’s difficult to see how Air Canada allowed this to get to court, but perhaps highlights the maturity of AI adoption in different sectors with different / less regulation.
There are likely many examples of AI in the collections space that are serving companies and their customers well. It will also be interesting to see the outcome of the first “mistake” in this space.
If you’re looking to implement AI or self-service options, Arum can help you navigate the complicated landscape of digital collections software and implement the right solution for your business.
About the author
Nick Walsh
Principal Consultant
Arum
Nick, a seasoned collections and recoveries professional, boasts over four decades of experience both domestically and internationally. His expertise has empowered numerous organisations, spanning various sectors and sizes, to swiftly adopt an optimal operating model tailored to their unique needs. This tailored approach carefully balances regulatory compliance with organisational limitations, whilst charting a more strategic roadmap for improvement. Nick, and Arum, ensure good outcomes for customers are prioritised in all the client engagements we undertake.