Editorial
The Gist
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The customer service industry is evolving. LLM chatbots have introduced new ways to automate, streamline and improve customer interactions.
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LLMs can introduce new capabilities. Distinct from previous AI chatbots, LLMs enable more natural, dynamic conversations that recognize tone and other conversational nuances.
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Considering the future. While powerful LLMs can revolutionize customer service, a human touch will always be fundamental.
Large language models (LLMs) look set to revolutionize how our businesses interact with customers by pioneering new contact center solutions. With their enhanced ability to understand language at scale, LLMs are opening up new possibilities for streamlining processes and delivering exceptional improvement in customer interactions.
However, while the use of LLMs in customer service (CS) has real potential to elevate the customer experience, there are important considerations regarding their implementation that cannot be swept aside.
Here I’ll look at how LLMs are reorganizing contact center operations, the opportunities they present, and their shortcomings, and then suggest some best practices brands should keep in mind when implementing AI in customer service.
Customer Interactions: Transforming Contact Center Operations
Traditionally, contact centers have relied on methods like phone trees or customer service chatbots to route customers through standardized paths, but LLMs like ChatGPT are fundamentally different from previous generations of AI customer service tools. This is mainly in their ability to understand language through large-scale training on relevant text (for example logs of past customer interactions). Unlike rule-based chatbots with scripted responses, LLM chatbots can engage in open-ended, natural conversations due to the pattern recognition achieved by training them on vast datasets.
LLMs can also improve contact center operations by recognizing subtle context and nuances that could previously only be sensed by humans. Drawing from intricate customer interactions, queries and preferences help generate highly coherent, appropriate responses and reactively generate individualized solutions.
By analyzing past customer interactions, thoughtfully integrated LLM chatbots can also anticipate customer needs, provide personalized recommendations and predict potential issues. This shifts customer service from a reactive model to a proactive model of achieving customer satisfaction.
It can also increase the capability of human customer service agents to help them work more quickly, accurately and consistently.
Related Article: ICYMI: AI's Transformation of Customer Interactions, New Personalization Strategies
How LLMs Can Support Customer Interactions
LLM’s potential to improve the operational efficiency of customer interactions can be realized through the following:
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Streamlining the onboarding process. With AI taking care of the common requests, training of new staff can jump straight to the more complex matters that require human thought and experience.
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Reducing customer wait time. With an LLM chatbot taking care of routine requests almost instantaneously, customers aren’t left hanging around waiting for basic issues to be resolved.
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Around-the-clock availability. LLM chatbots can be ready to resolve customer issues 24/7.
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Returning consistently accurate responses. AI won’t succumb to stress, tiredness or other factors that may force simple mistakes for routine questions.
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Reducing overheads. Assigning common requests to chatbots reduces the workload of human customer service agents, bringing down the cost of training and hiring staff.
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Improving analytics. LLMs can gather useful insights from conversations that can be drawn to identify pain points and make service improvements.
While the pros are plenty, this is a fledgling development. It demands cautious, responsible adoption that considers the risks and limitations of its use. In the customer-facing role that such AI customer service chatbots will be applied, safeguards and controls must be applied to ensure compliance with privacy and other legal obligations.
Related Article: AI in Customer Service: Alleviate Fear of AI
Challenges to Consider
What challenges should customer service leaders consider implementing AI customer service chatbots?
- Privacy and security of data. Customer data must be subject to strict security measures, which means any private information used to train your LLM chatbot must be anonymized.
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Time and resources to help chatbots perform well. It’s crucial to carefully weigh the cost/benefit and ensure the quality of datasets your LLM is trained on — this requires human resources.
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Editing out bias. It's crucial to actively seek out and compensate for any biases in your LLM system that could reduce service quality or cause other difficulties.
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Hallucinations. Occasionally, AI will fill in the blanks, “guess” facts, provide false answers or “hallucinate." While this is an inherent weakness of all artificial intelligence, it can somewhat be mitigated by directing the LLM to refer to an index of human-written text before generating a response.
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Wholesale replacement of human customer service. LLMs in customer service should enhance, not replace, human interaction. On-the-spot problem-solving, real-world understanding and experience, empathy and the ability to use reason remain irreplaceable strengths of the human mind.
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Continuous input and training. It will be necessary to feed new datasets to the LLMs to not only refine their capabilities but also keep them current with the changes to your market and customer needs.
Related Article: What You Should Know About AI Customer Service Tools
AI Chatbots Can Improve Customer Service Efficiency
When it comes to improving customer interactions, thorough preparedness and awareness of the potential pitfalls of LLMs are prerequisites. We can’t get carried away with the cost-saving aspect of the technology and cut corners. To limit the complications of adoption (while still taking advantage of LLMs to support your customer service team), you can:
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Test LLM chatbots before launch and validate responses to avoid embarrassing and costly errors once live.
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Provide sufficient and up-to-date data to train your LLM — a vast amount of specific data associated with your specific organization is necessary. You cannot, for example, take customer service data from company A and use it for company B.
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Be transparent. Make sure that your customers are aware that they are interacting with an AI while making it easy for them to reach a human customer service agent if progress is not being made.
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Apply risk mitigation controls and monitor carefully for risks such as inappropriate or sensitive content, bias, “hallucinations” or false information.
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Make privacy a top priority and anonymize any personal data the AI uses for training.
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Consider AI chatbots as complements to human customer service agents, not complete replacements; recognize the limitations and strengths of both and synergize them.
Related Article: Super-Power Your Teams With Generative AI in Customer Service
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The Future of Customer Service
With careful implementation, LLMs present an exciting opportunity to improve customer interactions and drive customer service to new levels of efficiency and scale. The developing abilities of AI to engage in helpful, resourceful and natural conversations while having instantaneous access to vast knowledge bases can resolve customer concerns faster than ever before.
However, we must apply this technology responsibly. The more we rely on AI, the harder it will become to find experienced human customer service agents to provide high-quality customer service that meets the unique demands of the present day. If all rudimentary customer service is carried out by machines, how do we develop experienced customer service agents to provide the next level of support?
This is an important question that needs to be addressed and underlines what is possibly the most important consideration: What happens next?
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