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Article Summary: How Generative AI Is Already Transforming Customer Service


Business leaders must plan for the disruptive impact of generative AI on customer service, explain the authors of a new research paper from Boston Consulting Group. While large language models (LLMs) have the potential to improve customer satisfaction and enhance productivity, businesses must also navigate a slew of challenges, including machine bias and inaccuracies. Glean valuable insights into how organizations are transforming their customer service offerings, while taking steps to protect themselves from the unpredictable aspects of this rapidly evolving technology.


  • Embed generative AI into customer care processes in five phases.
  • LLMs make mistakes and can contain harmful biases, so human oversight is needed.
  • Generative AI systems are rapidly evolving and disrupt business models.

Article Summary: How Generative AI Is Already Transforming Customer Service


Embed generative AI into customer care processes in five phases.

Ever since OpenAI launched its large language model (LLM) ChatGPT, companies have become increasingly aware of the potential of generative AI to disrupt customer service. Organizations have already begun harnessing the potential of LLMs in their customer service platforms, with sustainable energy provider Octopus Energy using generative AI to automate drafting email responses to customers, boosting customer happiness scores by 18%. Once organizations leverage the power of generative AI at scale, they could boost customer service productivity between 30% and 50%.

“Driven by a host of enablers – new AI algorithms, increases in computing power and cheaper cloud-computing infrastructure – emerging AI-powered customer care applications will be able to provide answers and solutions to customers faster and in a much more human-like manner.”

There are five phases organizations go through on the path to fully adopting AI-enabled customer service solutions:

  1. “Self-service for simplest transactions” – At this stage, companies don’t yet use generative AI, enabling self-service only for simple transactions, involving the company’s online portal, app or contact center.
  2. “Self-service for simple journeys” – Companies still don’t use generative AI when relying on tools such as basic chatbots.
  3. Human-like support for more complex situations – Humans remain available to resolve more complex queries, with support from AI augmentation and human-like chatbots.
  4. Proactive AI-enabled assistance – Businesses embrace a shift to proactive AI-powered problem-solving, using AI generation and predictive analytics to draft and deliver prompts to customers.
  5. Continuous AI-enabled assistance – AI-powered support will serve your company across nearly all aspects of the user journey, giving you a clearer picture of your consumer life cycle.

LLMs make mistakes and can contain harmful biases, so human oversight is needed.

Generative AI has potential to significantly expand customer service capabilities, but it also poses considerable challenges. First, LLM-based applications sometimes make factual errors, delivering them with what respondents interpret as high levels of confidence. Second, they aren’t immune to bias, and could thus treat certain customers unfairly. Human oversight is still needed to ensure customer interactions run smoothly. Generative AI poses a third threat: It could inappropriately reveal sensitive customer data, intellectual property or proprietary information.

“Should companies buy an industry-specific ready-to-use solution or a system from one of the major tech companies offering platforms that incorporate LLM capabilities? Or should they invest time and resources in fine-tuning their own model?”

If you’re offering support in a more general industry, consider simply purchasing an off-the-shelf model. But if you’re using LLMs in regulated industries with heightened data restrictions, consider fine-tuning a foundational language model to better control the output of generative AI. Train the model for your specific data needs, optimizing the LLMs constraints and carefully selecting data sets to bolster accuracy, while expanding your own control by defining your desired keywords in advance.

Generative AI systems are rapidly evolving and disrupt business models.

Generative AI will disrupt business models, as data-driven and nimble organizations use enablers such as AI algorithms to provide customers with fast, human-like customer service. When generative AI systems learn about you and your organization, they gain the capability to predict consumer behavior, prompting them to reach out to customers when they anticipate their needs. If businesses maximize the potential of generative AI, they could create differentiated customer experiences, identifying more opportunities for personalization.

“Whatever happens, it will happen fast. Are you ready?”

In the not-so-distant future, having support from generative AI assistants could become a ubiquitous part of everyday life. For example, the in-development generative AI “sidekick” will help automate routine tasks, execute customer support requests and access product information. Generative AI’s usages could potentially expand beyond customer service into other business aspects, such as resource planning, working with suppliers directly and production. Companies that can keep up with the rapid pace of technological change will be those that anticipate how generative AI’s evolving capabilities might transform their relationship with customers and workforces.

About the Authors

Simon Bamberger is a Los Angeles-based managing director and partner. Nicholas Clark is a London-based partner and associate director of service and support operations. Sukand Ramachandran is a London-based managing director and senior partner. Veronika Sokolova is a London-based project leader. All are professionals with the Boston Consulting Group.

Alex Lim is a certified book reviewer and editor with over 10 years of experience in the publishing industry. He has reviewed hundreds of books for reputable magazines and websites, such as The New York Times, The Guardian, and Goodreads. Alex has a master’s degree in comparative literature from Harvard University and a PhD in literary criticism from Oxford University. He is also the author of several acclaimed books on literary theory and analysis, such as The Art of Reading and How to Write a Book Review. Alex lives in London, England with his wife and two children. You can contact him at [email protected] or follow him on Website | Twitter | Facebook

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