Generative AI Use Cases in Customer Service

2 min read

Amidst the current AI hype, industry analysts are starting to sound the alarm bells. For example, Gartner predicts that at least 30% of generative AI (GenAI) projects will be abandoned after proof of concept (PoC) by the end of 2025.

Some of the common challenges encountered include:

Misalignment with business goals — Many projects fail because they aren’t tied to specific business objectives. Without a clear connection to improving efficiency, reducing costs, or driving revenue, GenAI initiatives struggle to demonstrate value.
Technical complexity — GenAI is not a simple solution. It requires significant expertise to develop, train, and deploy models. Organizations often underestimate these complexities, leading to frustration and project abandonment.
Data issues — High-quality, diverse data is crucial for GenAI success. Poor data quality or lack of access can result in inaccurate outputs, diminishing the project’s perceived value.
Unrealistic expectations — The hype around GenAI often leads to inflated expectations. When results fall short, organizations may lose faith and abandon the project.

To avoid these pitfalls and getting struck in PoC purgatory, companies should instead start small, and then scale fast. Similar to other technology rollouts, the choice of the initial use case is critical to the success or failure of the GenAI initiative. Fortunately, there are now a ton of resources on GenAI use cases depending on your industry or business function.

For example, in customer service, the top GenAI use cases include: