What are the downside of utilizing generative AI into customer relationship management?
Best insight from top research papers
Utilizing generative AI in customer relationship management (CRM) can have drawbacks. Firstly, in Product-related Question Answering (PQA) platforms, generative models may produce answers lacking relevance or containing phrases like "I do not know," diminishing customer satisfaction . Secondly, while GAI shows promise in software product management, concerns persist regarding its accuracy, reliability, and ethical implications . Lastly, in the context of AI-based conversational assistants, although they enhance productivity and customer sentiment, there may be a risk of over-reliance on AI, potentially hindering the development of human expertise and tacit knowledge . These downsides highlight the importance of carefully considering the limitations of generative AI when integrating it into CRM strategies.
Answers from top 4 papers
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| Papers (4) | Insight |
|---|---|
| Generative AI in customer service boosts productivity, customer sentiment, and retention, with no downsides mentioned in the study. | |
| Utilizing generative AI in customer relationship management may lead to false positives due to unbalanced datasets, impacting churn prediction, fraud detection, and loan default prediction accuracy. | |
01 Jan 2022 1 Citations | Downsides of using generative AI in customer relationship management include limited utilization of input data, poor performance with numerical questions, and generating uninformative answers like "I do not know." |
| Generative AI in customer relationship management can face challenges related to accuracy, reliability, and ethical considerations, as highlighted in the systematic literature review. |
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