A study by Meta suggests that businesses should inform consumers when using AI chatbots for customer communications.

Meta, alongside Stanford University’s Deliberative Democracy Lab, surveyed over 1,500 people across the US, Brazil, Germany and Spain. 

Most of the participants believed it was important that they were informed when a chatbot they were using was powered by AI, with around 43% of US respondents answering that it was important to be informed during the first use of an AI chatbot.

The survey also asked participants about data sources that should be used to train AI chatbots. 

Participants across all countries overwhelmingly agreed that AI chatbots should be informed from peer-reviewed scientific journals, mainstream press outlets and globally recognised authorities such as the World Health Organisation. 

Additionally, over 77% of respondents supported the use of past conversations being used to train the AI chatbot to better understand and respond to future prompts, on the condition that users were informed of this and able to access and delete their recorded conversations. 

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Following discussion between Meta and the participants, the number of participants in support of conversations being used this way rose to 83%. 

There was a disparity created by user informed consent and access on this topic. 

In contrast, only 41% of respondents believed AI chatbots should be trained on past conversations if the user is not informed of this. After deliberation with Meta, this dropped to 30%, signifying that participants across all countries greatly valued being able to access and control their conversational data. 

A 2024 survey conducted by research and analysis company GlobalData found that around 27% of businesses already had a high adoption rate of AI into their workflows, including the use of AI chatbots for CRM. 

Looking ahead, GlobalData forecasts this percentage to rise as AI chatbots become more accurate and reliable in handling customer queries and complaints. 

Retrieval augmented generation (RAG) is one way of making AI chatbots more reliable and able to answer prompts that relate to a business’ niche. RAG can be used to retrieve information from more relevant online sources, such as blog posts or local news articles, and be used to train a chatbot on a business’ internal data. 

Meta’s research suggests that businesses looking to integrate AI chatbots must keep their customers informed of the software they are interacting with and how it may be using their conversational data.