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GuidesResearch methodsArriving at actionable user insights faster with AI-powered researchConcerns about AI-supported customer insights

Arriving at actionable user insights faster with AI-powered research

Last updated

9 January 2024


Claire Bonneau

Reviewed by

Jean Kaluza


1. Current state of AI adoption in user research
2. Concerns about AI-supported customer insights
4. A 7-step research strategy framework

Valid concerns about AI-supported customer insights

In navigating the evolving AI landscape, researchers must recognize the importance of ethical considerations, continually adapting to the challenges posed by this powerful technology.

Private businesses and AI companies must use AI carefully, particularly when protecting personal data. Some sectors, like healthcare and fintech, are responsible for protecting especially sensitive user data. 

As a society, we’re in a major transition time—businesses face the challenge of being both explorers and regulators.

While using AI, collaboration, adaptability, and regular monitoring are necessary for building customer trust and improving safety and privacy.

And let’s not forget that AI tools are still inconsistent. Sometimes, they make up things, giving out bad data or bizarre information. 

A study from Vectara found that even with increasingly stringent guardrails in place, large language models (LLMs) like ChatGPT can make up details (hallucinate) about 3% of the time.

In addition, since generative AI (GenAI) trains on massive amounts of human data, there’s a risk of introducing all sorts of biases, such as racial, gender, and age discrimination.

For example, a recent study of ChatGPT revealed gender biases when generating imaginary descriptions of male and female personnel—the chatbot tended to use descriptors like “expert” and “integrity” for men versus “beautiful” or “warm” for women.

To avoid significant problems and maintain a trustworthy reputation, researchers still need to quality-check data, especially when leveraging new tools.

To ensure a smooth integration of AI, researchers and brands should aspire to transparency with customers, commit to comprehensive AI training for employees, and be vigilant about data protection. And, of course, leaders in the AI space must consistently address concerns as the tech advances.

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