by Madison Leonard, Michael Costa, and Jonny Frank
Left to right: Madison Leonard, Michael Costa and Jonny Frank. (Photos courtesy of StoneTurn)
Far from mere tools, employee surveys and interviews serve as key indicators of an organization’s overall health and success. They play a pivotal role in assessing corporate culture, gauging satisfaction, gathering feedback, improving retention, and measuring diversity. Surveys can quantify sentiment—often over multiple periods and population segments. Interviews supplement surveys by offering nuance, context, the opportunity to follow up and even multiple perspectives on a single topic. These qualitative responses are essential for identifying subtle concerns, uncovering issues not explicitly covered by standard questions, and understanding employee sentiment. Without these insights, assessments risk missing critical information about how employees feel about governance, compliance and other key topics.
Despite their importance, surveys and interviews come with their own set of challenges. They are resource-intensive and susceptible to human error. The process of choosing survey topics and questions is a delicate one. While quantitative survey results are clear-cut, the qualitative feedback from interviews and focus groups, often in the form of lengthy, nuanced responses, demands significant mental effort to categorize and interpret. The larger the dataset, the higher the risk of confirmation bias, where reviewers may focus on responses that align with their expectations or miss critical patterns in the data. This bias, coupled with the sheer volume of information to be processed, makes it difficult to conduct thorough and objective assessments, especially under time and budget constraints.
Artificial Intelligence (AI), specifically Large Language Models (LLMS), presents a compelling solution to these challenges. By automating both quantitative and qualitative data analysis, AI models can swiftly sift through large datasets, identifying patterns, contradictions, and sentiment across thousands of responses in a fraction of the time it would take a human reviewer. This time-saving aspect of AI is particularly beneficial in today’s fast-paced business environment, where efficiency and productivity are paramount.
In addition to speed, AI helps mitigate the risk of human error and bias. It allows professionals to review qualitative data thoroughly and impartially. Unlike the human eye, a well-trained AI model can efficiently process voluminous and complex text, uncovering insights without overlooking critical information.
But, as powerful as AI may be, human judgment and experience remain critical. Human input is necessary to refine, train and ultimately verify the accuracy of an AI model.
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