There has been a lot of focus lately on generative AI and its ability to do seemingly everything, from writing content to automating workflows to language translation and localization. In the context of research and insights, generative AI is a powerful tool to unlock the value of qualitative data. It can save researchers and analysts hours of time by automatically creating and maintaining text topics and categories. And when large language models are trained properly, generative AI can analyze large amounts of unstructured data and accurately understand customer feedback, emotions, needs, wants, and motivations.
But there is something that generative AI is NOT great at. Math. You may have even seen some of the “fails” shared around where generic generative AI tools couldn’t do math equations or get word counts correct (sorry, kids - you’ll need to go back to using a calculator).
This is because LLMs don’t actually use math apparatus to do the math. When you ask the model to sum two numbers, it doesn't use a calculator and doesn't explicitly perform summation. Despite its vast knowledge, the LLM is still sampling and referencing the data set to “complete the sentence” - not actually answering the question.
So, while generative AI works great for something like summarization it could be completely wrong if you wanted to quantify this summarization. For example, let’s say you are analyzing some customer feedback data. Generative AI might be able to determine that people are talking about Prices and Faster delivery, and even how people feel about those topics, but it won’t know to say with reliability that 50% talk about delivery vs. only 10% about Prices. It gets even more complicated if you want to compare segments and look at trends, answering questions like “How much did our NPS go up this month and why?”
Generative AI is a powerful technology, but if used alone as an analytics tool, it will fall short. To help organizations make more confident decisions, research and analysis leaders need to deliver insights that combine the valuable “why” information from qualitative with the robustness of quantitative analysis.
Beehive AI offers a comprehensive analytics platform along with generative AI to enhance it. Additionally, we ensure that all open-ended insights from the language model (LLM) are supported and validated by the analytics platform, which includes structured data. We utilize a combination of methods, including LLM, LLM applied to tabulated data, and pure tabulated analytics. This unique approach provides research and analysis leaders a powerful tool to analyze both qualitative and quantitative data, and combine it into more actionable, trustworthy insights.
Learn how Beehive AI combines the power of Generative AI with quantitative analysis to generate more actionable, trustworthy insights.
Unlock the answers in your data with the modern analysis platform built on generative AI. With self-learning language models validated by human experts and built-in statistical analysis, Beehive AI helps research and insights leaders quickly, accurately, and safely generate actionable insights from their quantitative and qualitative data.
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