Generative vs. Conversational AI: A Guide for Insights Pros

Have you been coming across exciting new "AI-based" products aimed at market research, CX, UX, or VoC applications? Or perhaps some specific to qualitative or quantitative research? That’s great—but what kind of AI are they using? Generative, Conversational, or both? And are they using general LLMs or specialty ones? Understanding these distinctions can bring much-needed clarity when evaluating and comparing these products and their AI claims.

Generative AI vs. Conversational AI: How Customer Insights Researchers Can Use Both—And Yes, They Are Different

Generative AI creates new content—such as data analysis, text, data visualizations, images, or even music—based on existing data. It uses machine learning models to generate something new, drawing on inputs, patterns, and structures in the data it was trained on. Example: Imagine an AI writing a new biography of Stevie Ray Vaughan, tailored specifically to appeal to hardcore blues music aficionados.

Conversational AI, on the other hand, is designed for dialogue. It receives, interprets, and responds to human input—which can be typed, spoken, or even (though still rarely) visual—enabling chatbots, virtual assistants, and other interactive tools to simulate conversations through text, voice, or even video avatars. Example: Picture verbally asking a virtual B.B. King chatbot about his musical influences, and having the lifelike video avatar speak back the answer.

Clarifying Conversational AI vs. Non-AI Systems

It's important to note that not all conversational systems are true AI. A system can be conversational without being "AI." For example, a simple rule-based chatbot uses predefined scripts and decision trees (rules) to respond to inputs. It follows fixed logic based on keywords or pre-set paths and doesn't involve learning, adaptability, or pattern recognition. These systems lack the flexibility of true Conversational AI, which involves machine learning or data-driven decision-making to generate more context-aware and human-like interactions.

Specialty LLMs for Market Research & Insights

Specialty LLMs (Large Language Models) can also be used in generative or conversational AI-based products to perform tasks specific to certain domains, such as consumer behavior, market research, healthcare, or legal fields. In generative AI, these models create new content (such as text analysis, reports, or data visualizations), while in conversational AI, they enable more accurate and context-aware dialogue. These models are trained on large, domain-specific datasets, allowing them to provide more precise, relevant outputs for specialized applications.

In contrast, general-purpose LLMs, like GPT-4 which is the LLM behind ChatGPT, are trained on diverse datasets that cover a wide range of topics. This broad training makes them highly versatile and able to handle many different types of queries. However, while these general-purpose models offer flexibility, they do not provide the same level of domain-specific accuracy as specialty LLMs (unless they are fine-tuned with additional training on industry-specific data). Fine-tuning allows these general models to perform more like specialty LLMs in targeted use cases, while still benefiting from their broad knowledge base.

Why Does Conversational AI Sometimes Lag?

Delays in Conversational AI often occur because these systems need time to process the input, analyze the context, and generate an appropriate response—particularly when complex or domain-specific language models (such as specialty LLMs) are involved. Users of Conversational AI products may experience a 2 to 5 second response lag, depending on a number of factors, though speed is always improving.

Looking Ahead: AI in Market Research & Insights

As more AI-based tools emerge, expect a mix of those using Generative AI, Conversational AI, or both. We will also start to encounter specialty LLMs tailored to areas like consumer behavior, social-sourced attitudinal data, methodology repositories, or research databases—similar to how other knowledge-based industries like law and accounting are using specialized AI for their specific needs. The landscape is evolving quickly, and these tools will become powerful assets for researchers in market research, CX, UX, VoC, and related fields.

Evaluating AI-based Tools

For those in customer insights, AI tools are becoming an increasingly integral part of our work. We’re already seeing exciting new options for data collection, analysis (of both structured and unstructured data), knowledge management, and yes, even training. When evaluating these tools, it's important to be clear about whether they use Generative AI, Conversational AI, or specialty LLMs. This understanding will allow for more precise comparisons between products based on the type of AI technology they employ—and help you spot the true AI rockstars from the impersonators.

Check out the impressive examples of AI use in other knowledge-based professions for inspiration about what is possible for market research and insights. Here's a great article from Bloomberg Law (AI for Legal Professionals), and one from Thomson Reuters (How do different accounting firms use AI?).