There are a lot of conversations about AI and most of them are focused around Generative AI. But because AI is a very large ecosystem, I think we need to start to break it down into some of its subcomponents when we’re looking at how to most effectively apply it to the enterprise. I’d like to start by separating “Analytical AI” from “Generative AI.” Both have transformative potential, but their roles and impacts differ significantly, especially when it comes to practical applications within organizations.
When I refer to Analytical AI, I’m talking about AI technologies designed to analyze, search, and process existing data to generate insights and automate tasks. This form of AI should be used by enterprises to enhance decision-making, improve efficiency, and optimize operations. One example is Retrieval-Augmented Generation (RAG), which combines traditional AI models with search and retrieval techniques to pull relevant information from vast datasets. RAG doesn’t create new content; it surfaces existing data in ways that provide value, such as by quickly delivering relevant answers or identifying patterns that might go unnoticed by human analysts.
Generative AI has a different purpose. This subcomponent is designed to create new content, like text, images, code, or even ideas, based on patterns learned from existing data. Tools like OpenAI’s GPT-4 or DALL-E are perfect examples; they generate output based on the data they’ve been trained on. Generative AI can and should be used across the business where the acceleration of content generation can benefit them. This can be something as simple as an email response or as complex as a customized how-to video for a specific customer.
Understanding these two types of AI technology is important for organizations so they know when and how to apply them. Analytical AI is about optimizing what already exists, streamlining processes, enhancing accuracy, and boosting productivity. Generative AI is about quickly creating content to help augment or enhance your existing processes. Whether it’s Generative or Analytical AI, applying the right technology is a critical part of any AI strategy.
