The analytics landscape has evolved significantly during the past decade. Many organizations have progressed from basic statistical modeling to machine learning, and some have added deep learning to their toolkits as well. In this context, the emergence of generative AI — with its ability to create humanlike text, generate images, and write code — introduces new possibilities and new questions.

While generative AI promises to revolutionize everything from customer service to product development, its optimal role alongside predictive AI tools (that is, machine learning and deep learning tools) remains a work in progress. That often leaves leaders asking what the right approach is for addressing a particular problem. This article presents a set of guidelines to help leaders and organizations navigate this tricky and crucial decision.

Machine Learning Versus Deep Learning Versus GenAI

Let’s begin with a quick overview of machine learning, deep learning, and generative AI, focusing on their respective strengths and limitations.

Machine learning: This type of AI involves identifying patterns from historical data using statistical and computational techniques to make predictions or decisions without being explicitly programmed to do so. Encompassing a range of techniques, including regression analysis, decision trees, random forests, and gradient boosting, its primary strength lies in handling tabular/structured data — data that can be arranged in the rows and columns of a spreadsheet or a database table. In tabular data, the columns — known as independent variables or features — are either naturally numeric (such as levels of LDL cholesterol for a patient, or the average credit balance for a loan applicant) or can be represented numerically. (For example, if a patient has a family history of heart disease, it is represented by a value of 1; otherwise, the value is zero).

Since problems with tabular input data are ubiquitous in business, machine learning has had a tremendous positive impact. Retailers use machine learning to forecast product demand and inventory needs by analyzing historical sales data and seasonal patterns. Subscription-based businesses use machine learning for customer-churn prediction and prevention. Financial institutions use machine learning to predict the risk of loan defaults.

But machine learning doesn’t work well if the input data is unstructured (such as images, natural language text, or audio). To effectively use traditional machine learning with unstructured data, the data must be manually structured — an expensive task that makes machine learning unattractive for business use cases where the input data is not tabular.

Deep learning: A particular type of machine learning based on neural networks, deep learning is a seminal advancement in analytical capabilities. Deep learning models can process unstructured data such as images, audio, and natural language without the need for upfront manual processing, thereby making numerous use cases viable. Deep learning can accommodate tabular inputs as well. Its ability to handle both structured and unstructured data makes it particularly valuable for tasks where the input data naturally appears in different modalities. A model for disease detection, for example, should be able to process image data (such as radiology scans) alongside tabular data, such as patient test results. But deep learning tends to be more “data-hungry” than machine learning, and it can be more challenging to understand and interpret due to the complexity and size of the underlying neural networks.

Generative AI: GenAI is distinguished from predictive AI by its ability to generate new content rather than simply making predictions. Built on a breakthrough deep learning architecture known as a transformer, these systems can generate coherent text, realistic images, and even functional code and, as a result, promise widespread applicability to a broad swath of knowledge work. For example, a marketing department might use GenAI to draft advertising copy, create visual content variations, or generate personalized customer communications at scale.

The inputs and outputs of generative AI systems like LLMs are typically unstructured. Most commonly, they comprise text and/or image data and, more recently, videos. Note that the text being analyzed by and created from generative AI tools encompasses an astonishing range of types, such as software code, protein sequences, music notation, mathematical expressions, and chemical formulas.