AI has become a key component of operational efficiency and decision-making in businesses, and the rise of generative AI (GenAI) will further transform innovation across industries. At the core of many AI tools is classical machine learning (ML), also known as traditional ML, which enables capabilities like clustering, recommendation systems, and predictive analytics. With GenAI, the creation of new outputs produces more variability and originality, and it’s crucial to distinguish these functions from classical ML. As 90% of senior data leaders increase investment in GenAI, businesses must understand the scope of its capabilities to maximize productivity gains and customer experience.  

Classical Machine Learning vs. Generative AI 

Understanding the key differences between classical machine learning and generative AI enables businesses to make informed decisions about how to integrate AI into the workplace. 

What is Classical Machine Learning (ML)? 

Machine learning is a subset of artificial intelligence that involves teaching computers to learn from data so they can perform relatively autonomous tasks. These tasks include predictive modeling, fuzzy matching, forecasting, and anomaly detection. Instead of requiring detailed programming for each specific task it must complete, ML programs can learn from examples, reducing the manual labor involved. 

ML systems can have four main functions: descriptive, using data to explain what happened; diagnostic, using data to explain why something happened; predictive, using data to explain what will happen; or prescriptive, using data to make suggestions about what action to take. These ML algorithms are beneficial because they can digest large volumes of data extremely quickly to produce consistent outputs.  

LVMH, for example, is utilizing ML for supply chain planning and pricing optimization to adjust based on declining consumer demand for luxury goods. Predictive ML models allow LVMH to forecast demand and optimize inventory based on current market conditions and consumer behaviors. 

What is Generative AI (GenAI)? 

Generative AI is based on deep learning, a subset of machine learning, that’s trained to create new data instead of primarily making predictions or classifications about a dataset, like ML. GenAI can create content including text, images, audio, and videos, taking ML a step further with its ability to answer questions and solve problems. 

ChatGPT is one of the most notable GenAI tools because it uses a large language model (LLM) to respond to a wide range of user prompts in real-time. Large language models are a type of GenAI designed to understand, generate, and predict human language. ChatGPT and other LLMs are revolutionizing businesses by accelerating tasks from genetic data analysis for stem cell donor matching to customization for fitness wearables. 

Because GenAI is relatively new, businesses are still learning how to effectively implement it across various use cases. GenAI can perform a wide range of tasks leveraging Natural Language Processing (NLP), document intelligence, information retrieval, and task automation through agentic AI. As these capabilities expand, businesses must be aware of risks like output quality issues and “hallucinations” that can undermine decision-making 

In addition to using ML, retailer LVMH is incorporating specific GenAI tools to stay competitive. Its design team uses GenAI to create mood boards for inspiration, and the marketing department uses it to personalize marketing copy for eCommerce sites. LVMH also launched MaIA, its companywide GenAI agent, which receives more than 2 million requests a month from about 40,000 employees. 

Comparing Classical ML and Generative AI 

At its core, GenAI is based on ML – ML’s ability to recognize patterns enables GenAI to synthesize new material. When it comes to primary functions, ML analyzes existing data to make predictions, classifications, or decisions, while GenAI creates new content or data. On a practical level, ML systems excel at analyzing business data to address user questions, and GenAI virtual assistants build on this by answering calls with users to meet their needs. 

Though classical ML and GenAI both have wide-ranging applications across many industries, they differ in the types of data they typically work with and in their associated risks. ML often works best with structured and semi structured data, but GenAI works with all types of data, including unstructured. The compatibility of GenAI with more varied data creates opportunities for businesses to innovate and extract deeper insights from sources. 

There are new risks associated with GenAI that are less prominent with classical methods, including bias and copyright infringement. Businesses need to be cautious about implementation and strategic about ensuring that human oversight keeps GenAI in check. 

These differences don’t mean that ML and GenAI are mutually exclusive, however. ML is still capable of structuring messy data, despite LLMs working more precisely for text. On the other hand, while ML is traditionally utilized for forecasting, GenAI is increasingly enhancing accuracy by managing complex time series data. Their capabilities can even be combined through compound AI systems with multiple interacting components. Businesses ultimately should leverage the strengths of both ML and GenAI while understanding areas for potential overlap. 

Looking Ahead 

Defining both classical machine learning and generative AI is necessary for businesses to understand where each contributes to efficiency and when the two can be used together. ML is the backbone of prediction and operational analytics, while GenAI powers content generation and conversational tools.   

When organizations adopt both the pattern recognition capabilities of ML and the pattern creation of GenAI, they gain a competitive edge that drives strategic decision-making and innovation. For more information on how to get started, contact Clarkston today. 

Contributions from Hannah Yang