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Beyond GenAI: Why Real Solutions Combine AI, ML, and Analytics

Jeremy Winters, Principal Architect, breaks down how GenAI, ML, and advanced analytics each play a distinct role - and why the most effective real-world AI solutions use all three.

Author

Jeremy Winters
Principle Architect - Data & AI Practice

Highlights:

  • Understand the real differences between GenAI, ML, and Advanced Analytics
  • See how modern solutions combine all three technologies to solve complex problems
  • Learn why SQL and ML, not GenAI alone, are the right tools for large structured datasets
  • Explore real-world examples: customer segmentation and predictive maintenance
  • Discover how to leverage existing SQL talent while building toward AI maturity

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March 24, 2026

GenAI gets all the attention. But what actually powers the best enterprise AI solutions? This article cuts through the hype to clarify how Generative AI, Machine Learning, and Advanced Analytics each contribute something the others can't. From customer segmentation to predictive maintenance, Jeremy Winters walks through the architectures behind real-world solutions, showing where SQL does the heavy lifting, where ML handles classification, and where GenAI adds the reasoning that ties it all together. If you're evaluating AI use cases, this is the framing you need before you start building.

GenAI is the new solution that has everybody looking at their problems to see where it can be applied. In the process, organizations are uncovering use cases previously considered impractical or impossible.

As a consultant, I discuss use cases with prospective customers all the time. GenAI is where the conversation starts... but the end solution often involves a combination of GenAI, ML, and Advanced Analytics.

First... let's clarify the meaning for each of these terms.

Machine Learning

Machine Learning encompasses a broad set of techniques that enable systems to learn patterns from data and make predictions or decisions without being explicitly programmed. ML models are often used for fraud detection, customer segmentation, recommendation engines, and forecasting, as well as visual processing such object detection and facial recognition.

Generative AI

Generative AI is a subset of Machine Learning, leveraging approaches such as deep learning and neural networks. In this context, we focus on Large Language Models (LLMs), which are trained on vast amounts of text data, learning the patterns and relationships between tokens that represent words and phrases. In operation, an LLM accepts text as input and predicts the most statistically likely text to follow — generating a response one token at a time.

LLMs are trained with massive data sets, but runtime inferences must fit within a context window which limits the number of tokens that can be processed at once. While some models can process up to one million tokens in a single inference, this is still a relatively small amount of data.

Advanced Analytics

Advanced Analytics goes beyond pivot reports and visualization with complex SQL, statistical modeling, non-SQL code, multi-step processes, and other approaches. Advanced Analytics tends to be descriptive, providing insights based on observed data, as opposed to predictive, which is where ML and AI come in.

Advanced analytics operates on data volumes ranging from small, curated datasets to petabyte-scale data warehouses. Analytical queries may scan millions to billions of rows, making data organization strategies like partitioning, indexing, and aggregation critical. The emphasis is on how efficiently meaningful insights can be extracted from large, structured historical datasets.

Real World Solutions

In practice, a modern customer segmentation application might involve the following steps:

  • SQL aggregation of all transactions down to a summary row per customer.

  • ML clustering to create customer segments based on similarity of attributes.

  • LLM inference to describe the segments in natural language.

A predictive maintenance application would look somewhat different:

  • SQL queries aggregate time-series sensor readings from equipment into operational metrics (temperature, vibration, cycle counts) by machine and time window.

  • ML anomaly detection model flags machines exhibiting abnormal patterns, scoring each with a failure risk probability.

  • SQL queries to summarize flagged machines by facility, equipment type, and severity

  • LLM inference generates maintenance recommendations and root cause hypotheses for each flagged asset.

Using them all!

  • SQL does the heavy lifting of handling large data sets.

  • ML is used for classification of entities.

  • GenAI inferences provide human reasoning and communication where needed.

SQL and ML are not just making up for weak points of GenAI. By offloading some of the processing, GenAI can focus on one of its biggest strengths, which is analyzing and summarizing data provided within the context window.

Working with Structured Data at Scale

The use cases that branch into ML and Advanced Analytics we've put forward involve structured data, such as transactions, event logs, and IoT (or other) streams. GenAI can play an important role in answering questions about these data sets, but keep in mind that LLMs:

Can be inefficient when processing an inference for millions of single data points

Are only capable of processing data that fits within the context window

SQL and ML classification provide scalability enabling better input data to the LLM context window.

Conclusion

When approaching your use cases, consider that the best solution may involve a combination of GenAI, ML, and Advanced Analytics. GenAI enables these previously impractical solutions, but it may not be the only technology in play for the solution.

The resulting solution will cost less and be more efficient, with much more room for future scaling.

From an organizational perspective, this allows for leveraging existing SQL talent, while highlighting the need for some data science upskilling.

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