Beyond generative AI: the Machine Learning and NLP that already ran your business

We've been working with artificial intelligence in our daily operations for years. Today's noise is almost entirely about generative AI and foundational models, but Machine Learning and Natural Language Processing (NLP) have been pulling the strings of the software we use in business for more than a decade.

This is how the tools we interact with every day actually operate:

Siri showed us this back in 2011. It turned speech into text and executed OS actions by mapping our intent through acoustic models and deep neural networks.

In the Amazon ecosystem, we depend on platforms like Keepa, Helium 10 and Jungle Scout, which are essentially predictive-model and time-series engines. Since Amazon closes the door on third-party sales data, these tools apply regression algorithms. They cross Best Sellers Rank (BSR) history, category, price fluctuations and seasonality to project inventory, demand and revenue estimates.

For structuring and analyzing operations, Power BI and Excel embed traditional Machine Learning. They use statistical algorithms like ARIMA or ETS to run forecasts, decision trees to isolate the variables that actually impact the business and identify key influencers, and pattern recognition to clean databases in seconds.

In SEO, Semrush applies NLP to cluster terms semantically and understand the user's real search intent. Its traffic projections and keyword-difficulty scores are built with machine-learning algorithms, trained by processing terabytes of navigation data and topological analysis to read link authority.

The technical summary is that until recently we operated on discriminative and predictive AI: systems built to read historical data, classify and guess what was going to happen. Today's radical leap is generative AI, backed by architectures like Transformers, which no longer just analyzes but has the ability to create information, code or text from scratch.