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9 Examples of AI in Data Analytics – November 2025

Author/Source: Tableau See the full link here

Takeaway

This article explores nine practical applications of Artificial Intelligence in data analytics, demonstrating how AI tools empower businesses to gain deeper insights and make smarter decisions. It shows how AI automates complex tasks, enhances data exploration, and improves predictive capabilities without replacing human expertise.

Technical Subject Understandability

Intermediate

Analogy/Comparison

Imagine AI in data analytics as having a super-smart assistant who can instantly sort through millions of documents, highlight the most important patterns, and even predict what might happen next, all while clearly explaining findings so you can make the best choices.

Why It Matters

AI in data analytics matters because it transforms raw data into actionable intelligence, helping organizations solve real-world problems more effectively. For instance, a retail company can use AI to analyze sales data and predict consumer trends, allowing them to optimize inventory and marketing strategies to avoid shortages or overstocking, ultimately boosting profits and customer satisfaction.

Related Terms

Artificial Intelligence (AI)
Machine Learning (ML)
Natural Language Processing (NLP)
Augmented Analytics
Predictive Modeling
Data Visualization
Data Prep

Jargon Conversion:
Artificial Intelligence (AI): Computer systems that can perform tasks normally requiring human intelligence, like learning, problem-solving, and understanding language.
Machine Learning (ML): A subset of AI where computer systems learn from data without being explicitly programmed, improving performance on a task over time.
Natural Language Processing (NLP): A field of AI that allows computers to understand, interpret, and generate human language.
Augmented Analytics: AI-driven features in data analytics tools that automate data preparation, insight generation, and explanation to augment human intelligence.
Predictive Modeling: Using statistical techniques and machine learning to forecast future outcomes or behaviors based on historical data.
Data Visualization: The graphical representation of information and data to help users see and understand patterns and trends.
Data Prep: The process of cleaning, structuring, and enriching raw data into a usable format for analysis.

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