6 books on AI for Business Intelligence [PDF]
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AI is forcing business intelligence analysts to look in the mirror and ask: what's the value of manually creating and distributing charts and graphs? Large language models can generate queries and code to create compelling charts based on natural language prompts in seconds. They have perfect knowledge of query syntax and BI library packages and readily respond to every request from CEOs to create charts at any time of day.
But what's behind every chart? Essentially, a chart compiles a large amount of data into easily understandable information that humans can hopefully act upon. The viewer of a chart must be confident that its creator has taken the time to ensure it is correct and relevant. Otherwise, the viewer would have to go through their own analytical process to obtain this information themselves. Great analytics isn't about quickly creating charts; it's about building confidence in decisions through careful data exploration.
AI still often fails - it simply produces something different from what humans asks. If you work in business analytics, these books will help you better understand how to collaborate with AI tools, where to stay engaged and how to design your workflows and data models so that both humans and machines can generate more reliable data. In the AI era, only those analysts who embrace AI as a powerful coding tool while recognizing that the most valuable analytical work is thinking, questioning and contextualizing remains fundamentally human - will succeed.
1. Business Intelligence and Data Analysis in the Age of AI
2025 by Arshad Khan
This book is a set of AI-generated lists about what Business Intelligence is, what tasks, problems and solutions there are in this area. Only the last small section is devoted to the use of AI for BI and there is a reason for this, because today, in 2025, the use of AI (by which we mean not ML algorithms, but language models) has not yet become commonplace for BI specialists and BI system developers. How can language intelligence help in BI? Two options come to mind. The first is processing unstructured data. This makes sense and the 5-10% of errors that AI agent can make when performing this task can well justify saving time and money on the salaries of data taggers. The second is using LLM for language queries in the BI system, in response to which it must build an individual visualization or give an exact numerical value. Here, AI cannot be a reliable assistant yet, because 5% of hallucinations may affect important aspects of the data and the manager will get the wrong answer. Rather, such a language interface can be useful for a data-scientist, who can check its correctness before presenting the results to the manager or marketer.
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2. Artificial Intelligence with Microsoft Power BI
2024 by Jen Stirrup, Thomas J. Weinandy
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3. Artificial Intelligence for Business Analytics: Algorithms, Platforms and Application Scenarios
2023 by Felix Weber
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4. AI-Powered Business Intelligence
2022 by Tobias Zwingmann
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5. Decision Intelligence Analytics and the Implementation of Strategic Business Management
2022 by P. Mary Jeyanthi, Tanupriya Choudhury, Dieu Hack-Polay, T P Singh, Sheikh Abujar
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6. Machine Learning and Cognition in Enterprises: Business Intelligence Transformed
2017 by Rohit Kumar
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How to download PDF:
1. Install Gooreader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
But what's behind every chart? Essentially, a chart compiles a large amount of data into easily understandable information that humans can hopefully act upon. The viewer of a chart must be confident that its creator has taken the time to ensure it is correct and relevant. Otherwise, the viewer would have to go through their own analytical process to obtain this information themselves. Great analytics isn't about quickly creating charts; it's about building confidence in decisions through careful data exploration.
AI still often fails - it simply produces something different from what humans asks. If you work in business analytics, these books will help you better understand how to collaborate with AI tools, where to stay engaged and how to design your workflows and data models so that both humans and machines can generate more reliable data. In the AI era, only those analysts who embrace AI as a powerful coding tool while recognizing that the most valuable analytical work is thinking, questioning and contextualizing remains fundamentally human - will succeed.
1. Business Intelligence and Data Analysis in the Age of AI
2025 by Arshad Khan
This book is a set of AI-generated lists about what Business Intelligence is, what tasks, problems and solutions there are in this area. Only the last small section is devoted to the use of AI for BI and there is a reason for this, because today, in 2025, the use of AI (by which we mean not ML algorithms, but language models) has not yet become commonplace for BI specialists and BI system developers. How can language intelligence help in BI? Two options come to mind. The first is processing unstructured data. This makes sense and the 5-10% of errors that AI agent can make when performing this task can well justify saving time and money on the salaries of data taggers. The second is using LLM for language queries in the BI system, in response to which it must build an individual visualization or give an exact numerical value. Here, AI cannot be a reliable assistant yet, because 5% of hallucinations may affect important aspects of the data and the manager will get the wrong answer. Rather, such a language interface can be useful for a data-scientist, who can check its correctness before presenting the results to the manager or marketer.
Download PDF
2. Artificial Intelligence with Microsoft Power BI
2024 by Jen Stirrup, Thomas J. Weinandy
Download PDF
3. Artificial Intelligence for Business Analytics: Algorithms, Platforms and Application Scenarios
2023 by Felix Weber
Download PDF
4. AI-Powered Business Intelligence
2022 by Tobias Zwingmann
Download PDF
5. Decision Intelligence Analytics and the Implementation of Strategic Business Management
2022 by P. Mary Jeyanthi, Tanupriya Choudhury, Dieu Hack-Polay, T P Singh, Sheikh Abujar
Download PDF
6. Machine Learning and Cognition in Enterprises: Business Intelligence Transformed
2017 by Rohit Kumar
Download PDF
How to download PDF:
1. Install Gooreader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
