How AI Increases the Value of Business Intelligence

Article Summary: Artificial intelligence is increasing the value of business intelligence by enhancing data analysis and enabling powerful predictive and prescriptive insights into a corporation's data. AI automates processes, improves data analysis, and helps companies rise up the analytics escalator so they can better forecast business trends as well as implement demand pricing strategies to substantially increase corporate profitability.

Last Updated: December 5, 2025

Introduction

Although business intelligence (BI) has been around for decades, the recent addition of artificial intelligence (AI) into BI platforms like Qlik, Tableau, PowerBI, and Tibco’s Spotfire has substantially increased the capabilities of these tools. Today, it’s not the lack of data that’s the issue, it’s the sheer volume of data flowing that’ it’s the problem. AI can increase the value of BI by expanding a company’s BI toolset to include advanced analytics and scenario modeling.

“Having all the information in the world at our fingertips doesn’t make it easier to communicate: it makes it harder,” says Cole Nussbaumer Knaflic, an author who writes about business intelligence, data visualization, and data storytelling. AI can help businesses cut through the deluge of data flowing through their systems. It can provide deep visibility into a company’s data, helping them makes sense of it.

History of Business Intelligence

In 1865, Richard Millar Devens coined the term “Business intelligence” in Cyclopædia of Commercial and Business Anecdotes to describe the process of analyzing data to deliver actionable intelligence for a business. Today, BI is considered the field of knowledge discovery and data visualization, and it can be traced back to the late 1980s. According to Tableau, one of today’s leading BI vendors, “Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. Additionally, it provides an excellent way for employees or business owners to present data to non-technical audiences without confusion.”

AI-Powered Business Intelligence

In the world where real-time data and Big Data produce a deluge of data, artificial intelligence (AI) is becoming a pre-requisite for BI tools. Analyzing the massive amount of information flowing through a company’s data warehouse and making proactive data-driven decisions on that information would be impossible without AI.

In their article Business Intelligence Transformation through AI and Data Analytics, Eboigbe et al. claim, “the study highlighted a paradigm shift from traditional data processing methods to AI-driven predictive analytics, significantly enhancing the efficiency, accuracy, and predictive capabilities of BI tools. This evolution has redefined business operations, offering unprecedented insights and fostering more informed decision-making processes.”

In his article Increasing the Business Value of Business Intelligence, Mark Mosley argues, “Organizations making full usage of their ‘basic’ BI technology should definitely explore expanding their BI tool suite to include ‘advanced analytics’ tools for statistical analysis, data mining, predictive analysis, and scenario modeling.” AI increases the value of BI by enhancing data analysis and enabling powerful predictive insights into the data. It can automate processes as well as greatly improve data analysis.

AI can process huge amounts of structured, unstructured, and semi-structured data much faster than humans can. AI modeling techniques can detect patterns, trends, and anomalies in a dataset that would normally escape human perception.

Predictive & Prescriptive Analytics

One of the biggest impacts AI can have on BI is in the area of predictive analytics. AI can forecast trends, uncover market demands, and provide a window into individual customer behavior. Demand forecasting models built on AI can predict the most optimal price to sell perishable products, like a hotel room. After all, a hotel room produces no revenue if it sits empty all day and all night. An airplane plane seat is also worthless once the plane lifts off the ground and heads to its destination.

AI helps businesses make proactive, data-driven pricing decisions that take into account all supply and demand factors affecting a product’s price. Descriptive analytical models can recommend businesses take a particular course of action that might result in more optimized results. AI can also automate manual processes as well as reduce the repetitive tasks associated with typical BI.

Data extraction, data cleansing, and report generation can all be time intensive endeavors. AI can help free up BI professionals so they can focus on higher-level analysis and complex strategy. AI-powered BI systems can automatically generate insights and update visualization dashboards with data tailored to a user’s unique needs. AI enables BI systems to provide personalized insights, recommendations and experiences to users based on their past consumer history. This makes BI more accessible and actionable to a wider audience.

AI tools can automatically extract and enrich corporate metadata, improving data classification and data tagging. Patterns in a data set can be analyzed and data can be classified according to company data requirements and compliance. This ensures consistency and better data discoverability, which can reduce the manual workload for data stewards.

Business Intelligence Limitations

Although AI can increase the value of BI in many industries, it does have its limitations. These include:

- Limited Data Scope: Traditional BI tools primarily analyze structured data, which restricts their ability to process unstructured or semi-structured data. With so much customer data available online, this limitation can prevent organizations from gaining a comprehensive view of a customer even though that data is often easily attainable.

- Batch Processing: Most traditional BI systems batch process their data rather than access and utilize it in real-time. Because batch processing occurs intermittently, timely decision-making can be adversely affect. This is especially true in fast-paced environments where real-time data use is critical, like in the finance, cyber security, and logistics industries.

- Slow Query Performance: Traditional BI tools can struggle with large datasets and complex data queries, resulting in slow decision-making. Today, business users expect decisions to be made in real-time. Slow query performance will frustrate users and customers, limiting the overall effectiveness of a BI system.

- Limited Flexibility and Scalability: Many traditional BI systems are rigid and don’t scale well. Although some BI cloud offerings can alleviate these problems, many businesses can put their sensitive customer data on the cloud. With data volumes growing exponentially, scalability issues are only going to increase.

- Data Quality Issues: BI and analytical effectiveness is heavily dependent on data quality. Poor data quality leads to inaccurate insights, biased data, and poor decision-making. Traditional BI tools often lack built-in processes that cleanse data and ensure its integrity.

- Lack of Advanced Analytics: Although traditional BI tools have embraced AI and advanced analytics, they can still be limited in their insights and their data modeling won’t be as sophisticated as most cloud offerings.

- High Total Cost of Ownership: The implementation and maintenance costs of traditional BI systems can be high. Although the BI software space is crowded, companies like Domo, Qlik, Tableau, TIBCO, and PowerBI have made substantial R&D investments in their products and tools. They are now looking to pass along this cost to their customers. New data consumption pricing models are being rolled out to replace perpetual license ones. These costs can be a barrier to small businesses, who are looking to leverage their data with a BI tool.

Conclusion

“The purpose of visualization is insight, not pictures,” says Ben Shneiderman, an American Association for the Advancement of Science Fellow. A sentiment certainly shared by Richard Millar Devens, the man who coined the term “Business Intelligence” to describe how a banker he knew profited handsomely by acting on information he had gathered in his normal business endeavors. The banker used the information he collected before any of his competitors could and profited handsomely. Even in 1865, time meant money.

At its core, BI is a tool that transforms data into actionable intelligence. Adding AI increases the value of BI. Retrospective reporting tools become forward-looking strategic assets. By automating analysis, uncovering hidden insights, and personalizing experiences, AI makes BI more powerful, efficient and valuable. In a world rich in data, companies that get the most out of their data best will be the ones gaining a significant competitive edge over their competitors.

Dr. David Marco, IIM Fellow, CBIP, CDP, IIBA Global Analytics Expert

Best known as the world’s foremost authority on data governance and metadata management, he is an internationally recognized expert in the fields of data management, AI governance, data literacy, advanced analytics, and data stewardship. Dr. David Marco has earned many industry honors, including Crain’s Chicago Business “Top 40 Under 40”, named by DePaul University as one of their “Top 14 Alumni Under 40”, and he is a Professional Fellow in the Institute of Information Management. In 2022, CDO Magazine named Dr. Marco one of the Top Data Consultants in North America and IDMMA named him their Data Management Professional of the Year. He is the president of DataManagementU.com and is their lead contributor. He is IIBA’s Analytics Expert. In 2023, LinkedIn named Dr. Marco LinkedIn’s Top Business Intelligence (BI) Voice. In 2024, he received the BIG Innovation Award for Data Management and CDO. In 2024, Dr. Marco was named LinkedIn’s Top Data Governance Voice.

David Marco is the author of the widely acclaimed two top-selling books in metadata management history, “Universal Meta Data Models” and “Building and Managing the Meta Data Repository” (available in multiple languages). In addition, he is a co-author of numerous books and has published hundreds of articles, some of which are translated into Mandarin, Russian, Portuguese, and others. He has taught at the University of Chicago and DePaul University.