The State of AI in Business Intelligence and Analytics

Business intelligence (BI) allows companies to make informed, data-driven decisions by analyzing past performance and current trends. Artificial intelligence (AI) applications elevate this capacity, transforming raw data into actionable insights faster.

These applications give you access to more data—often in real-time—and can help you quickly adapt your business strategy to handle market changes.¹

By integrating AI and machine learning into BI solutions, businesses gain access to broader and more complex datasets, allowing for quick responses to market shifts and improved strategic planning.

These advanced business intelligence solutions provide a competitive edge, as they can process unstructured data, perform sentiment analysis and support both technical and non-technical users in making valuable insights more accessible.

This article will explore how AI is shaping the future of business intelligence and analytics, providing companies, and individuals, with powerful tools to manage data, improve data quality and refine their approach to decision-making.

Business intelligence has been around since the 1950s, but it wasn’t until the 1970s and 1980s that data warehouses were widely used to drive business strategies. During this time, they primarily provided historical data for retrospective reporting and analysis.²

In the early 2000s, BI technology began to advance toward its current state, in which it incorporates big data for predictive analytics, cloud computing and AI.²

Artificial intelligence and business intelligence technologies automate data collection from a variety of sources, including business transactions, online interactions, and Internet of Things (IoT) devices. Sophisticated algorithms can use this data to identify patterns from complex relationships and generate predictions and insights that business leaders can use to formulate their strategies.³

Advanced AI tools are driving modern BI applications, including machine learning, natural language processing and automated data visualization.

Predictive analytics use data to answer questions about what might happen in the future. Businesses can use predictive analytics to forecast their inventory, allocate resources, create effective marketing campaigns, detect fraud and more. Machine learning algorithms train on large datasets to develop predictive models.⁴

Natural language processing (NLP) is a type of artificial intelligence that allows computers to read, process, understand and generate human language. These programs let people naturally interact with data, such as asking questions, without needing to use structured programming languages.⁵

Data visualization tools can help you make sense out of millions of data points that would be extremely difficult to understand in other formats. These automated tools are simple to use and are included in many BI software programs. They can take the form of dashboards, charts, graphs, maps and other visual formats.⁶

Business intelligence tools have become essential for organizations seeking to derive actionable insights from vast and complex datasets. These AI-powered tools integrate with a variety of data sources, from structured data in data warehouses to real-time data streams, enabling users to work with both historical and current information for more dynamic analysis.

Modern BI tools support data workers by automating time-consuming tasks, making it easier to monitor performance metrics, streamline data governance policies and ensure robust security measures are in place.

By combining advanced capabilities such as fraud detection and predictive analytics, today’s BI systems are equipped to handle the intricacies of big data. As businesses operate in increasingly complex and data-rich environments, these tools empower both technical and non-technical users to make faster, more informed decisions that align with strategic goals.

Here are a few top BI tools widely used for data analysis and decision-making:

• Tableau: Renowned for its powerful data visualization capabilities and user-friendly interface
• Microsoft Power BI: Offers strong integration with Microsoft products, real-time analytics and customizable dashboards
• Looker: Specializes in data exploration and embedded analytics, popular for SQL-based modeling
• Qlik Sense: Provides advanced AI capabilities and associative data modeling for deep insights and intuitive data exploration
• Domo: An all-in-one platform with data integration, visualization and collaboration features designed for business users
  

These tools enable organizations to extract valuable insights, manage data efficiently and enhance strategic planning.

As technology evolves, some trends have emerged to make using AI in business analytics easier and more accessible. One of these trends is the rise of self-service analytics powered by AI. These tools allow business leaders to ask analytical questions and generate reports on their own.⁷

AI tools are also increasingly being used for real-time data processing and decision-making. Many applications of AI in data analytics rely on historical data, which is valuable for analyzing past performance but falls short when real-time insights are needed. You can use this information to make split-second decisions based on the most current data.⁸

AI brings additional functionality to traditional business intelligence. It gives you more accurate insights from larger datasets. AI systems can break tasks down into smaller chunks that they can work on simultaneously. This parallel processing allows programs to perform an analysis faster and more efficiently than traditional programs. Because the analysis you get from AI programs is of a higher quality, you’ll be able to make better decisions.¹

Although business intelligence and AI applications can drive effective business strategies, they also come with challenges that businesses need to consider when choosing to deploy them.

The quality of your data analysis depends on the quality of your data. Even the best models won’t give you good results if you’re working with flawed data.

There are many components to high-quality data.

Most importantly, it needs to be accurate. Errors in the data can cause inaccurate results. It also needs to be in a standard format so the algorithms can efficiently process it.

Completeness is another factor for gauging whether data is high in quality or not, since incomplete data won’t give you the full picture.

Finally, the data needs to be current and relevant to the problem under consideration.⁹

As AI algorithms are increasingly being used to make decisions that affect people’s lives, ethical concerns are coming to the forefront. A big part of these concerns is the nature of AI programs. Because the programs teach themselves, in many cases, even the programmers don’t know how they make decisions. They may contain biases or prejudices.¹⁰

Another major ethical concern is data privacy. AI programs have access to a tremendous amount of data and can uncover hidden insights and personal details that wouldn’t be apparent through traditional data processing methods.¹⁰

AI applications are so efficient that they may lead to significant job losses and negatively impact society as a whole. There are no easy answers to these ethical concerns, but they’ll need to be addressed by policymakers, business and community leaders, and other stakeholders.¹⁰

Despite the increase in self-service portals, AI systems still need to be managed by trained professionals with skills from an advanced program that understands the importance of AI in business analytics.

This can represent a significant expense and obstacle for smaller businesses. However, many business intelligence platforms are offered on a software-as-a-service (SaaS) basis, which can make them more affordable and manageable^.1

Today’s business leaders need to understand how to apply AI-powered insights to make strategic decisions. You can learn these skills with William & Mary’s Raymond A. Mason School of Business Online MBA.

Our expert faculty are leaders in their fields and our program is consistently ranked in the top 10% nationally.¹¹

You can learn from anywhere, at your own pace and connect with other business professionals through our extensive alumni network. Contact an admissions outreach advisor today to learn more about the admissions process and to apply.