AI in Analytics: Examples, Benefits, and Real-World Use Cases

Key takeaways

• Artificial intelligence (AI) in analytics uses technology to analyze data to gain insights and guide data-informed decisions.

• Using AI in business analytics requires using AI methods like deep learning and natural language processing, and tools such as Tableau AI, Microsoft Power BI, and ChatGPT.

• You can use AI in data analytics in various ways, with use cases that continue evolving and include democratizing data access across organizations, enhancing analysis, and automating data management.

AI enhances the analytics workflow, offering benefits such as improved decision-making, increased productivity, more accurate predictions, and enhanced interfaces. Learn more about the role of AI in data analytics below, including its uses, benefits, and how it's already being used in the real world. Afterward, if you want to start building your own AI skills for data analysis, consider enrolling in IBM's Generative AI for Data Analysts Specialization. This beginner-friendly program can help you expand your knowledge and gain practical experience with using prompts, tools, and techniques to enhance data analytics.

What is AI in analytics?

AI analytics is a form of data analytics that leverages artificial intelligence, particularly advanced forms of machine learning, for business intelligence (BI) purposes and real-time predictions and analysis. Although it is meaningfully distinct from the traditional data analysis methods used by many organizations, AI analytics is focused on achieving the same end: analyzing data sets to produce actionable insights and guide data-driven decisions.

The core difference between AI analytics and traditional data analytics lies in the kinds of technology used to create and access these insights. Today, many organizations rely on traditional BI tools like dashboards to synthesize and display data points for easy reference when making decisions. Yet, while these tools are impactful, they often provide a static view of the data for most users, lean heavily on statistical analysis to generate insights, and require analysts to draw conclusions themselves rather than rely on the technology.

AI analytics uses advanced AI methods, such as natural language processing (NLP) and deep learning, to analyze large data sets, develop insights, and guide decision-making in a dynamic manner that responds directly to user interactions. For example, an AI-enabled analytics platform could allow business users to directly ask questions like “What was our sales performance last quarter?” and “What is the average number of sales made during the Q4 holiday season over the last three years?” In response, the system would be able to answer these queries dynamically without requiring human intervention.

AI analytics, then, is simply about applying the latest commercial developments in AI to the practice of data analysis already established within many organizations.

AI in data analytics: Common types of analysis

AI-powered data analytics automates some of the processes and uses AI to monitor data to identify patterns and irregularities, and, like traditional data analytics, it relies on four primary types of data analysis. These include:

• Descriptive analytics: The examination of data to understand what is currently happening or happened during a defined period in the past. Descriptive analytics is all about describing the state of something as indicated by relevant data points, such as a company’s sales to date or a team’s success rate in hitting their OKRs last quarter.

• Diagnostic analytics: The analysis of data to understand why something happened. Much as a doctor diagnoses the cause of a health condition, diagnostic analytics is all about identifying the root cause for a particular trend or pattern, such as the reason that sales fell during the last quarter or a digital marketing campaign hit a record-high engagement rate.

• Predictive analytics: Data analysis with a goal of predicting trends and patterns in order to forecast future outcomes. As its name suggests, predictive analytics is used to better understand how something might develop in the future, such as to project an organization’s growth over the next five years or the total number of sales that may be made in an upcoming quarter.

• Prescriptive analytics: The analysis of data to identify strategies to achieve a particular objective. Just as a doctor may prescribe a course of treatment to cure a patient’s illness, prescriptive analytics is used to define the course of action required for an organization to hit a particular objective, such as what they need to do to meet their sales targets next year or to increase their marketing reach with limited resources.

How to use AI in data analytics

To use AI in analysis, it helps to begin by defining your goals, which lends focus to the process and enhances your ability to measure the results. Then, collect and prepare the data and use it with AI-powered tools to complete the analysis. You’ll likely continually review, monitor, and refine your processes to ensure they remain accurate, effective, and relevant over time.

Additionally, it’s notable that you’ll find diverse uses of AI in data analytics, and they continue to evolve along with the advancement of the field of artificial intelligence.t. Explore three of these uses in more detail for a better understanding of how using AI in analytics may help you.

1. Democratize data

To make efficient, data-driven decisions, business professionals need access to relevant data points and insights as soon as the need arises. Despite this, many professionals within organizations lack the technical skills required to effectively work with data and get the answers they need out of it.

AI analytics tools can help democratize data in the workplace through interactive interfaces that allow you to query the platform directly. Rather than having to sift through the backend, generative AI tools like chatbots can answer your questions directly, giving you the insights you need, when you need them.

Read more: How To Write ChatGPT Prompts: Your Guide

2. Efficiently analyze big data sets

The world generates more than 400 billion gigabytes of data every day [1], and many organizations are awash in data. Yet, rather than helping workers make the best decisions, many professionals instead find that the sheer volume of data at their fingertips hinders their decision-making rather than helps it.

According to research from Demandsage, more than 97 percent of organizations invest in big data, yet only 24 percent use that data to drive decisions [1]. Further, a 2026 study by Precisely, for example, 51 percent of organizations surveyed cite making data-driven decisions as a priority, with 52 percent citing AI as a significant source driving their data strategies; however, 33 percent lack trust in the quality of their data [2]. To make matters worse, the volume and complexity of data can create further complications that potentially limit organizations' success rather than help it.

AI can help remedy the data overwhelm many workers experience by quickly and efficiently analyzing the large amounts of data organizations are currently struggling with. It can perform an analysis in minutes rather than days or weeks, typically with a lower margin of error. In effect, workers can feel confident that the information in front of them is both reliable and up to date, empowering them to make sound decisions when the time calls for it.

3. Automate data management

Some of the most tedious, time-consuming tasks in data analysis are also the most foundational: extracting, cleaning, and entering data into databases are all necessary steps to complete before effective analysis can occur. As a result, many data professionals spend much of their time accomplishing these necessary steps rather than focusing on the most complex and impactful aspects of their work.

Thankfully, AI is well-suited to data management tasks such as data cleaning, entry, and classification, and can do them automatically without human input. Furthermore, AI can detect anomalies and errors in data sets and resolve these issues to ensure a database’s integrity, so data professionals can have confidence in their results and insights.

Benefits of AI in analytics

Integrating AI into analytics can help make it faster and easier to process data, spot patterns, and analyze data coming from various sources. Additional advantages that organizations may experience include:

• Improve decision-making: AI analytics democratizes data, enabling business professionals without a background in data analytics to gain insights when needed. In turn, it has the power to improve decision-making for a wider number of workers than many of those BI solutions in use today.

• Increase productivity: AI tools can perform tedious data analysis tasks that take data professionals a significant amount of time. Free from these tasks, data scientists and analysts can focus on the most complex and impactful projects for their employers.

• Greater predictive accuracy: As AI advances, so do its capabilities. In particular, deep learning and machine learning models can predict outcomes more accurately than many of the predictive techniques business professionals used in the past.

• Dynamic and personalized interfaces: Business professionals often use BI dashboards as everyday tools. However, while many traditional dashboards have only limited interactive features, AI-enabled interfaces go beyond the limited interactivity associated with traditional dashboards, offering the potential to respond dynamically to users and provide them with a personalized view so they can do their best work.

AI analytics example

In retail, you might use AI analytics to segment customers according to their purchasing patterns and analyze sales to help gain insights you can use to predict demand. In health care, you might use AI analytics for improved workforce planning after analyzing staffing and admissions data or parse through data to identify quality issues as part of your risk management strategy. Likewise, you can find examples of AI in analytics across industries in areas like fraud detection and credit risk analysis, predictive maintenance and quality control efforts, and marketing campaign optimization.

For some organizations, in fact, it’s actually core to their brand and business model. Netflix, for example, uses machine learning to provide customers with personalized recommendations to encourage them to stay on the platform. Not satisfied with just using AI to create an engaging customer experience, the company also uses it to design its movie catalog, identify the characteristics of successful content, and inform its advertising spend, among other things.

As AI becomes more user-friendly, many of the techniques that made companies like Netflix successful may likely become more prevalent in medium-sized and small businesses, too.

AI analytics tools

Now that you have a better understanding of AI analytics, it’s time to start exploring the tools that can help you implement it in your own organization. Some common AI analytics tools you might consider exploring include:

• ChatGPT

• Tableau AI and Tableau Pulse

• Power BI with AI Insights

• IBM Cognos Analytics

• Jupyter AI

• Anthropic Claude

• Google Gemini

• Microsoft Copilot

Explore our free AI resources

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