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

Key takeaways

Artificial intelligence (AI) analytics uses AI to analyze data and get meaningful insights from it, enabling informed decision-making.

Despite data-driven decision-making remaining a priority for 76 percent of organizations, 67 percent don’t trust their data [1]. 

Using AI in analytics can help democratize data and increase the efficiency with which you can analyze large data sets to decrease overwhelm and increase confidence in using data-driven insights.

You can learn how to use AI by experimenting on your own, enrolling in a formal program, or working to build AI-related skills. 

Learn about the role of AI in data analytics, including its uses, benefits, and how it's already being used in the real world. When you’re ready to begin building AI skills, including writing prompts, responsible AI use, and common tools, consider enrolling in the Google AI Essentials Specialization. This five-course, beginner-friendly program can help you become familiar with the technology and begin developing strategies to remain updated on the many emerging uses of AI. In the end, you'll earn a shareable certificate you can add to your resume or LinkedIn profile.

What is AI in analytics? 

AI analytics is a form of data analytics that leverages artificial intelligence —in particular, advanced forms of machine learning—for business intelligence purposes. 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 business intelligence (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

There are four primary types of data analysis that are possible with both traditional data analytics and AI analytics. These include: 

• Descriptive analytics: Data is examined 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 the relevant data points, such as a company’s sales-to-date or a team’s success rate at hitting their OKRs last quarter. 

• Diagnostic analytics: Data is analyzed 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 is analyzed to predict trends and patterns in order to forecast future outcomes. As its name suggests, predictive analytics is used to understand better 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: Data is analyzed to identify how a particular objective may be achieved. 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 

The uses of AI in data analytics are vast, diverse, and continually evolving as the field of artificial intelligence advances. Nonetheless, there are some uses of AI that are becoming increasingly prevalent in the business world that managers and organizational leaders should know about. Below, we explore three of these uses. 

1. Democratize data

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

Fortunately, AI analytics tools can help democratize data within the workplace with interactive interfaces that allow users to simply query the platform itself. Rather than having to sift through the backend, then, generative AI tools like chatbots can answer users’ questions directly so that they can get the insights they need, when they need them. 

Read more: How To Write ChatGPT Prompts: Your Guide

2. Efficiently analyze big data sets

Many organizations are awash in data. Yet, rather than helping workers make the best decisions, many professionals instead find the sheer volume of data at their fingertips hinders their decision-making rather than helps it. 

According to a 2025 study by Precisely, for example, 76 percent of organizations surveyed cite making data-driven decisions as a priority; however, 67 percent lack trust in the quality of their data [1]. 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. In effect, workers can feel confident that the information in front of them is both reliable and up-to-date, empowering them to make strong 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

There are many benefits to using AI for data analytics. Some of the most common that organizations may experience include: 

• Improve decision-making: AI analytics has the power to democratize data, so business professionals without a background in data analytics can 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 a significant amount of time for data professionals to complete. 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 does its capabilities. In particular, deep learning and machine learning models have the ability to more accurately predict outcomes than many of the predictive techniques business professionals used in the past. 

• Dynamic and personalized interfaces: BI dashboards are a common tool used by business professionals every day. But, while many traditional dashboards have only limited interactive features, AI-enabled ones have the potential to respond dynamically to users and provide them with a personalized view so they can do their best work. 

AI analytics example 

There are many ways businesses can implement AI analytics into their existing workflows. 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, it’s safe to assume that many of the techniques that made companies like Netflix successful will 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

Read more: 9 Business Intelligence Tools You Need to Know

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