What is AI analytics?

Key AI analytics technologies includegenerative AI (gen AI) and AI agents. These and other innovations provide advanced capabilities that offer multiple advantages over traditional analysis methods.

For example, with AI, enterprises can analyze larger and more diverse datasets; they can improve the speed and efficiency of their data analysis; and they can make data analytics accessible to more users.

Also referred to as AI data analytics or AI augmented analytics, AI analytics is a specialized area within data analytics, which is part of the broader fields of business analytics and business intelligence. These fields all focus on transforming data into actionable insights.

Implementing data quality measures and responsible AI practices can help organizations successfully incorporate AI analytics in everyday practices, from optimizing inventory management to diagnosing medical conditions.

Enterprises rely on data-driven insights for smart decision-making and competitive differentiation. For years, achieving those insights meant relying on basic statistical analysis techniques like regression analysis, hypothesis testing and descriptive statistics.

These methods illuminated data patterns and relationships, but with notable drawbacks: they relied on structured data (typically data organized in relational databases); they were limited in producing actionable predictions; and they required labor-intensive manual execution. The latter made data analytics difficult to scale—only so much data can be processed manually in standard spreadsheet software.

AI-driven analytics has changed the scope of what’s possible. AI empowers organizations to process and analyze much larger volumes of data, including big data—complex datasets measured in terabytes or petabytes.

It supports the analysis of data compiled from diverse sources—ranging from Internet of Things (IoT) sensors to social media to financial transactions—in a variety of formats, including structured, semi-structured and unstructured. And AI has made data analytics faster: Machine learning models and rule-based engines support real-time analytics, enabling organizations to act on insights as events unfold.

Deep learning, powered by neural networks with many layers, underpin modern AI analytics platforms. Deep learning models consist of multiple layers of interconnected nodes (artificial neurons) that transform data. The distributed, highly flexible structure of deep learning—which is inspired by the human brain—powers state-of-the-art AI, including AI technologies used for analytics.

Two AI technologies have become increasingly prominent in the analytics space: generative AI and AI agents. According to a survey-based research report from MIT Sloan Management Review, the greatest value offered by genAI to business and data leaders is “its ability to analyze and improve strategic business decisions.” And in a 2025 survey of operations executives by the IBM Institute for Business Value (IBV), 90% said that by 2027, AI agents will enable business operations professionals to perform insightful analytics for real-time optimization.

There are four main types of data analytics, and AI can be used to enhance all four.

• Descriptive analytics
• Diagnostic analytics
• Predictive analytics
• Prescriptive analytics

AI-powered descriptive analytics provides a clear and comprehensive understanding of past performance. With machine learning algorithms and natural language processing (NLP), AI can sift through large volumes of structured and unstructured data to identify trends, patterns and correlations.

Consider a retailer using AI to analyze customer data and uncover insights about purchasing trends and preferences. An AI system can process millions of sales records and social media trends to identify which products are most popular during certain seasons or which items are frequently bought together.

This customer behavior analysis can then be visualized on dashboards, showing sales performance over time, customer segmentation based on purchasing habits and the effectiveness of marketing campaigns.

Generative AI can streamline this process, making it more user-friendly by generating automated reports and creating dynamic data visualizations. Overall, by providing a detailed and comprehensive view of past customer experiences, AI-powered descriptive analytics helps retailers make data-driven decisions to optimize inventory management, tailor marketing strategies and enhance customer satisfaction.

AI improves diagnostic analytics by rapidly identifying underlying causes and correlations within complex collections of raw data, enabling more accurate and timely problem-solving. 

Healthcare offers a range of examples of how AI improves diagnostic analytics. AI-powered tools can interpret medical images to detect physiological information that human physicians can’t see, detecting everything from blood pressure to disease risk from retinal scans, for instance.

AI can also be deployed to derive insights from text data: One study found that generative AI such as ChatGPT’s GPT-4 model has outperformed physicians in assessing clinical vignettes—descriptions of medical cases—to determine diagnoses, according to a 2024 Stanford University study.

But perhaps the most powerful diagnostic AI tool comes in the form of multimodal large language models (MLLMs), which can integrate imaging and (textual) clinical data. In radiology, they are being deployed to propose diagnoses and draft preliminary reports.

AI enhances predictive analytics by using advanced algorithms to analyze historical data and identify patterns, enabling more accurate forecasts of future trends and outcomes.

In financial analysis and reporting, for instance, financial modeling AI agents can analyze historical data and build predictive models to produce cash flow projections. In the IBV operations executives survey, respondents said that predictive financial modeling will improve forecast accuracy by 24% by 2027.

Another example of AI-powered predictive analytics can be found in the public sector. In the US, NASA teamed up with IBM to create AI geospatial foundation models to analyze satellite images and create customized maps of environmental changes. Potential applications of the models include estimating climate-related risks to crops and developing predictive models that help enterprises adapt to climate change.

Analysts can take advantage of AI to not only analyze data but to provide actionable recommendations for better decision-making processes.

An example of AI used in prescriptive analytics is in supply chain management, where AI systems analyze data on inventory levels, demand forecasts and shipping conditions. These systems recommend optimal order quantities and delivery schedules, thereby minimizing costs and improving efficiency.

Prescriptive analytics can also be used to make pricing decisions. For instance, IBM developed an AI-powered analytics solution that produces pricing recommendations for IBM hardware and software products. The solution, designed for IBM sellers and business partners, optimizes pricing for each individual customer based on their geographic location and buying patterns.

AI-powered analytics typically follows these steps:

Practitioners must first identify what the AI model is expected to predict. This helps them to select the most appropriate approach. Different models are tailored for specific use cases, and choosing the right model and data is crucial for achieving optimal results quickly and cost-effectively.

Workflows begin with data scientists or data analysts gathering relevant data from both internal data sources and third-party providers. The accuracy and effectiveness of the models depend on the quality and quantity of the data collected.

Once gathered, data preparation and preprocessing methods, including data cleaning and transformation, ensure it’s ready for analysis. Common steps include addressing missing values, removing duplicates, standardizing formats and encoding categorical variables to ensure that the data is consistent and ready for modeling.

Before applying prescriptive analytics, analysts generally perform descriptive analytics to review past performance and predictive analytics to project future outcomes.

Descriptive analytics involves summarizing and visualizing data to gain insights into historical trends and patterns, while predictive analytics uses statistical and machine learning models to forecast future events or behaviors.

Prescriptive analytics involves constructing mathematical models and optimization algorithms to recommend business decisions aimed at achieving the best possible outcomes. These models consider various factors such as constraints, objectives, uncertainties and tradeoffs, building on insights from descriptive and predictive analyses to suggest how an organization should respond to different scenarios.

After evaluation, the models are integrated into operational systems or applications to provide real-time predictions and recommendations. This might involve incorporating the models into existing software systems, APIs or dashboards to automate decision-making processes or offer prescriptive insights to users, facilitating a more seamless use of insights.

Continuous monitoring and maintenance of the models are necessary to ensure their ongoing effectiveness and relevance. This includes tracking model performance, updating with new data, periodically retraining and refining the models to adapt to changing conditions or evolving data patterns.

Deploying AI for analytics offers a host of benefits for organizations and individual users.

• New insights: From medical diagnostics to dynamic pricing, AI analytics enables organizations to discover patterns and trends that might otherwise go unnoticed—and seize new opportunities as a result.

• More diverse data: Unlike traditional analytics, AI analytics supports the analysis of unstructured and semi-structured data in addition to structured data, allowing organizations to unlock more value from enterprise data.

• Faster decisions: AI analytics, especially in conjunction with real-time analytics, helps enterprises access intelligence fast—sometimes in milliseconds—for critical, time-sensitive decisions.

• Greater efficiency: Through technologies such as machine learning, generative AI and AI agents, enterprises can analyze data more efficiently than through cumbersome manual processes.

• Bigger scale: AI analytics enables the analysis of enormous and diverse datasets that have become increasingly common in the digital age.

• Quality management: Enterprises can use AI analytics for root cause analysis, anomaly detection and fraud detection, enabling a more proactive approach to identifying and addressing problems.

• Democratized analysis: Through generative AI and natural language processing, business users without data science expertise can use ordinary language to interact with chatbots and obtain analysis and insights.

While AI analytics helps organizations cull insights from greater volumes and more diverse data than traditional analytics, ensuring the data quality is critical to achieving accurate analysis. Data governance measures help organizations maintain high-quality data required for AI systems.

In addition to considering data quality, organizations collecting data for AI analytics initiatives should take steps to ensure compliance with applicable data privacy measures, such as the EU’s General Data Protection Regulation. Enterprises can implement a number of measures to protect data privacy, including using synthetic data and implementing responsible AI practices.

Users often feel more comfortable relying on AI analytics conclusions and predictions when they understand how machine learning models achieved those outputs. Explainable AI (XAI) techniques can build trust and confidence in AI analytics results.