AI Analytics

What it is, why it matters, and use cases. This guide provides definitions, examples, and practical advice to help you understand and practice modern AI analytics.

AI Analytics - Hero Image

What is AI Analytics?

AI analytics refers to the application of artificial intelligence techniques and algorithms to automate analysis processes, analyze and interpret data, derive insights, and make predictions or recommendations. It involves leveraging advanced technologies like machine learning, natural language processing, and data visualization to enhance decision-making capabilities. AI data analytics can help you lower costs, reduce errors, improve accuracy, and free up human resources to focus on more strategic tasks.

Traditional vs AI Data Analytics

Traditional analytics can be performed without relying on artificial intelligence techniques and is broken out into 2 main types:

Traditional Types

Question Answered


What happened?


Why did it happen?

AI can enhance analytics by leveraging advanced algorithms and machine learning models to handle complex and unstructured data, identify patterns, and make accurate predictions.

AI Analytics Types

Question Answered


What will happen?


What should we do?

AI can also accomplish analytics tasks significantly faster than a human could, tasks such as data processing or performing exhaustive tests on all possible data combinations to identify hierarchies of relationships within the data.

Glossary Topic: Augmented Analytics - Move Beyond the Hype in AI Analytics

Move Beyond the Hype in AI Analytics

Use this checklist when you’re evaluating data analytics platforms to make sure you get the most possible value from AI.

Key Elements of AI Analytics

AI analytics (also known as augmented analytics) enhances each aspect of data analysis by automating processes, improving accuracy and efficiency, enabling advanced techniques, and delivering specific insights and recommended actions.

Key Elements of AI Analytics

These key elements include:

Data Collection and Preparation: AI enhances data collection and preparation by automating the process of gathering, cleaning, and integrating data from various sources. AI algorithms can analyze large volumes of data to detect errors or inconsistencies, and suggest data cleaning techniques, thereby improving the efficiency and accuracy of data preparation.

Deployment and Integration: Technologies, such as containerization and cloud-based services, simplify the deployment of AI models in production environments. AI frameworks also offer integration capabilities with existing systems and provide APIs for seamless integration with other applications.

Data Exploration and Visualization: Advanced algorithms for data analysis and visualization enhance data discovery, data mining and visualization. AI techniques, such as clustering and anomaly detection, can help uncover hidden patterns and outliers in the data. AI-powered visualization tools offer interactive and intuitive visual representations, enabling you to explore complex data sets and gain meaningful insights more effectively.

Natural Language Processing (NLP): NLP supports data analytics in various ways: resolving language ambiguities and structuring data, enabling computers to communicate with you in your language for tasks like reading, speech recognition, and sentiment analysis, and providing immediate and understandable responses through NLG. Additionally, NLP aids research by analyzing vast amounts of text-based data to extract key facts, relationships, and summaries, while also performing tasks like sentiment analysis to determine the positivity, negativity, or neutrality of text.

Natural Language Generation (NLG): NLG enables analytics tools to offer easily understandable responses and generate written reports in your language, automating routine analysis to save time and money. It also aids compliance teams in identifying crucial information from structured data and providing context, explanations, and potential next actions, while advanced NLG tools with machine learning capabilities can offer in-depth answers to complex questions.

Machine Learning and Statistical Analysis: Machine Learning (ML) is a type of AI which automates predictive model building by allowing software to learn from historical data, identify patterns, and make predictions and decisions with little to no human guidance. ML models are the basis for most AI data analytics applications such as insight recommendations and natural language, search-based analytics. The best BI tools integrate an AutoML capability that allows you to build custom ML models without extensive training. AI algorithms, such as neural networks, support advanced techniques like deep learning, enabling more accurate and complex modeling capabilities for various use cases.

Model Evaluation and Optimization: AI automates and accelerates model evaluation and optimization. AI algorithms can automatically evaluate model performance using various metrics, perform hyperparameter tuning, and optimize model architectures. AI techniques like Bayesian optimization and genetic algorithms help efficiently search the hyperparameter space, improving the overall performance of your AI models. Explainable AI (XAI) refers to techniques and processes that help you understand the rationale behind the output of your machine learning algorithm.

Predictive and Prescriptive Analytics: AI enables more accurate and powerful prediction models. With AI algorithms, predictive models can analyze large and diverse data sets, capture nonlinear relationships, and handle complex features more effectively. AI techniques, such as ensemble learning and deep neural networks, can improve the accuracy and robustness of predictive and prescriptive models.

Prescriptive Analytics: Challenges & Solutions

Learn how to overcome the top 14 challenges you'll face.

AI Analytics Example

In these examples, a sales leader needs to gain insights such as the sales and cost by product in a specific category. Artificial intelligence analytics capabilities found in a modern BI tool helps him more efficiently gain the insights he needs.

See how natural language processing makes it easy for a sales leader to understand the sales revenue and cost of sale by product.

As shown in this example, you can type a question in regular language and NLP immediately determines the meaning of your text and generates relevant insights, including visual representations. You can then refine the resulting analytics and explore your data in ways you hadn’t considered before, to help you make the best business decisions.

Use Cases

A wide range of industries and job roles leverage AI analytics techniques. Here are some common predictive analytics examples across different industries.

Insurance companies

may use AI analytics to assess and predict risks associated with policy applications and determine the likelihood of future claims.

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Financial services firms

can utilize it to predict loan default probabilities, detect fraud, and forecast market movements for better investment decisions.

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Retailers and CPG companies

can leverage it to analyze past promotions’ effectiveness and predict which offers will be most successful in the future.

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Healthcare companies

may employ it to forecast patient admissions and readmissions, enabling better management of patient care and resource allocation.

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Energy and utilities sectors

can utilize it to analyze historical equipment failures and predict future energy demands based on past consumption patterns.

Life sciences organizations

can employ it to develop patient personas and predict the likelihood of nonadherence to treatment plans.

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Manufacturing and supply chain operations

can use it to forecast demand, optimize inventory management, and identify factors leading to production failures.

The public sector

can leverage it to analyze population trends, plan infrastructure investments, and make informed decisions for public works projects.


AI in analytics can have a significant impact on your organization. By harnessing the power of AI, you can gain a competitive edge, drive innovation, and achieve better outcomes in various domains and industries.
Here are some key benefits:

Enhance Data Analysis: AI enables you to analyze vast amounts of data quickly and efficiently. AI algorithms can uncover patterns, trends, and correlations that may be difficult for humans to identify manually. This leads to more accurate and comprehensive data analysis, providing deeper insights and enabling data-driven decision-making.

Improve Decision-Making: AI models can process complex data sets, perform predictive analysis, and provide recommendations based on historical data and patterns. This gives you actionable insights, helping you make strategic choices that drive business growth and competitive advantage.

Automate Repetitive Tasks: AI automates repetitive and time-consuming tasks involved in data analysis. This frees up valuable time for data analysts and domain experts to focus on higher-value activities such as interpreting results, formulating strategies, and generating innovative ideas. Automation also reduces the risk of human error, leading to more reliable and consistent results.

Increase Efficiency and Productivity: It accelerates data processing and analysis, helping you derive insights faster and make decisions more quickly. With AI algorithms handling complex tasks, you can streamline operations, improve productivity, and achieve more efficient resource allocation. This efficiency gain translates into cost savings and better utilization of resources.

Enhance Customer Experience: It can provide you with a deeper understanding of customer behavior, preferences, and needs. By analyzing customer data, AI models can personalize marketing campaigns, optimize pricing strategies, and improve customer service. This leads to enhanced customer experiences, increased customer satisfaction, and improved customer retention.

Identify New Opportunities: It can uncover hidden opportunities and potential areas for growth. By analyzing data from various sources, AI algorithms can identify market trends, customer segments, and emerging patterns that may not be apparent through traditional analysis. This helps you proactively identify new business opportunities, innovate, and stay ahead of the competition.

Mitigate Risk: AI models can analyze historical data to detect anomalies, predict potential risks, and provide early warning indicators. This allows you to take preventive measures, develop risk mitigation strategies, and improve decision-making under uncertain conditions.

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What are the main challenges of AI in analytics?

The first challenge is ensuring the availability of high-quality data that is relevant, accurate, and representative of the problem at hand. Second, there is a shortage of skilled professionals who can effectively develop, deploy, and interpret AI models. Third, ethical considerations arise in terms of data privacy, fairness, and bias, requiring you to establish robust governance frameworks. Next, the interpretability of AI models remains a challenge as complex models like deep learning neural networks are often regarded as black boxes. Lastly, AI models need ongoing monitoring to detect and address concept drift and to ensure that they remain accurate and unbiased as new data becomes available.

What are AI Analytics Tools?

AI analytics tools are software applications or platforms that enable you to leverage artificial intelligence techniques for data analysis and decision-making. These tools encompass a range of capabilities, including data collection and preparation, data exploration and visualization, machine learning algorithms, predictive analytics, natural language processing, and model evaluation and optimization. These tools often provide user-friendly interfaces, drag-and-drop functionalities, and visualization features to simplify the process of analyzing and interpreting data. They help you extract insights, make predictions, and generate actionable recommendations from complex and large-scale datasets. These tools contribute to improved efficiency, enhanced accuracy, and better decision-making, allowing you to harness the power of AI for data-driven insights and business success.

What does an AI data analyst do?

An AI data analyst is a skilled professional who specializes in extracting and analyzing statistical data from existing datasets. They play a crucial role in collecting, processing, and cleaning the data, utilizing machine learning models and advanced analytical techniques. Their primary objective is to enhance efficiency and performance by identifying meaningful patterns in the data, and they also generate informative reports to support decision-making. Given the promising future of the field, the career path of an AI data analyst is highly sought after due to the increasing demand for their expertise.

Is AI replacing data analysts?

Although AI is set to transform specific areas of data analysis, it’s not likely that it will completely replace data analysts. Instead, AI can augment the capabilities of data analysts by automating repetitive tasks like data processing and visualization, thereby freeing up their time for more valuable endeavors. Due to the current limitations of machines in comprehending context and adapting storytelling to diverse scenarios, data analysts' expertise and human judgment remain indispensable in the field.

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