Top 9 AI tools for Data Analysis in 2025

Consider what AI can do for your job. As your business grows, you’ll need to increasingly rely on advanced analytics to give you actionable insights and drive informed decision-making. Combining data analytics tools with AI can open up a world of possibilities that will allow you to enhance your data exploration and empower your end-users to find the most value in the data. 

AI tools combined with data analysis can help your teams and customers explore data more naturally, having a conversation and asking questions to help find answers. It can also make it easy to create and manage AI models that give you the flexibility you need for your data. 

Many AI tools will empower your data analysis. Knowing which to choose can mean a lot of research, time, and testing to see what will work for your business. We’re giving you a head start by reviewing some of the top tools and features — from predictive analytics to natural language processing and features designed to extract meaningful insights from complex datasets — so you can choose what will work best for you and your data.  

Whether you’re a small startup or a multinational corporation, these tools are essential for your data and to give you a competitive edge. Let’s dive into what you should look for from the top nine AI tools for data analysis in 2025. 

 How to use AI for data analysis

Want to know how you can use AI tools to help analyze data? The good news is that there are a lot of possibilities. While your data is full of rich insights and information your company can take immediate action on, it can sometimes be a time-consuming and tool-intensive process to get everything possible out of the data.

Utilizing AI tools to analyze data allows organizations to sift through massive amounts of data faster and discover insights more naturally and intuitively. Even the most advanced data intelligence platforms like Domo, Tableau, or Power BI use AI tools to enhance user experiences, surface more insights, and ensure users find answers to their business questions.

Here are some ways AI can help you harness even more data analysis power: 

• Predictive analytics: You can use AI algorithms to analyze historical data and forecast future trends, enabling your business to anticipate market shifts, customer behavior, and operational needs.
• Prescriptive analytics: By combining AI with optimization algorithms, your team can determine the best course of action to achieve your business goals through optimizing resources and maximizing efficiency.
• Anomaly detection: Use AI algorithms to identify outliers within your datasets. This will flag potential errors, fraud, or emerging patterns that require attention.
• Natural Language Processing (NLP): NLP enables AI systems to interpret unstructured data, such as text or speech, extracting insights from customer feedback, social media interactions, and other text-based sources.
• Image and video analysis: Use AI algorithms to analyze images and videos and extract valuable insights — like object recognition, sentiment analysis, and demographic profiling — then categorize your media files for further analysis.
• Time series forecasting: AI models can analyze sequential data points over time to forecast future trends and patterns, aiding in inventory management, resource planning, and financial forecasting.
• Automated data preparation: AI streamlines data cleaning, transformation, and integration processes, accelerating data preparation tasks and ensuring quality data for analysis.

Best 9 AI tools to analyze data

Now that you’re thinking about how you can use AI tools to analyze data, let’s look at the top tools on the market in 2025 and how they can help your business grow. Here is an overview of the tools and pros and cons to consider when deciding what will work best for you. 

1. Domo

Domo is an end-to-end data platform that supports cleaning, modifying, and loading data to build customizable data apps to consume data and insights right where users are working. This comprehensive suite of tools and features has an AI service layer that helps users deliver data to their teams in a more streamlined way and guides users to insights through AI-enhanced data exploration. This AI foundation supports the creation, training, and integration of AI models, ensuring responsible AI deployment with built-in governance and usage analytics. It also includes an intelligent chat, allowing users to ask questions about their data and find deeper, customized insights within their data. Domo also has pre-built AI models that users can integrate into their data to support accurate forecasting and sentiment analysis.

Pros

• AI-enhanced tools across every aspect of the data journey
• Intuitive interface for data exploration and AI model management
• Built-in analytics and governance to ensure you can deploy AI responsibly
• Utilizes AI to support comprehensive data governance and security
• Allows you to bring in external models to work with your data
• Large, active user community

Cons

• As an end-to-end data platform, Domo can be more feature-heavy than some businesses need

The cost may be high for businesses looking only for data analysis

2. Microsoft Power BI

For users already well-integrated with the Microsoft Office suite of applications, Microsoft’s Power BI integrates well with current applications and ensures you can seamlessly add AI into your data analysis across your tools. Like other tools, non-expert users can have difficulty accessing some of the more advanced features and getting the full value from the tool. Native integrations like Azure Machine Learning with Excel allow users to work within their current applications seamlessly. Plus, Power BI can import data from nearly any source. Microsoft Power BI is a powerful data visualization and business intelligence tool that allows users to create interactive reports and dashboards.

Pros

• User-friendly interface, especially for those already familiar with Microsoft products
• Integration with the Microsoft Office suite of applications
• Scales to handle large data sets

Cons

• Can become costly with premium features
• Learning curve for advanced functionalities, including AI tools
• Integration with non-Microsoft data, though possible, can require additional tools and steps to fully integrate

3. Tableau

Tableau is one of the leading business intelligence platforms in the market. One of the historical frustrations of using Tableau is that while it’s very feature-rich, it’s difficult to use for new users or business users who aren’t as experienced with data. Tableau uses AI to change that and give users more intuitive and natural paths for finding answers and insights within their data. Tableau’s AI capabilities — including Tableau GPT and Tableau Pulse — enhance data analysis, preparation, and governance by leveraging advanced AI models from Salesforce (Tableau’s parent company) and OpenAI. These features give users powerful, efficient methods for managing complex data and delivering smart, personalized insights directly within the workflow. This can go a long way to making data analysis more intuitive and accessible, even for beginners.

Pros

• Advanced visualizations with an intuitive drag-and-drop interface
• Feature-rich with AI tools across the product
• Seamlessly integrates with Salesforce data

Cons 

• High cost for enterprise versions
• Can still require a steep learning curve for new users
• AI tools only offer limited customization

4. Polymer

Polymer is a relatively new tool that simplifies data analysis through automation and AI. It empowers users to upload spreadsheets and instantly converts them into streamlined, searchable, and interactive databases. Users can then filter, sort, and query this data for analysis. Polymer’s AI capabilities can be used for more than simple data transformation. Advanced users can employ AI algorithms to automatically identify patterns and relationships within the data, with AI tools providing insightful visualizations and analytics.

Pros

• Simple and intuitive interface
• Strong focus on automation and AI
• Quick to set up and use
• Cost-effective for small to medium-sized teams

Cons

• Limited advanced analytics features
• Smaller range of integrations
• Less mature product with a smaller user base

5. Qlik

We’ve ranked Qlik behind tools like Domo, Power BI, and Tableau due to its higher cost and comparatively limited AI functionalities. But for users who already use Qlik or want to understand its AI features, Qlik offers multiple data exploration features, a user-friendly interface, and collaborative tools that cater to both technical and non-technical users. Qlik offers robust analytics through its associative data model, which allows users to explore data freely and gain insights quickly.

Pros

• Associative data model for flexible data exploration
• Allows data users to embed data in external applications
• Provides enhanced collaboration tools for teams within the application

Cons

• Offers a comparatively lower AI feature set than some competitors.
• Steeper learning curve

6. IBM Cognos Analytics

IBM Cognos Analytics is an integrated self-service solution that enables users to create dashboards and reports, leveraging AI-powered automation and insights. With IBM Watson Analytics, it offers automated pattern detection, natural language query support, and advanced analytics capabilities, allowing teams to make data-driven decisions based on real-time insights. The AI assistant helps users describe data needs and build visualizations, transforming business teams into power users and enabling data analysts to uncover deeper insights. Though a powerful tool, it is ranked here for a lack of customization on the AI features, and it can be a very complex tool with a steep learning curve.

Pros

• Integrates with the IBM tools and IBM Watson
• Supports natural language inquiries 

Cons

• Complex interface with a steep learning curve
• Can be prohibitively expensive for small to mid-sized companies

7. AnswerRocket

AnswerRocket is a search-powered AI data analytics platform. Like the name implies, it’s focused on allowing business users to ask questions in natural language and leveraging AI and machine learning to automate tasks and provide rapid insights. Its primary features are built around helping businesses efficiently get insights from their data. It has Max, an AI Copilot, that helps with tasks like sales analysis, business performance reporting, ad hoc analysis, and forecasting.

Pros

• Easy to use even for users with limited data backgrounds though its natural language querying
• Quick insights and report generation
• Good AI-driven analytics capabilities
• Suitable for business users without technical expertise

Cons

• Lacks the advanced features and functionalities compared to more established tools on this list like Domo, Power BI, and Tableau
• Integration options can be restrictive
• Smaller user community and support resources

8. Bardeen.ai

Bardeen.ai focuses on automating repetitive tasks and data workflows using AI. It can help users simplify data analysis by connecting hundreds of SaaS and website sources, enabling AI-driven automation for tasks like scraping website data, synchronizing sources, and analyzing unstructured data. It integrates well with platforms you likely use regularly, such as Google Sheets, LinkedIn, and HubSpot. Bardeen also has ready-to-use templates, allowing teams to get off the ground quickly.

Pros

• Easy to integrate with popular apps and tools
• Focused on automating repetitive tasks
• No coding required

Cons

• Limited to workflow automation rather than deep analytics
• Fewer data visualization and analysis features
• Template lacks customization features

9. Talend

With ready-to-use machine learning (ML) components, Talend is a data platform that can help users efficiently integrate a variety of AI algorithms with their data. Its Real-Time Big Data platform integrates machine learning algorithms for analytics without hand coding, enabling quick deployment of ML results to solve business problems.

Pros

• Excellent data integration and management tools

Cons

• Limited AI feature set
• Can be complex to set up and configure
• Limited direct visualization and analytics capabilities
• Requires technical expertise to fully utilize AI features

As we head toward 2025, the potential of AI in data analysis continues to revolutionize how organizations derive insights from their data. From predictive and prescriptive analytics to natural language processing, these tools equip businesses—from startups to multinational corporations—with the capabilities needed to make informed decisions and drive growth. By understanding the unique features and benefits of each tool, you can select the best fit for your organization, ensuring that your data analysis processes are efficient, insightful, and aligned with your strategic goals. Embrace the power of AI with Domo, and let it transform your data into a competitive advantage in this rapidly evolving landscape.