The demand for AI data analytics software is skyrocketing as organizations race to unlock deeper insights, automate reporting, and make smarter decisions faster. However, as this field is still emerging, it can be challenging to determine what you need from your AI data analysis tool and how to find it.
This guide to AI data analytics software will help you understand the different features available, the problems they help you solve, and how to select the best tool for your specific use case.
What Is AI Analytics Software
AI analytics, or AI data analytics, is the process of using artificial intelligence (AI) tools to accelerate data analysis. AI data analytics software, therefore, refers to the various tools that embed AI in the data analysis process. These tools are typically powered by the following:
- Natural language processing (NLP): This is the AI that understands and works with human language, whether spoken or written. It is what has made ChatGPT so popular and enabled the rise of AI text analytics.
- Machine learning: This is a core subset of AI that enables software to automatically learn from historical data, identify patterns, and make predictions with minimal human intervention.
- Deep learning: An advanced subfield of machine learning that leverages artificial neural networks with multiple layers - often called deep neural networks - to solve complex problems. Find out more here.
Together, these AI technologies enable modern analytics software to automate data preparation, accelerate insight generation, and provide predictive and prescriptive recommendations, allowing organizations to make smarter, faster, and more data-driven decisions than ever before.
How AI Data Analytics Platforms Work
Artificial intelligence, more specifically machine learning, has long played a role in data analysis. For example, Displayr has integrated commonly used machine learning and multivariate statistical methods, such as random forest and decision trees, within the platform for almost a decade. These capabilities have enabled users to perform advanced segmentation, predictive modeling, and pattern recognition without needing to write code or rely on external tools.
However, more advanced AI data analysis software that combines machine learning with NLP and deep learning is a more recent development. These tools typically include features such as:
- Automated data preparation that quickly cleans and organizes raw data - even handling missing values - to get datasets ready for analysis.
- Smart reporting that updates insights and visualizations in real time as new data comes in.
- User-friendly dashboards that allow teams to interact with data, explore trends, and share findings instantly.
The result is a new generation of AI analytics software that not only accelerates traditional quantitative analysis, but also unlocks actionable insights from complex, unstructured data sources. Displayr is just one example of this shift, offering an all-in-one platform where users can seamlessly integrate, analyze, and visualize both structured and unstructured data, automate reporting, and collaborate in real-time - all powered by state-of-the-art AI.
This convergence of machine learning, NLP, and deep learning is transforming how organizations approach analytics, enabling them to uncover deeper insights, automate repetitive tasks, and make smarter decisions faster than ever before.
When To Use AI Data Analysis
AI-powered data analysis enables a faster and more accurate transition from raw data to actionable insights. However, the biggest limitation on the effectiveness of AI data analytics is the quality of the data.
Data cleaning is an essential step when performing data analysis - whether it's AI-powered or not. It's how you improve the accuracy and reliability of the analysis by removing errors, inconsistencies, and potential outliers that could disrupt the results.
Although it is now possible to automate some of the data cleaning process, you should always still manually review your data file before you begin analysis.
Looking for AI tools focused on charts and dashboards? Check out our guide to AI Visualization platforms.
Key Features to Look for in AI Data Analytics Software
Choosing the right AI data analytics software means focusing on features that maximize efficiency, accuracy, and usability for your specific needs. If you're a market researcher, you'll want a tool that makes it easy to analyze both quantitative and qualitative data. If you're a financial analyst, the best tool for you will likely prioritize robust forecasting, real-time dashboards, and the ability to handle large, complex datasets with high security and compliance standards. That said, there are some features that tend to make a solution stand out across multiple industries. These include:
- Seamless data integration from multiple sources: Including URL, SQL, databases, web scraping, third-party apps, and more. This ensures that all relevant data can be seamlessly brought together for analysis.
- Automated analysis and reporting powered by AI: Automate key steps in the analytics process, from data cleaning and preparation to trend detection, anomaly identification, and predictive modeling.
- Collaboration features for teams: Effective analytics platforms support real-time collaboration, role-based access, and shared workspaces. This makes it easier for teams to collaborate on data projects, provide feedback, and ensure that everyone has access to the latest insights.
Best AI Data Analytics Software in 2025
Displayr
Displayr is leading the way when it comes to AI data analytics, providing market researchers with AI-powered, purpose-built tools that excel when working with survey data. Instead of spending days building slides or wrangling charts, researchers simply provide their questions - and the AI does the rest. It automatically identifies the right data, performs the analysis, creates visualizations, summarizes results, and even delivers strategic recommendations. Designed to accelerate every stage of the research process, Displayr’s all-in-one platform empowers teams to go from raw data to decision-ready reports in a fraction of the time. For anyone working with survey data, Displayr is the most powerful and future-proof choice available.
Verdict: Best for market researchers or consumer insights professionals who want an AI-powered research partner for faster, smarter reporting.
Akkio
Akkio is a no-code tool for creating predictive models from spreadsheets or connected data. The platform focuses on forecasting outcomes such as customer churn or sales performance. The interface is straightforward, but the platform offers limited customization compared to full-service analytics solutions. It is mainly aimed at non-technical teams that need simple predictive outputs rather than in-depth, flexible analysis.
Verdict: Most suited for lightweight predictive tasks where ease of use is more important than depth or control.
Obviously AI
Obviously AI is positioned as a no-code data science assistant. Users can upload datasets, and the platform automatically applies machine learning models, returning results in plain language alongside basic charts. While it lowers the barrier for non-technical users, the trade-off is a lack of transparency and control over methods. It can provide quick directional answers, but may not meet the needs of advanced analysts or researchers who require robust, customizable models.
Verdict: Best for quick, surface-level predictions but less suitable for complex analysis.
Powedrill
Powerdrill is another tool designed to help users upload data files (supported files limited to Excel, CSV, and TSV) and transform them into insights with a few clicks. Featuring a no-code interface, the tool automatically processes data for analysis and then provides AI-generated summaries and insights to help users explore trends and patterns. It’s best suited for individuals or small teams seeking a lightweight, fast, and intuitive way to create simple dashboards and reports without requiring deep technical expertise.
Verdict: Best budget option and for quickly generating visuals from small to mid-sized datasets.
Ajelix
Ajelix is a tool designed specifically for Excel. Positioning itself as 'Excel AI', it lets users build reports using conversational AI. With a focus on spreadsheet workflows, it allows users to ask questions about their data, generate summaries, and create formulas or scripts without needing to code. Ajelix is ideal for solo analysts or small teams who live in Excel and want to boost productivity with AI - without moving to a more complex analytics platform.
Verdict: Best for Excel-only users.
IBM Watson Studio
IBM Watson Studio is a powerful platform for data scientists who need to build, train, and deploy custom machine learning and deep learning models at scale. It offers advanced capabilities for predictive analytics, automated data prep, and model governance. Best suited to enterprises with strong technical teams, Watson Studio supports use cases ranging from fraud detection to forecasting and advanced personalization.
Verdict: Best for enterprise data science teams.
KNIME
KNIME is an open-source, visual analytics platform that enables users to build data pipelines through drag-and-drop workflows. It supports ETL, modeling, statistics, and integrates with tools like Weka, H2O.ai, Keras, Spark, R, and Python. KNIME is modular and highly scalable, making it applicable for a wide range of analytics—from research projects to large enterprise use cases.
Verdict: Best for users who need flexible, modular analytics workflows without necessarily writing code.
SAS Viya
SAS Viya is a cloud-native analytics platform that provides AI, machine learning, and data management capabilities with low-code or no-code interfaces. It supports integration with open-source tools (like R, Python, GPT-4, and Gemini Pro) and features tools for model auditing, bias detection, and compliance—often used in regulated industries.
Verdict: Best for enterprises needing AI governance and integration with open-source modeling frameworks.
Supermetrics
Supermetrics focuses on marketing analytics, providing tools that automate data extraction, transformation, and scheduling from multiple sources into destinations like Google Sheets or Looker Studio. It supports AI-powered data transformations via natural-language prompts and can streamline marketing reporting workflows.
Verdict: Best for marketing teams needing automated analytics pipelines without manual query building.
FAQs About AI Data Analytics
What is AI data analytics software?
AI data analytics software uses artificial intelligence techniques to speed up the process of preparing data, identifying patterns, building models, and summarizing results. These tools reduce manual effort and help teams move from raw data to insights faster.
How is AI data analytics different from AI data visualization?
AI data analytics focuses on generating insights, predictions, and recommendations from data. AI data visualization focuses on displaying information in charts, dashboards, or infographics. Analytics platforms may include visualization features, but their core purpose is to analyze data and support decisions, not just to present it.
What are the main benefits of AI in data analysis?
Key benefits include faster turnaround from raw data to results, automation of repetitive tasks like cleaning or preparing data, scalable modeling across large datasets, and the ability to generate predictive and prescriptive insights that improve decision-making.
Can AI replace human data analysts?
No. AI can automate repetitive steps and highlight patterns, but human analysts are still needed to validate results, provide context, choose the right methods, and communicate findings in ways that align with organizational goals.
Which industries benefit most from AI data analytics?
AI analytics is widely used across industries, including market research (for survey analysis and tracking studies), finance (for forecasting and risk modeling), healthcare (for patient outcomes and operations), marketing (for campaign attribution and media mix), and retail or supply chain (for planning and optimization).
What is the best AI data analytics tool for survey data?
Teams working with survey data are best served by platforms designed specifically for research workflows. These tools handle weighting, cross-tabulation, significance testing, and text analysis alongside AI-driven reporting features, making them more suitable for consumer insights and market research projects than general-purpose platforms.