10 Must-Have AI Tools to Boost Your Business in 2024

Artificial intelligence (AI) is transforming businesses of all sizes and across all industries. By automating tasks, analyzing data, and providing insights, AI enables organizations to work smarter and more efficiently. According to IDC, worldwide spending on AI is forecast to reach $97.9 billion in 2023. With the rising adoption of AI, companies now have access to powerful tools and platforms that leverage machine learning and deep learning algorithms to solve real-world business problems.In this article, we will cover the top 10 AI tools that are making an impact in various business functions and processes:

  1. Anthropic
  2. DataRobot
  3. H20.ai
  4. Dataiku
  5. IBM Watson
  6. SAS
  7. Sisense
  8. RapidMiner
  9. Alteryx
  10. MathWorks

1. Anthropic

Anthropic is an AI startup that develops natural language processing models. Their flagship product is Claude, an AI assistant that can summarize documents, write content, answer questions, and more.

Overview and Capabilities

Claude is powered by Constitutional AI, Anthropic’s self-supervised learning technique. Unlike other AI systems, Claude is designed to be helpful, harmless, and honest. Anthropic claims Claude will not be biased or toxic thanks to built-in safety constraints.

Claude can generate content, answer natural language questions, summarize documents, classify and moderate content, transcribe audio, translate between languages, and more. It aims to be a versatile AI assistant for knowledge workers.

Key Features

  • Content generation: Claude can write high-quality content on a wide variety of topics when given a prompt.

  • Summarization: It can analyze documents and summarize the key points concisely.

  • Question answering: Claude answers natural language questions accurately by synthesizing information from various sources.

  • Toxicity detection: Anthropic has trained Claude to identify harmful, biased and misleading information.

Use Cases

  • Automating business processes: Claude can generate reports, emails, support documents to improve efficiency.

  • Market research: Quickly analyze customer feedback, social media, news articles with Claude’s summarization.

  • Writing assistance: Claude helps create content briefs, drafts, emails to augment human writing.

  • Customer support: Claude can answer customer queries by referencing databases, documents and past conversations.

  • Moderating content: Claude can review user-generated content to identify policy violations.

2. DataRobot

DataRobot is an automated machine learning platform that enables users to build and deploy highly accurate machine learning models. It is designed to make AI accessible to every business user and organization.

Overview and Capabilities

DataRobot automates the end-to-end machine learning workflow, including data preparation, feature engineering, model building, model validation, and deployment. Its key capability is automated machine learning, which allows users with no data science expertise to build highly accurate models for various use cases like prediction, forecasting, classification, and more.

The platform supports all the leading machine learning algorithms including regression, classification, time series, clustering, deep learning, and more. It can process structured and unstructured data. The automated ML empowers users to build models with a simple drag and drop interface.

Key Features

  • Automated machine learning – AutoML allows non-technical users to build ML models without coding through an intuitive UI. It scans through hundreds of modeling options to select the best algorithm and parameters for each dataset.

  • Machine learning model management – DataRobot centralizes the model building, testing, deployment, and monitoring processes for governance and collaboration.

  • Explainable AI – It explains machine learning predictions and provides transparency into how models work. This enables users to trust and act on model predictions.

  • Bias detection – Detects bias in models to ensure predictions are fair and ethical. This prevents discrimination against protected groups.

  • Natural language processing – Perform text analysis for sentiment analysis, topic modeling, named entity recognition, and other NLP tasks.

  • Visual AI – Generate accurate predictions from image data for computer vision use cases.

Use Cases

DataRobot is used across industries like banking, insurance, healthcare, retail, manufacturing, and more to build machine learning models for:

  • Predictive maintenance – Forecast equipment failures before they occur.

  • Customer churn prediction – Identify customers likely to churn and take proactive retention measures.

  • Fraud detection – Detect fraudulent transactions, claims, accounts etc.

  • Risk modeling – Assess risk for credit, insurance, and other areas.

  • Personalization – Deliver personalized recommendations and experiences.

  • Forecasting – Improve forecasting for sales, demand, inventory and other metrics.

  • Text analytics – Extract insights from customer feedback, surveys, social media, and documents.

  • Image recognition – Categorize images, detect objects, faces etc. for computer vision applications.

3. H2O.ai

H2O.ai is an open source, artificial intelligence and machine learning platform. It aims to democratize AI by providing access to advanced algorithms and models through an easy-to-use interface.

Overview and capabilities

H2O.ai offers automatic machine learning to build and deploy models quickly without requiring data science expertise. It has capabilities for deep learning, boosted trees, random forest, generalized linear modeling, K-Means clustering, and more. The platform works with common data sources and handles tasks like data preprocessing and feature engineering automatically.

Key features

  • Automated machine learning – H2O AutoML automatically trains and tunes models to provide the best algorithms for your data. This removes the need to have deep data science knowledge.

  • Open source machine learning – As an open source platform, H2O.ai makes advanced ML accessible. Users can extend capabilities, inspect models, and customize as needed.

  • Cloud or on-premise deployment – H2O.ai offers deployment flexibility including on public or private clouds like AWS, Azure, and GCP or directly on premises.

  • Model interpretability – Explainable AI capabilities provide transparency into model predictions and behaviour. This ensures models can be understood and tweaked.

Use cases

H2O.ai is well suited for a range of AI applications including:

  • Predictive modeling – Build models for tasks like predictive maintenance, customer churn, fraud detection etc.

  • Computer vision – Develop image and video recognition models for manufacturing, healthcare, and more.

  • Natural language processing – Create chatbots, analyze text data, and extract insights from unstructured content.

  • Anomaly detection – Identify anomalies and outliers in time series data coming from IoT devices, networks, and applications.

The platform’s automated ML makes it accessible for companies without deep data science expertise. H2O.ai is used by organizations across many industries to make better data-driven decisions.

4. Dataiku

Dataiku is an end-to-end platform for data analysts, data scientists, and engineers to collaborate and deliver value from data. It provides capabilities across the data value chain – data preparation, exploration, analytics and reporting, machine learning, and deployment.

Overview and capabilities

Dataiku enables collaboration so teams can work together on data projects. It connects to data sources, prepares data for analysis, builds and compares models, and deploys analytical solutions. The visual interface allows users to develop workflows with drag and drop. Dataiku automates and scales data processing pipelines. The platform is enterprise-ready with governance, security, and management capabilities.

Key features

  • Visual workflow editor for data preparation and modeling
  • Automated machine learning to speed model development
  • Centralized model management and deployment
  • Collaboration tools for teams
  • Connectors to data sources and business tools
  • Governance and explainability for models
  • Scalability to handle large data volumes

Use cases

Dataiku helps organizations build and scale AI by operationalizing analytics. Some examples of use cases include:

  • Predictive maintenance: Monitoring equipment sensor data to predict failures before they occur. Models are built in Dataiku then deployed to detect anomalies.

  • Customer churn prediction: Analyzing customer usage patterns and other data to identify those likely to churn. Dataiku builds models to score customers on propensity to churn.

  • Fraud detection: Continuously analyzing transaction data to detect fraudulent activities. Dataiku enables both real-time and batch analysis of data from multiple sources to flag potential fraud.

  • Personalization: Analyzing user data to build models that tailor content or recommendations. Dataiku integrates these models into applications to serve personalized experiences.

5. IBM Watson

IBM Watson is one of the most well-known AI platforms for business applications. It offers a suite of AI services including machine learning, natural language processing, speech recognition, and more.

Overview and Capabilities

Watson was originally developed by IBM to compete on the TV quiz show Jeopardy!, which it famously won in 2011. Since then, IBM has turned Watson into a robust commercial AI platform. Its capabilities include:

  • Natural language processing – Understand, interpret, and generate human language
  • Machine learning – Build custom models to make predictions and decisions
  • Speech recognition – Convert speech to text
  • Vision services – Analyze images and video for insights
  • Chatbots and virtual agents – Automate customer service and other conversations

Watson can process and analyze vast amounts of unstructured data to extract insights. It supports multiple development languages and frameworks. The platform is secure, scalable, and deployable on premises or in the cloud.

Key Features

Some key features of IBM Watson include:

  • Pre-built industry models – Watson offers pre-trained models for industries like healthcare, financial services, marketing, and more. This allows for quicker time to implementation.

  • Easy integration – Watson APIs make it simple to connect and integrate Watson services into existing business applications and processes.

  • Hybrid cloud deployment – Watson can be deployed on premises, in a private cloud, in the IBM Cloud, or across hybrid environments. This provides flexibility.

  • Secure and compliant – Watson meets key industry compliance standards including HIPAA, GDPR, and more. It provides enterprise-grade security.

Use Cases

IBM Watson is used across many industries to power AI applications such as:

  • Call center automation – Watson Assistant chatbots handle common customer service queries to improve efficiency.

  • Drug discovery – Watson analyzes vast amounts of medical research data to identify potential new drug candidates.

  • Financial risk management – Watson monitors markets to detect fraud, make recommendations, and optimize trading.

  • Talent acquisition – Watson conducts video interviews and vets resumes to identify best candidates.

  • Personalized marketing – Watson analyzes customer data to deliver tailored recommendations and offers.

IBM Watson enables businesses to harness the power of AI and cognitive computing to solve complex problems and drive digital transformation. With its robust capabilities and enterprise focus, it is one of the top AI platforms for business.

6. SAS

SAS provides a suite of tools for data management, machine learning, forecasting, optimization, and more.

Overview and Capabilities

SAS has been a leader in analytics for over 40 years. Its breadth of capabilities includes data mining, predictive modeling, forecasting, optimization, text analytics, image analysis, and more. The platform is designed to help users prepare data, build models, and deploy AI across the organization.

Key Features

  • Drag-and-drop interface for building machine learning pipelines
  • Automated modeling to find the best algorithms for your data
  • Visual data mining for exploring relationships in data
  • Text analytics for analyzing sentiment, topics, and relationships in text
  • Forecasting capabilities for time series modeling and prediction
  • Optimization engines for scheduling, pricing, portfolio analysis and more

Use Cases

SAS is used across industries for AI applications such as:

  • Predictive maintenance – Analyzing sensor data to predict equipment failures before they occur
  • Fraud detection – Identifying patterns in transactions to detect fraudulent activity
  • Customer churn – Determining customers likely to churn and taking proactive retention measures
  • Personalized marketing – Targeting customers based on predictive insights about their preferences
  • Clinical trials analysis – Optimizing clinical trial design and analyzing results to detect safety signals faster
  • Supply chain forecasting – Predicting demand to optimize inventory levels across the supply chain

The breadth of capabilities makes SAS useful for organizations looking for an end-to-end AI and analytics platform. Its ease of use makes it accessible for both data scientists and business users.

7. Sisense

Sisense is a business intelligence and analytics platform that aims to make complex data easy for users to understand and utilize. It provides self-service data analytics tools for non-technical users to easily create interactive dashboards and visualizations.

Overview and Capabilities

Sisense was founded in 2004 and is headquartered in New York. It is used by companies across various industries like finance, manufacturing, healthcare, retail, and more. The platform connects to multiple data sources like databases, cloud apps, etc. and handles large complex datasets with its in-memory technology. It then prepares the data for analysis using machine learning algorithms.

The drag-and-drop interface allows users to easily build interactive dashboards with advanced visualizations like charts, graphs, gauges, maps, and more. Users can also create custom widgets to meet their specific needs. Dashboards and analytics can be accessed on both desktop and mobile devices.

Sisense has robust collaboration features like annotating, sharing, alerting and white-labeling of dashboards. It also provides options to embed analytics into internal portals or customer-facing apps.

Key Features

  • Connects to multiple data sources
  • Scalable in-memory data engine
  • Drag-and-drop dashboard designer
  • Advanced visualizations and widgets
  • AI-driven analytics and insights
  • Real-time alerting and notifications
  • Role-based access control and security
  • White-label and embed analytics options
  • Mobile analytics across devices

Use Cases

Sisense is used across departments like sales, marketing, finance, operations, etc. to track KPIs, identify trends and patterns, forecast future outcomes, and make data-driven decisions.

Specific use cases include:

  • Sales analytics to track performance, pipelines, forecasts
  • Marketing analytics for campaign measurement, channel efficacy, customer segmentation
  • Financial reporting and analysis for budgets, projections, cost optimizations
  • Supply chain analytics to identify bottlenecks, reduce costs, streamline operations
  • Customer analytics to understand behavior, improve satisfaction, reduce churn
  • Product analytics to track adoption, usage, performance to guide development
  • HR analytics for turnover, recruitment, diversity, compensation analysis
  • IT analytics to track assets, system health, capacity planning, security

Sisense enables organizations to tap into the value of their data by making complex analytics accessible. With an intuitive interface and AI-powered insights, it empowers every employee to make data-driven decisions.

8. RapidMiner

RapidMiner is an end-to-end data science platform that provides a visual workflow designer for building machine learning models and putting them into production. Some key capabilities and features include:

Overview and Capabilities

  • Provides all steps of the data science process in one platform – data prep, ML modeling, deployment and monitoring
  • Extensible via R and Python scripts as well as REST APIs
  • Available both on-premise and in the cloud
  • Built-in support for automation, collaboration and reproducibility

Key Features

  • Intuitive visual workflow designer for building ML models without coding
  • Automated machine learning (AutoML) to automatically select algorithms and tune models
  • Over 500 data prep, modeling, evaluation and deployment operators
  • Real-time model scoring for putting models into production
  • Model management and versioning capabilities
  • Collaboration features like sharing and commenting on workflows

Use Cases

  • Building predictive models for tasks like forecasting, classification and recommendation
  • Deploying and monitoring models in production applications
  • Automating machine learning to increase productivity of data scientists
  • Enabling citizen data scientists to build models without coding expertise
  • Operationalizing analytical models and workflows for business users

RapidMiner provides a complete platform for data science teams to build, deploy and manage advanced analytics and machine learning models faster. The visual workflow design and automation make it easier for users of all skill levels to leverage AI.

9. Alteryx

Alteryx is a data analytics and data science platform that aims to make advanced analytics accessible to any data worker. It provides an end-to-end workflow for data preparation, blending, analytics, and visualization without the need for coding.

Overview and Capabilities

Alteryx offers a code-free environment to prep, blend, enrich, and analyze data. It connects to hundreds of data sources and leverages an intuitive visual interface to design workflows. Alteryx automates repetitive tasks and empowers users to perform predictive, prescriptive, and spatial analytics using drag-and-drop building blocks.

Key capabilities include:

  • Data preparation: Clean, normalize, and combine data from multiple sources.
  • Advanced analytics: Apply predictive modeling, regression analysis, clustering, and other techniques.
  • Data visualization: Create interactive reports, charts, and dashboards.
  • Sharing and collaboration: Publish and share workflows and analytics.

Key Features

Some of the key features of Alteryx include:

  • Drag-and-drop interface: Easily build workflows without coding.
  • Connectivity: Connect to data sources like cloud applications, databases, files, etc.
  • Data blending: Combine data from multiple sources into a unified view.
  • Data preparation: Tools for cleansing, transforming, and enriching data.
  • Analytics tools: Apply predictive, prescriptive, and spatial analytics out-of-the-box.
  • Visualization: Interactive reporting and dashboarding capabilities.
  • Collaboration: Share workflows, results, and insights across the organization.

Use Cases

Alteryx is used across industries for a variety of use cases:

  • Data warehousing: Build data pipelines for warehousing and analytics.
  • Self-service analytics: Enable business users to prep, blend, and analyze data without IT help.
  • Data science: Prep and pre-process data for machine learning and AI.
  • Marketing analytics: Analyze customer data for segmentation, targeting, and campaign optimization.
  • Financial analytics: Perform risk analysis, fraud detection, portfolio optimization.
  • Operational analytics: Optimize supply chain, manufacturing, and other operations.

In summary, Alteryx empowers organizations to accelerate insights from data through automation and easy-to-use analytics tools for both business and technical users. Its code-free environment makes advanced analytics accessible to citizen data scientists.


In this article, we covered 10 leading business AI tools that are empowering organizations to leverage data and make smarter decisions. From automated machine learning platforms like DataRobot and H20.ai to end-to-end platforms like IBM Watson, businesses now have access to cutting-edge AI capabilities without needing an army of data scientists.

Some key takeaways for applying AI in business effectively include:

  • Start with well-defined business problems or opportunities where AI can provide value
  • Ensure sufficient data quality and volume to train AI models
  • Combine AI with human oversight and domain expertise for optimal results
  • Focus on augmenting human capabilities rather than full automation
  • Iterate quickly using agile principles to build trust and adoption of AI

The future looks bright for AI’s role in business. As tools continue advancing and lowering barriers to adoption, AI will become a standard part of business operations and decision making. More data and computing power will fuel innovations we can’t yet imagine. But ultimately, AI will remain most powerful when guided by human goals, ethics and ingenuity. Businesses who harness AI as a collaborative technology, rather than just automation, will lead the next era of data-driven growth and innovation.

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