Top AI Software Solutions for Business Operations and Workflows

The demand for artificial intelligence software (AI) has increased significantly in recent years, and organizations of all sizes are adopting artificial intelligence to stay competitive. During the past couple of years that I’ve spent researching this type of technology, I’ve discovered a range of incredible AI tools, and at what often feels like a moment’s notice, these tools optimize their features and introduce new capabilities to meet the growing needs and demands of users.

I’ve analyzed various artificial intelligence software solutions for different use cases — primarily focusing on business scenarios — to help you determine the best and most relevant AI applications for your needs. This list considers software from AI companies that serve both technical and non-technical teams. Let’s take a look.

Comparison Chart: Top AI Software

The top AI software solutions on the market today cover a broad spectrum of industries and specific use cases. To provide a point of comparison across these categories, we’ve summed up the key characteristics and strengths of each of our top choices in the table below.

Tool: Azure Machine Learning Studio
Company: Microsoft
Best For: Data Scientists & Azure Users
Key Features & Capabilities: Automated machine learning, Integration with Azure cloud & products, Drag-and-drop designer interface

Tool: Databricks Data Intelligence Platform
Company: Databricks
Best For: Collaborative Data Workflow Management
Key Features & Capabilities: Data intelligence engine with semantic understanding, AI & data governance, Data lakehouse storage & infrastructure

Tool: Amazon SageMaker
Company: Amazon
Best For: AI & ML Model Deployment
Key Features & Capabilities: Foundation model building, Fully managed infrastructure for AI & ML model lifecycle management, Human-in-the-loop & quality management features

Tool: DataRobot AI Platform
Company: DataRobot
Best For: Rapid Model Building & Model Lifecycle Management
Key Features & Capabilities: In-platform model building & governance, Predictive modeling, Modeling data preparation

Tool: IBM watsonx
Company: IBM
Best For: AI Governance & Explainability
Key Features & Capabilities: Open, hybrid, governed data store, Foundation & fine-tuned AI/ML model-building studio, AI assistant for coding, orchestration, & other tasks

Tool: H2O AI Cloud
Company: H2O.ai
Best For: AutoML
Key Features & Capabilities: Custom generative AI modeling, Distributed, in-memory processing, Managed & hybrid cloud deployment options

Tool: C3 AI Platform
Company: C3 AI
Best For: Industry-Specific Model Development
Key Features & Capabilities: Enterprise AI applications & development, Data governance & lifecycle management, ModelOps & DevOps workflows

Tool: Glean
Company: Glean
Best For: Organizational Knowledge Management & Search
Key Features & Capabilities: Workplace vector search with semantic-understanding-driven LLMs, More than 100 prebuilt business application connectors, Generative AI assistant

Tool: Microsoft Copilot
Company: Microsoft
Best For: General Workplace Task Assistance
Key Features & Capabilities: Built-in assistant in Microsoft products, like Microsoft 365, DIY AI copilots & Copilot Studio, Enterprise-grade data protection

Tool: Jasper
Company: Jasper
Best For: Digital Marketing Agencies & Teams
Key Features & Capabilities: AI-powered social media & blog writing with smart content strategy, Deep learning for brand voice & style guides, Generative AI chatbot & art generation tools

Tool: Fireflies
Company: Fireflies.ai
Best For: Notetaking Assistance
Key Features & Capabilities: Video meeting transcriptions & summaries, AI-powered content searches, Conversational intelligence & analytics

Tool: Salesforce Einstein
Company: Salesforce
Best For: Unified Sales, Marketing, & Service Support
Key Features & Capabilities: Einstein Copilot, AI-powered conversation & data insights, Cloud/hub-specific AI tools & use cases

Tool: Chorus
Company: ZoomInfo Technologies
Best For: Client-Facing & Sales Teams
Key Features & Capabilities: Sales call analysis, Deal intelligence & pipeline management, Snippet tool

Tool: Tidio
Company: Tidio
Best For: Customer Success Teams
Key Features & Capabilities: Lyro AI chatbot, FAQ Wizard for ML-generated FAQs, Smart prioritization of customer conversations

Tool: GPT-4 (ChatGPT)
Company: OpenAI
Best For: Overall Generative AI Platform
Key Features & Capabilities: Multimodal content generation, Workspace & collaborative versions, Free access to GPT-4o in all subscriptions

Tool: Vertex AI & Gemini
Company: Google (Alphabet)
Best For: Integrated Generative AI & Internet Experience
Key Features & Capabilities: Gemini model library access, Multimodal content generation, Real-time internet connection & data updates

Tool: Claude 3
Company: Anthropic
Best For: Ethical Generative AI Strategy
Key Features & Capabilities: Free online version available, Content generation with large context windows, Constitutional AI approach

Tool: GitHub Copilot
Company: GitHub (Microsoft)
Best For: Generative Coding & Developer Assistance
Key Features & Capabilities: Vulnerability prevention system, AI-assisted coding & code completion, Messaging & natural language approach

Tool: Cohere Command
Company: Cohere
Best For: Enterprise API Usability
Key Features & Capabilities: Business-focused model customizations, REST API with code samples, Command R & R+ model options

Tool: AI21 Studio
Company: AI21 Labs
Best For: Task-Specific Models
Key Features & Capabilities: Task-specific language modeling, Custom modeling & fine-tuning, Playground environment

7 Best AI Software for Model Building & Governance

Cloud and other technology companies are racing to develop AI tools and solutions both for their customers and internal use cases. To meet these goals, a growing number of business users are investing in AI software that helps them build their own models, manage their training data, and govern all aspects of the modeling lifecycle for better outcomes. These are the best solutions I’ve found for model building and governance:

Azure Machine Learning Studio
Best for Data Scientists & Azure Users
Azure Machine Learning Studio is an AI solution designed to help ML engineers and data scientists train and deploy models and manage the MLOps lifecycle. With this tool, users can create a model in Azure Machine Learning or use a model built from an open-source platform, such as PyTorch, TensorFlow, or scikit-learn. They can also design and build custom models and algorithms to gain insights from data, deploy them in production, and monitor the models’ analytics results as they evolve. Azure Machine Learning Studio offers several features that simplify data science and machine learning. These include automated machine learning, model management, and interactive visualizations. The Studio version of the tool is designed to be user-friendly and low-code/no-code, so in some ways, its capabilities are limited; the greater Azure Machine Learning environment offers support for more complex use cases and experienced developers who want to go further with their models in Azure.

Pros & Cons

Pros: Usability of tool & drag-and-drop designer, Comprehensive documentation & support
Cons: Learning curve for new users, Can be pricey
Pricing: Azure Machine Learning Studio (classic) is available in two tiers: Free and Standard. The standard plan costs $9.99 per ML studio workspace per month, or $1 per studio experimentation hour. Production Web API pricing works like this: Dev/Test: $0 per month, Standard S1: $100.13 per month plus overage rates of $0.50 per 1,000 transactions and $2 per API compute hour, Standard S2: $1,000.06 per month plus overage rates of $0.25 per 1,000 transactions and $1.50 per API compute hour, Standard S3: $9,999.98 per month plus overage rates of $0.10 per 1,000 transactions and $1 per API compute hour.
Key Features: Data labeling and preparation capability, Automated machine learning with monitoring and analysis, Drag-and-drop designer, Open-source libraries and frameworks, Hybrid and multi-cloud model training and deployment

Databricks Data Intelligence Platform
Best for Collaborative Data Workflow Management
The Databricks Data Intelligence Platform is an innovative solution that combines the strengths of Databricks’ data lakehouse storage infrastructure with several different AI and data workflow management features, including a comprehensive layer of AI, data, and analytics governance capabilities. Many enterprise teams are opting for this solution to develop and manage all aspects of AI technology for their businesses, as it is one of the best tools on the market for prepping and getting to know your data at all levels. Built on the Databricks open data lakehouse foundation, the Data Intelligence Platform combines the strengths of a lakehouse with generative AI to create its trademark Data Intelligence Engine, which quickly learns and operates in your organization’s data style and lingo. The platform also includes a range of capabilities to support business intelligence, ETL, data warehousing, data analytics, orchestration, and data science. Most recently, Databricks updated the platform with a new addition: DBRX, an open LLM that is now available to all users and is competitive with tools like GPT-3.5.

Pros & Cons

Pros: Connection to high-powered, industry-leading data lakehouse, Comprehensive unified governance layer
Cons: Steeper learning curve for less-technical users, Databricks Unit (DBU) pricing structure can get expensive & confusing
Pricing: The Databricks platform itself appears to be “free,” but to actually do anything with it, you’ll need to pay for DBU-based product usage. Pricing looks like this, but can be variable, depending on selected cloud, region, volume discount privileges, and other factors: Workflows: Starting at $0.15 per DBU, Delta Live Tables: Starting at $0.20 per DBU, Databricks SQL: Starting at $0.22 per DBU, Interactive Workloads: Starting at $0.40 per DBU, Mosaic AI Training and Serving: Starting at $0.07 per DBU
Key Features: Databricks Unity Catalog for unified governance layer that covers data and AI, Mosaic AI for AI lifecycle management, Vector search and feature engineering, Platform built on data lakehouse, Model creation, tuning, and deployment

Amazon SageMaker
Best for AI & ML Model Deployment
Amazon SageMaker is an ML-focused platform from one of the largest and most successful tech companies in the world. With its impressive network of third-party partners and solutions, as well as the tools and capabilities directly available from Amazon and AWS, users can effectively manage the model lifecycle through deployment in several different formats, including edge device, geospatial-data-driven, and embedded AI deployment. SageMaker includes a diverse array of tools and features to help users prepare their data, build out their models, train models, deploy models, and govern their models, all with supportive features for end-to-end ML. It is one of the best platforms for dedicated deployment support. The platform’s dedicated deployment capabilities include Model Deployment; Pipelines, for CI/CD-driven workflow management; and Edge, to help users learn how to deploy and manage models that run on edge devices on an ongoing basis.

Pros & Cons

Pros: SageMaker Ground Truth supports built-in human-in-the-loop reviews, Some features available in AWS Free Tier
Cons: Limited features & capabilities for generative AI modeling, May need to pay separately for each minor component of the platform
Pricing: Some SageMaker features and capabilities may be available in a limited format through AWS Free Tier. Otherwise, pricing is typically based on per-hour usage and is highly variable, depending on the global region in which you operate and the version of the tool you select. Sample pricing in the United States may look like this for Amazon SageMaker Studio Classic: Standard Instances: Between $0.05 and $6.509 per instance per hour used, Compute Optimized: Between $0.102 and $3.672 per instance per hour used, Memory Optimized: Between $0.151 and $7.258 per instance per hour used, Accelerated Computing: Between $0.94 and $28.50 per instance per hour used
Key Features: Data preparation, governance, and lifecycle management, including for geospatial data and ML, SageMaker Notebooks and Studio Labs to comprehensively test model ideas, HyperPod, purpose-built AI/ML training infrastructure, End-to-end ML with MLOps and Studio interfaces, Ground Truth solution for human-in-the-loop feedback mechanisms

DataRobot AI Platform
Best for Rapid Model Building & Model Lifecycle Management
DataRobot AI Platform is an automated, end-to-end platform for developing and deploying AI models, helping organizations process and analyze data to derive important business insights. The platform provides tools and resources for data science teams to build, tune, and deploy models, and it allows users to explore models from various algorithms to select the best-fit models for their datasets. The DataRobot AI Platform can be deployed as multi-tenant SaaS, single-tenant SaaS, self-managed VPC, or self-managed on-premise. It is a helpful tool for teams — including inexperienced teams — to get up and running with AI models quickly, as many of its features are automated and unified for both generative and predictive modeling. The platform includes features for deployment and operations, AI governance and management, and model building and fine-tuning. Additionally, the platform comes with particularly user-friendly visuals that are color-coded and clearly labeled.

Pros & Cons

Pros: Real-time predictions & monitoring, Helpful, automated compliance documentation
Cons: Somewhat inflexible retraining automation, Difficult to set up
Pricing: DataRobot doesn’t advertise specific pricing on its page. However, they do mention two plans: Essential 9.0 and Business Critical 9.0. More specific pricing information is available upon request.
Key Features: Modeling data preparation, Model building, validation, performance monitoring, and governance, Integration with third-party services like GitHub, Hugging Face, Streamlit, Azure Machine Learning, Amazon SageMaker, mlflow, and Apache Airflow, Support for and compatibility with data platforms like Snowflake, Google Cloud Storage, Amazon S3, Amazon Redshift, Google Big Query, Azure Synapse Analytics, SAP, Databricks, and Microsoft Azure Data Lake, Supported business apps and intelligence solutions include SAP, Salesforce, Power BI, Tableau, and ThoughtSpot

IBM watsonx
Best for AI Governance & Explainability
IBM watsonx is a new generation of Watson’s AI products and solutions that span across AI model-building, open data lakehouse storage, AI governance, conversational AI chatbots, task and efficiency management, and code generation and assistance. It is a highly modular platform, with users getting to choose between watsonx.ai, watsonx.data, watsonx.governance, watsonx Assistant, watsonx BI Assistant, watsonx Orchestrate, watsonx Code Assistant, or some combination of these tools and AI assistance services. AI governance, AI ethics, and regulatory-compliance-focused features include a commitment to only using trusted datasets for training, hands-on data and AI governance tools for users, and a hybrid and governed large-scale data store. With watsonx.governance specifically, users can benefit from a platform-agnostic governance approach that works on platforms like Amazon SageMaker, Amazon Bedrock, Google Vertex, Microsoft Azure, and watsonx.ai. Especially as AI regulations ramp up in the coming years, this is an impressive tool for organizations that want to quickly scale their governance practices.

Pros & Cons

Pros: Comprehensive, enterprise-ready features, Various model health and governance management features
Cons: Confusing pricing approach, Limited non-English capabilities
Pricing: Pricing for most watsonx platform features is based on the number of virtual processor cores (VPCs) a user requires. Three SaaS tiers are currently available: Trial, Essentials, and Standard. These tiers include watsonx.ai, watsonx.data, and watsonx.governance: Trial: Free, limited trial of watsonx.ai and watsonx.data that includes limited ML functionality and inference tokens per month, as well as 2,000 free watsonx.data Resource Units. It also now includes limited Resource Units and features for watsonx.governance. Essentials: A $0 per month tier fee plus RU-based token pricing per 1,000 tokens, ML tools and runtime based on Capacity Unit Hours used per billing month, supporting services at $3 per hour, cache optimized node at $2.80 per hour, and compute optimized node at $6.50 per hour. watsonx.governance usage costs $0.60 per Resource Unit. Standard: A $1,050 per month tier fee plus similar component-based pricing to the Essentials plan.
Key Features: AI and ML model building, training, fine-tuning, and validation, Hybrid-cloud-compatible data lakehouse store with shared metadata layer and workload optimization, Automated model and workflow documentation, Model health, accuracy, drift, and bias management, Governance features that include approval workflows, risk scorecards, and model metadata management solutions

H2O AI Cloud
Best for AutoML
H2O AI Cloud is an enterprise AI and ML platform that comes from H2O.ai, a leading AI cloud company with over 10 years of experience developing AI and ML solutions. The company’s goal is to democratize AI and make it accessible to organizations of all sizes. In addition to the core H2O AI Cloud platform, H2O.ai also offers the open-source generative AI solution, h2oGPT, which provides tools (H2O LLM Studio, a framework and no-code GUI) for data scientists and developers to build and deploy large language models and chatbot applications. Most recently, the company also released Danube2-18B, an open-source LLM that is both user-friendly and enterprise-quality. H2O AI Cloud provides comprehensive automated machine learning (autoML) capabilities and no-code deep learning engines through a flexible and scalable cloud platform. The autoML approach in particular makes it possible for users of all different backgrounds to work with the interface’s no-code UI to develop usable workflows and pipelines.

Pros & Cons

Pros: Quality customer support, Collaboration promoted through open-source technology
Cons: No pricing transparency, Limited & ineffective documentation
Pricing: H2O.ai doesn’t advertise its rates on its website. They encourage interested buyers to request a demo, and quotes will be sent after the demo based on the buyer’s needs. Publicly available pricing information from AWS Marketplace shows that H2O AI Cloud costs $50,000 per unit, with a minimum of four AI units.
Key Features: Distributed, in-memory processing, AutoML capability, Various algorithms, including Random Forest, GLM, GBM, XGBoost, GLRM, and Word2Vec, for distributed computing and for both supervised and unsupervised techniques, No-code deep learning engines, Fully managed and hybrid cloud deployments

C3 AI Platform
Best for Industry-Specific Model Development
The C3 AI Platform is a unique enterprise AI solution that supports digital transformation projects on an enterprise scale while still remaining accessible to both technical and non-technical team members. C3 AI enables users to ideate, plan, build, deploy, and maintain enterprise AI applications, including for highly specific industries and industrial use cases. For example, one set of C3 AI applications is prebuilt and designed for manufacturing problems and challenges, including inventory parameter management, supply chain variable calculations, and automated recommendations for inventory analysts. C3 AI Platform customers primarily come from highly regulated sectors with complex workflows, including manufacturing, oil and gas, utilities, financial services, defense and intelligence, government, healthcare, telecommunications, transportation, and retail. The platform includes a democratized AI studio space,