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Top 20 AI Software of 2024: Best Picks for Business Users

Friday May 31, 2024. 03:00 PM , from eWeek
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.

TABLE OF CONTENTS
ToggleComparison Chart: Top AI Software7 Best AI Software for Model Building & Governance4 Best AI Software for Business Operations & Workflows3 Best AI Software for Customer Service & User Experience6 Best AI Software for Enterprise Generative AIAI Software BenefitsHow to Choose the Best AI SoftwareBottom Line: AI Software Offers Competitive Advantages



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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
Company
Best For
Key Features & Capabilities

Azure Machine Learning Studio
Microsoft
Best for Data Scientists & Azure Users

Automated machine learning
Integration with Azure cloud & products
Drag-and-drop designer interface

Databricks Data Intelligence Platform
Databricks
Best for Collaborative Data Workflow Management

Data intelligence engine with semantic understanding
AI & data governance
Data lakehouse storage & infrastructure

Amazon SageMaker
Amazon
Best for AI & ML Model Deployment

Foundation model building
Fully managed infrastructure for AI & ML model lifecycle management
Human-in-the-loop & quality management features

DataRobot AI Platform
DataRobot
Best for Rapid Model Building & Model Lifecycle Management

In-platform model building & governance
Predictive modeling
Modeling data preparation

IBM watsonx
IBM
Best for AI Governance & Explainability

Open, hybrid, governed data store
Foundation & fine-tuned AI/ML model-building studio
AI assistant for coding, orchestration, & other tasks

H2O AI Cloud
H2O.ai
Best for AutoML

Custom generative AI modeling
Distributed, in-memory processing
Managed & hybrid cloud deployment options

C3 AI Platform
C3 AI
Best for Industry-Specific Model Development

Enterprise AI applications & development
Data governance & lifecycle management
ModelOps & DevOps workflows

Glean
Glean
Best for Organizational Knowledge Management & Search

Workplace vector search with semantic-understanding-driven LLMs
More than 100 prebuilt business application connectors
Generative AI assistant

Microsoft Copilot
Microsoft
Best for General Workplace Task Assistance

Built-in assistant in Microsoft products, like Microsoft 365
DIY AI copilots & Copilot Studio
Enterprise-grade data protection

Jasper
Jasper
Best for Digital Marketing Agencies & Teams

AI-powered social media & blog writing with smart content strategy
Deep learning for brand voice & style guides
Generative AI chatbot & art generation tools

Fireflies
Fireflies.ai
Best for Notetaking Assistance

Video meeting transcriptions & summaries
AI-powered content searches
Conversational intelligence & analytics

Salesforce Einstein
Salesforce
Best for Unified Sales, Marketing, & Service Support

Einstein Copilot
AI-powered conversation & data insights
Cloud/hub-specific AI tools & use cases

Chorus
ZoomInfo Technologies
Best for Client-Facing & Sales Teams

Sales call analysis
Deal intelligence & pipeline management
Snippet tool

Tidio
Tidio
Best for Customer Success Teams

Lyro AI chatbot
FAQ Wizard for ML-generated FAQs
Smart prioritization of customer conversations

GPT-4 (ChatGPT)
OpenAI
Best Overall Generative AI Platform

Multimodal content generation
Workspace & collaborative versions
Free access to GPT-4o in all subscriptions

Vertex AI & Gemini
Google (Alphabet)
Best for Integrated Generative AI & Internet Experience

Gemini model library access
Multimodal content generation
Real-time internet connection & data updates

Claude 3
Anthropic
Best for Ethical Generative AI Strategy

Free online version available
Content generation with large context windows
Constitutional AI approach

GitHub Copilot
GitHub (Microsoft)
Best for Generative Coding & Developer Assistance

Vulnerability prevention system
AI-assisted coding & code completion
Messaging & natural language approach

Cohere Command
Cohere
Best for Enterprise API Usability

Business-focused model customizations
REST API with code samples
Command R & R+ model options

AI21 Studio
AI21 Labs
Best for Task-Specific Models

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.

Visit Azure Machine Learning Studio

Pros & Cons

Pros
Cons

Usability of tool & drag-and-drop designer.
Learning curve for new users.

Comprehensive documentation & support.
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.

Error monitoring can be highly visual and intuitive for users of Microsoft Azure Machine Learning Studio. Source: Microsoft.

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.

Visit Databricks Data Intelligence Platform

Pros & Cons

Pros
Cons

Connection to high-powered, industry-leading data lakehouse.
Steeper learning curve for less-technical users.

Comprehensive unified governance layer.
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.

With the Unity Catalog approach in the Data Intelligence Platform, Databricks users can more easily manage permissions and privileges from a single, manageable interface. Source: Databricks.

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.

Visit Amazon SageMaker

Pros & Cons

Pros
Cons

SageMaker Ground Truth supports built-in human-in-the-loop reviews.
Limited features & capabilities for generative AI modeling.

Some features available in AWS Free Tier.
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.

While SageMaker includes a wide range of relevant AI and ML lifecycle management features, I am most impressed with Ground Truth, which offers users several hands-on ways to do human-in-the-loop or reinforcement learning from human feedback (RLHF). Source: Amazon AWS.

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.

Visit DataRobot AI Platform

Pros & Cons

Pros
Cons

Real-time predictions & monitoring.
Somewhat inflexible retraining automation.

Helpful, automated compliance documentation.
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.

As part of the DataRobot AI Platform’s main console, users can easily check system health and other performance metrics with color-coded charts and visuals. Source: DataRobot.

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.

Visit IBM watsonx

Pros & Cons

Pros
Cons

Comprehensive, enterprise-ready features.
Confusing pricing approach.

Various model health and governance management features.
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.

As you can see from this screenshot, watsonx.governance users can get a comprehensive overview of model health, compliance, and governance metrics in granular detail. The color-coded visuals are particularly helpful. Source: IBM.

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.

Visit H2O AI Cloud

Pros & Cons

Pros
Cons

Quality customer support.
No pricing transparency.

Collaboration promoted through open-source technology.
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.

H2O AI Cloud has a very accessible interface with tabs that are simple to navigate. I particularly appreciate how easy it is to pin and spot your most important AI apps. Source: H2O.ai.

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, open architecture, and a shared data and model ontology to speed up and improve model deployments.

Visit C3 AI Platform

Pros & Cons

Pros
Cons

Purpose-built tools available for complex & industry-specific use cases.
Purportedly high initial investment costs.

Robust security, model operationalization, and enterprise-ready features.
Somewhat limited customization opportunities.

Pricing
C3 AI is not transparent with its pricing information or plans. Prospective buyers will need to contact the company directly for more information.
Key Features

AI feature store and model development tools, including C3 AI Studio.
Model ops and DevOps workflows.
Deployment platform services.
Industry-specific, prebuilt AI application sets.
Data integration, preparation, visualization, and governance capabilities.

The C3 AI Platform has one of the most sophisticated and impressive dashboards on the AI software market. I was particularly impressed with this visual that helps enterprise teams identify localized outages, downtimes, and other performance issues. Source: C3 AI.
4 Best AI Software for Business Operations & Workflows

Glean
Best for Organizational Knowledge Management & Search
Glean is a generative AI platform that focuses on organizational knowledge management and enterprise search, giving employee users at all levels the tools they need to find, access, save, and share different kinds of business documents and data. All users receive access to the workplace search feature, which is accompanied by an AI assistant that can help users find answers and content that is geared for their specific role, search query, and search history and habits.
From an administrative perspective, the platform also does a compelling job of storing information in such a way that it’s easy to update outdated resources, manage privacy and compliance, and create teams and environments for productive collaboration. Many users also appreciate how Glean’s knowledge graph, plug-ins and connectors, and work hub platform combine the best of both ease of use and customizable workplace search and knowledge management.

Visit Glean

Pros & Cons

Pros
Cons

Smart and customizable enterprise search solution.
Limited customer support availability.

Intuitive UI for technical & non-technical users.
Can get expensive.

Pricing
According to a Glean representative, the product is priced per user per month and billed at an annual flat rate. Though the website does not transparently list plans or rates, the Glean customer service representative with whom we chatted shared that different packages are available based on company size and scalable goals. The company typically requires a $40,000 annual contract minimum for teams with fewer than 100 users.
Key Features

Customizable collections and Go Links for organization-specific knowledge management.
Vector search with semantic understanding and NLP for plain-language user queries.
Data governance and compliance features such as DLP reports, GDPR and CCPA compliance, and user access reviews.
Workplace search, AI assistants, and work hub for UX-focused enterprise knowledge management.
Slack and other third-party connectors for knowledge management across business applications.

Glean’s built-in AI assistants work with your organizational data to help you create specific content and fulfill tasks based on the most relevant and up-to-date information. Source: Glean.

Microsoft Copilot
Best for General Workplace Task Assistance
Microsoft Copilot is a built-in AI assistant that works within several Microsoft products. A free chat interface is available and works similarly to ChatGPT and Gemini, offering several great multimodal capabilities. However, Microsoft’s copilot technology really shines in Microsoft 365 in particular, where tools like Word, Teams, Excel, and others have built in smart assistance, content completion, automations, and workflow support for some of the most common daily and routine business tasks.
Because so many businesses rely on the Microsoft Suite tools for their business operations, this tool has quickly grown in popularity. Microsoft Copilot is also available in a handful of other Microsoft business tools, including PowerApps, Dynamics 365, and Power BI (in preview).

Visit Microsoft Copilot

Pros & Cons

Pros
Cons

Built-in AI assistance across Microsoft products.
Expensive to scale features for larger business teams.

Copilot that can expertly handle various business tasks & automations.
Most features require Microsoft subscriptions, leading to additional costs & vendor lock-in.

Pricing
Pay-as-you-go pricing is a key component of many Microsoft products, including the account users sign up for to use Microsoft Copilot business tier products. The online chat interface of Microsoft Copilot can be freely accessed or upgraded to Copilot Pro for $20 per user per month.
For the most popular business-specific Microsoft Copilot solutions, here’s what pricing looks like:

Copilot for Microsoft 365: $30 per user per month, billed annually, in addition to Microsoft 365 subscription costs.
Copilot Studio: $200 for 25,000 messages per month.
Microsoft Copilot for Sales: $50 per user per month; annual commitment required.

Key Features

Copilot for Microsoft 365 for built-in AI assistance and workflow automations.
GPT-4-powered content generation.
Microsoft Graph grounding.
Microsoft Copilot Studio for customizable copilots.
Enterprise data protection and scalability.

Microsoft Copilot can easily assist Word users with content creation, relying on existing materials to create a better document. Source: Microsoft.

Jasper
Best for Digital Marketing Agencies & Teams
Jasper is a suite of AI copilot tools that focuses on supplementing digital and content marketing efforts across social media and web channels. Its tools cover a range of features and functions, including art and image generation for ads and thumbnails, AI content templates, chatbots that provide customer and user assistance, automated end-to-end marketing campaigns, and smart understanding for brand voice and style guides.
Many users select Jasper because the platform can generate content based on the context users provide, including descriptions of what they want content to include, knowledge about specific products or services, instructions on tone of voice and audience, SEO guidance, and style guides.
Many digital marketing teams and agencies also select Jasper for its ease of use and ability to integrate with existing marketing tools. The Jasper API, Chrome browser extension, and a variety of collaboration tools make the Jasper For Business version of the platform a particularly effective tool for holistic marketing strategies and extensibility.

Visit Jasper

Pros & Cons

Pros
Cons

Multiuser & multilingual format.
Somewhat confusing interface.

Smart brand guide & styling tools.
Few features for video marketing.

Pricing
Jasper is available in three main subscription plans:

Creator: $39 per month, billed annually, or $49 billed monthly. Includes one seat and one brand voice. A 7-day free trial is also available.
Pro: $59 per month per seat, billed annually, or $125 per seat, billed monthly. Includes one seat and the ability to add up to five seats; also includes three brand voices, 10 knowledge assets, and three Instant Campaigns. A 7-day free trial is also available.
Business: Prospective buyers will need to contact Jasper’s sales team for pricing information.

Key Features

AI-powered content writing, editing, social copywriting, and content strategy.
Instant and automated marketing campaigns.
Brand voice guide uploads and content application.
SEO integrations and content optimization.
SEO, social media, content management, and other digital-marketing-friendly integrations, as well as Jasper API for custom integrations and embeds.

Jasper does a great job of creating original marketing content based on existing style guides, audience information, and other inputs from the user. Source: Jasper.

Fireflies
Best for Notetaking Assistance
Fireflies is an AI-powered note-taking platform that automates the process of capturing meeting information, including audio transcripts, text notes, and highlights. It uses machine learning and natural language processing (NLP) to transcribe meeting recordings and provide users with actionable notes to save them hours of manual post-meeting note-taking and follow-up.
Beyond its basic content summarization features, Fireflies has also continued to expand its conversation analytics and search capabilities. With these two features, users can get a more enterprise-level experience out of an otherwise-simple AI tool, learning more about what was said and how while also quickly pulling important conversational snippets out of longer transcripts. Most recently, Fireflies has come out in a mobile app version.

Visit Fireflies

Pros & Cons

Pros
Cons

Easy to set up & use.
Issues with search capability.

Speaker-focused analytics.
Occasional lags.

Pricing
Four pricing tiers are available for Fireflies:

Free: No cost for up to 800 mins of storage.
Pro: $10 per seat per month, billed annually, or $18 per seat per month, billed monthly.
Business: $19 per seat per month, billed annually, or $29 per seat per month, billed monthly.
Enterprise: $39 per seat per month, billed annually; no monthly billing option.

Key Features

Chrome extension.
CRM, Zapier, and Slack integrations.
Team and security management capabilities.
Meeting clips as sound bytes.
Topic tracker and analytics.

Fireflies allows administrators to set up team permissions to make meetings and meeting data more secure. Source: Fireflies.ai.
3 Best AI Software for Customer Service & User Experience

Salesforce Einstein
Best for Unified Sales, Marketing, & Service Support
Salesforce Einstein is a family of AI tools and solutions that are available across the Salesforce ecosystem. Whether you use Salesforce for marketing, sales, service, e-commerce, or some combination of all of these, dedicated Einstein AI capabilities have been designed to automate customer data and experience management in smart ways.
The design of Salesforce itself, with its separate clouds that work seamlessly in a united platform, makes its Einstein AI technology particularly practical and effective. Businesses can easily manage customers throughout their buyer lifecycle, from the time they’re a prospective buyer who requires dedicated marketing campaigns until they become a loyal customer who benefits from frequent nurture campaigns or customer service workflows.

Visit Salesforce Einstein

Pros & Cons

Pros
Cons

AI features incorporated into all Salesforce cloud applications.
AI only works with Salesforce products.

Trust Layer for best practices, privacy, & security.
Some mobility limitations.

Pricing
Certain Saelsforce cloud subscription tiers include access to Einstein capabilities. These are your best options for Einstein access:

Einstein 1 Sales: $500 per user per month, billed annually.
Einstein 1 Service: $500 per user per month, billed annually.
Marketing Cloud Growth Edition: $1,500 per month.

Key Features

Generative and predictive intelligence through Sales AI features.
AI for Customer 360.
Conversational analytics and recommendations.
Smart chatbot building technology.
Automated marketing campaign generation.

For the Sales AI portion of Salesforce Einstein’s solutions, business leaders can more easily predict how the whole team and individual players are performing against their sales goals. Source: Salesforce.

Chorus
Best for Client-Facing & Sales Teams
Chorus is a conversational intelligence and recording solution that helps users analyze all customer engagements across video conferencing, phone calls, and email channels to learn about their performance in these engagements. With this in-depth data, teams can work to convert prospects into paying customers and forecast future outcomes more accurately.
Chorus comes with features like sales call analysis, deal intelligence, relationship management focused on CRM data, and other advanced AI-driven insights to help teams modify actions for better customer conversations. Since its acquisition by ZoomInfo, Chorus also includes more advanced integrated intelligence about customer contacts and the company, which benefits go-to-market teams even more.

Visit Chorus

Pros & Cons

Pros
Cons

High-quality transcription & recording.
Limitations for non-native English speakers.

Useful deal intelligence & pipeline management.
Issues with search functionality.

Pricing
ZoomInfo, which acquired Chorus.ai, doesn’t publicly advertise its pricing on its website unless you fill out an information
https://www.eweek.com/artificial-intelligence/ai-software/

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