13 Best Artificial Intelligence Software, Tools, and Platforms of 2024

Over the past few decades, artificial intelligence (AI) has made significant inroads into the world of business software and will most likely continue to do so into the foreseeable future.

AI applications apply machine learning and deep learning to help users automate everyday tasks, helping them save time and energy, in addition to simplifying their job and improving their efficiency and productivity.

The assumption that AI will replace many jobs couldn’t be further from the truth. Instead, AI will simply make a lot of jobs easier and create several other new jobs.

If you are interested in building your own AI applications, this post provides reviews of the 13 best artificial intelligence software, tools, and platforms used to create such functionality in 2022.

What is AI Software?

Whether we realize it or not, we all use AI in one form or the other in our day-to-day lives. Applications often have AI and machine learning capabilities embedded to provide users with such functionalities as automation and prediction.

But there is a difference between AI-enabled applications and applications that are used to develop intelligent applications. Such applications that provide developers with the tools to develop intelligent applications are known as AI software.

These tools can help developers add machine learning or speech recognition functionality to an existing solution or create entirely new solutions from scratch. The software is usually code frameworks, libraries, algorithms, or developer kits.

Essentially, these tools are a set of services that provide support throughout the machine learning cycle. This includes support for data gathering, data preparation, training, testing, deployment, and scaling of machine learning models.

Eventually, AI will become the norm, so much so that it will no longer be a luxury but a necessity.

Types of AI Software

There are many different types of AI software such as AI platforms, chatbots, machine learning, and deep learning. Each type offers a different functionality that is potentially useful to businesses of all kinds

1. AI Platforms

AI platforms are the best solution for developers seeking to build intelligent applications on top of an existing platform. They provide drag-and-drop functionality like any other standard application platform and come with frameworks and prebuilt algorithms to ease and speed up the development process.

That being said, these platforms can be challenging to use for people without sufficient development knowledge, despite the presence of a drag-and-drop builder.

2. Deep Learning

Deep learning algorithms are different from machine learning algorithms in that they do not require human training and use artificial neural networks for making decisions and predictions.

Deep learning is a broad category of AI with its subcategories that include computer vision (image recognition), natural language processing (NLP), and voice recognition.

3. Chatbots

Chatbots are most commonly used for customer experience automation. They interact with customers via text and voice conversations by utilizing natural language processing.

Usually, they are the first thing a customer interacts with before being put in touch with a live chat customer service agent or call center. The more chatbots interact with users, the more they learn and improve their general intelligence. This is all thanks to the machine and deep learning functionality within the software.

4. Machine Learning

This category consists of a broad selection of libraries and frameworks that allow applications to make decisions and predictions based on data fed into the system.

Because these predominantly open-source algorithms learn —often with supervised or reinforcement learning— based on the data provided to them, they require some element of human training.

Top Artificial Intelligence Software

1. MATLAB

Best AI Software for Engineers and Scientists.

MATLAB is a numerical programming platform that allows users to analyze data, develop mathematical algorithms, and create models. Built around its matrix-based programming language, it allows users to analyze large data sets as well as design and test models rigorously.

Designed with engineers and scientists in mind, it is often applied to a wide range of purposes, including machine learning and deep learning, image processing, computational finance, IoT analytics, predictive maintenance, and more.

MATLAB is particularly beneficial for financial organizations as it develops risk and stress testing models that are high-quality, documented, transparent, and replicable.

The system provides easy-to-use tools that make it easy to create models in a matter of days instead of much longer. This is more important today than ever, given the rapidly shifting nature of the regulatory and business environments.

Because the software lets users incorporate risk-aware developer best practices, model and operational risks can be avoided when implementing model control and automation.

You can service multiple compliance regimes as well as front and middle office functions with a single risk model stack, thereby making your organization more productive.

MATLAB is also useful in the field of robotics as it can design and tune algorithms, automatically generate code, and model real-world systems all in a single integrated platform.

The software allows users to connect with and control their robots with developed algorithms. There are also tools to connect to the Robot Operating System and generate hardware-agnostic algorithms.

And users can analyze multiple data types or send control signals by connecting to a range of sensors and actuators.

Pricing

MATLAB offers four pricing plans. There’s a Standard plan, which costs $2,250 for a perpetual license or $940 for an annual license. There’s an Academic Use plan, which costs $550 for a perpetual license or $275 for an annual license.

There’s a Home plan, which costs a flat $95. Then there’s the Student plan, which costs $29 for a student license or $940 for a student suite license.

Pros

• The online community forum is a great support resource
• Intuitive data processing and analysis
• Rich feature set
• Easy to use

Cons

• Steep learning curve
• Software is resource-heavy and takes up too much storage and memory
• Proprietary programming language is not open-source
• Can very quickly get expensive

2. New Relic

Best AI Software for App Performance Monitoring.

New Relic is one of the most well-established players in the app performance monitoring (APM) space. Founded in 2008, it has grown rapidly into a program that thousands of users rely on to deliver software performance insights.

Besides its APM functionalities, New Relic also offers a suite of other tools: Infrastructure, Browser, Insights, Mobile, and Synthetics.

There are also plugins for popular technologies such as Elasticsearch, Amazon Web Services, NGINX, HAProxy, and MySQL. Though these plugins are not officially developed or supported by New Relic, they are maintained by partners and third-party users.

New Relic Mobile is specifically tailored towards helping mobile developers and improve their app performance. If your mobile app is very important to the success of your business, you should consider using New Relic’s Mobile Monitoring tool.

Pricing

After a 14-day free trial, New Relic’s APM costs anywhere between $9.37 to $200 monthly per host when billed annually. The pricing varies according to the number of hours each host/instance is running, the total number of hosts/instances, and the size of the host/instance.

Pros

• Most popular APM on the market
• Offers several products for different monitoring needs
• Plentiful tutorials to help you get set started with the system

Cons

• Reliance on Apdex scores comes with a steep learning curve and can be meaningless or misleading to most users
• Pricing depends on several factors, making it difficult to estimate how much the program will cost you
• Each product is priced separately so costs can quickly add up if you need to invest in all of them

3. TensorFlow

Best Open-Source AI Library for Numerical Computation and Large-Scale Machine Learning.

Created by Google Brain for internal company use, TensorFlow is now an open-source symbolic math library designed for machine learning operations.

TensorFlow is fully capable of doing things like recognizing places in images, offering on-point translations, providing accurate search results, and accurately identifying voices.

Although originally used by Google in a variety of their applications, it has since been adapted for several other uses since becoming an open-source platform. With it, teams and individuals can develop their own intelligent applications and enjoy the results based on cognitive foundations.

While TensorFlow works best on GPUs, it is perfectly capable of drawing power from the CPUs of computers that do not have a dedicated graphics processor. In other words, developers with limited computing resources can still use the software to create projects.

Also, TensorFlow recently introduced eager execution which is a feature that makes debugging a simplified process and eases the building and training of dynamic graphs. Additionally, users can use Python control flow within the software’s API and instantly inspect and debug their graphs.

Pricing

TensorFlow is free to use

Pros

• Eager execution feature simplifies the debugging process
• Excellent customer support
• Boasts industry-leading performance

Cons

• Does not support Windows
• No CPU support for Nvidia
• Very steep learning curve
• Only supports the Python programming language

4. InRule

Best AI-Powered Business Decision Platform.

InRule is a decision platform with a business rule engine (BRE) that helps organizations to automate and change their business logic without any programming effort. With the platform, users can integrate predictive logic and automated decisions into their applications.

InRule makes business decisions more transparent by separating business logic and predictive outcomes from the application code. Businesses can deliver personalized, highly complex, and contextually rich experiences.

Because InRule creates a single source of truth, businesses can quickly adapt to changing regulations, competitive threats, or shifting market conditions. Operational efficiency is improved and billions of mission-critical decisions can be executed from anywhere in the world in a flexible and scalable manner, whether on-premises or in the cloud.

Its author-first approach means that both technical and non-technical authors can write and manage decision logic. This improves the accuracy and consistency of determinations and that the complexity of regulations is consistently agile, actionable, and auditable.

With the vocabulary feature, users can create business-specific and plain language phrases to custom functions, expressions, complex mathematical equations, and even machine learning models. All of these can be used to author rules in any way supported by the platform.

Users can also create and save data for reusable test scenarios, check decision logic, and get explainable results without having to maintain a separate testing environment.

Moreover, the irCatalog feature allows organizations to set up roles and permissions, as well as version rule applications. Multiple authors can safely edit and merge changes transparently and with ease. And, if the administrator chooses, rules can be created in a development environment and only pushed to production after review.

Pricing

Pricing is on a quote basis so you’ll need to contact the vendor for pricing information.

Pros

• Great customer experience with speedy response times
• Excellent training and documentation
• Top-notch security with OAuth/SAML standard for Access Management

Cons

• Debugging capabilities can be improved
• No pricing plan for enterprise users. Licensing is only available per user.

5. ArcGIS

Best AI-Powered Geographical information Software.

ArcGIS is a geographical information software that allows organizations to create, analyze, manage, and share spatial information. Organizations can connect maps, people, data, and apps to make data-driven decisions.

Being a cloud-based solution, it is accessible anytime and anywhere. It features tools like spatial analysis & data science, field operations, imagery & remote sensing, 3D GIS, and mapping.

ArcGIS features advanced mapping capabilities such as 2D maps, 3D storytelling, 3D landscapes, 3D animation, and more. Furthermore, you can create maps based on the geospatial data stored in the Esri database.

It is easy to build accurate and dynamic maps with easy access to powerful features and tools like layouts, multiple maps, and multi-threaded geo-processing. Advanced 3D visualizations also allow you to create beautiful and highly functional maps that you, your colleagues, and your clients can appreciate.

The software features an intuitive, sophisticated, ribbon-based contextual UI that puts all the features you need right at your fingertips whenever you need them.

Moreover, the interface can be tailored to suit you and other users’ exact preferences. You can set up your workspace so that features that you use most frequently are within easy reach.

You can also set up the system to automate repetitive tasks. ArcGIS allows you to streamline everything, accelerate your tasks, and get more done in less time.

Additionally, you can instantly share any and every map you create within ArcGIS as a web scene or web map via ArcGIS Online or ArcGIS Enterprise. This makes collaboration between departments, teams, and individuals a breeze.

Pricing

Pricing is on a quote basis; so, you’ll need to contact the vendor to get a plan that best meets your needs.

Pros

• The platform is incredibly powerful
• Reporting is impeccable
• Boasts advanced statistical tools
• Can effectively handle large amounts of vector data

Cons

• Prices for the standard license can be scary high
• Not open-source
• Crashes occur often
• UI is not nice to look at

6. Clarifai

Best AI Software for Computer Vision.

Founded in 2013 by Mark Zeiler and headquartered in New York, Clarifai specializes in AI-powered computer vision technology. It works mainly using convolutional neural networks.

Although Clarifai initially only offered an API, they added custom training and visual search functionality to the platform in 2016. They also launched a mobile SDK in 2017. And in 2018, they released their on-premises solution.

The platform features Automatic Tagging, which is a feature that shows all the relevant classes and subclasses that an image falls under. A content modification feature provides all the information relevant to an image via pre-trained deep neural network architecture.

The platform also offers Workflow Management. You can upload images, videos, or text data onto the platform, share it with your team members, and they can annotate it with the tools present on the platform.

Clarifai contains a model gallery with pre-trained models that can be used to speed up your workflow. You can evaluate your models and retrain them to get rid of misclassifications.

Other features include ready-to-use models such as vehicle and people detector; demographics detection (that is, the ability to predict the age, gender, and ethnicity of people by their faces); face and text embeddings; NSFW content detection; food, color, and textual detection; and so on.

Pricing

Clarifai is available in four plans: the Community plan, which is free forever and offered for personal and academic use; the Essential plan, which starts at $30/month; the Professional plan, which starts at $300 per month; and the Enterprise plan, which is priced on a quote basis.

Pros

• Offers similarity searching and rapid tagging within the UI
• Intuitive and easy-to-use UI design

Cons

• Exporting models for offline usage requires the purchase of support and SDK services

7. Wipro HOLMES

Best AI Platform for Business Process Automation.

Wipro HOLMES is a machine learning and AI platform that offers cognitive services for business automation. It allows businesses to automate repetitive tasks so that employees are more productive and efficient.

Wipro HOLMES equips companies for future challenges through its predictive system. This is made possible by algorithms that allow it to read, analyze, and understand data so that it can spot patterns and make fairly accurate predictions for future outcomes based on existing trends.

The application depends on knowledge databases to make smart decisions and help users perform their tasks speedily. In other words, users can access knowledge that has been accumulated and aggregated by AI over time.

Wipro HOLMES also offers a control panel for drones and robots.

Pricing

Wipro HOLMES is priced on a quote basis. So, you’ll have to reach out to the vendor for a quote.

Pros

• It uses Arago Ai
• Offers a wide range of user interaction methods, including ChatOps

Cons

• Integration among components is lacking
• Reporting functionality can be improved
• Scalability is an issue

8. IBM Watson Studio

Best Code-Optional AI Platform for Businesses.

IBM Watson Studio is a data science and machine learning platform built for AI-powered businesses.

It helps organizations scale their data science operations by simplifying the process of experimentation to deployment, thereby speeding up data exploration and preparation, as well as model development and training.

IBM Watson Studio is code-optional, which means that both techies and non-techies can take advantage of the platform’s open-source tools via its visual drag-and-drop tools.

The platform offers automated data preparation, essentially eliminating repetitive, time-consuming tasks. There’s also text analytics which is useful for gleaning insights from unstructured data. And you get a drag-and-drop visual model builder with SPSS Modeler.

Additionally, you get broad data access, including flat files, spreadsheets, and major relational databases. Plus, there’s an advanced graphics engine for creating stunning data visualization, as well as support for Python 3 Notebooks.

There are several options for deployment. You can have the platform downloaded straight to your Windows or Mac machine via the IBM Watson Studio Desktop app. Or you can opt to have it run completely on IBM’s cloud via IBM Watson Studio Cloud.

There’s the option to have it installed on any cloud of your choosing via IBM Cloud Pak for Data. And finally, you can opt for the IBM Watson Studio Premium for IBM Cloud Pak for Data, which adds IBM SPSS Modeler, IBM Decision Optimization, and Hadoop Execution Engine.

Pricing

You will need to contact the vendor for pricing information as pricing is quote-based.

Pros

• Wide variety of tools
• Integrates nicely with many other technologies

Cons

• Frequently unintuitive UI

9. Salesforce Einstein

Best AI-Powered Sales Data Processing Software.

For organizations that require AI-powered sales data processing, Salesforce Einstein is the platform of choice. The platform delivers accurate predictions and suggestions that are tailored to a business’s specific needs based on the information that it is fed.

It also comes with automation features to offer in-depth insight into your customers and help you connect with them on a much deeper level. The platform allows sales reps to take advantage of data science features, even without any prior training.

You also get myEinstein, which is a platform that allows users to create their intelligent applications. This means that businesses can create custom solutions that are tailored to their exact needs.

Salesforce Einstein provides accurate predictions with sales marketing data, equipping sales reps with the knowledge necessary to make smart recommendations to customers and keep them engaged throughout the customer journey.

The application analyzes your customers’ behavior and stays a step ahead of them at all times by tailoring the journey to each individual.

On top of that, the software can provide insights into how your products are used by processing images containing the products on social media, even if the name of the product, company, or relevant keywords are not shown on the image or used in the text.

The platform empowers sales teams to be more productive by providing data to help them identify the most profitable opportunities and arrange their priorities accordingly. It is a sales team’s personal data scientist that helps them identify new contacts and stay up-to-date on business news.

Pricing

The platform is integrated with Salesforce Cloud products. You’ll need to contact the vendor for a quote.

Pros

• Allows you to generate predictions based on existing data sets

Cons

• Expensive

10. Microsoft Azure Machine Learning Studio

Best Machine Learning Software for Collaboration.

Microsoft Azure Machine Learning Studio is a collaborative, drag-and-drop machine learning software designed to help developers and data scientists design, test, and execute predictive analytics solutions within minutes.

It is a simple but powerful application that publishes models as web services that can be used by BI tools and applications like Microsoft Excel. It offers best-in-class algorithms and a visual authoring environment for non-techies to execute ideas quickly without writing any code.

Built for applied machine learning, Microsoft Azure Machine Learning Studio features hundreds of inbuilt packages and support for adding snippets of custom code into your machine learning experiments.

It also features other salient features like an intuitive GUI, anomaly detection, support for R scripts, predictive modeling, disaster recovery, and valuable documentation.

Users who deal with Big Data and AI will find Microsoft Azure Machine Learning Studio to be an ideal predictive analysis tool. Thanks to its advanced cloud-based analytics, it makes machine learning more accessible for businesses.

One of its most endearing features is its interactive workspace. This feature allows you to pull data from multiple sources to create predictive analysis models.

Transforming and analyzing your data via statistical functions and data manipulation is an interactive and intuitive process, and you can come easily up with results.

Pricing

Microsoft Azure Machine Learning Studio is available in two plans: a Free plan and a Standard plan, which starts at $9.99/seat/month, and $1/studio experiment hour.

Pros

• Very user friendly
• It is cloud-based; so, it does not rely on your available computer resources
• Relatively inexpensive, especially for a low-code tool
• Supports R and Python for custom code embeds
• Interconnects perfectly with the Microsoft Office environment.

Cons

• Compared to other machine learning programs like R, it is quite limited
• No easy way to connect with Tableau
• Always requires a stable internet connection, being cloud-based

11. Dataloop

Best AI-Powered Video and Image Annotation Software.

Dataloop is a video and image annotation platform designed to help businesses manage data workflows, generate training datasets, and run production models to accelerate vision AI.

Organizations can classify entire clips, selected scenes, or individual frames, and duplicate annotations across multiple image sequences on a unified platform.

Enterprises can streamline communication between data managers, annotators, and engineers by using embedded validation and quality tools.

Using Python SDK and REST API, businesses can create custom plugins and assemble event-driven automation pipelines. Also, managers can prioritize items for review or annotation using the sorting and filtering capabilities of the platform.

Moreover, administrators can track the time spent on each label, dataset, and action to gain insights into workforce productivity. They can review annotations, manage, upload, and export query data, and create QA workflows to ensure data validation.

Dataloop also includes several other features such as deep learning, document classification, data quality control, image segmentation, machine learning, ML algorithm library, and process workflow automation.

Pricing

Dataloop is priced on a quote basis. Contact the vendor directly for pricing information.

Pros

• Excellent customer support
• Support for Python SDK
• All-in-one products for building successful AI models

Cons

• UI can sometimes be unintuitive

12. Amazon Alexa

Best AI Software for Home Automation.

Amazon Alexa is a voice-controlled virtual assistant that accepts voice commands to perform tasks of all kinds ranging from simple and mundane ones to complicated ones. You can ask questions in natural language, order items online, set reminders, and create to-do lists.

Alexa comes bundled with the Amazon Echo and Dot smart speakers. There are over 100 distinct products that have Amazon Alexa built in.

The list of skills Alexa can perform increases daily as Amazon allows users to create their own skills through Amazon Blueprints and publish them in the Amazon Skills Store. Other people can find these skills in the store and use them on their Alexa devices.

Alexa can also be useful in helping people establish bedtime and morning routines. All you have to do is assign a specific command, and that command will trigger a series of events that you program into the system.

For example, you could set the system to turn all house lights off, turn on an ambient noise machine, and arm the security system simultaneously when you say, “ Alexa, goodnight.”

Alexa also comes with Alexa Guard, which is a helpful security feature that monitors the activity inside and outside your home while you are away.

So, if the system picks up on unusual sounds, such as breaking glass, for example, Alexa will immediately send you a notification to check things out.

Alexa Guard Plus, which is the paid version of this feature, will give you access to Amazon's emergency hotline, and several other security tools.

Pricing

Amazon Alexa comes bundled with Amazon-designed services and products like Amazon Echo, Echo Plus, Echo Show, Echo Dot, and other devices in the Echo family.

It also comes with Amazon Fire TV, Fire Tablet, and other third-party devices that have Amazon Alexa built-in.

Pros

• Alexa is hands-free

Cons

• Software updates can take several days to reach your device
• Echo has no internal battery and so must be plugged in to use

13. Google Cloud AI

Best Overall AI Platform.

Google Cloud AI is a platform that includes AI building blocks, accelerators, and other AI Solutions. The AI solutions are aimed at business managers rather than data scientists and are a fairly recent addition to the platform. These services include consulting from Google or any of its partners.

Business managers can use the AI building blocks without intimate knowledge of programming or data science. But even skilled data scientists use them for practical reasons, especially when they need to get things done without extensive model training.

These building blocks are easy-to-use components that you can add to any of your applications to add language, sight, conversation, and structured data. Several of the building blocks are pre-trained neural networks.

But if they don't fit your requirements out of the box, they can be customized with transfer learning and neural network search.

AutoML Tables automate the process data scientists use to find the best machine learning model for a tabular data set.

The AI platform and accelerators require coding skills, training time, and data preparation techniques. As such, they are generally for serious data scientists.

Pricing

Use the pricing calculator to estimate your training and prediction costs.

Pros

• Easy to set up
• Integrates well with the Google BigQuery and Google PubSub
• COmes with CV algorithms and video processing modules/APIs that make it a good choice for image/video processing

Cons

• Some of the built-in AI modules do not have up-to-date documentation
• Customization of existing modules and libraries is difficult and has a steep learning curve
• Support for Python and other programming languages can be improved

Popular Artificial Intelligence Software Categories

AI & Machine Learning Operationalization Software

With AI and machine learning operationalization software, users can manage and monitor machine learning models as they are integrated into business applications Many of these tools also facilitate the deployment of models.

These tools allow businesses to take machine learning models and algorithms built by data scientists and put them into action. The software allows you to automate the deployment of models, monitor the performance and accuracy of models, and improve on those models.

Some of this software allows you to do all of this in a collaborative way, which means that businesses can scale machine learning across the entire company.

They may also provide provisioning, security, and governance functionality. So, only team members with appropriate authorization can make version changes or deployment adjustments.

Some may even offer a way to manage all the machine learning models across your entire business from a single location.

All that being said, AI and machine learning operationalization software are more focused on the maintenance and monitoring of models, as opposed to deployment.

Some providers of AI and machine learning operationalization software include InRule, Algorithmia, Cortex Certifai, UbiOps, cnvrg.io, Spell, and many others.

Conversational Intelligence Software

Conversational intelligence software records and analyses conversations between sales reps and customers to help sales teams quickly find key information from conversations, speed up the process of data entry into their CRM, and pass information to other team members.

For managers and executives of sales and customer support teams, it makes coaching and training team members easier as they can listen to key points of conversations (such as house sales reps answering questions and responding to objections).

Conversational intelligence software can also bridge the alignment gap between the sales and customer success teams and help pass along key insights to other teams within the organization.

This software is different from a standard call recording or cloud meeting software because it analyzes conversations for you, automatically takes notes during conversations, and allows you to retrospectively access important moments from conversations.

Some providers here include IBM Watson Assistant, TARS, Zendesk Support Suite, Conversica, Ada, Azure Text-to-Speech API, and many more.

Data Labeling Software

Data labeling software refers to a set of tools that businesses can use to turn unlabeled data into labeled data and build corresponding AI algorithms. They are also known as data annotation software, training data software, data tagging software, or data classification software.

When a user inputs a data set within such a tool, the user can provide a label themselves or have the software provide a label through machine learning-assisted labeling or a human task force.

Some platforms allow users to choose a combination of all three labeling methods. On such platforms, the user or the system itself can choose who or what does the labeling depending on specific requirements like quality, price, and speed.

Depending on the type of data supported (for example, image, audio, video, and text), data labeling tools differ.

The type of annotation also varies and may include name entry recognition (NER) and sentiment detection for text data; image segmentation and object detection for image data; and transcription and emotion recognition for speech annotation.

Metrics like consensus, ground truth, and others are often used to assess the quality of labels.

Some providers here include Clarifai, Dataloop, Appen, Amazon Sagemaker Ground Truth, DataSaur, SuperAnnotate, and many more.

Data Science and Machine Learning Platforms

Data science and machine learning platforms are used to build, deploy, and monitor machine learning algorithms. They combine intelligent algorithms with data, thereby empowering developers to create business solutions.

Some feature drag-and-drop modeling and visual interfaces with pre-built algorithms and simplistic workflows. Others require a greater knowledge of programming and coding.

Algorithms on these platforms can include functionality for natural language processing, image recognition recommendation systems, and voice recognition, in addition to other machine learning functionalities.

By their very nature, data science and machine learning platforms enable non-techie users without any programming skills to take advantage of machine learning. In this regard, they are similar to platforms as a service (PaaS) which allow basic application development.

Some providers here include MATLAB, RStudio, RapidMiner, Alteryx, Qubole, IBM SPSS Modeler, and many more.

Benefits of Using AI Software

There are many benefits of AI software and some of them are outlined below:

1. Automation

Organizations may use machine learning and AI to automate tedious and repetitive tasks. By doing this, they free up time for their employees to focus on more productive, human-necessary tasks.

2. Prediction

AI software is also intelligent enough to predict outcomes of particular situations based on data that is fed into the system. AI software can generally do this faster and better than humans can.

3. Intelligent Decision-Making

While predictive AI Solutions can make intelligent decisions, this particular benefit of AI software is intended to help human beings make intelligent decisions instead of having software do it for them. AI and machine learning can take the guesswork out of making critical business decisions by analyzing data and outcomes in similar past situations.

4. Personalization

With machine learning algorithms, software developers can create high levels of personalization and offer unique experiences to their users. This allows for powerful recommendation systems such as those used by Netflix for film recommendations and Amazon for product recommendations.

5. Creating conversational interfaces

Amazon’s Alexa, Apple's Siri, and Google Home examples of popular consumer conversational interfaces. And the use of such conversational interfaces is gradually seeping into the B2B world.

Trends Related to Artificial Intelligence Software

AI is one of the biggest tech trends of the past decade and there are quite a few trends within the trend. Two of the most important ones are embedded AI and machine learning as a service (MLaaS).

1. Embedded AI

Whether users are aware of it or not, machine learning and AI are becoming embedded in nearly all types of software. We see it in software like CRM, marketing automation, and analytics solutions.

They allow users to automate certain tasks, streamline processes, and gain a competitive edge with their predictive functionalities.

The upward trend in embedded AI will continue over the years to come, just as cloud deployment and mobile have done over the past few years. In fact, it will become so commonplace that it will be assumed and even expected by consumers.

2. Machine learning as a Service (MLaaS)

In recent years, there has been a trend in the software industry towards a much more granular, microservices structure, especially with regards to DevOps needs.

This is evidenced by the boom in public cloud infrastructure services as offered by Google, Amazon, and Microsoft who offer development and infrastructure services to other businesses on a pay-as-you-go model.

It is no different for AI and machine learning as those same companies are offering machine learning as a service (MLaaS) to other companies with pre-built algorithms and solutions that developers can take advantage of by feeding in their own data and gleaning insights.

Effectively, this trend will mean the democratization of AI as smaller businesses can save time, resources, and capital by not having to hire skilled machine learning developers.

MLaaS will only continue to grow as businesses continue to rely on microservices and the need for AI increases.

AI Software FAQ

Which Artificial Intelligence Software Should I Pick?

Whatever your needs are for AI software, you must pick one that meets those exact needs and can scale to accommodate your future needs. Here are some recommendations:

• The best overall AI software is Google Cloud AI.
• MATLAB was made for engineers and scientists.
• For monitoring app performance, New Relic is your best bet.
• It doesn’t get better than Salesforce Einstein for sales data processing.
• To automate business decisions, it’s difficult to do better than InRule.
• And for home automation, Amazon Alexa leads the pack.

Explore Further

• 13 Best Text-to-Speech Software of 2023
• 10 Best AI OCR Software of 2023 (Free & Paid Tools)
• The Ultimate List of Artificial Intelligence Statistics for 2023
• Machine Learning Statistics for 2023

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