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Best Artificial Intelligence Software

Artificial intelligence (AI) software has emerged as a transformative force that has revolutionized how businesses interact with data and technology. Machine and deep learning algorithms have been integrated into intelligent applications, enabling them to perform various user tasks automatically. Automating business processes saves users’ time and energy, simplifying their jobs and allowing them to work more efficiently.

AI software's continuous learning ability enables it to adapt, evolve, and improve over time, rendering it invaluable for tackling complex industry challenges.

Best artificial intelligence software at a glance:

Best machine learning software: VertexAI
Best MLOps platform: VertexAI
Best natural language processing software: Amazon Comprehend
Best bot platform: IBM watsonx Assistant
Best image recognition software: Cloud Vision API
Best AI video generation software: Synthesia
Best data science and machine learning platform: Databricks Data Intelligence
Best data labeling software: SuperAnnotate
Best AI writing assistant: Grammarly Business

These software solutions are ranked using an algorithm that calculates customer satisfaction and market presence based on reviews from our user community. For more information, please check out G2’s Research Scoring Methodology.

To qualify for inclusion in the AI Platforms category, a product must:

Provide a platform for building intelligent, AI-capable applications
Allow users to create machine learning algorithms and/or offer pre-built machine learning algorithms for more novice users to build applications
Present a way for developers to connect data to the algorithms for them to learn and adapt

What is AI software?

Artificial intelligence software imitates human intelligence and performs tasks that require human cognitive skills. It works on complex algorithms, machine learning (ML) models, and neural networks to analyze massive volumes of data, recognize patterns, and make intelligent decisions.

Applications embed AI and machine learning capabilities to automate tasks and provide predictive features. These intelligent applications make business processes simpler and easier with AI.

However, it’s essential to differentiate between AI-enabled tools and those that help develop intelligent applications. AI software is the latter.

The best AI software provides developers with tools to build intelligent applications, whether adding ML or speech recognition to a solution or creating a new application from scratch using an AI platform. These developer tools are often built-in algorithms, libraries, or frameworks of code and developer kits that make machine and deep learning functionality for the software.

The use of AI software will eventually become nothing more than a norm: a feature that is not considered revolutionary but one that is deemed necessary. The tech industry is striving to reach that norm using AI developer tools.

Here’s an overview of the AI platforms, machine learning algorithms, and deep learning frameworks needed for artificial intelligence software development:

The debate surrounding AI's impact on the job market remains contentious and complex. Some people fear that AI adoption in business could lead to job displacement as machines might replace human workers. Others argue that AI simplifies jobs by automating tasks and streamlining processes. It will create new opportunities for workers to focus on higher-value, creative, and strategic roles.

Striking a balance between AI’s ability to augment human capabilities and ensuring the workforce's resilience and adaptability remains a significant challenge.

How to use artificial intelligence software

Artificial intelligence software can be used to solve complex problems, automate manual processes, and optimize decision-making with data analysis. It’s perfect for tasks like pattern recognition, natural language understanding, and predictive modeling. AI software improves efficiency, reduces errors, and uncovers insights that may be challenging for traditional methods.

Here are a few key steps involved in the use of AI software:

Identify needs: Start by determining the problems the company wants to solve using AI. It can involve data processing, process automation, image recognition, etc.
Choose the right tool: Once the problem area and business needs have been identified, pick the software that can offer the right solutions. Several AI algorithms like decision trees and neural networks are available, so research well and pick what suits the organization’s requirements.
Collect and input data: The next step for the user is to collect relevant, accurate, and comprehensive data that can be used to train their AI model. This data will help their software recognize patterns, make predictions, and perform all the necessary tasks.
Setup and configure tool: The user is advised to install their software along with all its dependencies and configure it properly according to their specific use case. It is also recommended that the tools be modified as required.
Train the AI software: During this period, users need to input the collected data to train the AI model. This step is necessary to improve the accuracy of the algorithm.
Test the AI software: After completing the training period, the user should test the AI software for accuracy and reliability. If the results are unsatisfactory, they can refine their data, adjust configurations, or retrain the AI model to improve its performance.
Deployment: Now that the software is trained and tested, deploy it to a production environment. This involves integrating the model into a web application, mobile app, or other systems.

ArtificiaI intelligence software features

AI software comes with features designed to mimic human intelligence and improve operational efficiencies. However, these features can vary based on their intended use for different AI software.

Natural language dialogue: This feature enables seamless and human-like communication between users and machines. It helps users engage with data and discover new insights in a conversational flow.
Process automation: Artificial intelligence solutions boost productivity by automating repetitive tasks and processes. It results in improved efficiency and reduces the need for manual intervention.
Security and privacy: This feature ensures that the security of data and models is maintained. It also addresses privacy concerns, especially when dealing with sensitive information.
Customization: AI capabilities allow users to customize algorithms and parameters to suit specific requirements. It can also extend its functionality through third-party plugins and extensions.
Monitoring and logging: With this feature, users can monitor the deployed AI models, capture and log relevant information, and generate performance reports.
Predictive analytics: AI software can use data to predict future events. This feature proves handy in sales forecasting, risk assessment, and preventative maintenance.
Real-time processing: Through this feature, the AI tool processes data and provides results in real-time. This is important for applications like fraud detection or self-driving cars.
Data preprocessing: AI technology can also be used to clean, transform, and prepare data that trains machine learning models.
Feedback mechanisms: Collecting feedback on the AI model's performance is integral to the process. It allows for continuous improvement and optimization.

Types of artificial intelligence software

There are many types of AI software, like chatbots, AI and machine learning platforms, and deep learning solutions. Each subcategory offers valuable AI features and functionalities that can help businesses advance.

Chatbots

Chatbots are one of the more refined areas of AI software and have specific applications in the business world, particularly in customer experience enhancement and process automation.

Chatbots utilize the technology of natural language processing (NLP) to conduct customer conversations via text and voice mediums. Chatbots increase human productivity by automating tactical tasks, freeing time for creativity and innovation. Chatbots are the primary point of contact for call centers and live customer service chats on websites. Businesses can help customers or prospects by building a chatbot that determines the severity of a request or the reason for contact. These solutions analyze the general theme of requests and direct customers to the right person equipped to respond to their inquiries.

Chatbots can also serve as virtual assistants or customer support tools. Because of their machine and deep learning abilities, chatbots keep improving their vocabulary and general intelligence as they interact with humans.

AI platforms

AI platforms are a set of integrated technologies and software frameworks that allow the development, testing, deployment, and management of AI applications, ML, and deep learning models. They are the ideal solution for developers trying to build intelligent applications on top of other platforms. AI platforms offer a centralized environment that provides prebuilt algorithms and code frameworks to assist in making the application from scratch.

AI platforms are a mix of open-source and proprietary products and differ from cloud platforms as a service (PaaS) software. While both these technologies provide tools and services for application development, PaaS platforms don’t specialize in AI development. With AI platforms, developers can add machine and deep learning libraries and frameworks when constructing an application, giving them an intelligent edge.

However, even with drag-and-drop functionality, these platforms may be challenging for beginners without sufficient development knowledge.

Machine learning platforms

The ML category comprises various libraries and frameworks that develop, deploy, and improve machine learning models and algorithms. These algorithms require human training and often use supervised or reinforcement learning models to learn from big data sets.

Combining ML algorithms with data sets enables businesses to derive insights and deploy new solutions at scale. ML platforms can be embedded into software as open-source algorithms, allowing the applications to make smarter decisions and accurate predictions. However, building applications through this process requires high development skills and technical expertise.

Standard machine learning algorithms include association rule learning, Bayesian networks, and clustering and decision tree learning.

Deep learning solutions

Deep learning algorithms are a subset of ML algorithms but don’t require human training. Instead, they use artificial neural network (ANN) technology to learn from data sets automatically and make predictions. ANN allows sophisticated algorithms to make decisions like the human brain. However, this is done on a small scale because it’s impossible to mimic the neural connections in the human brain.

Deep learning uses multiple layers of interconnected nodes to recognize complex patterns effectively. It’s widely used in subcategories like image recognition (computer vision), NLP, and voice recognition. These technologies use ANN and depend on the networks' deep layers of neural connections to enhance their learning capabilities.

With image recognition algorithms, applications are trained to accurately identify, interpret, and categorize visual information within images. It’s commonly used for facial recognition and object detection in autonomous vehicles. NLP enables machines to analyze and respond to human language and speech meaningfully and relevantly. This technology makes the human-machine communication more intuitive and easy. NLP is commonly found in applications like iPhone's Siri, Amazon’s Alexa, or Google Assistant.

Generative AI

The advent of generative AI has transformed the content creation game. This technique uses neural networks to identify the patterns and structures within existing data to generate new synthetic content. The input data can include text, image, sound, animation, and 3D models and is categorized and translated using deep learning and NLP. It can convert text inputs into images, turn an image into a video, and turn a video into text.

Generative AI is ideal for streamlining and automating workflows for professionals in various industries and companies. It can be used to create synthetic data to train existing AI and ML algorithms. It can also analyze complex data, allowing businesses to discover hidden trends and patterns.

Artificial intelligence software cost

The cost of AI software varies depending on factors like the application, the complexity of the tasks it performs, the number of users, and the vendor's pricing model. It can range from freely available open-source tools to enterprise-level platforms that cost thousands to even millions of dollars annually.

Here’s a general overview:

Free or open source: Some basic AI tools are available free of charge or as open-source software, particularly those intended for developers and data scientists.
Low to mid-range: Some AI software solutions are based on a subscription model. These can range from as low as $10 to several hundred dollars per month. These may include chatbot builders, simple predictive analytics tools, and other lower-end AI solutions.
Mid to high range: Advanced AI applications used by medium to large businesses usually cost around several thousand dollars annually. However, the cost can fluctuate based on the number of users, the volume of data processed, and other factors.
Enterprise-level: Larger corporations use comprehensive and custom-built AI solutions that cost several hundred thousand to millions of dollars. These solutions often include extensive customization, ongoing support, and other premium features.

What are the benefits of artificial intelligence software?

AI software builds intelligent applications that augment human capabilities and provide enhanced experiences. It allows users to implement general machine learning or more specific deep learning capabilities into software applications.

While this is the primary and somewhat apparent reason, there are many motivations behind this rationale, with the following being some of the most common themes:

Intelligent decision-making: AI solutions help humans make intelligent decisions. Machine learning provides analytical evidence and anticipated results, eliminating the uncertainty in making crucial business decisions. This feature minimizes the chances of human error in decision-making and equips users with the data necessary to justify their choices.
Personalization: ML algorithms enable software developers to create a high level of personalization, providing unique experiences for all users. By creating applications that recognize user interactions, developers benefit from implementing powerful recommendation systems to improve their software, similar to Amazon personalizing consumer shopping or the content recommendation capabilities of Netflix.
Conversational interfaces: Given the popularity of consumer conversational AI offerings, like Amazon's Alexa, Apple's Siri, and Google Home, conversational interfaces are making their way into the B2B world. AI software is the place to start for software companies trying to innovate and keep up with these advancements. Using speech recognition makes user interactions with applications more efficient and distinctive.
Predictive capabilities: Predictive functionality allows an AI solution to generate an outcome that is assumed to be correct instead of requiring a human to do so manually. This can be as simple as expense management solutions automatically adding an expense to a report. With AI and ML, the software can understand the recurring charges a user inputs every month. So, the software automates the task by predicting the charge and adding it to the report.
Task automation: Businesses automate redundant and manual employee tasks by implementing AI and machine learning. This delegation to AI solutions enables employees to focus on more critical parts of their jobs that require human expertise and experience.

Who uses artificial intelligence software?

Gone are the days when developers primarily used AI software to create intelligent apps. The use cases and applications of AI software have expanded with the rise of large language models (LLMs) and generative AI tools like ChatGPT.

From finance to healthcare, there isn’t any industry that’s not leveraging the power of AI to reinvent its processes. Financial institutions use AI algorithms to detect fraud, assess risks, and develop robust investment strategies. AI also supports medical professionals in predicting patient outcomes, designing personalized treatment plans, and diagnosing diseases.

Businesses use AI-powered virtual assistants and AI chatbots to support customer service by simplifying interactions and enhancing user experiences. Companies also employ AI to improve processes like data analysis, predictive modeling, and customer relationship management.

AI is also widely used in marketing and e-commerce to analyze customer behavior, optimize advertising campaigns, and personalize user experiences. In addition, government and research institutes harness AI for data analysis, climate modeling, and security purposes.

Industries like manufacturing, logistics, and transportation incorporate AI to reduce costs and improve supply chain efficiency.

Challenges with artificial intelligence software

Many potential users assume that the best artificial intelligence software can do everything right out of the box, but that's false. Even free AI software requires time and a great deal of data to learn and perform according to expectations. Users must train machine learning algorithms using reinforcement, supervised, and unsupervised learning to build a truly intelligent application.

For instance, a computer vision model cannot determine whether an image is a cat or a dog unless it has learned what a cat and a dog look like.

There is also a need for more people who understand how to build these algorithms and train them to perform the actions they need. The common user cannot simply fire up AI software and have it solve all their problems. Instead, it takes substantial software development and machine learning knowledge.

However, as the need for these professionals increases, so will the number of qualified candidates and the capabilities of the applications they are building.

AI technology trends

Over the past five years, AI has been one of the most significant tech trends. However, as the marketing for AI continues, the buzzwords can be overwhelming. G2 has determined a few trends within AI software: embedded AI and machine learning as a service (MLaaS).

Embedded AI

Through embedded AI, developers can integrate machine learning and deep learning into systems and network devices. This smart functionality allows applications to run AI models at the device level and perform “intelligent” tasks based on the results. Embedded AI is used in software like CRM, marketing automation, and analytics, allowing for streamlined processes, automated tasks, and predictive capabilities.

The trend toward embedded devices pairs artificial intelligence with data generation, improving efficiency, privacy, and application performance. The demand for embedded AI will rise with time, similar to trends like cloud deployment and mobile capabilities. Down the road, machine learning benefits won’t be counted as a special vendor offering but an expected feature.

Machine learning as a service (MLaaS)

MLaaS is part of a broader trend of delivering software services through the cloud. The boom in public cloud infrastructure has enabled companies like Google, Amazon, and Microsoft to offer development and infrastructure services to other businesses with a pay-as-you-use model.

MLaaS is a cloud-based offering that provides machine learning tools and algorithms as a service, enabling individuals to use ML capabilities without needing the expertise. Developers can use these prebuilt algorithms and solutions to gain data insights. Small businesses can save time, resources, and money by using systems built by other enterprises instead of hiring developers on their budgets. With the need for AI rising, businesses will continue relying on MLaaS.

Researched and written by Matthew Miller

Reviewed and edited by Shanti S Nair

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