Artificial Intelligence platform provides tools and technologies to build applications with AI-rich capabilities. The algorithms used for formulating the AI platform provide logical models for application developers to fabricate various innovative applications with capabilities, such as speech and voice recognition, text recognition, and predictive analytics.

Top 10 Artificial Intelligence Platform in 2020:

  1. MICROSOFT Azure AI
  2. GOOGLE Cloud Machine Learning Engine.
  3. IBM Watson
  4.  AMAZON ML platform services
  5. SAP Leonardo Machine Learning
  6. INTEL Nervana Platform
  7. Salesforce Einstein suite
  8. Qualcomm 
  9. HPE C3 AI Suite
  10. SAS 

The global market for the Artificial Intelligence platform is projected to reach a market size of USD 9.88 billion by 2022, from USD 2.61 billion in 2017. This growth is expected at a Compounded Annual Growth Rate (CAGR) of 30.5%. The factors likely to drive the Artificial Intelligence platform market are the substantial increase in data generation, high demand for AI-based solutions, the need to enhance customer experience, and the increased operational efficiency & reduced cost that the best Artificial Intelligence platforms offer.

VISIONARY LEADERS

Vendors who fall into this category receive high scores for most of the evaluation criteria. They have a strong and established product portfolio and a very strong market presence. They provide mature and reputable Artificial Intelligence platform software. They also have strong business strategies. Microsoft Azure AI, Amazon ML platform services, Intel Nervana Platform, Google Cloud Machine Learning Engine, IBM Watson, SAP Leonardo Machine Learning, Salesforce Einstein suite, and Qualcomm have been placed in this category.

DYNAMIC DIFFERENTIATORS

They are established vendors with very strong business strategies. However, they are low in their product portfolios. They focus on a specific type of technology, related to the product. HPE C3 AI Suite, Infosys Nia, and Wipro Holmes have been placed in this category.

INNOVATORS

The innovators in the MicroQuadrant consist of vendors who have demonstrated substantial product innovations as compared to their competitors. They have much-focused product portfolios. However, they do not have very strong growth strategies for their overall business. Ayasdi, Absolutdata NAVIK Artificial Intelligence platform, Oracle, and SAS have been placed in this category.

EMERGING COMPANIES

They are vendors with niche product offerings. They are starting to gain their positions in the Artificial Intelligence platform market. They do not have strong business strategies when compared to the other established vendors. They might be new entrants in the market and require some more time before gaining significant traction. BigML, Artificial Solutions, CrowdFlower, Kasisto, msg.ai, Rainbird Technologies, Vital AI, Faculty, RapidMiner, and DataRobot have been placed in this category.

Market Overview

By tools, the Machine Learning (ML) segment is expected to hold the dominant position in terms of market share as well as CAGR during the forecast period. the best artificial Intelligence platform tools are used to deploy AI-enabled algorithms across the Banking, Financial Services, and Insurance (BFSI), healthcare, and retail & eCommerce verticals to analyze large amounts of data.

Among services, the highest CAGR and largest contribution are expected from the managed services segment. The growth of this segment can be attributed to the availability of 2 deployment models—on-premises and cloud—which require maintenance and infrastructure support by providers of managed services.

By deployment model, cloud deployment is expected to dominate in terms of market share as well as CAGR, since this mode provides a variety of agile solutions in the Artificial Intelligence platform market.

By application, forecasts & prescriptive models is projected to be the most significant contributor, while text recognition is expected to grow at the highest rate. Analysis of customer behavior and identification of specific patterns in data are possible with the use of forecasts & prescriptive models.

Among end-users, the BFSI segment is projected to have the largest share, while healthcare is expected to have the highest growth rate during the forecast period. Both these sectors use the Artificial Intelligence platform to obtain insights from significant amounts of data and analyze trends from this data to enhance decision making as well as customer experience. End users from the manufacturing, retail & eCommerce, transportation, and research & academia sectors use AI platforms to access real-time data to reduce response time to customer queries and enhance business processes. These sectors are also expected to grow significantly during the forecast period.

By region, the Artificial Intelligence platform market is studied for North America, Asia Pacific (APAC), the Middle East & Africa (MEA), and Latin America. Among these, the highest revenue is expected from North America. This can be attributed to the high focus on innovation and the competitive nature of the market in developed economies such as the US and Canada. In terms of growth rate, APAC is projected to lead as a result of the increasing technological advancements and the expansion of domestic enterprises in the region.

Restraints that the market faces include the increasing issue of data privacy, especially in emerging economies, as well as the lack of a skilled workforce to enable organizations to perform optimally.

Benefits of Artificial Intelligence Software

  • Data Mining & Business Insights
  • Outcome Prediction
  • Real-time Assistance
  • Operational Automation
  • Improving Shopping Experience
  • Minimizing Errors
  • Increased Business Efficiency
  • Make Data Interpretation Easy
  • Automating Customer Experience
  • Improved Recruitment Process

Features in Top Artificial Intelligence Platform

  • Machine Learning- Machine learning solutions can learn complex decision systems; find patterns and anomalies in data, raise alerts and much more.
  • Automation- In the field of artificial intelligence and machine learning ability to automate the manual processes will save time and money.
  • Bot Design and Deployment- Chatbots as well as transactional, informational, and entertainment bots can be designed to provide valuable information and keep customers engaged.
  • Natural Language Processing (NLP) and Natural Language Understanding (NLU)- Being able to convert audio to text and using the data will deliver multiple benefits, including analysis and understanding of multiple languages and dialects.
  • Cloud Infrastructure- Cloud offers the scalability needed to access resources to deploy the most complex artificial intelligence and machine learning solutions.
  • Price- If the solution delivers in its ROI, then it will be worth the price.

How to choose Best  Artificial Intelligence Software

Following factors should be considered while selecting the best AI software:

Gaining an understanding of AI and what it means to your business

  • Does the vendor have a true understanding of the service provider network and its multiple parts, which is the core medium through which you operate your business and transport services?
  • How much experience and/or credibility do they have in the software itself? How about in analytics and AI – the technologies that will serve as the “brain” that drives intelligence?
  • Given the significance of the project and potential risks involved (new technology, new application) -- does the vendor have the capability and resources to stand behind you throughout the entire project lifecycle?

Projecting tangible business value and benefits

  • Does the vendor/solution provide the ability and flexibility for you to take a controlled, manageable and phased approach to deployment?
  • Do they provide financial modeling services to help ensure you are making well-informed investment decisions along the way?
  • Can they provide any ROI examples and/or models or have current customers successfully benefiting from their solutions and/or services that are specific to analytics and AI?

Beyond initial deployment, expanding into new domains as business needs evolve

  • Does the solution provide a firm foundation for expansion and is it flexible enough to grow and adapt to my changing business priorities without significant disruption?
  • Will the vendor/solution enable me to acquire deep knowledge and expertise so I can program my own network and take control of its transformation?
  • Will I be provided with the technical and expert resources I need, where and when I need them?

What’s trending in Artificial Intelligence Software 2020?

  • Operationalization will be the name of the game
  • Data governance will get even better
  • AI pros will shine
  • Data modeling will move to the edge
  • Emerging AI User Interface
  • Intelligent Automation
  • Partnering AI Workforce
  • AI medical diagnostics – AI decentralization
  • Data access enabling ubiquity
  • Boosting Cybersecurity
  • AI complementing humans

Best Artificial Intelligence Software in 2020

Comparing 26 vendors in Artificial Intelligence Platform across 232 criteria.
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Microsoft offers AI platform services and tools to help developers in creating AI-enabled models. Moreover, it is said to be heavily investing in services, tools, and platforms to help bring AI and data-driven intelligence into every application. Apart from that, the company launched new tools and services, such as Azure data and cloud services, to help developers in modernizing applications. Additionally, the company’s focus areas for investment include intelligent edge and intelligent cloud. It also opened the Microsoft Research AI lab, which focuses on solving hurdles in the AI space. The technologies involved in the research are ML, NLP, decision-making, and visual perception.
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Google Cloud Platform offers cloud computing services that comprise ML, data analytics, and data storage capabilities. The platform provides features, such as secure and cost-effective infrastructure, along with data and analytics capabilities, for better product development. Moreover, Google offers the open source library, TensorFlow, to carry out numerical calculations with the help of data flow graphs. TensorFlow helps users from various industry backgrounds in using different applications, ranging from language translation to early detection of diseases. Apart from that, Google’s ML Engine enables developers to create ML models on any size of data. Additionally, its Cloud Machine Learning Engine offers managed services that negate the need for infrastructure and provide the basis for model development and prediction. It also adds portability, flexibility, and the ease of use. Additionally, Google’s Cloud Video Intelligence API is designed to concentrate on video analytics for the new audience. Furthermore, the Cloud Video Intelligence API makes the video searching process easier.
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IBM Watson suite enables organizations to combine AI into their applications, and also helps with data management in the cloud. It offers the PowerAI platform, which provides various AI capabilities. These capabilities negate the need for developing AI solutions. Moreover, the PowerAI platform provides AI-rich capabilities, such as deep learning, which allows organizations to fulfill the technological requirements. The IBM Power Systems software combined with the PowerAI platform allows enterprises to deploy PowerAI with deep learning capabilities for enhanced performance.

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AWS Managed ML Platforms offers data scientists and developers a way to create models without investing in infrastructure management. Amazon ML removes the need to learn complex technologies and ML algorithms, along with visualization tools and wizards to help guide in the process of building ML models. Apache Spark on Amazon EMR is an open source distributed processing system, which focuses on big data workloads. It offers various features, such as enhancing the performance and enabling the quick development of applications, such as libraries, to help develop applications for various uses cases. Amazon Web Services also offers intelligent services to build application, such as Amazon Lex, Amazon Polly, and Amazon Rekognition. The services are used to turn text into speech, as well as, help study the images to recognize faces, objects, and scenes.

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SAP Leonardo Machine Learning platform is created on SAP Cloud Platform and comprises the capabilities of ML to help organizations in finding connections and patterns in the data. It comprises services that offer the capabilities to learn from data as well as gain knowledge. Moreover, it allows taking advantage of the intelligent capabilities for developing enterprise applications and removes the need for data science skills in the process. The SAP Leonardo ML platform offers the basis to create and manage intelligent applications under a common infrastructure. Moreover, SAP offers SAP CoPilot, the virtual assistant designed to help customers. The virtual assistant analyzes the unstructured speech to offer users with relevant data.

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Intel has adopted the acquisitions strategy to strengthen its Artificial Intelligence Platform platform offerings; for instance, it acquired Nervana Systems, an expert in ML capabilities. Apart from the acquisition’s strategy, the company is also said to be making capital investments to accelerate the AI innovation in companies, such as AEye, Element AI, and CognitiveScale. Moreover, the company has made investments in startups to enhance its AI platform’s technological capabilities. Apart from acquisitions and investments, the company formed a partnership with Tata Consultancy Services to build the architecture for AI, IoT, cloud, and 5G. With such strategies, Intel is trying to sustain in a competitive position in the AI platform technologies, such as deep learning, neural networks, and ML. Moreover, to enhance its product offerings, the company focuses on the upcoming Intel Nervana ASIC (Application-Specific Integrated Circuit) engine, designed to enhance the deep learning capabilities of neural networks. Moreover, the engine helps in speeding up the process of AI training.
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Salesforce Einstein suite offers data modeling, preparation, and infrastructure processes, which can be embedded into predictive models and applications to benefit from capabilities. The Einstein platform services offer the basis to create AI-driven applications by making available, the capabilities of image recognition and NLP to the users. The Marketing Cloud Einstein allows the marketers to take benefits of tools, such as Predictive Scoring, Predictive Audiences, and Automated Send-time Optimization, to analyze the target audience, contents, and channels while designing campaigns. Furthermore, the Analytics Cloud Einstein helps in the discovery of future patterns for business processes and provides insights from a large chunk of data. These platforms remove the need to build algorithms and mathematical models. Moreover, by using Service Cloud Einstein, enterprises can achieve intelligent, automated, and predictive customer engagement experience. The Community Cloud Einstein offers customers a way to find the information and offers recommendations about the contents.

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Qualcomm has been focusing on bringing ML and AI technologies to various industrial applications. For instance, to enhance the capabilities of the Artificial Intelligence Platform technology across various industries, such as manufacturing and finance, the company acquired Scyfer B.V., which possesses developed capabilities in ML. Qualcomm also helps bring AI-enabled applications to various industries. Qualcomm has raised a funding of USD 14 million, under Series C, for its BrainOS platform. The platform is a software built with the help of sensors and hardware, designed to offer a basis for creating autonomous robots. Apart from this, the company is working on building AI technologies in various devices, such as robots and cars, to negate the requirement of network or Wi-Fi. Additionally, the company’s consistent efforts in R&D in AI is evident from the fact that it started the Qualcomm Research in the Netherlands in 2014, as well as, acquired Euvision Technologies. Moreover, in 2016, it collaborated with Google to speed up TensorFlow, Google’s open source library. Qualcomm, with the help from the University of Amsterdam, created a research lab. Additionally, in 2017, the company announced its support for deep learning frameworks, such as Caffe2 and TensorFlow.
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The company focuses on the launch of AI-enabled platforms, which is evident from the fact that, HPE unveiled a software solution, Investigate Analytics, which comprises big data and AI technologies, designed to help the financial firms with identifying risk and fraudulent behaviors. This offering was launched at the LegalTech conference in New York City to help identify such frauds and risks by analyzing large amounts of data. Moreover, the company launched hardware, services, and software in June 2017, which were designed to deliver high-performance computing and AI technologies. These hardware, services, and software also help scientific institutes and organizations in gaining insights into large amounts of data. Additionally, these solutions focus on helping organizations with the security and cost factors. HPE partnered with BASF SE. As a part of this partnership, HPE would help BASF SE develop supercomputers. HPE’s Apollo system would assist in creating and developing the modeling and simulation approach, so that BASF SE can find new opportunities in the process of simulation and complex modeling development during its research process.
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SAS Visual Data Mining and Machine Learning offers an innovative solution that combines the most advanced analytics, data prep, visualization, model assessment and model deployment in a single environment. It also supports programming from popular open source languages. This reliable, collective environment produces desired outcomes, helping improve organizational procedures and discover new opportunities for growth.

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The company is focused on adopting the software automation platform, along with AI-based automation techniques, to increase the efficiency of its projects. Infosys’ Mana platform of AI was adopted by many its clients to achieve efficiency and automation. Moreover, the company invested in the Danish AI startup, UNSILO, which leverages NLP and ML to gain insights into numerous texts for increasing the efficiency and the speed of workers. Additionally, to help clients take advantage of the AI-based technologies, Infosys launched the AI platform, Nia.
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The company is focused on the innovation and development of AI platform technologies, which is evident from the fact that it partnered with Tel Aviv University’s (TAU) business engagement center, Ramot, in July 2017. The partnership focused on the research of the continuously developing AI technologies. Moreover, Wipro has made investments through the Horizon Program (intrapreneurship program) to nurture AI platform technologies in the organization. Apart from that, the company is focused on its investment in AI technologies for achieving a differentiation in IT services business. Additionally, it launched the automation service for SAP software to automate the business processes that run on SAP’s Enterprise Resource Planning (ERP) software. The automation service includes the capabilities of Wipro’s AI platform, HOLMES.
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Oracle provides readymade AI cloud applications with intellectual features that drive better business outcomes. It offers a full suite of cloud services to build, deploy, and manage AI-powered solutions. It automate security patching, backups, and improve database query performance, which eliminate human error and repetitive manual tasks. so organizations can focus on higher-value activities.

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Ayasdi introduced its AI platform in the healthcare and financial sectors by partnering with Deloitte. Both the companies worked to increase the adoption of AI across various enterprises. Additionally, Ayasdi benefits from Deloitte’s capabilities in cognitive as well as the other emerging technologies for discovering business applications. Moreover, Ayasdi is said to be experiencing a rapid growth in its healthcare vertical, and to cater to the growing demand, the company has made additions to its healthcare staff. The addition of the staff would be useful in increasing the company’s place in key analytical challenges for its solutions, such as patient risk stratification and fraud, clinical variation, and denials management.
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Absolutdata has been updating its product offering in the market; for instance, the company launched the upgraded version NAVIK Converter 2. 0, which comprises technologies, such as advanced analytics and ML, to help brands in designing campaigns. Furthermore, the company launched its AI platform, NAIVIK, as well as, AI-based tools for helping the sales and marketing team in achieving efficiency. Apart from that, to increase its expertise around analytics and achieve a steady growth, the company added more staff. It is believed that the company is attempting to develop more innovative AI platforms to offer better experiences to its clients.
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RapidMiner provides a comprehensive solution on a integrated platform that supports the whole Machine Learning workflow from data preparation through model deployment to ongoing model management. It is quick-to-learn and simple-to-use workflow designer accelerates end-to-end data science for improved productivity.
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DataRobot automated machine learning platform provides knowledge, experience, and best practices to deliver unmatched levels of automation and ease-of-use for machine learning initiatives. DataRobot allows users of all skill levels, from business people to analysts to data scientists, to build and deploy highly-accurate predictive models in a fraction of the time of traditional modeling methods.

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The company provides a highly interactive conversational AI betting solution that engages customers over any channel, device, service, and language. It uses linguistic and machine learning techniques for maximum performance on any platform.
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Kasisto offers a conversational AI platform to create virtual assistants and bots that deliver personalized, delightful and intelligent experiences across multiple channels. It enables a network of intents wired at runtime to enable human-like, cross-intent conversational experiences. KAI includes a deep-learning analytical toolset for data collection and analysis, model training, testing, and deployment.

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Faculty Platform allows to manage and schedule model training and execution pipelines natively, and deploy models into staging and production with one simple workflow. It use a browser or command line interface that integrates with favourite IDE and version control systems for easy interface customisation.

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Vital AI Development Kit (VDK) offers a suite of software to reorganize the flow of data across application architecture and integrate with analytical frameworks using the Vital Service API. The key tool is VitalSigns, which provides a consistent data model used by all software modules.

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Msg.ai Artificial Intelligence respond quickly to issues that are repeatable, while enabling human agents to focus on high-impact work. It ensure a best experience to customers whenever there is a  an issue, a question or a need. It can collaborates with human agents to offer high-quality resolutions to customer queries on email, mobile, and chat.      

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BigML offers robust engineered Machine Learning algorithms proven to solve problems by applying a single, standardized framework across company. It reduces the dependencies on many disparate libraries that increase complexity, maintenance costs, and technical debt in projects. BigML enables unlimited predictive applications across industries. It automatically adjusts resources to seamlessly meet computational needs in a cost-effective manner, while abstracting away infrastructure concerns from end-users.

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Rainbird is an AI-powered automated decision-making platform. It goes beyond other rules engines that can only make simple decisions, and limited 'black box' machine learning that can't explain. The platform drives smarter decision-making by enabling semantic links between different Rainbird knowledge maps. It enables to 'join-up' previously siloed knowledge and deliver a more holistic and strategic system, capable of automating complex decisions.
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Figure eight generates high-quality customized training data and automates business process with easy-to-deploy models. It offers products and services like self-driving cars, intelligent personal assistants, medical image labeling, content categorization, customer support ticket classification, social data insight, CRM data enrichment, product categorization, and search relevance.

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SenseFace is an integrated solution for target surveillance, population management, trajectory analysis, and is powered by deep learning algorithms.
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