Artificial Intelligence Systems Spending Market

Introduction

The global artificial intelligence systems spending market is estimated to be valued at USD 11.7 Billion in the year 2017 and is slated to touch a value of USD 516.2 Billion by the end of the year 2027, exhibiting a CAGR of 46.1% over the period of assessment (2017 to 2027).

Attributes	Details
Estimated Market Value (2017)	USD 11.7 Billion
Projected Market Revenue (2027)	USD 516.2 Billion
Value-based Market CAGR (2017 to 2027)	46.1%

Global Artificial Intelligence Systems Spending Market Attractiveness Index, by Technology

Amongst all technologies, deep learning accounts for a 50.8% value share in the global artificial intelligence systems spending market by 2027 end, followed by machine learning with a 24.8% value share. Deep Learning is expected to show a higher incremental value during the forecast period as compared to other technologies.

Deep Learning segment is projected to exhibit a CAGR of 49.3% over the forecast period. Natural language processing segment is expected to account for an 11.3% value share by 2027 end and will register a CAGR of 42.9% over the forecast period.

Unlimited access to computing power through cloud technology

The next generation cloud computing model built around the AI capabilities should be able to run deep learning or AI applications. The potential for cloud computing is to lower computing costs and increase business flexibility. AI uses large volume of data stored and can be utilized for cloud robotics, automation, intelligent actions and machine learning.

Artificial Intelligence with cloud computing add advancements using new use cases to improvise the systems developed so far. The current AI cloud landscape is categorized into two groups-AI cloud services and cloud machine learning platforms. AI Cloud Services include technologies such as Microsoft Cognitive Services, IBM Watson etc.

Convergence of AI and Big Data

The convergence of AI and big data is an important development that is shaping the future of companies deriving business value from data and analytics capabilities. Lack of data availability, limited sample size and inability to analyze massive amount of data in milliseconds limit the scope of AI and machine learning. The availability to access greater volumes and sources of data with agility and ready access has enabled the capabilities of AI and machine learning.

Big data provides fascinating possibility for the technology by analyzing the data in real time. MetLife, one of the largest global providers of insurance, employee benefit and annuities programs, has also adopted AI technology with big data.

Investment in the skilled workforce

Major companies are focusing on high investment on hiring artificial intelligence engineers due to its rapid growth across the globe. Artificial intelligence is majorly used to transform the enterprise businesses and reduce the reaction time.

Various companies such as IBM Corp, Salesforce.com Inc., Google Inc., Facebook Inc. and many other companies are paying high salary to retain their highly skilled employees. Hence, the one of major restraining factor of artificial intelligence is high investment on a highly skilled workforce.

Growth of AI systems in commerce and retail

Artificial intelligence is changing the way e-commerce retailers store and operate as it delivers a modern way of analyzing big data, helping e-commerce retailers to get involved with their customers in a deeper level and provide excellent customer experience. AI helps to deliver the messages to the right customers at the right time, which helps to increase the overall revenue. For Instance, IBM Watson artificial intelligence system provides e-commerce solutions in the marketplace.

Table of Content

• 1. Executive Summary
• 2. Market Introduction
• 3. Global AI Systems Spending Market Analysis (2012 to 2016) and Forecast (2017 to 2027)
• 4. Market Dynamics
• 5. Global AI Systems Spending Analysis (2012 to 2016) and Forecast (2017 to 2027), By Industry Type
  • 5.1. BFSI
  • 5.2. Discrete & Process Manufacturing
  • 5.3. Healthcare
  • 5.4. Retail
  • 5.5. Wholesale
  • 5.6. Professional & Consumer Services
  • 5.7. Transportation
  • 5.8. Media & Entertainment
  • 5.9. Telecommunications & Utilities
  • 5.10. Government
  • 5.11. Education
  • 5.12. Others (Construction, Resource Industries)
• 6. Global AI Systems Spending Analysis (2012 to 2016) and Forecast (2017 to 2027), By Technology
  • 6.1. Deep Learning
  • 6.2. Machine Learning
  • 6.3. Natural Language Processing
  • 6.4. Machine Vision
  • 6.5. Artificial General Intelligence (AGI)
  • 6.6. Artificial Super Intelligence (ASI)
• 7. Global AI Systems Spending Analysis (2012 to 2016) and Forecast (2017 to 2027), By Market Type
  • 7.1. Hardware
  • 7.2. Software
  • 7.3. Services
• 8. Global AI Systems Spending Analysis & Forecast, By Region
  • 8.1. North America
  • 8.2. Latin America
  • 8.3. Western Europe
  • 8.4. Eastern Europe
  • 8.5. Asia Pacific Excluding Japan
  • 8.6. Japan
  • 8.7. MEA
• 9. North America AI Systems Spending Analysis & Forecast
• 10. Latin America AI Systems Spending Analysis & Forecast
• 11. Western Europe AI Systems Spending Analysis & Forecast
• 12. Eastern Europe AI Systems Spending Analysis & Forecast
• 13. APEJ AI Systems Spending Analysis & Forecast
• 14. Japan AI Systems Spending Analysis & Forecast
• 15. MEA AI Systems Spending Analysis & Forecast
• 16. Competition Landscape
  • 16.1. Google Inc.
  • 16.2. Microsoft Corporation
  • 16.3. Facebook, Inc.
  • 16.4. IBM Corporation
  • 16.5. Apple Inc.
  • 16.6. Amazon.com Inc.
  • 16.7. Intel Corporation
  • 16.8. Infosys Limited
  • 16.9. Wipro Ltd
  • 16.10. Salesforce.com Inc.
  • 16.11. IPsoft Inc.
  • 16.12. Anki, Inc.
  • 16.13. Cognitive Scale Inc.
  • 16.14. Ayasdi, Inc.
  • 16.15. Appier Inc.
  • 16.16. OpenText Corp.
  • 16.17. Nuance Communication
  • 16.18. Digital Reasoning Systems, Inc.
  • 16.19. AIBrain, Inc.
  • 16.20. Palantir Technologies Inc.
• 17. Acronyms and Assumptions Used
• 18. Research Methodology