Artificial Intelligence Systems Spending Market is anticipated to display a CAGR of 46% by 2027


The known players in the global artificial intelligence systems spending market include Google LLC, Microsoft Corporation, and Apple Inc., among others.

The Artificial Intelligence (AI) systems spending market refers to the market for businesses and organizations spending on AI technologies and solutions. AI systems spending covers a wide range of areas including hardware, software, and services. The market is driven by the increasing demand for automation and data-driven decision making in various industries such as finance, healthcare, retail, and manufacturing.

Key drivers of growth in the AI systems spending market include advancements in AI technology, increased availability of data, and growing recognition of the business value of AI. Major players in the AI systems spending market include Amazon Web Services (AWS), Google Cloud, Microsoft Azure, IBM, and Alibaba Cloud.

The market for artificial intelligence systems is expected to reach US$ 516.2 billion by the end of 2027, representing a CAGR of 46.1% over the assessment period, from an estimated value of US$ 11.7 billion in 2017. (2017-2027). By the end of 2027, deep learning will account for a 50.8% value share of all technologies used in artificial intelligence systems, followed by machine learning with a 24.8% value share. In comparison to other technologies, Deep Learning is anticipated to demonstrate a larger incremental value during the projection period. Over the forecast period, the Deep Learning segment is expected to display a CAGR of 49.3%. The segment for natural language processing is anticipated to have a value share of 11.3%.

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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.

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.

Market Taxonomy

By Industry Type:

  • BFSI
  • Discrete & Process Manufacturing
  • Healthcare
  • Retail
  • Wholesale
  • Professional & Consumer
  • Service
  • Transportation
  • Media & Entertainment
  • Telecommunications & Utilities
  • Government
  • Education
  • Others (Construction, Resource Industries)

By Technology:

  • Deep Learning
  • Machine Learning
  • Natural Language Processing
  • Machine Vision
  • AGI
  • ASI

By Market:

  • Hardware
  • Software
  • Services

By Region:

  • North America
  • Asia-Pacific excluding
  • Japan
  • Western Europe
  • Eastern Europe
  • Latin America
  • Middle East and Africa
  • Japan

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Table of Content

1. Executive Summary

1.1. Market Overview

1.2. Market Analysis

1.3. Wheel of Fortune

2. Market Introduction

2.1. Market Definition

2.2. Market Taxonomy

3. Global AI Systems Spending Market Analysis(2012-2016) and Forecast(2017-2027)

3.1. Market Size(US$ Mn) and Forecast

4. Market Dynamics

4.1. Macro-Economic Factors

4.2. Drivers

4.2.1. Supply Side

4.2.2. Demand Side

4.3. Restraints

4.4. Trends

4.5. Forecast Factors- Relevance and Impact

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Published in

Technology

Published on

Mar 22, 2023

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