1 December 2023
Enterprise Artificial Intelligence Market

Market Overview

The global enterprise artificial intelligence market is expected to grow at a CAGR of 34.60% from 2023 to 2030, reaching a value of USD 156.0809 billion by 2030. The growth of the market is being driven by the increasing demand for AI-based solutions and platforms, and the need to analyze large and complex data sets.

Enterprise AI is the use of AI technologies to automate and improve business processes and operations. It can be used to improve customer service, optimize supply chains, detect fraud, and make better decisions.

Key Companies in the Enterprise AI Market

Some of the key players in the enterprise AI market include:

  • IBM
  • Microsoft
  • Google
  • Amazon Web Services (AWS)
  • Oracle
  • SAP
  • Salesforce
  • Intel
  • Nvidia
  • Honeywell
  • Cisco Systems

These companies are developing and offering a wide range of AI-based solutions and platforms for enterprises. They are also investing in research and development to develop new AI technologies.

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The major factors driving the growth of the enterprise AI market include:

  • The increasing demand for AI-based solutions and platforms: AI is being used to develop a wide range of solutions and platforms, such as chatbots, virtual assistants, and predictive analytics tools. These solutions are helping businesses to improve customer service, automate tasks, and make better decisions.
  • The need to analyze large and complex data sets: Businesses are generating more data than ever before. This data can be used to gain insights into customer behavior, identify trends, and make better decisions. AI can help businesses to analyze this data more effectively.
  • The growing adoption of cloud computing: Cloud computing is making it easier for businesses to deploy and manage AI solutions. This is driving the adoption of AI in enterprises.
  • The increasing availability of skilled professionals: The availability of skilled professionals is another factor driving the growth of the enterprise AI market. There is a growing demand for AI professionals, and businesses are investing in training their employees on AI.

Market Segmentation

The enterprise AI market can be segmented by deployment type, technology, application, industry vertical, and region.

  • By deployment type, the market can be segmented into cloud-based and on-premises. The cloud-based segment is expected to grow at a faster rate during the forecast period due to the increasing adoption of cloud computing by enterprises.
  • By technology, the market can be segmented into machine learning, natural language processing, image processing, and speech recognition. The machine learning segment is expected to grow at the highest rate during the forecast period due to its wide range of applications in enterprises.
  • By application, the market can be segmented into customer service, fraud detection, predictive analytics, and risk management. The customer service segment is expected to grow at the highest rate during the forecast period due to the increasing demand for chatbots and virtual assistants.
  • By industry vertical, the market can be segmented into BFSI, healthcare, retail, manufacturing, and IT and telecom. The BFSI segment is expected to grow at the highest rate during the forecast period due to the increasing use of AI in areas such as fraud detection and risk management.
  • By region, the market can be segmented into North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. North America is expected to hold the largest market share during the forecast period due to the early adoption of AI technologies by enterprises in this region.

Top Impacting Factors

The following are some of the top impacting factors of the enterprise AI market:

  • Increasing demand for AI-based solutions and platforms
  • Need to analyze large and complex data sets
  • Growing adoption of cloud computing
  • Increasing availability of skilled professionals
  • Government regulations
  • Data privacy and security concerns

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