The Enterprise Artificial Intelligence Market grows with the adoption of AI for business process automation, and predictive analytics

AUSTIN, TX, UNITED STATES, December 5, 2024 /EINPresswire.com/ -- Market Scope and Overview

The Enterprise Artificial Intelligence Market is expected to boom by USD 19.4 billion in 2023 and reach USD 284.9 billion by 2032. It expected to grow at a CAGR of 34.8% from 2024 to 2032.

The enterprise artificial intelligence market is moving forward due to the wide-scale adoption of AI technologies across industries, as well as increased accessibility of advanced AI tools and cloud infrastructure.

Market Drivers

The rapid acceleration in the Enterprise AI market is mainly attributable to the growing demand for organizations to automate the operations of their businesses and to adopt a data-driven approach toward decision-making to optimize business processes, reduce human costs, and provide a better customer experience. The last couple of years have demonstrated how AI technologies like natural language processing (NLP), machine learning (ML), and image recognition are helping enterprises automate complex tasks, predict market trends, and provide customized services. In addition, the growth in big data and the increase in availability of cloud-based AI solutions has played an especially big role in pushing the adoption of AI within organizations large and small.

At the same time, healthcare, manufacturing, retail, finance, and other industries are seen turning to AI more and more to enhance their internal processes, customer experience, and products. With more than a decade of AI technology maturing, generic and targeted platforms, and scalable solutions that can be aligned to specific requirements, the market growth is accelerating quickly.

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Some of the Major Key Players Studied in this Report are:

✦ SAP SE (SAP AI Core, SAP Leonardo)
✦ DataRobot, Inc. (DataRobot Automated Machine Learning, DataRobot MLOps)
✦ Microsoft Corporation (Azure AI, Microsoft Cognitive Services)
✦ Oracle Corporation (Oracle AI, Oracle Digital Assistant)
✦ NVidia Corporation (NVIDIA AI Enterprise, NVIDIA Clara)
✦ Amazon Web Services, Inc. (AWS) (AWS SageMaker, AWS Rekognition)
✦ Intel Corporation (Intel OpenVINO, Intel Xeon Scalable Processors with AI)
✦ Hewlett Packard Enterprise (HPE Ezmeral Machine Learning Ops, HPE GreenLake for AI)
✦ IBM Corporation (IBM Watson, IBM Cloud Pak for Data)
✦ Alphabet Inc. (Google) (Google Cloud AI, TensorFlow)
✦ C3.ai, Inc. (C3 AI Suite, C3 AI CRM)
✦ Wipro Limited (Wipro HOLMES, Wipro AI Studio)
✦ Apple Inc. (Core ML, Siri Intelligence)
✦ IPsoft Inc. (Amelia, 1Desk)
✦ Salesforce, Inc. (Salesforce Einstein, Tableau AI)
✦ Adobe Inc. (Adobe Sensei, Adobe Experience Platform AI)
✦ ServiceNow, Inc. (Now Intelligence, Predictive Intelligence)
✦ SAP HANA Cloud Services (SAP HANA Cloud, SAP Business AI)
✦ Zoho Corporation (Zia, Zoho Analytics AI)
✦ Alibaba Cloud (ET Brain, Alibaba AI Platform)
✦ Others

Market Segmentation

By Deployment

The cloud deployment model was the leading model in the Enterprise AI market in 2023, accounting for nearly 66% of global revenue. Enterprises now opt for the cloud-based deployment model due to its scalability, flexibility, and affordability. Cloud platforms make this possible by making available on demand the AI capabilities that previously would have required a large upfront investment in hardware or infrastructure. Cloud-based AI also has the advantage of being more easily integrated into other enterprise systems, making it an appealing option for organizations that want to roll out their AI workflows across their business.

Cloud-based AI solutions offer the advantage of real-time data processing, very critical for businesses that need to make quick decisions and need to be responsive. In addition, cloud deployment enables businesses to be up to date with innovative AI technologies and updates to remain competitive in the fast-changing AI landscape.

By Technology

The natural language processing (NLP) segment dominated the market in 2023 accounting for around 32% of the overall market share. Natural Language Processing (NLP) technologies allow machines to understand and process natural language, making this technology a key for organizations that aim to enhance their customer interaction, automate customer communication & improve business intelligence. NLP is used in chatbots, sentence analysis, voice assistants, customer service automation, etc. With the increasing need for enterprises to achieve customer engagement and have simplified support, the demand for NLP solutions forms a significant blower. Deep learning and neural networks are further increasing NLP technologies' efficiency and accuracy and, consequently, their adoption in industries.

In addition to NLP, more AI tech like machine learning (ML) and computer vision gets to high enterprise use. In predictive analytics, fraud detection, and recommendation systems, machine learning enables systems to learn and improve with the help of data over time. Computer vision, the ability of machines to identify and interpret visual input from the world, is applied across industries, including manufacturing, retail, and healthcare.

Market Segmentation and Sub-Segmentation Included Are:

By Deployment
✦ Cloud
✦ On-premises

By Organization size
✦ Large Enterprises
✦ Small And Medium Enterprises

By Technology
✦ Natural Language Processing (NLP)
✦ Machine Learning
✦ Computer Vision
✦ Speech Recognition
✦ Others

By End-use
✦ Media & Advertising
✦ Retail
✦ BFSI
✦ IT & Telecom
✦ Healthcare
✦ Automotive & Transportation
✦ Others

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Regional Analysis

North America led the Enterprise AI market in 2023, holding the largest market share of around 42%. The region’s dominance can be attributed to the presence of major technology players, including Google, IBM, Microsoft, and Amazon, which are driving AI innovation and providing enterprises with advanced AI solutions. Additionally, North America’s robust digital infrastructure, high internet penetration, and significant investments in AI research and development have positioned the region as a leader in AI adoption. In the United States, the rapid digital transformation across industries such as finance, healthcare, and retail has fueled the demand for enterprise AI solutions. The government's focus on AI-driven innovation, along with favorable policies and funding initiatives, has further supported the growth of the AI market in the region.

The Asia-Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid urbanization, increasing internet penetration, and the growing adoption of digital technologies across countries like China, India, Japan, and South Korea. As businesses in the region embrace digital transformation, the demand for enterprise AI solutions is expected to rise, particularly in industries such as manufacturing, retail, and healthcare. The growing number of startups and technology companies in the region is also contributing to the rise in AI adoption. In addition, government initiatives aimed at boosting AI development and investment in research are further fueling market growth in Asia-Pacific.

Recent Developments

✦ In October 2024 IBM and Microsoft expanded their collaboration to provide AI-powered solutions for enterprises, combining IBM’s AI-driven cloud solutions with Microsoft’s Azure cloud platform, enabling businesses to access AI tools for automation, data analytics, and decision-making.

✦ In September 2024, Google introduced a new AI-powered analytics platform aimed at enterprises, designed to provide real-time insights from big data and automate critical business processes, enhancing operational efficiency.

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Table of Contents- Major Key Points

1. Introduction

2. Executive Summary

3. Research Methodology

4. Market Dynamics Impact Analysis

5. Statistical Insights and Trends Reporting

6. Competitive Landscape

7. Enterprise Artificial Intelligence Market Segmentation, By Deployment
7.1. Cloud
7.2. On-premises

8. Enterprise Artificial Intelligence Market Segmentation, By Organization Size
8.1. Large Enterprises
8.2. Small and Medium Enterprises

9. Enterprise Artificial Intelligence Market Segmentation, By Technology Device
9.1. Natural Language Processing (NLP)
9.2. Machine Learning
9.3. Computer Vision
9.4. Speech Recognition
9.5. Others

10. Enterprise Artificial Intelligence Market Segmentation, By End-use
10.1. Media & Advertising
10.2. Retail
10.3. BFSI
10.4. IT & Telecom
10.5. Healthcare
10.6. Automotive & Transportation
10.7. Others

11. Regional Analysis
11.1. Introduction
11.2. North America
11.3. Europe
11.4. Asia-Pacific
11.5. The Middle East & Africa
11.6. Latin America

12. Company Profile

13. USE Cases and Best Practices

14. Conclusion

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