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Global Enterprise AI Market Size, Share, Drivers, Trends, And Competitors 2024-2032

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Global Enterprise AI Market Size By Component (Solution, Services), By Deployment Type (Cloud, On-Premises), By Application (Security And Risk Management, Marketing Management), By Geographic Scope And Forecast

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Enterprise AI Market Size And Forecast

Enterprise AI Market size was valued to be USD 10.52 Billion in the year 2023 and it is expected to reach USD 158.81 Billion in 2031, at a CAGR of 47.16% over the forecast period of 2024 to 2031.

  • Enterprise AI encompasses the utilization of artificial intelligence (AI) technologies and solutions within the context of an enterprise or business environment, aimed at improving operational efficiency, streamlining processes, and facilitating data-driven decision-making.
  • Comprising a diverse array of AI technologies such as machine learning, natural language processing, computer vision, and predictive analytics, Enterprise AI solutions are integrated into various business operations and applications.
  • These solutions find applications in sectors including customer service (utilizing chatbots, and virtual assistants), sales and marketing (enhancing lead generation, and personalization), supply chain management (optimizing demand forecasting, and inventory management), and finance (detecting fraud, assessing risks).

Global Enterprise AI Market Dynamics

The key market dynamics that are shaping the Enterprise AI Market include:

Key Market Drivers

  • Increased Data Generation and Availability: The adoption of AI solutions is driven by the vast amount of data generated by enterprises from various sources, such as customer interactions, transactions, and operational processes, enabling data-driven decision-making and valuable insights extraction.
  • Requirement for Process Automation and Operational Efficiency: Enterprises are actively seeking AI-powered solutions to streamline operations, automate repetitive and complex tasks, and optimize processes, leading to enhanced productivity, cost reduction, and efficiency improvement.
  • Rising Demand for Personalized Customer Experiences: Enterprises are leveraging AI technologies, including natural language processing and machine learning, to analyze customer data and deliver highly personalized offerings, meeting the increasing expectations of customers for tailored experiences.
  • Competitive Advantage and Innovation: AI is acknowledged by enterprises as a driver of innovation, enabling the creation of new business models, faster decision-making, and predictive analytics, thereby gaining a competitive edge in their respective markets.
  • Advancements in AI Technologies and Computing Power: Continuous enhancements in AI algorithms, frameworks, and computing power facilitate the development of more sophisticated AI solutions, enabling enterprises to adopt and integrate these technologies into their operations.

Key Challenges:

  • Data Quality and Governance: Ensuring the quality, accuracy, and completeness of data used for training AI models poses a significant challenge for enterprises, as poor-quality or biased data can result in unreliable AI outputs, diminishing the effectiveness of these solutions.
  • Integration with Existing Systems and Processes: Seamlessly integrating AI solutions with legacy systems, data silos, and established business processes presents a complex and time-consuming task for enterprises, requiring them to address compatibility issues and facilitate smooth data exchange.
  • Ethical and Regulatory Concerns: The use of AI in enterprises raises ethical concerns related to data privacy, algorithmic bias, transparency, and accountability, necessitating compliance with a complex regulatory landscape and ethical considerations to maintain trust and adhere to relevant laws and guidelines.
  • Talent Shortage and Upskilling Requirements: The demand for skilled professionals in AI, machine learning, and data science surpasses the available supply, resulting in a talent shortage for enterprises. Challenges include attracting and retaining AI talent, as well as upskilling existing employees to effectively utilize AI technologies.

Key Trends:

  • Integration with Cloud Computing: A significant trend being observed involves the integration of enterprise AI solutions with cloud computing platforms, allowing scalability, cost-effectiveness, and seamless access to computational resources for training and deploying AI models.
  • Adoption of AI-Powered Automation: Momentum is growing in the incorporation of AI technologies into business process automation, with intelligent automation solutions being utilized for tasks such as data entry, document processing, and workflow optimization, resulting in increased efficiency and productivity.
  • Explainable AI and Trustworthy AI: Recognizing the increasing presence of AI systems in decision-making processes, efforts are underway to develop AI models that are transparent, ethical, and accountable, addressing the need for explainable and trustworthy AI.
  • AI-Driven Cybersecurity: A crucial trend emerging is the application of AI in cybersecurity, with AI-powered threat detection, vulnerability assessment, and automated response mechanisms being implemented to enhance an organization’s security posture and mitigate cyber risks.
  • AI for Predictive Analytics: Embracing the use of AI in predictive analytics involves employing machine learning algorithms to analyze large datasets, identify patterns, and generate accurate forecasts, enabling data-driven decision-making across various business functions.

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Global Enterprise AI Market Regional Analysis

Here is a more detailed regional analysis of the Enterprise AI Market:

North America:

  • The dominance of the enterprise AI market in North America is observed due to the early adoption of advanced technologies, the presence of major tech giants spearheading AI innovations, and digital transformation embraced by a large enterprise base.
  • Global leadership in the enterprise AI market is seen particularly in the United States, with tech behemoths like Google, Microsoft, Amazon, IBM, and NVIDIA headquartered in the region, driving advancements in AI technologies.
  • Growth of the enterprise AI market in North America is propelled by the widespread adoption of AI solutions across various industries such as healthcare, finance, retail, and manufacturing, as organizations seek competitive advantages through data-driven decision-making and process automation.
  • Dominance of North America in the enterprise AI market is contributed to by the region’s robust ecosystem of AI startups, access to venture capital funding, and a skilled talent pool.

Asia Pacific:

  • Rapid growth in the enterprise AI market is experienced in the Asia Pacific region, fueled by factors such as increasing digital transformation initiatives, rising investments in AI research and development, and adoption of advanced technologies by enterprises.
  • A surge in enterprise AI adoption is witnessed in countries like China, India, Japan, and South Korea, as businesses strive to leverage AI capabilities to enhance operational efficiency, improve customer experiences, and gain competitive advantages.
  • Market growth in the Asia Pacific region is driven by leading technology companies such as Alibaba, Tencent, Samsung, and Huawei, actively developing and deploying AI solutions tailored for local markets and industries.
  • Increasing availability of AI-related skills and talents, coupled with government initiatives and supportive policies aimed at promoting AI adoption, further boosts the growth of the enterprise AI market in the Asia Pacific region.
  • The region’s large population and rapidly growing economies, combined with increasing demand for intelligent automation and data-driven decision-making, are expected to fuel the growth of the enterprise AI market in the coming years.

Global Enterprise AI Market Segmentation Analysis

The Global Enterprise AI Market is segmented based on Component, Deployment Type, Application, and Geography.

Enterprise AI Market, By Component

  • Solution• Services

Based on Component, the market is bifurcated into Solutions and Services. The Solution segment is dominating the market as more and more enterprises opting for it. Once the market for the solution segment is mature the service segment will rise along the way. the solution and service both the segments will have a significant growth in the CAGR during the forecast period.

Enterprise AI Market, By Deployment Type

  • Cloud• On-premises

Based on Deployment Type, the market is bifurcated into Cloud & on-premises. Enterprises are moving rapidly to the Cloud for their storage. The cloud segment is anticipated to witness tremendous growth owing to the increasing trend of adoption among enterprises.

Enterprise AI Market, By Application

  • Security and risk management• Marketing management• Customer support and experience• Human resource and recruitment management• Analytics application• Process automation

Based on Application, the market is bifurcated into Security and risk management, Marketing management, Customer support and experience, Human resource and recruitment management, Analytics application, and Process automation. With the increasing trend of adoption of AI applications in enterprises, the segments are highly likely to grow at a rapid pace during the forecast period.

Key Players

The “Global Enterprise AI Market” study report will provide valuable insight with an emphasis on the global market. The major players in the market are IBM, Microsoft, AWS, Intel, Google SAP, Sentient Technologies, Oracle, HPE, and Wipro.

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with product benchmarking and SWOT analysis. The competitive landscape section also includes key development strategies, market share, and market ranking analysis of the above-mentioned players globally.

Enterprise AI Market Key Developments

  • In May 2021, Vertex AI, a fully managed cloud platform from Google Cloud, simplifies the deployment and management of machine learning models. Compared to competitor systems, Google claims that Vertex allows models to be trained with up to 80% fewer lines of code.
  • In November 2021, Oracle announced the launch of Oracle Cloud Infrastructure (OCI) AI services, making it easier for developers to incorporate AI into their applications without needing data science knowledge. Developers can choose between using out-of-the-box models that have been pre-trained on business-oriented data or custom training the services using their data using the new OCI AI services.
  • In May 2022, with a new center of excellence, IBM and MBZUAI are collaborating to advance AI research. This center will give our academics and students a helpful resource and a collaborative setting to expand their AI work. IBM has a long technological innovation history, and they look forward to collaborating on their most recent initiatives to improve AI technology and commercialization for mutual benefit.
  • In March 2022, Nuance Communications Inc., a leader in conversational AI and ambient intelligence across industries such as healthcare, financial services, retail, and telecommunications, was acquired by Microsoft Corp. With our security-focused, cloud-based solutions integrated with strong, vertically optimized AI, Microsoft and Nuance will empower enterprises across industries to accelerate their business goals with our outcomes-based AI.

Report Scope

REPORT ATTRIBUTES DETAILS
Study Period

2019-2031

Base Year

2023

Forecast Period

2024-2031

Historical Period

2020-2022

Unit

Value (USD Billion)

Key Companies Profiled

IBM, Microsoft, AWS, Intel, Google SAP, Sentient Technologies, Oracle, HPE, and Wipro

Segments Covered

By Component, By Deployment Type, By Application, And By Geography

Customization Scope

Free report customization (equivalent up to 4 analyst’s working days) with purchase. Addition or alteration to country, regional & segment scope

Research Methodology of Market Research:

To know more about the Research Methodology and other aspects of the research study, kindly get in touch with our .

Reasons to Purchase this Report

• Qualitative and quantitative analysis of the market based on segmentation involving both economic as well as non-economic factors• Provision of market value (USD Billion) data for each segment and sub-segment• Indicates the region and segment that is expected to witness the fastest growth as well as to dominate the market• Analysis by geography highlighting the consumption of the product/service in the region as well as indicating the factors that are affecting the market within each region• Competitive landscape which incorporates the market ranking of the major players, along with new service/product launches, partnerships, business expansions, and acquisitions in the past five years of companies profiled• Extensive company profiles comprising of company overview, company insights, product benchmarking, and SWOT analysis for the major market players• The current as well as the future market outlook of the industry with respect to recent developments which involve growth opportunities and drivers as well as challenges and restraints of both emerging as well as developed regions• Includes in-depth analysis of the market of various perspectives through Porter’s five forces analysis• Provides insight into the market through Value Chain• Market dynamics scenario, along with growth opportunities of the market in the years to come• 6-month post-sales analyst support

Customization of the Report

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Frequently Asked Questions

Enterprise AI Market was valued to be USD 10.52 Billion in the year 2023 and it is expected to reach USD 158.81 Billion in 2031, at a CAGR of 47.16% over the forecast period of 2024 to 2031.
Increased Data Generation and Availability, Requirement for Process Automation and Operational Efficiency, Rising Demand for Personalized Customer Experiences are the factors driving the growth of the Enterprise AI Market.
The major players are IBM, Microsoft, AWS, Intel, Google, SAP, Sentient Technologies, Oracle, HPE, and Wipro.