The global artificial intelligence (AI) software market is forecast to grow rapidly in the coming years, reaching around *** billion U.S. dollars by 2025. The overall AI market includes a wide array of applications such as natural language processing, robotic process automation, and machine learning. What is artificial intelligence? Artificial intelligence refers to the capability of a machine that is able to replicate or simulate intelligent human behaviours such as analysing and making judgments and decisions. Originated in the computer sciences and a contested area in philosophy, artificial intelligence has evolved and developed rapidly in the past decades and AI use cases can now be found in all corners of our society: the digital voice assistants that reside in our smartphones or smart speakers, customer support chatbots, as well as industrial robots. Investments in AI Many of the biggest names in the tech industry have invested heavily into both AI acquisitions and AI related research and development. When it comes to AI patent applications by company, Microsoft, IBM, Google, and Samsung have each submitted thousands of such applications, and funding for AI related start-ups are raking in dozens of billions of dollars each year.

As per Cognitive Market Research's latest published report, the Global Machine Learning market size was USD 24,345.76 million in 2021 and it is forecasted to reach USD 206,235.41 million by 2028. Machine Learning Industry's Compound Annual Growth Rate will be 42.64% from 2023 to 2030. Market Dynamics of Machine Learning Market

Key Drivers for Machine Learning Market

Explosion of Big Data Across Industries: The substantial increase in both structured and unstructured data generated by sensors, social media, transactions, and IoT devices is driving the demand for machine learning-based data analysis.

Widespread Adoption of AI in Business Processes: Machine learning is facilitating automation, predictive analytics, and optimization in various sectors such as healthcare, finance, manufacturing, and retail, thereby enhancing efficiency and outcomes.

Increased Availability of Open-Source Frameworks and Cloud Platforms: Resources like TensorFlow, PyTorch, and scalable cloud infrastructure are simplifying the process for developers and enterprises to create and implement machine learning models.

Growing Investments in AI-Driven Innovation: Governments, venture capitalists, and major technology companies are making substantial investments in machine learning research and startups, which is accelerating progress and market entry.

Key Restraints for Machine Learning Market

Shortage of Skilled Talent in ML and AI: The need for data scientists, machine learning engineers, and domain specialists significantly surpasses the available supply, hindering scalability and implementation in numerous organizations.

High Computational and Operational Costs: The training of intricate machine learning models necessitates considerable computing power, energy, and infrastructure, resulting in high costs for startups and smaller enterprises.

Data Privacy and Regulatory Compliance Challenges: Issues related to user privacy, data breaches, and adherence to regulations such as GDPR and HIPAA present obstacles in the collection and utilization of data for machine learning.

Lack of Model Transparency and Explainability: The opaque nature of certain machine learning models undermines trust, particularly in sensitive areas like finance and healthcare, where the need for explainable AI is paramount.

Key Trends for Machine Learning Market

Growth of AutoML and No-Code ML Platforms: Automated machine learning tools are making AI development more accessible, enabling individuals without extensive coding or mathematical expertise to construct models.

Integration of ML with Edge Computing: Executing machine learning models locally on edge devices (such as cameras and smartphones) is enhancing real-time performance and minimizing latency in applications.

Ethical AI and Responsible Machine Learning Practices: Increasing emphasis on fairness, bias reduction, and accountability is shaping ethical frameworks and governance in ML adoption.

Industry-Specific ML Applications on the Rise: Custom ML solutions are rapidly emerging in sectors like agriculture (crop prediction), logistics (route optimization), and education (personalized learning).

COVID-19 Impact:

Similar to other industries, the covid-19 situation has affected the machine learning industry. Despite the dire conditions and uncertain collapse, some industries have continued to grow during the pandemic. During covid 19, the machine learning market remains stable with positive growth and opportunities. The global machine learning market faces minimal impact compared to some other industries.The growth of the global machine learning market has stagnated owing to automation developments and technological advancements. Pre-owned machines and smartphones widely used for remote work are leading to positive growth of the market. Several industries have transplanted the market progress using new technologies of machine learning systems. June 2020, DeCaprio et al. Published COVID-19 pandemic risk research is still in its early stages. In the report, DeCaprio et al. mentions that it has used machine learning to build an initial vulnerability index for the coronavirus. The lab further noted that as more data and results from ongoing research become available, it will be able to see more practical applications of machine learning in predicting infection risk. What is&nbs...

AI And Machine Learning Operationalization Software Market size was estimated at USD 6.12 Billion in 2024 and is projected to reach USD 36.25 Billion by 2032, growing at a CAGR of 35.2% from 2026 to 2032.

Key Market Drivers

Surging Adoption of AI & ML: The widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) across various industries is driven primarily by the surge in demand. With AI and ML increasingly leveraged by organizations for tasks like automation, decision-making, and process optimization, there is a growing demand for MLOps software to effectively manage and operationalize these models.

The global Ai And Machine Learning Operationalization Software Market size was estimated at USD 1.61 billion in 2024 and is anticipated to grow at a CAGR of 37.1% from 2025 to 2034.

Discover Market Research Intellect's Ai Machine Learning Operationalization Software Market Report, worth 4.5 billion USD in 2024 and projected to hit 12.3 billion USD by 2033, registering a CAGR of 15.2% between 2026 and 2033.Gain in-depth knowledge of emerging trends, growth drivers, and leading companies.

Global AI And Machine Learning Operationalization Software market size is expected to reach $28.48 billion by 2029 at 39.0%, segmented as by cloud-based, public cloud, private cloud, hybrid cloud

Get key insights on Market Research Intellect's Ai Machine Learning Operationalization Software Market Report: valued at USD 5.2 billion in 2024, set to grow steadily to USD 15.3 billion by 2033, recording a CAGR of 16.5%.Examine opportunities driven by end-user demand, R&D progress, and competitive strategies.

Newsle led the global machine learning industry in 2021 with a market share of ***** percent, followed by TensorFlow and Torch. The source indicates that machine learning software is utilized for the application of artificial intelligence (AI) that allows systems the ability to automatically or "artificially" learn and improve functions based on experience without being specifically programmed to do so.

Machine Learning Data Catalog Software Market Outlook

The Machine Learning Data Catalog Software market is witnessing robust growth, with a global market size valued at USD 1.5 billion in 2023, projected to reach USD 4.2 billion by 2032, expanding at a compound annual growth rate (CAGR) of 12.5% during the forecast period. The burgeoning need for efficient data management and storage solutions, coupled with the increasing adoption of machine learning technologies across various industries, is driving this market's growth trajectory. Enterprises are increasingly seeking advanced data catalog solutions that can leverage machine learning to enhance data discovery, governance, and overall data management efficiency.

One of the primary growth factors fueling the Machine Learning Data Catalog Software market is the exponential increase in data generation across industries. With businesses increasingly relying on data-driven decision-making, the demand for structured data management has become paramount. Machine learning data catalog software facilitates enhanced data discovery and management, allowing organizations to efficiently organize and retrieve vast data volumes. This has become even more critical as enterprises aim to derive actionable insights from unstructured data, driving the market's expansion. Furthermore, regulatory pressures towards data governance and compliance are compelling organizations to adopt sophisticated data catalog solutions to ensure data integrity and security.

Another significant driver is the growing integration of artificial intelligence and machine learning in data management processes. Machine learning algorithms enhance data cataloging by automating the tagging and classification of data, thus improving data accessibility and usability. This automation reduces the time and effort required for data management, enabling organizations to focus on strategic initiatives. Additionally, the rapid evolution of AI technologies is leading to the continuous improvement of data catalog software capabilities, making them more robust and versatile. This technological advancement is a crucial factor contributing to the sustained growth of the market.

The increasing adoption of cloud-based solutions also serves as a potent growth catalyst for the Machine Learning Data Catalog Software market. As organizations transition towards cloud infrastructure, the demand for cloud-compatible data catalog solutions is surging. Cloud-based data catalogs offer scalability, flexibility, and cost-effectiveness, making them attractive to enterprises of all sizes. This shift is particularly prominent among small and medium-sized enterprises (SMEs) that seek affordable yet powerful data management solutions. Furthermore, the cloud deployment model facilitates seamless collaboration and remote accessibility, which is increasingly important in the contemporary global business environment.

Regionally, North America currently dominates the Machine Learning Data Catalog Software market, owing to the presence of numerous leading technology companies and high adoption rates of AI-driven solutions. The region's mature IT infrastructure and strong focus on data-driven strategies further bolster this market leadership. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rapid digital transformation across emerging economies, particularly in countries like China and India. This transformation is characterized by increased investments in AI and machine learning technologies, creating lucrative opportunities for market expansion.

Component Analysis

In the Machine Learning Data Catalog Software market, the component segment is divided into software and services. Software solutions dominate the market due to their core functionality in data cataloging, which encompasses data indexing, tagging, and classification. The software component is essential for organizations seeking to automate data management processes, thereby reducing manual efforts and enhancing efficiency. The rapid technological advancements in software capabilities, such as the integration of AI-driven features, are further fueling the demand for these solutions. Additionally, the constant updates and innovations within software offerings ensure they remain aligned with the evolving needs of enterprises in various sectors.

On the other hand, the services segment, though smaller in market share compared to software, plays a critical role by providing essential support and customization options. Services include consulting, implementatio

The AI and Machine Learning (AI/ML) operational software market is experiencing robust growth, driven by the increasing adoption of AI/ML across diverse industries. The market, estimated at $50 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 20% from 2025 to 2033. This significant expansion is fueled by several key factors. Firstly, the rising volume of data generated across various sectors necessitates sophisticated tools for efficient data analysis and model deployment. Secondly, the growing need for automation in business processes is driving demand for AI/ML-powered solutions that optimize workflows and improve operational efficiency. Thirdly, the maturation of cloud computing infrastructure provides scalable and cost-effective platforms for deploying and managing AI/ML models, further accelerating market growth. Large enterprises are currently the major adopters, but the market is witnessing significant traction from Small and Medium Enterprises (SMEs) as access to user-friendly and cost-effective solutions becomes more prevalent. The preference for cloud-based solutions is rapidly overtaking locally-based deployments due to scalability and accessibility advantages. The market segmentation reveals distinct dynamics. Cloud-based solutions dominate due to their inherent scalability and accessibility. While North America currently holds the largest market share, driven by early adoption and technological advancements, regions like Asia Pacific are witnessing rapid growth, reflecting the increasing digitalization across developing economies. Competitive forces are intense, with established players like Microsoft, IBM, and SAP, competing with agile startups such as DataRobot and Alteryx. The market landscape is further shaped by open-source tools like Python and frameworks like TensorFlow and PyTorch, contributing significantly to the development and deployment of AI/ML models. Despite the rapid growth, challenges remain, including the need for skilled professionals to develop and maintain these systems, data security and privacy concerns, and the ethical implications of AI/ML applications. These factors, while posing challenges, also present significant opportunities for innovation and market expansion.

The Artificial Intelligence Software market is valued at USD 80.26 Billion in 2022 and will be USD 612.36 Billion by 2030 with a CAGR of 29.06% during the forecast period. Factors Affecting the Artificial Intelligence Software Market

Increasing demand for IoT solutions boosts Artificial Intelligence Software market growth:

Software that uses artificial intelligence is increasingly being used in industries from healthcare to defense, as it is one of the most effective ways to eliminate the need for human labor. To compete in the artificial intelligence software market, other well-known companies are also releasing new AI software at the same time. For instance, Amazon announced the release of a new artificial intelligence tool called “Create with Alexa '' in November 2022 to produce stories for children. This brand-new artificial intelligence program creates briefings that include animation, music, and pictures.

Increasing demand for AI in the healthcare sector

The health industry's application of artificial intelligence software improved the standard of living for workers. In the coming years, the market for artificial intelligence software will probably be driven by rising demand from the healthcare industry. Technology advancements also offer profitable chances for market expansion.

Restraint for Artificial Intelligence Software Market

The difficulty associated adoption of AI tools can hamper market growth:

The market's limiting factors include the absence of Al talent in emerging nations, difficulties with the all-at-once adoption of Al tools, and the black box effect. To combat these factors and end the "black box effect," businesses have improved their solutions with more moral and explicable Al models. The black box effect causes the Al algorithms to occasionally provide results that are difficult to verify. These algorithms' results could be biased in a subtle way that is hard to detect. Therefore, the results are not adequately explained. As a result, consumers frequently embrace Al tools without feeling secure or trusted

Impact of the COVID-19 Pandemic on the Artificial Intelligence Software Market:

The pandemic crisis altered the way businesses functioned and made them more complex. Businesses moved their business operations to the cloud to adapt pt this development., machine learning, and other cutting-edge technologies saw a spike in use as a result. One of the first industries to use this technology, which increased the precision and effectiveness of diagnoses, treatments, and predictions, was the healthcare industry. For instance, according to the study report from January 2022, researchers at Indiana University and the Regenstrief Institute discovered that machine learning (ML) models could aid in public health decision-making during the pandemic. What is Artificial Intelligence Software?

A mainframe program called artificial intelligence (Al) software imitates human behavior by gaining knowledge from various insights and data patterns. Artificial intelligence platforms, chatbots, deep learning software, and machine learning software are a few examples of different types of Al software. Additional features of Al software include voice and speech recognition, machine learning, and virtual assistants. To automate company procedures and organize data for better data insights, various sorts of enterprises utilize artificial intelligence software which is machine learning incorporated. In addition, the market for Al software is anticipated to increase exponentially in the future due to rising technological advancements.

Global AI & Machine Learning Operationalization Software is segmented by Application (Data Scientists, Machine Learning Engineers, DevOps Teams), Type (Model Deployment, Model Monitoring, Model Management, Model Versioning, MLOps Pipelines) and Geography(North America, LATAM, West Europe, Central & Eastern Europe, Northern Europe, Southern Europe, East Asia, Southeast Asia, South Asia, Central Asia, Oceania, MEA)

AI & Machine Learning Operationalization Software Market Outlook

The AI & Machine Learning Operationalization Software market size was valued at USD 4.5 billion in 2023 and is projected to reach USD 18.7 billion by 2032, growing at a CAGR of 17.2% during the forecast period. The robust growth of the market is driven by the increasing adoption of artificial intelligence (AI) and machine learning (ML) across various industries due to their ability to enhance operational efficiency and decision-making processes.

One of the significant growth factors in this market is the rising demand for automation and data-driven decision-making across industries. AI and ML operationalization software enables organizations to deploy and manage machine learning models at scale, which leads to improved performance, reduced costs, and enhanced customer satisfaction. The ability to leverage vast amounts of data to derive actionable insights is becoming increasingly crucial in today's competitive business environment, driving the adoption of these technologies.

Moreover, advancements in AI and ML technologies, coupled with the increasing availability of high-quality data, are further fueling the market's growth. The development of sophisticated algorithms and the integration of AI and ML with other emerging technologies such as the Internet of Things (IoT) and blockchain are opening new avenues for innovation and efficiency. These advancements enable more complex and accurate predictive models, which are critical for various applications ranging from predictive maintenance in manufacturing to personalized customer experiences in retail.

Another significant driver is the growing need for regulatory compliance and risk management. Industries such as BFSI and healthcare are under constant scrutiny from regulatory bodies, and the ability to operationalize AI and ML can help these organizations comply with regulations more effectively. AI and ML operationalization software provides robust tools for model monitoring, auditing, and governance, which are essential for maintaining compliance and managing risks in sensitive sectors.

From a regional perspective, North America is expected to dominate the market due to the early adoption of AI and ML technologies and the presence of major technology players in the region. However, the Asia Pacific region is anticipated to witness the highest growth during the forecast period, driven by rapid digital transformation, increasing investments in AI and ML, and supportive government initiatives.

Component Analysis

The AI & Machine Learning Operationalization Software market can be segmented by component into software and services. The software segment is anticipated to hold the largest market share, given the critical role that AI and ML software solutions play in enabling organizations to develop, deploy, and manage machine learning models. These software solutions encompass a wide range of functionalities, including data preprocessing, model training, deployment, and monitoring, which are essential for operationalizing AI and ML within an enterprise environment.

Within the software segment, end-to-end machine learning platforms are gaining significant traction. These platforms provide comprehensive tools and frameworks that simplify the entire machine learning lifecycle, from data ingestion to model deployment and monitoring. The convenience and efficiency offered by these platforms are driving their adoption across various industries. Additionally, the integration of AI and ML operationalization software with existing IT infrastructure and applications is further enhancing their value proposition, making them indispensable for organizations aiming to leverage AI and ML at scale.

On the other hand, the services segment is also expected to witness substantial growth, driven by the increasing need for professional services such as consulting, integration, and training. As organizations embark on their AI and ML journeys, they often require specialized expertise to navigate the complexities associated with AI and ML implementation. Professional services providers offer valuable support in areas such as strategy development, technology selection, model development, and operationalization, thereby facilitating the successful adoption of AI and ML technologies.

Another critical aspect of the services segment is the growing demand for managed services. Managed services providers offer ongoing support for AI and ML operationalization, including model monito

The Deep Learning System Software market is experiencing robust growth, driven by the increasing adoption of AI across various industries. The market's expansion is fueled by the need for efficient and scalable solutions to handle the massive datasets required for training sophisticated deep learning models. Key factors contributing to this growth include the proliferation of cloud computing services offering readily accessible deep learning platforms, the development of more powerful and energy-efficient hardware (GPUs and specialized AI chips), and the rising demand for automated decision-making systems in sectors like healthcare, finance, and manufacturing. The market is segmented by software type (e.g., frameworks, libraries, tools), deployment model (cloud, on-premise), and industry vertical. Leading players like Microsoft, Nvidia, Google (Alphabet), and Intel are actively investing in R&D and strategic acquisitions to strengthen their market positions. Competition is intense, with companies focusing on providing specialized solutions tailored to specific industry needs and improving the ease of use and accessibility of their software. While challenges remain, such as the need for skilled data scientists and the ethical considerations surrounding AI deployment, the overall market outlook remains positive, projecting significant expansion over the forecast period. Despite the positive outlook, several restraints could potentially hinder market growth. These include the high cost of implementation, the complexity of deep learning systems requiring specialized expertise, and concerns regarding data security and privacy. The need for continuous updates and maintenance to keep pace with technological advancements also presents a challenge. However, ongoing research and development in areas such as automated machine learning (AutoML) and edge AI are expected to mitigate some of these challenges. The market is likely to witness increased consolidation as larger players acquire smaller companies with specialized technologies. Furthermore, the growing importance of data annotation and model explainability will create new market opportunities for specialized service providers. The future of the Deep Learning System Software market is characterized by innovation, competition, and the ongoing need to address ethical and practical concerns. We expect the market to demonstrate a steady and considerable increase in value throughout the forecast period.

According to the market research firm Tractica, the European artificial intelligence software market is expected to experience significant growth in the coming years, with revenues increasing from around **** billion U.S. dollars in 2018 to an expected ***** billion by 2025. The overall AI market includes a wide array of applications such as natural language processing, robotic process automation, and machine learning.

AI & Machine Learning Market Outlook

The AI & Machine Learning market size is forecasted to grow from USD 128.9 billion in 2023 to USD 684.6 billion by 2032, at a compound annual growth rate (CAGR) of 20.5%. The market's rapid expansion is driven by the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies across various sectors, including healthcare, finance, and manufacturing, as these technologies become more integral to operations and decision-making processes.

One of the primary growth factors for this market is the continuous advancements in computational power and data processing capabilities. The exponential increase in data generated from various sources, such as IoT devices, social media, and enterprise systems, has created a substantial demand for sophisticated AI and ML algorithms to analyze and derive actionable insights. This surge in data, coupled with improvements in hardware, such as GPUs and TPUs, has made real-time analytics and complex model training more feasible and efficient, thereby fueling market growth.

Additionally, the increasing investments in AI and ML by both private and public sectors are significantly contributing to the market's expansion. Governments worldwide are recognizing the strategic importance of AI and ML technologies for national security, economic growth, and global competitiveness. Various initiatives and funding programs aimed at fostering AI research and development are being established, which, in turn, are encouraging startups and established companies to innovate and develop new AI-driven solutions. This influx of capital and resources is expected to sustain the market's growth trajectory over the coming years.

The proliferation of AI and ML applications across diverse industries is also a critical driver for market growth. In healthcare, AI is being used for predictive analytics, personalized medicine, and automated diagnostics, enhancing patient care and operational efficiency. In finance, AI and ML are employed for fraud detection, risk management, and algorithmic trading, offering significant cost savings and improved decision-making. The retail and e-commerce sectors leverage AI for customer behavior analysis, personalized recommendations, and inventory management, optimizing the overall shopping experience and operational workflow.

From a regional perspective, North America currently holds the largest market share, driven by technological advancements, significant R&D investments, and the presence of key market players. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. Increasing digitalization, growing adoption of AI-driven technologies in emerging economies like China and India, and supportive government policies are contributing to this rapid growth. Europe and Latin America are also expected to experience substantial growth, attributed to rising awareness and integration of AI and ML across various sectors.

Component Analysis

The AI & Machine Learning market is segmented by components into software, hardware, and services. Each of these segments plays a crucial role in the ecosystem, contributing to the overall functionality and deployment of AI and ML technologies. The software segment, which includes AI platforms, machine learning frameworks, and analytics tools, is the largest and fastest-growing component of the market. This segment's growth is primarily driven by the increasing demand for AI-powered applications and solutions that can automate processes, enhance decision-making, and provide predictive insights. Organizations are investing heavily in AI software to gain a competitive edge, streamline operations, and deliver innovative products and services to customers.

The hardware segment, comprising GPUs, TPUs, and other specialized AI processors, is also witnessing significant growth. These hardware components are essential for the efficient processing and training of complex AI models, enabling faster and more accurate data analysis. The advancements in hardware technologies are making it possible to handle large datasets and perform real-time analytics, which are critical for applications such as autonomous driving, natural language processing, and computer vision. The demand for high-performance hardware is expected to continue growing as AI and ML applications become more sophisticated and widespread.

The services segment includes consulting, implementation, and maintenance services that support the deployment and integ

The global Deep Learning Software market size was valued at USD XXX million in 2025 and is projected to grow at a CAGR of XX% during the forecast period 2025-2033, reaching a value of USD XXX million by 2033. The growth of the market is attributed to the increasing adoption of deep learning for various applications such as image recognition, natural language processing, fraud detection, and healthcare diagnostics. Key industry trends that are driving the market growth include the increasing use of artificial intelligence (AI) and machine learning (ML), the development of new deep learning algorithms, the availability of large datasets, and the reduction in the cost of computing. The major players in the market are Microsoft, Google, IBM, Amazon Web Services (AWS), Nuance Communications, and Clarifai. The market is segmented based on application into large enterprises and small and medium enterprises (SMEs), and based on deployment type into on-premise and cloud-based. The North American region holds the largest share of the market, followed by Europe and Asia Pacific. The increasing adoption of deep learning in the healthcare, retail, finance, and manufacturing industries is expected to drive market growth in the coming years.

The AI & Machine Learning Operationalization Software market is experiencing rapid growth, driven by the increasing need for businesses to effectively deploy and manage AI/ML models in production environments. The market's expansion is fueled by several key factors: the surge in data volume and variety demanding sophisticated analytical tools; the rising adoption of cloud computing, providing scalable infrastructure for AI/ML workloads; and the growing demand for improved model accuracy, reliability, and efficiency. This has led to the development of specialized software solutions that streamline the entire ML lifecycle, from model training and deployment to monitoring and maintenance. Key players are innovating with features like automated model deployment, real-time monitoring dashboards, and robust version control to address challenges around model explainability, bias mitigation, and operational efficiency. We estimate the market size in 2025 to be $2.5 billion, with a Compound Annual Growth Rate (CAGR) of 25% projected through 2033, indicating a substantial market expansion driven by increased adoption across various industries like finance, healthcare, and retail. This robust growth, however, is not without its restraints. The complexity of implementing and managing AI/ML systems presents a significant barrier for many organizations, requiring specialized skills and substantial investments. Data security and privacy concerns are also critical, demanding robust security protocols throughout the operational lifecycle. Furthermore, the need for continuous model retraining and adaptation to evolving data patterns adds to the operational complexity. Despite these challenges, the benefits of operationalizing AI/ML—including improved decision-making, enhanced operational efficiency, and the creation of new revenue streams—are driving widespread adoption. The market segmentation reflects this diversity, with solutions catering to specific needs across different industries and deployment environments (on-premise, cloud, hybrid). The competitive landscape is dynamic, with established players like IBM and Databricks competing alongside specialized startups, creating a vibrant ecosystem of innovation.

Global Artificial Intelligence (AI) Software market size is expected to reach $817.68 billion by 2029 at 29.3%, segmented as by on-premises, enterprise ai solutions, edge ai solutions, ai for data centers