PUBLISHER: MarketsandMarkets | PRODUCT CODE: 1608639
PUBLISHER: MarketsandMarkets | PRODUCT CODE: 1608639
The cloud AI market will grow from USD 80.30 billion in 2024 to USD 327.15 billion by 2029 at a compounded annual growth rate (CAGR) of 32.4% during the forecast period. Cloud AI transforms technology use across industries, including manufacturing, healthcare, finance, and retail. For instance, hospitals employ cloud AI to forecast health trends and quickly evaluate medical data, assisting physicians in making better decisions for their patients.
Scope of the Report | |
---|---|
Years Considered for the Study | 2019-2029 |
Base Year | 2024 |
Forecast Period | 2024-2029 |
Units Considered | USD (Billion) |
Segments | Offering, Technology Type, Hosting Type, Organization Size, Business Function, Verticals |
Regions covered | North America, Europe, Asia Pacific, Middle East Africa, and Latin America |
More businesses are using cloud AI since it offers powerful computing and data analysis without needing hardware investments. This allows companies to use AI for real-time insights, predictions, and automation, helping them work more efficiently, save money, and focus on core operations.
By offering, the AI as a service segment holds the highest CAGR during the forecast period.
AI-as-a-Service (AIaaS) is expected to grow the highest in the cloud AI market. It gives businesses access to advanced AI tools without investing in expensive infrastructure or specialized knowledge. A key advantage of AIaaS is its scalability and flexibility. Businesses can quickly change their AI capabilities as required, which works well for companies of all sizes. It also makes AI accessible to smaller businesses that don't have the resources to create and manage their systems. AIaaS providers often have simple interfaces and tools that make it easier to connect with existing systems, so businesses don't need extensive technical skills.
AIaaS is becoming more popular as businesses try to improve customer experiences and run their operations more efficiently. The growth of AutoML (automated machine learning) and pre-trained models in AIaaS is helping this trend by making it easier to develop and use AI applications. As businesses embrace data-driven decision-making, AIaaS will be crucial in navigating data complexities and driving innovation.
Based on vertical, the BFSI segment holds the largest market share during the forecast period.
Banks and financial services use cloud AI to improve security, customer service, and efficiency. Cloud AI helps them analyze data instantly, which is essential for detecting fraud, managing risks, and providing personalized services for each customer. AI models process large amounts of data in real-time to find unusual patterns and reduce the risks of financial crimes. Cloud AI improves customer service by offering personalized advice and chatbots, making banking faster and more effective in meeting the growing demand for digital services.
In insurance, cloud AI speeds up claims processing, predicts risks, and examines data, making work quicker and decisions more accurate. It also allows businesses to adjust resources without significant upfront investments in IT systems. This flexibility helps companies to adapt to changing market demands and follow new regulations. Overall, cloud AI improves security, lets companies offer personalized services, and helps them operate more efficiently to meet customer needs and keep up with a fast-changing digital world.
Based on the business function, the operations & supply segment holds the highest CAGR during the forecast period.
Cloud AI transforms how companies manage logistics, inventories, and efficiency in operations and supply chains. Companies could use AI systems to obtain real-time data about their supply chain, improve inventory management, and better predict demand. This reduces expenses, increases flexibility, and helps to satisfy customers' needs better. AI also helps businesses find potential problems and improve delivery routes, making the supply chain faster and more responsive.
Recent trends in the cloud AI market for operations and supply chain chains include the integration of Internet of Things (IoT) devices for real-time data collection, which enhances visibility across the supply chain. More businesses are using AI to automate tasks such as order processing and inventory management so their employees can focus on making key decisions. AI also helps with predictive maintenance, keeping equipment running smoothly and reducing expensive downtime. As companies work to be more eco-friendly, AI helps them cut waste and use resources more efficiently.
Breakdown of primaries
We interviewed Chief Executive Officers (CEOs), directors of innovation and technology, system integrators, and executives from several significant cloud AI market companies.
Some of the significant cloud AI market vendors are Google (US), IBM (US), AWS (US), Microsoft (US), Oracle (US), Nvidia (US), Salesforce (US), SAP (Germany), Alibaba Cloud (China), HPE (US), and Intel (US).
In the market report, we covered the cloud AI market across segments. We estimated the market size and growth potential for many segments based on offering, technology type, hosting type, organization size, business function, verticals, and region. It contains a thorough competition analysis of the major market participants, information about their businesses, essential observations about their product and service offerings, current trends, and critical market strategies.
With information on the most accurate revenue estimates for the whole cloud AI industry and its subsegments, the research will benefit market leaders and recent newcomers. Stakeholders will benefit from this report's increased understanding of the competitive environment, which will help them better position their companies and develop go-to-market strategies. The research offers information on the main market drivers, constraints, opportunities, and challenges, as well as aids players in understanding the pulse of the industry.
Analysis of key drivers (provide the necessary infrastructure and scalability for gen AI applications, allowing organizations to harness massive datasets and computational power), restraints (many businesses are cautious about adopting cloud-based AI solutions due to concerns over data ownership, encryption, and the potential misuse of AI-powered insights), opportunities (as technologies like the IoT the need for AI-driven solutions that can manage, analyze, and optimize the vast amounts of data generated by these innovations is increasing), and challenges (complexity of AI integration is a significant challenge for the cloud AI market, particularly for businesses with limited technical expertise).