The global AI Market is on the brink of exponential growth. In a new report from ResearchAndMarkets.com, the overall increase is estimated to be from $273.6 billion in 2025 to over $5.26 trillion by 2035 – a compound annual growth rate (CAGR) of 30.84%. The report, Artificial Intelligence (AI) Market Industry Trends and Global Forecast to 2035, also provides a comprehensive analysis of the overall AI landscape across offering types. These include deployment types, technology types, geographic locations or regions, and categorizations. The report will provide businesses with valuable insight into a fast-changing market.
Software Still Dominates, But Cloud AI Is Catching Up
Software is considered the largest segment in the AI Market. This is largely attributable to the use of AI tools, such as Machine Learning, natural language processing (NLP), and computer vision, all of which are now pervasive throughout industries like healthcare, finance, and automotive. Emerging technologies will typically be used for tasks such as diagnostics, fraud detection, and data with visual aspects. However, Cloud AI is the latest and fastest-growing driver of growth. Reports show that performance and penetration of cloud AI are outpacing on-premises growth.
Flexibility, scalability, and decreased capital expenditure are often cited as reasons companies are selecting cloud solutions over on-premises. In the case of small and medium-sized enterprises, cost savings extend beyond decreased operational costs to decreased capital costs. Cloud AI solutions do not require the purchase of costly infrastructure, allowing these enterprises to adopt AI tools and use them far faster than they otherwise could. The path toward cloud-based AI deployments has increasing momentum from an emerging trend toward adopting tools. This is provided as cloud-based tools and often an AI as a Service platform that can be quickly deployed, has fewer barriers to entry, and has fewer hurdles to identifying and deploying upgrades.
Machine Learning Leads the Tech Stack
Machine Learning continues to be the foundation for the growth of AI, enabling computers to learn from data, make predictions, and continuously improve without being programmed to do so. In the report, Machine Learning holds the most significant share of the overall technology market.
Its importance is ubiquitous. In the financial markets, Machine Learning is the basis for fraud detection and credit scoring. In logistics, it is the basis for demand forecasting and dynamic delivery routing. As more industries automate and shift to real-time analytics, Machine Learning will continue to be the driver for growth in AI.
AI Drives Value in BFSI and Marketing
From a usage perspective, marketing and sales comprise the largest market share in the AI Market for Applications. Companies use AI tools to analyze customer behavior, segment their audiences, and provide personalized offers and experiences. For the BFSI sector, Financial institutions use AI technology for risk management, fraud detection, and personalization. In addition to managing risk relative to brand reputation and saving costs associated with fraud, machine learning is useful. It provides financial organizations with the ability to process vast amounts of information and provide for automated decision-making.
Asia Races Ahead in AI Expansion
North America has the largest regional market, but Asia is expected to grow the fastest in the next ten years. Countries such as China, India, Japan, and Southeast Asia are ramping up spending and investments in AI infrastructure and Cloud Computing. Across all three industries, Cloud AI is seeing significant traction in Asia’s Fintech, logistics, and retail businesses. There is pressure from governments and emerging private enterprises to innovate and improve efficiencies for competitiveness. Global titans, including Google, Microsoft, OpenAI, AWS, NVIDIA, Meta, and Baidu, are rapidly expanding their AI footprints all over the globe. This is continuing to push innovation and investing heavily in R&D, AI, and Cloud deployment strategies.
About The Author
