Artificial Intelligence as a Service Market to Reach US$48 Billion by 2028

Future of Artificial Intelligence as a Service market: Growth drivers, market restraints and recent developments
Artificial Intelligence as a Service Market to Reach US$48 Billion by 2028

Artificial Intelligence as a Service Market Prediction: The Artificial Intelligence As a Service market size is expected to reach US$48.69 billion by 2028 from US$9.98 billion in 2023, with a CAGR of 37.31%. As organizations strive to harness the power of AI to enhance their operations, the demand for scalable, flexible, and cost-effective AI solutions continues to rise.

Among Wipro, Inuvo, Google, and Deloitte, there are plenty of companies offering groundbreaking AIaaS tools and services specifically aimed at business requirements. This article presents a thorough exploration of recent advancements, prominent partnerships, and a perspective on the future of Artificial Intelligence As a Service market, showcasing how these offerings will be transformative to industry and deliver economic value.

Factors driving the Artificial Intelligence As a Service market growth

The enormous increase in the data being created within organizations today, alongside the growing interest in data-driven decision-making, has made AIaaS a hot commodity for enterprise today. This huge amount of data, however, allows businesses to create insights that drive data-driven innovation using AI technology.

The historical practice of deploying AI was associated with a high cost of entry — requiring investments in hardware, infrastructure, and expertise. AIaaS overcomes all these hurdles by giving businesses easy and reasonable access to AI capabilities over the cloud, making it easier for businesses of all sizes to leverage the true potential of AI.

AI and big data have increased market opportunities. The risk of cyberattacks and data breaches increases with AIaaS, but that can freeze companies in their tracks as well. Moreover, with massive improvements in technologies like AI, IoT, big data market players also have something new to lure consumers into.

These technologies, combined with AIaaS, will help automate tasks, increase efficiency, reduce redundancy and ultimately save cost while discovering new insights that was previously impossible with older computing algorithms. Which in turn expected to provide new opportunities for businesses, which will propel the growth of the Market over the forecast period.

Moreover, growing need for business process optimization is fostering the growth in the AIaaS market. Digitization and automation are transforming the way industries operate, allowing maximizers to be their most complete selves by automating repetitive processes and commoditized jobs that optimize resource utilization and increase profit.

The transformation has largely been assisted by AIaaS services, which allows for a more flexible platform that helps scale, increase redundancy, cut costs, short upgrade time, downtime, security, and reliability.

Market restraints that may hinder the growth

Dealing with masses of data are an integral part of AIaaS, as a matter of fact, it has now opened a pandoras box when it comes to issues of data privacy and security. Yet, companies do not want to lose control of their data while they use AI services, but that can be an expensive proposition as securing data in an AI-driven world is not going to be cheap.

AIaaS requires hefty amounts of initial investment in hardware (GPUs, TPUs to streamline working with massive data), software (AI frameworks, platforms), and learning curves to get the staff on board. For smaller enterprises that do not have the expendable capital to invest in such technology, these costs can be out of reach.

Integrating AIaaS with existing systems and infrastructure is often complex and time-consuming. It requires specialized expertise in both the company’s existing IT systems and the new AI technologies. This process involves significant planning, development, and testing to ensure seamless integration and to avoid disruptions to business operations.

The gist is that demand is high but supply is low of AI professionals. It can hamper the adoption of AIaaS, as companies are struggling to get the personnel required to maintain and manage AI systems.

Most of the AIaaS providers have their own algorithms and data formats, making the end-users to be at the mercy of a single vendor. Migrating to another provider can be expensive and disruptive because it would require a complete rewrite or reengineering of an application to conform to the new provider.

To work well, an AI system needs good data, and lots of it. Incomplete, stale, or wrong data are nothing but the source of faulty predictions and conclusions based on which poor data-driven business decisions and the operational costs/efficiency might come.

AIaaS also has to pass a battery of laws and regulations, ranging from data privacy laws to the industry-specific chatographies. This makes compliance costly and difficult, and may even slow the adoption of AIaaS.

Due to the desire to take advantage of data wherever it strips within, AIaaS systems are arguably one of the vulnerabilities in the network, and constitute a valuable source for cyberattacks. Organizations must implement strong security controls to counter these risks.

Recent Developments in the Artificial Intelligence as a Service Market

The launch of is a standout example of recent advancements in the Artificial Intelligence as a Service market. Global EY organization It is a world-class platform that enables digital transformation by bringing together human expertise and AI capabilities. EY, supported by funding worth US$1.4 billion, has over one billion users globally.

The platform encourages responsible AI use, delivers broad training on AI for EY employees and integrates generative AI tools into its offerings. These developments have helped to strengthen client service, drive operational excellence and good data management, governance and compliance with industry regulations that position, as the market leader in AI-driven business solutions.

ReadyAI of Deloitte is a State-of-the-Art AI-as-a-Service Model introduced in the market designed to enable organizations to efficiently scale AI projects. To remove some of the common barriers to entry, ReadyAI also offers a subscription-based service model covering data preparation, advanced analytics, machine learning and model management.

Deloitte offers tailor-made AI solutions and has a team of more than 3,100 AI professionals who can provide a wealth of resources to boost AI adoption. With Ethical AI, they wants to provide a solution for organisations, enabling them to deploy AI at scale ethically and efficiently – taking the AI from a pilot to fully integrated into business.

Recent development in the Artificial Intelligence as a Service (AIaaS) market also include Inuvo's launch of self-serve availability for its IntentKey® models within demand-side platforms (DSPs). This new service allows brand and agency clients to access AI-driven audience selection and targeting directly, with real-time insights via a dedicated dashboard.

Inuvo offers two service plans: a managed services team and a self-serve solution enabling independent campaign activation. IntentKey's models use real-time online signals rather than third-party cookies, ensuring dynamic and privacy-centric media placement across the web. This advancement emphasizes transparency, customization, and performance optimization in AI-driven advertising technology.

Notable Partnerships in the Artificial Intelligence As a Service Market

Collaboration in the Artificial Intelligence as a Service (AIaaS) market is growing as well. Wipro Limited, partnering with IBM, introduced Wipro Enterprise AI-Ready Platform that seen leveraging IBM's watsonx AI and data platform aimed to help clients accelerate their AI adoption.

The new multi-year agreement further strengthens this collaboration, and sees the convergence of Wipro's industry verticals, enterprise services with IBM Cloud infrastructure and Watson technologies. They are working together to deliver co-innovated solutions to strengthen enterprise AI solutions that include industry-specific use cases and comprehensive AI governance.

Wipro and IBM are also co-investing in the IBM TechHub@Wipro, a hybrid cloud innovation center, to provide businesses in India with access to the expertise to integrate IBM Technology.

The partnership between Deutsche Bank and NVIDIA aims to accelerate the adoption of AI and machine learning in financial services, enhancing risk management, high-performance computing, and customer service.

Utilizing NVIDIA's AI Enterprise software suite and Omniverse Enterprise platform, Deutsche Bank plans to leverage advanced AI capabilities for risk model development, creating virtual avatars, and deriving insights from unstructured data.

The collaboration supports Deutsche Bank’s cloud transformation and underscores their commitment to AI-driven innovation and regulatory-compliant AI-powered services.

Google’s collaboration with the Government of Maharashtra aims to leverage AI in key sectors such as agriculture, healthcare, sustainability, education, and startups. Google will establish an AI Centre of Excellence at IIIT Nagpur, provide AI-enabled healthcare screening models, and deploy remote sensing technology for agricultural insights.

Additionally, Google will offer mentorship and training to startups and government IT professionals, fostering innovation and development across the state. This partnership underscores the role of AI in driving regional development and future-ready skills.


The AIaaS market is rapidly evolving, with major tech players and consulting firms introducing cutting-edge solutions to meet the growing demand for AI-driven capabilities. The collaborations between industry giants and regional governments, along with the launch of advanced AI platforms, underscore the market's potential to transform various sectors, from healthcare and agriculture to finance and advertising.

As AI technologies become more integrated into business strategies, the ability to deploy and manage AI models at scale will be crucial for maintaining competitive advantage. The future of the AIaaS market looks promising, with continuous innovation paving the way for more efficient, transparent, and impactful AI applications across the globe.


1. What is an example of artificial intelligence as a service?

An example of artificial intelligence as a service (AIaaS) is the use of AI-powered digital assistants and chatbots that allow users to interact with AI through natural language conversations.

These tools can be integrated into various applications and systems to provide personalized customer service, automate routine tasks, and enhance overall user experience. 

2. What is the meaning of artificial intelligence as a service?

Artificial intelligence as a service (AIaaS) refers to a cloud-based service that provides access to AI capabilities, such as machine learning, natural language processing, and computer vision, without organizations needing to build and maintain their own AI infrastructure.

3. Who provides AI as a service?

Several companies provide AI as a service (AIaaS), including IBM, Google, OpenAI, Salesforce, Amazon Web Services (AWS), Microsoft Azure, and innovative companies like Vsynergize.

These providers offer AI capabilities through cloud-based platforms, eliminating the need for businesses to invest in costly infrastructure and making AI technologies accessible to organizations of all sizes.

4. What are the benefits of AI as a service?

The key benefits of AI as a Service (AIaaS) include cost reduction by eliminating the need for in-house AI infrastructure and expertise, speed and simplicity in deploying AI solutions, scalability to easily adjust AI usage based on business needs, and access to advanced AI capabilities like machine learning and natural language processing without significant upfront investments.

5. Is ChatGPT AIaaS?

Yes, ChatGPT is an example of AI as a Service (AIaaS). It provides access to AI capabilities like natural language processing and machine learning through a cloud-based platform, eliminating the need for businesses to invest in costly infrastructure and expertise.

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