Artificial Intelligence-as-a-Service Market - Growth, Trends, COVID-19 Impact, and Forecasts (2021 - 2026)

Market Overview

The global Artificial Intelligence-as-a-Service market was valued at USD 3.91 billion in 2020, and it is expected to reach USD 43.298 billion by 2026, registering a CAGR of 48.9% during the period of 2021-2026. With the increasing number of enterprises and competition among them, companies are rigorously trying to integrate artificial intelligence (AI) technology to their application, business, analytics, and services.

• Moreover, companies are trying to reduce their operational cost to increase profit margins, due to which Artificial Intelligence-as-a-Service (AIaaS) is gaining more prominence over the cloud. Notably, companies are more interested in cloud-based machine learning, which helps in experimenting with their offerings.
• The rising trend of multi-cloud functioning and growing need for cloud-based intelligence services are also increasing the demand for AI as a service. According to IBM, by 2021, 98% of the organization's plan will adopt multi-cloud architectures, with only 41% having a multi-cloud management strategy and just 38% having procedures and tools to operate a multi-cloud environment. This creates a massive opportunity for AI services.
• Many government organizations, especially in emerging economies, are also understanding the benefits and power of AI; hence, they are extensively promoting AI-based infrastructure development. For instance, Niti Aayog in India launched a national program on AI, including R&D, with increased budget allocation for Digital India, to promote AI, machine learning, 3D printing, and other technologies.
• In addition, the companies are launching services associated with testing AI services. For instance, in April 2019, Google Cloud Platform started offering AI creators a new, shared, end-to-end environment for teams to test, train, and deploy models called the AI Platform. The company also upgraded its AutoML, a service for automating the creation of custom AI models.
• The outbreak of the COVID-19 pandemic has significantly affected several industries and markets globally. COVID-19 has overwhelmed customer service departments. It has impacted the customer experience, made callers less patient, and stressed out many call center agents. The demand for AI Infrastructure as a service has been gaining traction during the pandemic. AI and genomic studies are one essential tool in combating the pandemic.

Scope of the Report

Artificial Intelligence-as-a-Service (AIaaS) accounts as a third-party offering to outsource artificial intelligence. It allows companies or the end users to experiment with AI across various purposes by limiting the initial investment and with lower risk.

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Key Market Trends

BFSI is Expected to Occupy the Highest Share

• In recent years, AI technology has been increasingly adopted in the BFSI industry, primarily to enhance operational efficiency and enable rich consumer experience. AI is at the forefront of all innovations and will continue to remain so in 2020. 
• In the BFSI industry, AI is mainly used as chatbots, algorithmic trading, fraud detection, and customer recommendation. Banks, such as RBS, are implementing chatbots, which is likely to compel other financial institutions to invest in similar technology. As a result, it is expected to create demand for AI-based solutions, which, in turn, is expected to boost the investment by the industry players, thereby, fostering the market growth, over the next six years.
• Artificial intelligence in finance can act as a powerful ally when it comes to analysing real-time activities in any environment. The accurate predictions and forecasts that it provides are based on multiple variables, vital to business planning.
• For Instance, a US leasing company, Crest Financial, used artificial intelligence on the Amazon Web Services platform and immediately saw a significant improvement in risk analysis, without facing any deployment delays associated with traditional data science methods.
• Artificial Intelligence can also provide a faster, more accurate and unbiased assessment of a potential borrower, at a lower cost, by accounting for a wider variety of factors, which leads to a better-informed, data-backed decision.
• For example, Automobile lending companies in the U.S. have reported success with AI for their lending activities as well. A report by Zest Finance shows that atop auto lender in the US was able to cut losses by 23% annually bringing AI on board.

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