AI Infrastructure Market - Growth, Trends, COVID-19 Impact, and Forecasts (2021 - 2026)

Market Overview

The AI Infrastructure Market is expected to register a CAGR of 21% during the forecast period (2021 - 2026). Many businesses and IT executives across the globe are already making significant investments in AI technologies. AI is primarily changing everything, and as it is becoming more prevalent, organizations in the near future will be forced to come to grips with it on a macro level as it is changing the entire industry, and on a micro-level, as it impacting the strategic business decisions. With such significant changes happening at such a fast pace, the market is expected to witness robust growth.

• The organizations that have more experience with AI, or the ones that are looking to respond to the increasing needs across multiple areas of their businesses, are expected to adopt broader infrastructure solutions that can support the general AI workloads. This approach is similar to the platform architecture prevalent across IT, which provides a highly scalable infrastructure layer that is managed as a single pool, with the usage of virtualization and software-defined orchestration across server processing, storage, and networking.
• The networking infrastructure is expected to play a crucial role in providing high efficiency at the scale required to support AI, and organizations may likely need to upgrade their networks. Deep learning algorithms, as well as machine learning algorithms, help companies to achieve network efficiencies. For example, companies should deploy automated infrastructure management tools in their data centers.
• For instance, In February 2020, Ericsson has launched an AI-powered Energy Infrastructure Operations solution, which leverages AI and advanced data analytics to optimize energy consumption across network infrastructure for communications service providers.
• Further, the growth in automation in different end-user industries has also been fueling the growth of the market studied. According to the Capgemini Report, 2019, in Europe, 51% of top global manufacturers use at least a single use case of AI in their operations.
• Different companies have been offering AI infrastructure-related solutions in the market studied, has been enabling the company to leverage their AI infrastructure. For instance, in June 2020, Intel its 3rd Gen Intel Xeon Scalable processors and additions to its hardware and software AI portfolio, enabling customers to accelerate the development and use of AI and analytics workloads running in the data center, network and intelligent-edge environments that support their AI infrastructure.
• Moreover, various countries have been investing in the AI ecosystem that has been propelling the growth of the market studied. For instance, in January 2020, the Indian government think tank NITI Aayog has released a paper to set up the country's first AI-specific cloud computing infrastructure called 'AIRAWAT' (AI Research, Analytics, and Knowledge Assimilation platform). The platform aims to guide the research and development of new and emerging technologies in the field of AI.

Scope of the Report

As Artificial Intelligence technology moves from the experimentation phase towards the adoption phase, it will primarily require a significant amount of computing resources and advance infrastructure. AI infrastructure requires multiple resources, which can provide sufficient performance, the computing resources include CPUs and GPUs, high storage capacity, advanced networking infrastructure, and high security. The scope for the AI Infrastructure market primarily includes Hardware and Software offering having on-premise and cloud deployment along with various end-user industries, such as Enterprises and Government, among others, across different geographies.

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Key Market Trends

Enterprise Segment is Expected to Witness Significant Growth

• Enterprises are increasingly recognizing the value associated with the incorporation of artificial intelligence (AI) into their business processes. They improve operational efficiency and reduce cost through automation of process flows. 
• For instance, companies have been using autonomous processes to improve operations and change the face of customer service (for example, through AI-powered chatbots), while spurring innovation to new heights. According to an article published by Komando Technology, in 2020, chatbots are expected to cut business costs by USD 8 billion.
• Furthermore, as the focus of IT strategy moves from data management to intelligent action, enterprises have been increasingly recognizing the role of AI to support humans in problem-solving, decision making, and creative endeavors. Also, enterprises recognize that implementing and using AI is critical for their continued growth in the competitive environment with many potential opportunities, such as new opportunities using AI to drive innovation, make connections, identify, and foster new developments.
• Further, to leverage the AI opportunities, one of the first considerations for any enterprise is to have a suitable infrastructure to support AI developments. Moreover, AI solutions frequently demand new hardware and software integration to function. For instance, for collation and annotation of data source, scalable processing, or creating and fine-tuning models as new data become available requires AI solutions. Solutions include repurposing existing hardware and buying a one-off AI solution, building a broader platform to support multiple AI solutions, and outsourcing AI solution delivery. Thus, infrastructure plays a vital role in the growth of the AI landscape.

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