AI Infrastructure Market - Growth, Trends, COVID-19 Impact, and Forecasts (2023 - 2028)

AI Infrastructure Market Analysis

The AI infrastructure market was valued at USD 38.34 billion in 2021, and it is expected to reach USD 120.69 billion by 2027, registering a CAGR of 20.59% during 2022-2027. Increasing demand for AI hardware in high-performance computing data centers and rising application of machine learning and deep learning technologies are expected to drive the growth of the market.

• Enterprises increasingly recognize the value associated with incorporating artificial intelligence (AI) into their business processes, as they improve operational efficiency and reduce cost through automation of process flows. Thus, 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. AI is a set of algorithms that can solve specific problems and works best with a significant volume of high-quality Big Data. Chatbots can cut down the operational costs for businesses by up to 30%.
• Furthermore, as the focus of IT strategy moves from data management to intelligent action, enterprises have increasingly recognized AI's role in supporting humans in problem-solving, decision-making, and creative endeavors. Enterprises acknowledge 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, and identify and foster new developments.
• To take advantage of the increasing AI opportunities, one of the first considerations for any organization is to have a suitable infrastructure to support AI developments. Moreover, AI solutions frequently demand new hardware and software integration to function. For instance, collating and annotating data sources, scalable processing, or creating and fine-tuning models require AI solutions, such as 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.
• Different companies have been offering AI infrastructure-related solutions in the market, enabling the company to leverage its AI infrastructure. For instance, in February 2022, Spell, the leader in operationalizing AI for natural language processing (NLP), machine vision, and speech recognition applications, and Graphcore, a maker of the Intelligence Processing Unit (IPU) for next-generation AI compute, have announced a partnership to deliver the next generation of AI infrastructure. The new solution integrates Graphcore's IPU-POD scale-out systems with Spell's eponymous hardware-agnostic MLOps software platform for deep learning (DLOps) to make advanced AI development faster, easier, and less expensive.
• In August 2021, Dell Technologies announced the certification of Dell EMC VxRail for the newly available NVIDIA AI Enterprise software suite. NVIDIA AI Enterprise is an end-to-end, cloud-native suite of AI and data analytics software optimized, certified, and supported by NVIDIA to run on VMware vSphere with NVIDIA-Certified Systems. It includes critical enabling technologies from NVIDIA for rapid deployment, management, and scaling of AI workloads in the modern hybrid cloud.
• Furthermore, the COVID-19 pandemic caused many organizations to accelerate their migrations to public cloud solutions since cloud service elasticity can meet unexpected spikes in service demand. Migrations to the cloud helped companies reinvent the way they conduct their businesses in the time of COVID-19. The need for AI services has grown, and many cloud providers offer AIaaS and MLaaS. As a result, the global cloud market recorded significant growth in the healthcare segment in 2020. AI and ML technology is being used considerably to fight COVID-19. For instance, several researchers are using machine learning to create an intelligent monitoring system that tracks and detects suspected COVID-19 infected persons. One proposed system is a new framework integrating machine learning, cloud, fog, and Internet of Things (IoT) technologies to create a COVID-19 disease monitoring and prognosis system.

AI Infrastructure Industry Segments

The AI infrastructure market is segmented by offering (hardware and software), deployment (on-premise, cloud, and hybrid), end users (enterprises, government, and cloud service providers), and geography.

As artificial intelligence technology moves from experimentation to adoption, it will primarily require a significant amount of computing resources and advanced infrastructure. AI infrastructure requires multiple resources which can provide adequate 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 provides Hardware and Software offerings with on-premise and cloud deployment along with various end-user industries, such as Enterprises and Government, across different geographies.

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AI Infrastructure Market Trends

This section covers the major market trends shaping the AI Infrastructure Market according to our research experts:

Hybrid Deployment is Expected to Hold a Major Market Share

• The rising transition of companies providing AI solutions from SMEs to large enterprises bolsters the demand for on-premise solutions that enable vertically and horizontal scalability. This factor has created a significant need for hybrid integration solutions (that comprise a mix of on-premise applications and cloud-based services) among enterprises. ​
• The main advantage of using the hybrid model for AI solutions is that organizations can deploy solutions depending on the scalability they require for particular operations or applications. ​According to the Flexera 2020 State of the Cloud Survey Report, 93% of enterprises have a multi-cloud strategy, and 87% of the enterprises with a hybrid strategy, while organizations with a design of multiple public clouds or multiple private clouds grew slightly by 6%.
• According to Pure Storage, some of the prominent vendors in the market are Amazon, IBM, Google, and Microsoft, as their public cloud can be easily integrated into a hybrid cloud and multi-cloud, according to the data gathered to form a survey of more than 500 IT professionals. Similarly, with such growing demand, the company has announced the number of partners in their hybrid cloud environment. IBM and Amazon stand at a higher position as of May 2021, with 65 and 63 partners, respectively. This is a solid indication of hybrid cloud adoption in the market.
• It is also possible to create a data-centric IT architecture for AI, which further means modernizing IT environments with hybrid multi-cloud. The importance of a hybrid multi-cloud in creating an AI roadmap is the essential aspect enterprises consider while building their hybrid multi-cloud. Moreover, enterprises should integrate their public and private clouds and legacy data centers into their hybrid multi-cloud framework. An integrated hybrid multi-cloud enables a unified IA, improving both applications and data portability and interoperability. Hybrid multi-cloud also solves the issues of security, compliance, and latency. Besides these factors, enterprises must also be aware of certain imperatives of developing an AI strategy.
• Businesses are also designing a multi-pronged strategy for their hybrid multi-cloud. As a part of this step, they should identify cloud platforms with embedded AI capabilities and plan continuous training for their AI and hybrid multi-cloud teams to ensure success. As an entity with data authority on the hybrid cloud, NetApp AI solutions remove bottlenecks at the edge, core, and cloud to enable more efficient data collection, accelerated AI workloads, and smoother cloud integration, helping the hybrid cloud deployment to grow. For instance, in May 2021, Hewlett Packard Enterprise announced that Carestream Health, a global provider of medical imaging systems, selected HPE GreenLake to transform the healthcare platform using a hybrid cloud AI platform. Carestream will change X-ray systems across the globe using HPE GreenLake for ML Ops powered by HPE Ezmeral.

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