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The adoption of cloud technologies continues to accelerate. According to the newest report from Canalys, in Q2 2021, companies spent $5 billion more on cloud infrastructure services compared to the previous quarter. While a number of factors are responsible, including an increased focus on business resiliency planning, the uptick illustrates the effect AI’s embracement has had — and continues to have — on enterprise IT budgets.
In a recent survey, 80% of U.S. enterprises said they accelerated their AI adoption over the past two years. A majority consider AI to be important in their digital transformation efforts and intend to set aside between $500,000 to $5 million per year for deployment efforts. Organizations were projected to invest more than $50 billion in AI systems globally in 2020, according to IDC, up from $37.5 billion in 2019. And by 2024, investment is expected to reach $110 billion.
The cloud is playing a role in this due to its potential to improve AI training and inferencing performance, lowering costs and in some cases providing enhanced protection against attacks. Most companies lack the infrastructure and expertise to implement AI applications themselves. As TierPoint highlights, outside of corporate datacenters, only public cloud infrastructure can support massive data storage as well as the scalable computing capability needed to crunch large amounts of data and AI algorithms. Even companies that have private datacenters often opt to avoid ramping up the hardware, networking, and data storage required to host big data and AI applications. According to Accenture global lead of applied intelligence Sanjeev Vohra, who spoke during VentureBeat’s Transform 2021 conference, the cloud and data have come together to give companies a higher level of compute, power, and flexibility.
Cloud vendor boost
Meanwhile, cloud vendors are further stoking the demand for AI by offering a number of tools and services that make it easier to develop, test, enhance, and operate AI systems without big upfront investments. These include hardware optimized for machine learning, APIs that automate speech recognition and text analysis, productivity-boosting automated machine learning modeling systems, and AI development workflow platforms. In a 2019 whitepaper, Deloitte analysts gave the example of Walgreens, which sought to use Microsoft’s Azure AI platform to develop new health care delivery models. One of the world’s largest shipbuilders is using Amazon Web Services to develop and manage autonomous cargo vessels, the analysts also noted. And the American Cancer Society uses Google’s machine learning cloud services for automated tissue image analysis.
“The symbiosis between cloud and AI is accelerating the adoption of both,” the analysts wrote. “Indeed, Gartner predicts that through 2023, AI will be one of the top workloads that drive IT infrastructure decisions. Technology market research firm Tractica forecasts that AI will account for as much as 50% of total public cloud services revenue by 2025: AI adoption means that, ‘essentially, another public cloud services market will be added on top of the current market.’”
With the global public cloud computing market set to exceed $362 billion in 2022 and the average cloud budget reaching $2.2 million today, it appears clear that investments in the cloud aren’t about to slow down anytime soon. As long as AI’s trajectory remains bright — and it should — the cloud industry will have an enormous boom from which to benefit.
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