Infrastructure is the conversation du jour as companies struggle to manage the influx of tools and technologies at their disposal. Big data is a vital part of digital transformation, and AI provides a pathway for processing massive data volumes. According to a report from MarketsandMarkets, the AI infrastructure market will grow quickly to match over the next five years.
The survey predicts that the infrastructure market will grow to $96.6 billion by the end of 2027, equalling a CAGR of 27.5% over the next five years. Driving the market is an ever-increasing need for tools that can process and manage big data, as well as a rising focus on parallel computing in AI centers.
In addition, the pandemic launched more than its fair share of AI initiatives as companies struggled to keep up with the change. A growing number of cross-industry partnerships and collaborations can also explain the massive drive.
See also: AI Workloads Need Purpose-built Infrastructure
What companies gain from infrastructure
According to the survey, the most significant segment of the market is inference. Inference engines apply logical rules to input data to make new insights and understand patterns for the future. This could be a critical component as companies look for ways to manage disruption in real time.
Other drivers include an increasing appetite for cloud services. Cloud service providers will also form a large chunk of the expected market as they try to deliver more innovative services and meet customer needs. Although it doesn’t form the largest market share overall, it does make up the fastest-growing segment.
Cloud’s increasing popularity means companies need tools that adapt and learn while helping to manage data once and for all. They’re revamping traditional infrastructure in favor of something that can integrate with the latest tools and approaches. Infrastructure will continue to be an essential segment of the technology world, and this survey only highlights its importance.
Elizabeth Wallace is a Nashville-based freelance writer with a soft spot for data science and AI and a background in linguistics. She spent 13 years teaching language in higher ed and now helps startups and other organizations explain – clearly – what it is they do.