Is your AI Infrastructure Prepared to Meet Future Demands?

Written by: Senior Research Associate Jeremy Korn and Research Vice President Nick Patience

Many organizations are underprepared for the demands AI and machine learning applications will place on their infrastructure, but they are prepared to spend money to change that situation.

Those are just a couple of conclusions we can draw from our new Voice of the Enterprise: AI and Machine Learning Infrastructure 2019 survey. Almost half (45%) of enterprises indicate that their current AI infrastructure will not be able to meet future demands (see Figure 1), which prompts a few questions:

• Why is that?
• What do they propose to do about it?
• Are they prepared to spend money to fix the problem?

Figure 1
figure 1 status of enterprise infrastructure for ai















Why is that?


Broadly speaking, data is the reason infrastructure needs to be overhauled to deliver AI at scale, with 89% of respondents in our survey saying they expect the volume of data in using the machine learning workloads to increase in the next year, and almost half projecting an increase of 25% or more. Much of that growth will come from unstructured data, since the most transformative use cases of AI and machine learning involve gaining insight from unstructured data, be it text, images, audio or video.

What do they propose to do about it?

Organizations understand that, for them to take advantage of AI at scale, it is not simply a case of scaling existing infrastructure. New infrastructure is needed to cope with the demands of machine learning workloads, including new scalable storage, dedicated accelerators and low-latency networks. These need to be deployed across a variety of execution venues.

Enterprises also express a variety of concerns about their AI infrastructures, from the security of these systems to the opacity of data management capabilities. Overhauling AI infrastructure demands more than just buying better hardware; it will require new tools and updates to architectural paradigms.

Are they prepared to spend money to fix the problem?

Yes, they are. Our survey shows that 83% of responding enterprises say they will expand AI infrastructure budgets next year, with 39% of those projecting an increase of 25% or more. Spending on cloud-based AI platforms will lead the charge, with 89% of respondents planning to increase spending on them in the next year.

Our Voice of the Enterprise: AI and Machine Learning Infrastructure 2019 survey contains a lot more data on subjects such as spending decision-makers, the specific points in the machine learning process that put strain on infrastructure, the types of AI-specific infrastructure components organizations are looking to buy, the areas in which skill shortages are most acute, and how often and where machine learning models are trained and deployed.

For more insight, check out this free Market Insight report.

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