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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How, why, and when AI & machine learning will transform organizations

Written by: Nick Patience, 451 Research Founder and Research Vice President – Software

AI – and machine learning in particular – is set to be the most transformative technology existing over the next decade, but it’s only just getting started. 

We have been covering AI and machine learning at 451 Research since we started the company in 2000. Back then, we were primarily focused on text analytics around use cases in government intelligence scenarios and, in the future, in the legal industry. We branched out from there as additional information types – such as audio speech, images and video – became viable data types from which machine learning can extract insights. To enhance our coverage of this vast and evolving space, we launched our inaugural survey on the topic: Voice of the Enterprise: AI & Machine Learning survey – Adoption, Drivers and Stakeholders 2018

Our new survey brings together use cases, business benefits, barriers to adoption and information about how who is influencing and ultimately deciding when, how and why to adopt machine learning. Given it is an omni-purpose technology, it is not surprising that use cases for machine learning are spread across all industry verticals and all layers of organizations. Practically everybody surveyed – 97% – believe AI will have an impact on society and 75% of this group think it will have a moderate to significant impact within the next two years. The question is how much impact and where will it be felt?

PR Graphic VotE AI 2018 2 1
Industry Optimism
Respondents are broadly optimistic, with 69% of them believing AI will have a mostly or somewhat positive impact on society. The much-vaunted threat of job losses doesn’t seem to concern people that much. While 62% think AI will have some type of impact on their organization, only 7% of them think it will be negative, suggesting respondents aren’t worried about AI replacing their jobs in the near term. 

VotE AI 2018 blog image 2
As the domain gets closer to the individual situation, we find people becoming more ambivalent about the effect of AI. Our survey found only 36% or respondents believe it will have a significant or moderate impact and 40% are unsure whether the impact will be positive or negative.
Enterprise adoption
Although we are in the very early phases of machine learning adoption – probably in the flat bit at the start of an S curve by our reckoning – we found plenty of organizations already developing or deploying machine learning. Some 36% of respondents are currently either developing or deploying machine learning. Early adopters could be developing it themselves with in-house talent, using some sort of third party to develop it for them or buying applications with machine learning already built in – security tools of various types being a good example of the last of those. 
Exactly how they are developing and deploying is predictably varied, with no single approach dominating. Some respondents are using cloud-based tools and third-party systems, but a healthy portion are looking to buy applications with machine learning built in while others are looking to build in-house. Similarly, several different execution venues are available for both development and deployment, as are a proliferous set of tools. Cloud-based platforms are the most popular, with almost half using them, but breaking that down, we see where machine learning is being developed or deployed varies by the status of the initiative. For example, those that say they plan to use machine learning in the future are more inclined to choose cloud platforms than those already deploying machine learning. 
This survey paints an overall picture of what is going on in small to large enterprises in North America, Europe and Asia-Pacific. Future surveys will focus sharply on use cases and business benefits and delve even more deeply into the infrastructure changes that are required or already happening to make the promise of AI and machine learning attainable for all organizations.

Learn more about Voice of the Enterprise.
Written by: Nick Patience, 451 Research Founder and Research Vice President – Software
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