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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Has Integrated Automation conquered the land RPA and AI once battled for?

Contributed by Principal Analyst Carl Lehmann 

Much the way Winter came for the Game of Thrones heroes in the new season (we promise this is the only Game of Thrones reference and we will not share any spoilers), there is talk spreading in the tech industry that Integrated Automation has come to displace tools like robotic process automation (RPA). We certainly don’t disagree, in fact, we predicted back in 2017 that RPA companies would likely not survive as stand-alone vendors.

In this report from April 2017, we predicted that RPA vendors that focused only on automating repetitive tasks, while very welcome in many IT departments in the short term, would be less likely to survive as stand-alone vendors compared to more sophisticated platforms that can call upon various machine-learning (ML) technologies to add contextual awareness and guidance of unstructured interactions toward desired outcomes. Even RPA platforms that can automate based on rules, conditional routing and logical operations, and modify behavior based on their learnings were also considered tech that would likely be subsumed into ML platforms of hyperscale CSPs, IT leviathans and tool kits of larger systems integrators, according to our analysts. In our opinion, it was unlikely RPA would last long as a stand-alone product.

Again in August 2017, our analyst team noted a rising trend with BPM software transforming into a process- and content-oriented application development and runtime platform, which we coined as 'digital automation platform' (DAP). DAPs, as referenced in the report, will emerge as uniform development, integration and runtime environments that enable intelligent process automation (IPA) – a managerial discipline focused on intuitive user experiences, contextual awareness and transparent execution. Much like what others are describing as Integrated Automation today, DAP would require RPA capabilities – to create software 'bots' that automate repetitive human activities in business processes – and AI integration – to expose 'next best guess' activities for application developers and users (process stakeholders) and extract insight – in one solution. In particular, RPA was cited to “likely become a core enabling technology in several DAP vendors' offerings.” 

In short, DAPs and Integrated Automation sound less like the death of RPA and similar technologies, and more like the next logical evolution toward accelerating business operations and making them efficient. Both describe feature-rich development platforms for content- and process-oriented applications, and a method to extract knowledge from automated execution to meet the innovation and operational efficiency needs of enterprises. In fact, our most recent research highlighting this evolution (in this spotlight report, now available for public access) covers why we believe the core tools needed to discover and effect how value and advantage are created include next-generation DAPs, RPA technology, hybrid integration platforms (HIPs), and process mining technologies (PMT) platforms. 451 Research clients can access all Market Insight reports on RPA and DAP and beyond in our Research Dashboard. Don’t have access? Apply for a Trial.

Much the way Winter has come for the Game of Thrones heroes in the new season (we promise this is the only Game of Thrones reference and we will not share any spoilers), there is talk spreading in the tech industry that Integrated Automation has come to displace tools like robotic process automation (RPA). We certainly don’t disagree, in fact we predicted back in 2017 that RPA companies would likely not survive as a stand-alone vendors.

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Key Analyst Insight Roundup from Google Cloud Next ‘19

Contributed by Research Vice President Matt Aslett

Earlier this month, some of 451 Research's analysts across multiple channel disciplines joined 30,000+ Google Cloud Next attendees in San Francisco. As in previous years, the event allowed our team to ponder whether and how Google would be able to translate its popularity with developers into enterprise adoption.

Having attended all three of the Google Cloud Next events (as well as the preceding GCP NEXT event in 2016), we have found it interesting to watch the event become increasingly enterprise-focused while the Google Cloud business attempts to maintain its engagement with developers and startups. Although the latter are still able to experiment to their heart's content using Google Cloud services, there is a clear strategy shift from the company to focus its sales teams on major deployments that will be taken into production at scale. This will increasingly involve Google's internal engineers engaging more directly with enterprise customers, particularly for AI projects, to build playbooks for repeatable, transformational use cases. To do so will likely require the company to engage more fully with consulting and service providers, as well as build up its own professional services organization.

When thinking about overall enterprise strategy, we found that while Google Cloud undoubtedly has had some success with enterprise customers over the years, the flagship customers, such as Spotify, have tended to be digital-native. Deliberate attempts to refocus its sales strategy landed more traditional flagship accounts, but it is fair to say that engaging with enterprise customers while maintaining Google's relationship with developers has not been an easy balancing act. New CEO Thomas Kurian outlined how the company is hiring more enterprise sales staff, but that is only one aspect of how Google is changing – and had already begun to change prior to Kurian's appointment – to address enterprise customers.

Another notable change is Google’s more empathetic approach to meeting customers where they are – accepting that while many potential customers might like to ‘run like Google,' legacy on-premises investments combined with adoption of other cloud services often don't make that possible. The launch of the Anthos hybrid cloud platform for on-premises and multi-cloud application development and management is a good indication of that strategy change, while there has also been a detectable change of emphasis toward describing Google Cloud Platform less as a destination and more as an engine for digital transformation. There is also a greater focus on use cases and ‘solutions' rather than stand-alone products/services. This should serve the company well, particularly in relation to AI and ML, although it will also likely require an increased investment in consulting and professional services.

What we have provided here are only some of the high-level impressions from our analysts. 451 Research subscribers can access this Market Insight report in our Research Dashboard that features all the high-level takes on the most interesting developments from the conference across hybrid- and multi-cloud, AI and machine learning, data and analytics, security, workforce productivity, and IoT. Not a current subscriber? Apply for a Trial.
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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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Prepping for HCTS – Q&A with Research Vice President Matt Aslett

Next up in our “Prepping for HCTS – Q&A” series, I spoke with Research Vice President Matt Aslett. Matt has overall responsibility for the data platforms and analytics coverage and he last presented at HCTS in 2013.

Q: What will you be discussing in this session?

A: Our research shows that artificial intelligence (AI) and machine learning (ML) are among the highest priorities for enterprises and service providers as they seek to deliver data-driven efficiencies and competitive advantage – 29% of survey respondents ranked ML/AI as a priority for 2018 and 12% of respondents currently use these technologies (See Figure 1).Vote digital pulse 2017 Matt QA
AI and ML fundamentally change the relationship between humans and computers, because tasks thought previously beyond computers – and thus solely the preserve of humans – are rapidly becoming possible using software. In fact, 47% of survey respondents say they plan to use AI and machine learning within the next two years. I’ll be talking about 451’s perspective on AI and ML, the potential they have to drive significant long-term change, as well as the likely immediate impact on infrastructure and service provision in particular.

Q: Why should HCTS attendees find this session valuable/what can they hope to gain?

A: 451 Research believes that artificial intelligence is set to revolutionize not only the software industry, but also the way we live, work, learn and play. And yet, while the long-term implications of AI are truly revolutionary, the initial improvements are likely to appear relatively routine and narrow. This dichotomy has the potential to create disillusionment but is par for the course in terms of the history of technological revolution.

Humanity’s ability to perceive the future is often limited by our current reality and a tendency to simultaneously over-estimate short-term gains while underestimating long-term implications. Within this context, we’ll discuss some of the anticipated benefits of AI and machine learning for infrastructure and service provision in particular: from automating IT service desks to ensuring data centers are run as efficiently as possible to capacity forecasting. The potential to apply these technologies is so vast that the challenge can be knowing where to deploy resources first. As such, we’ll also provide a practical guide to early AI use-cases and examples, 451 Research’s five-step process for success with machine learning and cover key questions such as:
  • Why now for AI and machine learning and how to get started?
  • How and AI and ML help me run my data centers more efficiently?
  • How can elements of IT service provision be automated using AI and ML?

Q: Why are you excited to attend this year’s HCTS?

A: HCTS is always a great event with a mixture of interesting and educational content as well as the ability to socialize with like-minded decision-makers and thought-leaders. As usual, there’s a great agenda with interesting presentations and discussions involving both 451 Research analysts and industry luminaries. This year’s event promises to be particularly interesting given the emergence of the age on consumption, as well as the confluence of multiple industry trends, including cloud, the Internet of Things, and artificial intelligence.

To hear Matt discuss applications for AI within the enterprise, register for HCTS 2018 which will be held at the Bellagio in Las Vegas, September 24-26. Keep an eye out for the other posts in our ongoing series, including our Q&A with William Fellows.

Figure 1 from Voice of the Enterprise (VoTE) – Digital Pulse: Budgets and Outlooks 2017
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