Introduction

Artificial intelligence (AI) and machine learning (ML) are the next set of transformative technologies in the tech sector. While much as been prophesized about their impact, data points are few and far between. 451 Research's Voice of the Enterprise (VotE): AI and Machine Learning survey is a new biannual offering that addresses the need for quantitative metrics around these emerging technologies. It provides insight into the adoption patterns, as well as the benefits, barriers and applications, of these critical technologies. This report leverages results from the most recent survey to showcase regional variations in AI and ML adoption and use cases in the US, UK, Germany and France. This is the first survey where we've collected as many (500) responses from Western Europe as we have from the US, enabling in-depth regional and country-level analysis. The data comes from 1,000 respondents representing senior and mid-level IT and line-of-business professionals across North America and Europe, with a roughly 50/50 split between IT and LoB.

The 451 Take

Over the past year, overall AI and ML adoption has increased substantially as more enterprises across the globe recognize the value machine learning can bring to their business applications and operations. While we do see some similarities in regional adoption and deployment approaches, differences are also emerging; in fact, new data from 451 Research's VotE: AI & Machine Learning Use Cases survey suggests that US and European enterprises differ in many major areas, including adoption strategies, vendor selection and use cases across industries. Participants were asked to identify their organization's primary strategy for using machine learning, choosing one of four strategies – cloud-based AI platforms, AI-enhanced applications, open source tools or systems integrators – which we previously examined here. Figure 1 provides a sampling of their responses, showing the region with the highest adoption percentage for each strategy.
 

US

Of all regions, the US has the highest percentage of enterprises that identify using systems integrators as their primary ML strategy (14%); however, similar to other regions, cloud-based AI platforms remain the top strategy here, with security features and data management capabilities being the top drivers of purchasing decisions. IBM Global Business Services is the systems integrator of choice for US enterprises; however, our data suggests that, over the next two years, vendors further down the preference scale, such as Rackspace and Dimension Data, may become more popular.

Use cases for ML have expanded rapidly across all industries. Among US respondents, ML is used most in the manufacturing industry for use cases such as data security and intelligent logistics, and in healthcare for clinician workflow optimization and decision support. The leading business reason given by US respondents for using ML in their organizations is data security.

UK

While most enterprises are interested in adopting ML in some capacity, not all are interested in developing these applications from the ground up. In the UK, nearly 28% of respondents chose purchasing AI-enhanced applications as their primary ML strategy, with their top vendors being Microsoft, IBM and Adobe. Customer experience optimization is one of the top five business reasons for using machine learning in the UK, and congruent with this, our data shows a significantly higher percentage of respondents using Microsoft Dynamics in this region.

Compared with other regions, the UK seems to have the most established ML use cases in the retail sector, including customer engagement, behavioral targeting for marketing and advertising, and payments processing, among others. Quality assurance, a manufacturing use case, also shows strong adoption in this region.

France

Our survey results suggest that machine learning is gaining a lot of traction in France. Of all regions, France has the highest percentage of enterprises that deployed their first ML application less than six months ago, alluding to an emerging market in this space.

Nearly 61% of respondents in France identify using cloud-based Al platforms as their enterprise's primary ML strategy – the highest percentage of any region. When asked which services their organizations use for these platforms, respondents cited an array of vendors, including Microsoft Azure, AWS, SAS and Google Cloud Platform.

Fraud detection is among the top business reasons reported for using ML in France, yet fraud detection is also one of the most underdeveloped ML applications in this region; as a result, many enterprises may seek to address this over the next few years. France currently utilizes ML most in the financial services industry. Top use cases include back-end process automation, data security and customer service. Over the next two years, many respondents also indicate a desire to extend ML functionality into payment processing and underwriting.


Germany

When considering adoption of ML technologies, many enterprises look to employ strategies that minimize costs and maximize flexibility; utilizing open source tools is one way to achieve this. About 20% of respondents in Germany use open source tools as their primary ML strategy. Of enterprises not employing this strategy, many are utilizing SAP for their ML needs, significantly more so than other regions; this comes as no surprise since SAP is based in Germany.

Our data suggests that Germany has some of the strongest ML application across industries. Healthcare, in particular, has a high percentage of respondents in a variety of use cases, including patient data analysis, treatment development and clinician workflow optimization. In manufacturing, ML is used most for smart robotics, and in retail for product design and creation, reflecting Germany's status as an advanced manufacturing economy.

 

Nick Patience
Founder & Research Vice President

Nick Patience is 451 Research’s lead analyst for AI and machine learning, an area he has been researching since 2001. He is part of the company’s Data, AI & Analytics research channel but also works across the entire research team to uncover and understand use cases for machine learning.

Jeremy Korn
Associate Analyst

Jeremy Korn is an Associate Analyst for the Data, AI & Analytics Channel at 451 Research, where he covers artificial intelligence and machine learning in the enterprise. In particular, he focuses on the legal and ethical challenges raised by these emerging technologies. In addition, Jeremy helps lead the Voice of the Enterprise: AI and Machine Learning survey, which provides qualitative insights into AI adoption, use cases and infrastructure.

Rachel Dunning
Research Associate

Rachel Dunning is a Research Associate at 451 Research. Prior to joining 451 Research, she graduated from Fitchburg State University Magna Cum Laude with a BS in Cognitive Psychology and Economics. While attending school, she gained exposure to research methodology and data analytics through her involvement in several academic research projects. She is conversationally fluent in German.

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