Artificial intelligence (AI) and machine learning are the next set of transformative technologies in the tech market. And while much has been prophesized about their impact, data points are few and far between. 451 Research's Voice of the Enterprise (VotE): AI & 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 benefits, barriers and applications of these critical technologies. This report leverages the results of the most recent survey to discuss the current and future use cases for AI and machine learning in four key verticals: financial services, retail, healthcare and manufacturing. The data comes from responses from over 1,600 line-of-business and IT professionals in North America, EMEA and Asia-Pacific.

The 451 Take

The initial wave of AI adoption has centered on making legacy systems and processes more intelligent. Embedding machine learning into customer service and support, BI, customer experience, sales, marketing and security applications can make these processes more automated and efficient. While early adopters of AI and machine learning report high levels of success and cite improvements in key business functions, usage patterns demonstrate that there is growing enterprise demand for more niche, industry-specific AI applications. This is especially true in the deep dive of the financial, retail, manufacturing and healthcare sectors contained in this report.

451 Research's VotE: AI and Machine Learning survey provided participants in four verticals with industry-specific AI use case picklists and asked respondents to select all current and future use cases. Figure 1 below provides a glimpse into the results, showing the top use case today in each industry.

Figure 1: Top AI Use Cases Across Four Key Industries
Below, we evaluate the results from each of the four verticals in turn, providing insight into why these use cases bubbled up and what other use cases will do so in the future.


Financial Services 

In the financial services space, 42% of respondents are using machine learning for fraud detection, and another security-centered use case – digital and data security – also showed strong adoption. As a heavily regulated industry, it's not surprising to find that many financial firms are employing machine learning to shore up their assets and networks. Fraud detection is a natural use case for the technology, given the abundance of historical transaction data. Digital and data security will be a challenge as threat vectors proliferate, and machine learning will need to be part of any such solution.

Comparing current and future adoption rates, compliance and payment processing are two leading use cases in terms of expected net growth. The explosion of new regulatory frameworks, led by GDPR, necessitates a more intelligent approach to data compliance. Earlier attempts to offer compliance software in this sector involved rules-based approaches, but machine learning is transforming the segment with its ability to uncover patterns in large sets of unstructured data. In terms of payment processing, machine learning will enable efficiencies necessary for an increasingly diffuse and digital consumer landscape.

Finally, the survey data suggests that algorithmic trading is a more niche use case of interest to those in investment banks and brokerages. Other use cases in the survey include customer service, wealth management, loan/credit card approval, marketing, product recommendations, process automation and physical security.



Since retailers live and die by their ability to convert potential customers into repeat buyers, it's not surprising that retailers are focused on using machine learning to improve customer engagement. In fact, according to the survey, customer engagement was the most popular use case among respondents, with 45% currently deploying machine learning as part of next-generation customer engagement tools. Retailers have access to a wealth of historic transaction data that they can leverage to better understand how and when to engage customers.

The survey results also show that demand prediction and supply chain enhancement are areas getting a boost from the use of machine learning, where AI is working alongside humans to spot patterns in vast, disparate datasets. Payment processing is important for ensuring customer satisfaction, so it's not surprising that it is one of the retail-focused use cases with the largest expected net growth, especially given the fragmentation of the US payments market and the improvements machine learning offers.

Other more transformative use cases such as product design and creation seem to be more than a few years out, based on the survey results. In addition to those discussed above, other use cases cited include post-sales customer service, digital/data security, physical security and product recommendation engines.



It should come as no surprise that practitioners in the healthcare space are excited to apply machine learning at the point of care. According to the survey results, 46% of respondents are employing machine learning in their patient monitoring systems, the highest of any of the surveyed use cases. Another related use case – clinician workflow optimization – also polls very well. By applying the technology to the growing volume of data generated by IoT medical devices and other lifestyle data, healthcare providers hope that intelligent patient monitoring and clinician workflow optimization tools will allow doctors to focus on the higher-level decision-making that improves patient outcomes.

The survey data also demonstrates that investment in machine-learning-based tools is high among other use cases in the healthcare space, including operations management and treatment development. Disease diagnosis and analysis – one of the buzzier applications of machine learning in medicine – also polls quite well. Other use cases in the survey include physical security, financial and business analysis, digital/data security, and drug discovery and development.



Among respondents in the manufacturing segment, maintenance forecasting is the most popular use case for machine learning today, cited by 36% of respondents. The primacy of maintenance forecasting speaks to the value that machine learning can bring in terms of making predictions about complex systems. Reducing machine downtime through predicative maintenance helps lower costs and improve margins for manufacturers. Another popular machine learning use case that could help manufacturers at the margins is supply and demand forecasting, which also polled well in the survey.

Quality assurance and product design and creation are two future use cases that seem to excite manufacturers. Better optimized and automated QA processes not only reduce costs but also enhance product quality. Automated product design is a headier challenge that could allow users to gain competitive advantage in the market.

Assembly line optimization is one of the manufacturing use cases with the largest net gain, suggesting that respondents are interested in deploying machine learning to bolster this area in the future. Other use cases surveyed include digital/data security, physical security, and supply chain logistics and management.
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. Nick is also a member of 451 Research’s Center of Excellence for Quantum Technologies.

Jeremy Korn
Senior Research Associate

Jeremy Korn is a Senior Research Associate at 451 Research. He graduated from Brown University with a BA in Biology and East Asian Studies and received a MA in East Asian Studies from Harvard University, where he employed quantitative and qualitative methodologies to study the Chinese film industry.

Keith Dawson
Principal Analyst

Keith Dawson is a principal analyst in 451 Research's Customer Experience & Commerce practice, primarily covering marketing technology. Keith has been covering the intersection of communications and enterprise software for 25 years, mainly looking at how to influence and optimize the customer experience.

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