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In a new survey of over 700 C-suite executives and IT decision-makers examining AI adoption in the enterprise, Juniper Networks found that 95% of respondents believe their organization would benefit from embedding AI into their daily operations. However, only 6% of those respondents reported adoption of AI-powered solutions across their business.
The findings agree with other surveys showing that, despite enthusiasm around AI, companies struggle to deploy AI-powered services in production. Enterprise use of AI grew a whopping 270% over the past several years, Gartner recently reported, while Deloitte says 62% of respondents to its corporate October 2018 study adopted some form of AI, up from 53% in 2019. But adoption doesn’t always meet with success, as the roughly 25% of businesses that have seen half their AI projects fail will tell you.
According to the Juniper survey, top challenges around AI remain ingesting, processing and managing data. In other tech stack trends, 39% of respondents said that they were likely to collect telemetry data to improve “user experience” AI embedded in products, while 34% noted that AI tool capabilities are the most critical in order to enable AI adoption.
Juniper’s is the second recent report to peg data issues as the reason organizations fail to successfully deploy AI. Data scientists spend the bulk of their time cleaning and organizing data, according to a 2016 survey by CrowdFlower. And respondents to Alation’s latest quarterly State of Data Culture Report said that inherent biases in the data being used in their AI systems produce discriminatory results that create compliance risks for their organizations.
A majority (73%) of Juniper survey respondents said that their organizations were struggling with expanding their workforce to integrate with AI systems. C-level executives reported that they feel it’s more of a priority to hire people than to develop AI capabilities within their business.
Laments over the AI talent shortage have become a familiar refrain from private industry.
O’Reilly’s 2021 AI Adoption in the Enterprise paper found that a lack of skilled people and difficulty hiring topped the list of challenges in AI, with 19% of respondents citing it as a “significant” barrier. In 2018, Element AI estimated that of the 22,000 Ph.D.-educated researchers globally working on AI development and research, only 25% are “well-versed enough in the technology to work with teams to take it from research to application.” Tencent says that there are about 300,000 AI professionals worldwide but “millions” of roles available. And a 2019 Gartner survey found that 54% of chief information officers view this skills gap as the biggest challenge facing their organization.
On the topic of AI governance, 87% of executives told Juniper that they believe organizations have a responsibility to implement policies that minimize the negative impacts of AI. Despite this, executives ranked establishing AI governance, policies, and procedures as one of their lowest priorities. And only 7% of survey takers said their organizations have established a company-wide leader who oversees AI strategy and governance.
Growing evidence suggests that organizations are implementing AI less responsible than they internally assume. According to a recent Boston Consulting Group survey of 1,000 enterprises, less than half that achieved AI at scale had fully mature, responsible AI implementations, according to the same report.
The lagging adoption of responsible AI belies the value that these practices can bring to bear. A study by Capgemini found that customers and employees will reward organizations that practice ethical AI with greater loyalty, more business, and even a willingness to advocate for them — and in turn, punish those that don’t. The study suggests that there’s both reputational risk and a direct impact on the bottom line for companies that don’t approach the issue thoughtfully.
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