A lack of talent and employee trust are some of the largest barriers to artificial intelligence adoption in US businesses, according to an EY report.
How China tried and failed to win the AI race
China’s aggressive artificial intelligence plan still does not match up to US progress in the field in many areas, despite the hype.
US CEOs are increasingly investing in artificial intelligence (AI), but are faced with international challenges when it comes to automation dominance, according to a Tuesday report from EY.
Of the 500 US CEOs and business leaders surveyed, 85% describe themselves as “AI optimists,” with 87% reporting that their companies plan to invest in AI initiatives this year.
“AI is transforming businesses for the better, as CEOs and boards are seeing the bottom-line impact the technology is having on their businesses,” Jeff Wong, EY global chief innovation officer, said in a press release. “This drive for greater economic impact has led to a global race for adoption, scaling and greater efficiencies in the technology.”
SEE: Special report: How to implement AI and machine learning (free PDF) (TechRepublic)
When asked which country was leading the global AI race, 52% of US CEOs ranked the US first, the report found. Some 24% ranked China first, 9% put Japan in the top spot, and Canada and Germany were tied at 4% each.
US CEOs also said they believed that the US has the best long-term AI strategy to help win the AI race three years from now (50%), compared to China (24%), Japan (7%), and Canada (7%).
Despite this confidence, 47% of US CEOs selected China as the nation that posed the largest obstacle to AI advancement in the US, followed by Russia (14%) and Japan (12%).
The US and China have long been competing for AI dominance across the various elements involved, including chips, research, workforce development, and funding.
“While US business leaders believe that the US is leading this race, China is focused on becoming an AI leader by 2030 and the gap is already smaller than it seems for this aspiration to become a reality,” Wong said in the release. “For the US to maintain a strong position, business leaders need to advocate now for stronger AI education programs, collaboration among both the public and private sectors and focus on ensuring the reliability and performance of the technology.”
AI trust in the enterprise
While CEOs are eager to implement AI, employees are less enthusiastic, the report found. Top barriers to AI adoption in the enterprise include the following:
- Lack of AI talent (46%)
- Regulatory/security risk (40%)
- Inefficient infrastructure support (37%)
- Insufficient quality/quantity of data (36%)
- Employee trust (33%)
- Consumer trust (32%)
- Lack of support from senior leadership (26%)
When it comes to trusting AI within their company, 44% of CEOs ranked the reliability and performance of the technology as the most important factors. Business leaders also ranked security (38%), ethics (29%), new opportunities driven by AI (27%), and governance and supervision of the technology (27%) as key factors for trusting it.
The majority of CEOs (82%) said they expect AI to disrupt their business to some extent within the next three years, the report found. This means businesses must understand, govern, and protect all of the components involved with the technology, it noted.
“AI is about collaboration. This collaboration is imperative in the widespread adoption of the technology and it starts within the workforce,” Wong said in the release. “As the global AI race heats up and businesses increase their investment in AI technology, leaders need to work with their employees to ensure reliability and performance remain top of mind when integrating AI. Employees need to be able to trust, utilize and maximize the full potential of the technology, as well as see its benefits for scaled implementation to be successful in any organization. Beyond company walls, businesses, governments and academia need to build a road map for success that includes solutions for developing strong talent and upskilling the current workforce.”
For more, check out Special report: Managing AI and ML in the enterprise (free PDF) on TechRepublic.
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