![]() ![]() And AI high performers are 1.6 times more likely than other organizations to engage nontechnical employees in creating AI applications by using emerging low-code or no-code programs, which allow companies to speed up the creation of AI applications. They also often automate most data-related processes, which can both improve efficiency in AI development and expand the number of applications they can develop by providing more high-quality data to feed into AI algorithms. Also important, they are engaging more often in “frontier” practices that enable AI development and deployment at scale, or what some call the “ industrialization of AI.” For example, leaders are more likely to have a data architecture that is modular enough to accommodate new AI applications rapidly. 2 All questions about AI-related strengths and practices were asked only of the 744 respondents who said their organizations had adopted AI in at least one function, n = 744. Next, high performers are more likely than others to follow core practices that unlock value, such as linking their AI strategy to business outcomes (Exhibit 1). The findings indicate that this group is achieving its superior results mainly from AI boosting top-line gains, as they’re more likely to report that AI is driving revenues rather than reducing costs, though they do report AI decreasing costs as well. The proportion of respondents falling into that group has remained steady at about 8 percent. For the past three years, we have defined AI high performers as those organizations that respondents say are seeing the biggest bottom-line impact from AI adoption-that is, 20 percent or more of EBIT from AI use. We see more indications that these leaders are expanding their competitive advantage than we find evidence that others are catching up.įirst, we haven’t seen an expansion in the size of the leader group. Over the past five years we have tracked the leaders in AI-we refer to them as AI high performers-and examined what they do differently. ![]() This marks the fifth consecutive year we’ve conducted research globally on AI’s role in business, and we have seen shifts over this period.Ģ. Five years in review: AI adoption, impact, and spend AI talent tales: New hot roles, continued diversity woesġ.Five years in review: AI adoption, impact, and spend.The data show that there is significant room to improve diversity on AI teams, and, consistent with other studies, diverse teams correlate with outstanding performance. On talent, for the first time, we looked closely at AI hiring and upskilling. The results show these leaders making larger investments in AI, engaging in increasingly advanced practices known to enable scale and faster AI development, and showing signs of faring better in the tight market for AI talent. A set of companies seeing the highest financial returns from AI continue to pull ahead of competitors. has plateaued between 50 and 60 percent for the past few years. Adoption has more than doubled since 2017, though the proportion of organizations using AI 1 In the survey, we defined AI as the ability of a machine to perform cognitive functions that we associate with human minds (for example, natural-language understanding and generation) and to perform physical tasks using cognitive functions (for example, physical robotics, autonomous driving, and manufacturing work).
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