AI Adoption in the Enterprise 2020 report finds growth in mature AI adoption; weak investment in data governance
BOSTON—March 18, 2020—O’Reilly, the premier source for insight-driven learning on technology and business, today announced the results of its 2020 artificial intelligence (AI) survey, 鈥AI Adoption in the Enterprise 2020.鈥 The benchmark report uncovers trends in the evaluation, implementation, and outcomes of AI enterprise adoption over the past year.
Findings reveal that more than half of respondents are in the 鈥渕ature鈥 phase of AI adoption 鈥 defined by those currently using AI for analysis or in production 鈥 while about one third are evaluating AI, and 15% of report not doing anything with AI. These numbers demonstrate growth when compared with O鈥橰eilly鈥檚 2019 AI Adoption in the Enterprise report, which found just 27% of organizations in the 鈥渕ature鈥 adoption phase and 54% in the evaluation phase.
When it comes to data governance, more than 26% of respondents say their organizations plan to institute formal data governance processes and/or tools by 2021 and nearly 35% expect this to happen in the next three years. Currently, just one-fifth of respondent organizations report having formal data governance processes and/or tools to support and complement their AI projects, similar to findings uncovered in the O鈥橰eilly Data Quality Survey.
Difficulties in hiring and retaining people with AI skills was once again noted as a top barrier to AI adoption in the enterprise, down slightly from 18% in 2019. As in 2019, the biggest bottleneck to AI adoption was reported to be a lack of institutional support (22%), followed by 鈥淒ifficulties in identifying appropriate business use cases鈥 at 20%.
鈥淎I practices are maturing, and adopters are experimenting with sophisticated AI techniques and tools, which bodes well for the future advancement of AI in the enterprise,鈥 said Rachel Roumeliotis, O鈥橰eilly Strata Data & AI conference co-chair and strategic content director at O鈥橰eilly. 鈥淗owever, organizations will continue to struggle to expand and scale their AI practices if they don鈥檛 address the importance of data governance and data conditioning in ML and AI development.鈥
Other notable findings include:
- Among mature adopters, supervised learning was reported to be the most popular machine learning technique (73%), while deep learning (55%) is the most popular among organizations still in the evaluation stage of AI.
- The bulk of AI use is in research and development鈥攃ited by just under half of all respondents鈥攆ollowed by IT, which was cited by just over one-third. Another high-use functional area of AI is customer service, with just under 30% of share.
- By a 2:1 margin, respondents in companies that are evaluating AI cited an unsupportive culture as the primary bulwark to AI adoption, suggesting increased resistance for organizations who have yet to put AI into production. By contrast, AI adopters are about one-third more likely to cite problems with missing or inconsistent data as the biggest bottleneck.
- Respondents identified the most critical ML- and AI-specific skills gaps in their organizations as the shortage of ML modelers and data scientists (58%), almost exactly on par with findings in 2019. This was followed by the challenge of understanding and maintaining a set of business use cases (49%) and data engineering (40%).
- Unexpected outcomes/predictions were the single most common risk factor when building and deploying ML models, cited by close to two-thirds of mature鈥攁nd by about 53% of still-evaluating鈥擜I practitioners.
- TensorFlow remains the most popular tool for use in AI-related work, as reported by roughly 55% of respondents in both 2019 and 2020. Additionally, four of the five most popular tools for AI-related work are either Python-based or dominated by Python tools, libraries, patterns, and projects.
The full survey results are now available for download here: /radar/ai-adoption-in-the-enterprise-2020/.
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