In recent years, artificial intelligence has played an increasingly important role in enterprise applications, but its implementation has faced many challenges. Although many enterprise giants have invested heavily in cloud service AI products, they have not been able to fully solve problems such as interoperability, governance, cost and security. This article will analyze the results of a global survey released by DataRobot and CIO.com, and explore the practical difficulties encountered by enterprises in the process of AI application and how enterprises can deal with these challenges.
Against the backdrop of rapid digitalization today, artificial intelligence (AI) has become an important tool for enterprises to improve their competitiveness. However, despite major companies investing heavily in hyperscaler AI products, AI leaders still face many challenges.
Recently, DataRobot and CIO.com released a global survey, which showed that more than 50% of AI leaders plan to increase investment in cloud service providers to solve the major problems they are facing.
The survey, which covers more than 200 senior decision makers in the AI field, focuses on the difficulties of enterprises when applying AI technology. The survey results show that insufficient interoperability, limited governance and compliance capabilities, and high usage costs have become key factors that restrict enterprise AI applications.
Image source notes: The image is generated by AI, and the image authorized service provider Midjourney
Survey data shows that only 38% of the surveyed companies have achieved full application of AI across the organization, a proportion highlighting the difficulties faced by many companies when expanding their AI projects. At the same time, 47% of respondents said that the existing hyperscale cloud service AI tools are not satisfactory in project delivery speed, and only 22% believe that AI can effectively improve the quality of business decisions.
In addition, security issues are also the focus of widespread attention in the investigation. As many as 84% of respondents said they had difficulty verifying the security of their AI models when using hyperscale cloud tools, and almost half expressed concerns about the protection of data privacy. Data breaches, third-party security risks, and potential damage to brand reputation have become important challenges that enterprises must face in AI applications. In response, more than 50% of respondents said they plan to invest in the future to solve AI security and compliance issues.
In addition, more than 60% of respondents expressed concerns about vendor lock-in, especially compatibility issues in multi-cloud environments, which has led companies to try to introduce new technologies or integrate new AI applications into existing systems face obstacles. And 51% of AI leaders are upset about the high cost of maintaining AI projects, especially those that have invested in hyperscale cloud tools for more than three years.
Venky Veeraraghavan, chief product officer of data robots, said that companies often face difficult choices when facing AI implementation. He noted that building a comprehensive AI solution can ensure safety and compliance while reducing costs, thereby increasing overall productivity and collaboration efficiency.
To address these issues, data robots provide an enterprise AI solution designed to help organizations deliver AI applications more efficiently and increase the effectiveness of existing technologies. Through open architectures and customizable AI applications, data robots are committed to accelerating the deployment of AI, reducing implementation costs, and helping enterprises better address future challenges.
Points:
More than 50% of AI leaders plan to increase their investment in cloud services to address existing technology challenges.
84% of respondents encountered difficulties in the security verification of AI models, and data privacy issues attracted widespread attention.
51% of AI leaders are upset about the high costs of maintaining AI projects, and more than 60% are worried about vendor lock-in issues.
In short, enterprises face many challenges in the AI application process, including cost, security, compliance, and supplier lock-in. Solving these problems requires companies to adopt comprehensive strategies, choose appropriate solutions, and continue to pay attention to technological development trends in order to truly realize the value of AI.