Addressing deficiencies in enterprise data management and infrastructure, as well as internal rigidities in structure and processes and lack of talent, are among these challenges. Some 72% of technology executives we interviewed for this study say that if their companies are failing to meet their AI goals, data issues are more likely than not to be the cause. Improving data processing speeds, governance and quality, as well as their sufficiency for models, are key data imperatives to ensure AI can be scaled, respondents say at investigation.
This report sheds light on these and other data constraints that organizations must address to unlock the potential of AI for their business. It also identifies investments and other steps companies plan to take to more closely align their data capabilities with their AI ambitions. The study’s findings are based on a global survey of 600 CIOs, CTOs and other senior technology leaders. We also learned from in-depth discussions with 10 of these leaders.
Here are the main results of the study:
- Businesses view the wider adoption of AI as essential for their future. From today’s limited use of AI in business, surveyed executives predict major expansion of use cases across all core functions over the next three years. Well over half expect the use of AI to be pervasive or critical in their IT, finance, product development, marketing, sales and other functions by 2025. While most will pursue a wide variety of use cases, many also aim to enhance the impact of AI at the high end, increasing returns for revenue-generating uses.
- Successfully scaling AI is the number one priority of data strategy. The data and the AI strategies of the interviewed companies are closely linked. More than three-quarters (78%) of the executives we surveyed, and nearly all (96%) of the Leaders group, say scaling AI and machine learning use cases to create business value is their top priority for enterprise data strategy over the next few years. three years.
- Significant spending growth is expected to strengthen AI databases. The CIOs surveyed, especially those in the leading group, are planning significant increases in investment by 2025 to strengthen different parts of their databases and AI. Over the next three years, executive spending on data security will increase by 101%, 85% on data governance, 69% on new data and AI platforms, and 63% on existing platforms. (Similar numbers among the sample as a whole are 59%, 52%, 40%, and 42%, respectively.)
- Investment growth intentions are strongest in the financial services sector. Of the fourteen industries in the survey, AI leaders are most numerous among retail/consumer goods and automotive/manufacturing companies. The expected growth of investment in these sectors in the aforementioned areas of data management and infrastructure is higher than in others, with one exception: the increases expected by financial service providers will greatly exceed those of all the other sectors.
- Multi-cloud and open standards are integral to advancing AI. Most survey respondents (72%) – and almost all leaders (92%) – appreciate the flexibility that a multi-cloud approach to AI development offers. CIOs interviewed for the study also highlight the role of open architecture standards in supporting multi-cloud, and the importance of both in advancing their AI development.
Download the full report.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not authored by the editorial staff of MIT Technology Review.