AI is showing executives that a compelling data strategy is the foundation of technology success.
Generative AI, like predictive AI before it, is understandably catching the attention of business executives: The technology has the potential to add trillions of dollars to the world's annual economic activity, and its implementation in business applications promises to improve top lines, profits, or both for many organizations.
Generative AI offers an impressive and powerful new set of capabilities, but its business value is not a given. While some powerful underlying models are publicly available, these are not differentiators for companies that want to stay ahead of the competition and realize the full potential of AI. To reap these benefits, organizations need to power AI models with their own data to generate unique business insights and opportunities.
But preparing an organization's data for AI creates new challenges and opportunities. This MIT Technology Review Insights research report explores whether enterprise data foundations are ready to benefit from generative AI and the challenges of building the data infrastructure required for this technology. The study draws on insights from a survey of 300 C-level executives and senior technology leaders, as well as in-depth interviews with four leading experts.
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Key findings include:
Data integration is a top priority for AI enablement. In our survey, 82% of C-suite and other senior executives agreed that “expanding AI or generative AI use cases to create business value is a top priority for their organization.” According to survey respondents, the biggest challenge to becoming AI-ready is data integration and pipelines (45%). When asked about data integration challenges, respondents cited four: managing data volume, moving data from on-premise to cloud, enabling real-time access, and managing changes to data.
Executives are focused on data management challenges and lasting solutions. 83% of survey respondents said their organization has identified numerous data sources that need to be integrated to enable AI initiatives. For decades, data-dependent technologies have driven data integration and aggregation programs, which were typically tailored to specific use cases. But now, companies are looking for more scalable and use-case agnosticism. 82% of respondents prioritize solutions that “will continue to work in the future, regardless of other changes to data strategy or partners.”
Data governance and security are top concerns for regulated sectors. Concerns about data governance and security were the second most common data readiness challenge (44% of respondents). Respondents in highly regulated sectors were two to three times more likely to cite data governance and security as a concern, and Chief Data Officers (CDOs) are twice as likely to say it's a challenge than C-level executives. Our experts also agree that data governance and security must be addressed from the start of any AI strategy to ensure data is used and accessed appropriately.
This content was produced by Insights, MIT Technology Review's custom content division; it was not written by MIT Technology Review's editorial staff.
Download the full report.
