A new report by MIT Technology Review Insights explores the breakthroughs in data intelligence that will enable CIOs to reach their data and generative AI priorities across seven industries, namely retail and consumer packaged goods, healthcare and life sciences, manufacturing, financial services, telecommunications, media and entertainment, and the public sector.

The report, "Bringing breakthrough data intelligence to industries," is produced in partnership with Databricks, the data and AI company, and is based on a global survey of 600 CIOs, CTOs, CDOs, and technology leaders for large enterprises and public-sector organizations and features in-depth interviews with C-level executives. Among the organizations represented are AT&T, AXA, Condé Nast, Databricks, Dell Technologies, General Motors, Morgan Stanley, Regeneron Genetic Center, the United States Postal Service, and Walmart.

"While it's early in the race to AI, leaders across diverse industries recognize the profound potential and impact of AI," says Arsalan Tavakoli, co-founder and senior vice president of field engineering at Databricks. "Organizations investing in unified data and governance platforms to fuel their AI and empower their workforces are positioned to lap the competition in realizing AI-based results."

The findings are as follows:

Real-time analytics and secure sharing are priorities in every industry to unleash the power of data truly. Sixty-four percent of CIOs say the ability to securely share live data and AI assets across platforms is "very important." Across industries, executives see promise in technology-agnostic data sharing across an industry ecosystem supporting AI models and core operations that will drive more accurate, relevant, and profitable outcomes. An even larger share (72%) say that the ability to stream data for real-time analytics will be key to delight customers and gain competitive advantages.

All industries aim to unify their data and AI governance models to protect and enable innovation. 60% of CIOs say a single built-in governance model for data and AI is "very important," suggesting that many organizations struggle with a fragmented or siloed data architecture. Every industry will have to achieve this unified governance in the context of its own unique systems of record, data pipelines, and requirements for security and compliance.

Industry-specific requirements will drive the prioritization and pace of generative AI use case adoption. Supply chain optimization is the highest-value generative AI use case in manufacturing. At the same time, it is real-time data analysis and insights for the public sector, personalization and customer experience for M&E, and quality control for telecommunications. Generative AI adoption will not be one-size-fits-all, with each industry taking its own path. Still, in every case, value creation will depend on access to data and AI across roles within the organization.

Preserving data and AI flexibility by leveraging multicloud and open source is critical for managing risks and accelerating innovation. Sixty-three percent of CIOs believe that leveraging multiple cloud providers is at least somewhat important, while 70% feel the same about open source standards and technology. Given the fast-moving AI landscape and uncertain regulatory environment, executives firmly believe in the value of strategic flexibility.

"Today's technology leaders are making it clear: a unified governance model for data and AI is not just a priority; it's a necessity," says Laurel Ruma, global director of custom content for MIT Technology Review. "As we move forward, it's evident that real-time analytics, secure data sharing, and technology-agnostic ecosystems will play pivotal roles in shaping the future of innovation across all industries."