The Great Acceleration: CIO Perspectives on Generative AI

While AI was recognized as strategically important before generative AI became prominent, our 2022 survey found that CIOs’ ambitions were limited: while 94% of organizations were using AI in some way, only 14% aimed to achieve “enterprise-level” AI by 2025. Conversely, the power of generative AI tools to democratize AI, to spread it across every function of the business, to support every employee and engage every customer, heralds a tipping point where AI can grow from a technology employed for particular use cases to what truly defines the modern enterprise.

Therefore, information managers and technical leaders will have to act decisively: embrace generative AI to seize its opportunities and avoid giving up the competitive ground, also making strategic decisions on data infrastructure, model ownership, structure of the AI workforce and governance that will take long-term long-term consequences for organizational success.
This report explores the latest insights from chief information officers at some of the world’s largest and best-known companies, as well as experts from the public, private and academic sectors. Present their views on AI against the backdrop of our global survey of 600 senior data and technology executives.

Key findings include the following:

• A mine of buried, unstructured data is now readable, unlocking business value. Previous AI initiatives had to focus on use cases where structured data was ready and abundant; the complexity of collecting, annotating, and synthesizing heterogeneous datasets has made larger AI initiatives impractical. Conversely, the new ability of generative AI to surface and use once-hidden data will fuel dramatic new advances across the organization.

• The age of generative AI requires a flexible, scalable and efficient data infrastructure. To power these new initiatives, information managers and engineering leaders are adopting next-generation data infrastructures. More advanced approaches, such as data lakehouses, can democratize access to data and analytics, improve security, and combine low-cost storage with high-performance queries.

• Some organizations seek to leverage open source technology to build their own LLMs, while capitalizing on and protecting their data and intellectual property. CIOs are already aware of the limitations and risks of third-party services, including the release of sensitive information and reliance on platforms they don’t control or have no visibility into. They also see opportunities in developing bespoke LLMs and realizing value from smaller models. The most successful organizations will strike the right strategic balance based on a careful calculation of risk, comparative advantage and governance.

• Automation anxiety shouldn’t be ignored, but dystopian predictions are exaggerated. Generative AI tools can already complete complex and varied workloads, but the CIOs and academics interviewed for this report don’t expect large-scale automation threats. Instead, they believe the broader workforce will be freed from time-consuming work to focus on higher-value areas of knowledge, strategy, and business value.

• Unified and consistent governance are the tracks on which AI can accelerate. Generative AI carries commercial and social risks, including the protection of commercially sensitive intellectual property, copyright infringement, unreliable or unexplained results, and toxic content. To innovate rapidly without breaking things or anticipating regulatory changes, diligent CIOs must address the unique governance challenges of generative AI by investing in technology, processes, and institutional structures.

Download the full report.

This content was produced by Insights, the personalized content arm of MIT Technology Review. It was not written by the editorial staff of MIT Technology Review.