Preparing an organization's data for AI, however, unlocks a new set of challenges and opportunities. This MIT Technology Review Insights survey report investigates whether companies' data foundations are ready to garner benefits from generative AI, as well as the challenges of building the necessary data infrastructure for this technology. In doing so, it draws on insights from a survey of 300 C-suite executives and senior technology leaders, as well on in-depth interviews with four leading experts. Data integration is the leading priority for AI readiness. In our survey, 82% of C-suite and other senior executives agree that "scaling AI or generative AI use cases to create business value is a top priority for our organization."

AI and automation are making it easier for users to find the data they need. Those of us beyond a certain age remember when school research projects began in front of the library card catalog: that now-antique set of wooden cabinets with the long drawers full of well-thumbed cards that adhered to a standard bibliographic system. If you understood that system (or had the help of a good librarian), you could perform a surprising amount of research at a metadata level before having to hunt through the library stacks for the actual books you needed. You could use the system to understand relationships between book topics and perhaps discover an unexpected book that was perfect for your report. Library catalogs, along with an increasing number of pre-digital-age storage systems, have changed.

Databricks, the company behind analytics tool Apache Spark, is introducing new features to ease the management of security, governance and administration of its machine learning platform. Security and data access rights have been fragmented between on-premises data, cloud instances and data platforms, Databricks told us. And the new approach allows tech teams to manage policies from a single environment and have them replicated in the cloud, it added. "Cloud companies have inherent native security controls, but it can be a very confusing journey for these customers moving from an on-premise[s] world where they have their own governance in place, controlling who has access to what, and then they move this up to the cloud and suddenly all the rules are different." The idea behind the new features is to allow users to employ the controls they are familiar with, for example, Active Directory to control data policies in Databricks.