Despite the rapid proliferation of artificial intelligence tools, many organizations struggle to extract the expected returns from their investments. According to a poll by MIT Technology Review Insights, fewer than 16 percent of organizations are extracting transformative value from their AI deployments. While 40 percent of respondents say they are seeing moderate gains in specific tasks, a notable 23 percent report seeing minimal or no returns from their investments.
The survey results show that 38 percent of companies are still piloting AI use cases. While 35 percent use AI to assist employees with their work, the proportion of enterprises with AI agents in production remains at 12 percent. Only 3 percent of companies say they are using AI to do most of the work in key processes. Prukalpa Sankar, co-founder of Atlan, points out that there is a significant gap between what AI models are capable of and how useful they actually are inside organizations, emphasizing that general models are not inherently useful for most companies today.
According to Sankar, the root of this problem is the missing "context" layer, which determines how well AI understands a business. A context layer is defined as the architectural infrastructure that translates enterprise intelligence into machine-usable context for AI models. Without this foundational layer, deploying AI tools is compared to hiring the smartest intern in the world, blindfolding them, and throwing them into the organization.