Across businesses, multiple AI platforms, systems, and tools operate simultaneously—AI-powered customer platforms, conversational assistants, and workflow automation tools. Each is sophisticated and responsive, but outcomes often lack proper business understanding. An MIT Technology Review Insights poll reveals only 16% of organizations are extracting transformative value from AI, while 40% report moderate gains and 23% see minimal returns. Most companies are still piloting AI use cases (38%) or using it to assist employees with co-pilots and chat assistants (35%). Just 12% have AI agents in production and only 3% use AI to perform most work in key processes.
"There’s a big gap between what the models are capable of and how the models are actually being useful inside organizations," says Prukalpa Sankar, co-founder of Atlan. "For every real-life intent and purpose, artificial general intelligence is already here. It’s just not really useful in most companies today."
The missing layer is context—how well AI understands a business. A context layer sits between business systems and AI platforms, translating enterprise intelligence into machine-usable context for AI agents, helping knowledge compound. "Without a context layer for AI, it’s almost like you’re hiring the smartest intern in the world, blindfolding them, and throwing them into your organization," Sankar explains.
Source: MIT Technology Review AI · Summarized by HeadlinesBriefing