Across a business, dozens of AI platforms, systems, and tools may be at work simultaneously. These include customer-facing platforms, conversational assistants, and workflow automation tools. Each one is sophisticated, advanced, and quick to respond. But when set to work, the outcomes and actions that emerge may be wrong, confused, or lacking a proper understanding of the business. Many organizations find themselves in this situation. It could be one of the key reasons they struggle to extract returns on their AI investments.
An MIT Technology Review Insights poll finds that while every organization surveyed is using AI today to some degree, fewer than one in five (16%) are extracting transformative value from their deployments. The largest percentage of respondents (40%) say they are seeing moderate gains in specific tasks or teams. Twenty-one per cent report meaningful gains. The remaining 23% are seeing minimal returns or none at all.
Companies are mostly still piloting AI use cases, according to 38% of respondents. Thirty-five per cent say they are using it to assist employees’ work with tools like co-pilots or chat assistants. Just 12% of enterprises have AI agents in production. Three per cent say they are using AI to do most of the work in key processes.
The gap between capability and utility
“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, Sankar says, is context, or how well AI understands a business. A context layer is the architectural infrastructure that sits between an organization’s business systems, for example systems of record and data, and its AI platforms. It translates enterprise intelligence into governed, machine-usable context for AI agents or models and helps 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 says.
What it means
Organisations need a middle layer that connects raw data to AI tools. Without it, even powerful models cannot function effectively within specific company structures. This infrastructure ensures that AI agents have the necessary background information to act correctly.
