76% of companies have started production-level AI. But here's the stat that should worry the other 24%: among organizations that actually got AI workflows into production, 90% are using an integration platform to connect their AI tools to existing business systems.
A new report from MIT Technology Review Insights, commissioned by integration platform Celigo, surveyed 500 senior IT leaders at US companies with $50 million or more in annual revenue across nine industries. The findings paint a clear picture: AI that isn't wired into your existing data and workflows stays stuck in the pilot phase.
The Integration Divide
The numbers are stark. Companies with enterprise-wide integration have deployed AI across multiple departments 39% of the time. Companies without integration platforms? That figure drops to 1% or less.
Data access tells the same story. 59% of organizations with enterprise-wide integration pull from five or more data sources for their AI workflows. Among those without integration platforms, that number is literally zero.
"Enterprises are quickly realizing that AI strategy is really architecture strategy," said Ronen Vengosh, Celigo's Chief Strategy Officer.
From Demos to Departments
The report describes a pattern that will sound familiar to anyone who's watched enterprise AI rollouts: a successful pilot in one department, followed by months of stalling when the team tries to expand. The bottleneck isn't the AI model. It's the plumbing - getting clean data in, pushing results out, and maintaining governance across the whole pipeline.
95% of executives surveyed said their AI workflows already have some level of autonomy. But only 34% of those with enterprise-wide integration have reached "mostly autonomous" AI workflows. Without integration? The autonomous workflows barely exist.
The caveat here is obvious: Celigo sells integration platforms, so they have a direct interest in this narrative. But the underlying data point holds up against what I see across the AI tools market. The tools that succeed in production aren't always the most impressive in a demo. They're the ones that connect to the systems where work actually happens - your CRM, your project management tool, your data warehouse.
For small businesses and teams evaluating AI tools, the takeaway is practical: before asking "which AI model is best," ask "can this tool actually talk to the rest of my stack?" The answer matters more than benchmark scores.