Only 13% of Manufacturers Run a Unified Namespace in Production
A 2026 survey finds 68% stuck in pilots and proofs of concept, with legacy integration and data silos named the largest barrier by nearly half of respondents.
Survey findings published on 24 February 2026 report that 13% of industrial organisations have a unified namespace in production and 22% have MQTT widely deployed in production. Sixty-eight percent are stuck in pilots, proofs of concept, research, or have no overall AI strategy at all. Seven percent have AI embedded in core operational processes, while 44% expect to within three years.
The barrier respondents name most often is not the models. Forty-eight percent cite integration with legacy systems and data silos as the top obstacle — the single largest answer. Budget and ROI uncertainty follows at 39%, lack of leadership support at 20%, and 15% explicitly name scaling pilots into production as their key challenge.
A unified namespace is the architectural response to exactly that first problem: a single, structured, event-driven place where the current state of a plant is published, from which any consumer subscribes to what it needs, instead of a mesh of point-to-point integrations between every system and every other system. The distinction that makes it work is that the namespace is the source of current state rather than a message queue that happens to be shared — which is why "we already have MQTT" and "we have a unified namespace" are different claims.
Two cautions on the numbers. The survey comes from a vendor whose product sits in this layer, which is worth holding in mind — though a finding that only 13% of the market has adopted the thing you sell cuts against marketing incentive, and is therefore more credible than the reverse would be. And the report notes that organisations with established MQTT and namespace deployments are over-represented among those that scaled past pilots, which is a correlation and not evidence of direction.
The useful reading for anyone planning industrial AI work is the ordering. If nearly half the field says its blocker is getting at the data, then the data layer is the project, and the model is what happens afterwards.
Source: HiveMQ