Don't Underestimate Industrial AI Data Quality Challenges, Says Gartner

What Gartner Says Is Blocking Industrial AI

The manufacturing organizations moving fastest on AI are not doing so because they found a better model. They are doing so because they fixed their data foundation first. For every organization still running pilots that will not scale, the gap widens every quarter.

“Over 50% of AI projects fail to reach production. Data issues are the primary blocker for 40% of initiatives,” says Gartner's report, Manufacturing CIO's Guide to Industrial AI Data Readiness, which puts a number on the cost of waiting. And buried in the report's cautions section is a warning that applies directly to how most manufacturing teams are operating today: don't underestimate data quality challenges. AI-ready data requires ongoing validation and enrichment. It is not a one-time project. It is an infrastructure decision.

The question for manufacturing CIOs is not whether AI is on the roadmap. It is whether the data foundation beneath it can carry the weight.

The Three Building Blocks for Industrial AI Data Readiness

The Gartner research defines three data activities that must all be operational before AI can scale reliably across manufacturing environments.

Gartner®, Manufacturing CIO’s Guide to Industrial AI Data Readiness, by Bettina Tratz-Ryan, 16 December 2025. GARTNER is a trademark of Gartner, Inc. and/or its affiliates.

Data Curation: Unified Namespaces and OPC UA

Raw sensor data from the factory floor is high-volume, unstructured, and inconsistent across lines, sites, and machine generations. In the report, we see Gartner identifying Unified Namespaces (UNS) and OPC UA as the frameworks for converting this data into contextually enriched, AI-ready streams at the point of generation. Without this curation layer, AI models work from noise rather than signal.

AI Data Preparation and Delivery: MQTT for Real-Time Data

Within the report, we also see Gartner research calling out MQTT as the standard for real-time AI data delivery in industrial environments. MQTT streamlines connectivity and data flows between heterogeneous systems from edge to core analytics and AI engines, ensuring that AI models work from the most current operational information. In resource- and compute-constrained edge environments, MQTT manages the balance between low latency and increased AI inference demands.

OT/IT Metadata Management 

Traditional OT metadata management has been passive, manual, and static. In the report, we see Gartner calling for a transition to active metadata management: continuously updated, enriched and structured to automate a data fabric or digital thread for AI applications. This layer supports data lineage, semantic modeling and dynamic policy enforcement across the production lifecycle. Without it, AI agents lack the context to reason and act reliably.

Together, these three form what Gartner calls the Industrial AI Data Foundation. All three must be in place. A gap in any one of them limits what AI can do with the others.

Gartner®, Manufacturing CIO’s Guide to Industrial AI Data Readiness, by Bettina Tratz-Ryan, 16 December 2025. GARTNER is a trademark of Gartner, Inc. and/or its affiliates.

How HiveMQ Delivers Industrial AI Data Readiness 

HiveMQ is the Industrial Data Platform for Agentic AI. Built on MQTT, the platform is designed to connect, contextualize, analyze, and act on real-time operational data, which, in our view, maps directly to the three activities Gartner identifies as prerequisites for production AI.

• For Data Curation: HiveMQ's Unified Namespace backbone provides a single, structured, real-time source of truth for all operational data. Governance is enforced at the architecture level: schema validation, data lineage and semantic consistency across every connected asset and site.

• For AI Data Preparation and Delivery: HiveMQ Broker delivers the MQTT-native data streaming layer Gartner recommends for real-time AI data delivery. It handles controlled, low-latency data flows for multiple AI instances simultaneously, with quality-of-service levels tailored to edge AI applications in manufacturing environments.

• For Metadata Management: HiveMQ Pulse adds the active semantic intelligence layer. Ontology management, dynamic enrichment, and continuous data observability replace static metadata catalogs with a live data fabric that gives AI agents the context to reason, predict, and act with precision.

Audi, BMW, Ford, Mercedes-Benz, Siemens, and Eli Lilly trust HiveMQ to build the real-time operational data backbone that connects, contextualizes, and governs the data their AI initiatives depend on.

The Scaling Wall: Where Most Industrial AI Initiatives Stall

The caution from Gartner via this research is not theoretical. AI-ready data requires ongoing validation and enrichment. Every pilot that runs on ungoverned, unstructured or inconsistently delivered operational data is building on a foundation that will not hold at scale. The organizations that treat data quality as a deployment prerequisite rather than a post-launch fix are the ones reaching that 70% AI project production rate that Gartner documents.

P.S. Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research and advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. Source: Gartner, Manufacturing CIO's Guide to Industrial AI Data Readiness, Bettina Tratz-Ryan, 18 December 2025, ID G00841619.