Most Manufacturing Data Isn’t AI Ready, Fix the Data Foundation Not the Model

In manufacturing, the primary reason why AI is stalling is because the data foundation is not AI-ready. Signals are not typed, units are not consistent, and context does not travel from one site to the next. When you fix the data foundation, models deploy faster, validation shortens, and scale becomes predictable rather than experimental. An increasingly adopted approach for building this data foundation is the Unified Namespace with MQTT at its core. 

The Problem: Most Manufacturing Data Falls Short for AI

AI is only as good as the data it is trained on, and, in manufacturing, most of that data simply isn’t ready.

While factories generate vast amounts of information from sensors, machines, MES, and ERP systems, the reality is that this data is often unclean, unstructured, and siloed. According to Gartner, 60% of AI projects unsupported by AI-ready data will be abandoned by 2026. For manufacturing organizations, this means the majority of manufacturers chasing AI initiatives are already set up for failure, not because of their algorithms, but because of a fragile data foundation.

In regulated industries, such as pharmaceuticals, this issue becomes even more acute. Eli Lilly, for example, built a compliance-driven connectivity data foundation with HiveMQ, integrating hundreds of lab and manufacturing instruments through MQTT and Sparkplug B. By standardizing these data flows and laying the foundation for a Unified Namespace, Lilly ensured that critical process data was contextualized for downstream use.

Lack of AI-Ready Data Delays AI Initiatives

Deloitte’s 2024 survey found that 55% of industrial product manufacturers are already leveraging GenAI tools for operations, and over 40% plan to increase investment in AI and machine learning over the next three years. However, the same survey also cites that nearly 70% of manufacturers cited data problems, including quality, contextualization, and validation, as the top obstacles to AI implementation. Forrester echoes this, identifying poor data quality as the leading blocker for AI adoption in B2B enterprises, including manufacturing.

What Does “AI-Ready” Data Really Mean? 

AI-ready data uses consistent data types and physical units; is contextualized across asset lines, machines, and time; stays aligned across sites; is governed as it flows; and remains traceable with clear lineage. If any one of these elements is missing, teams end up proving equivalence again and again instead of shipping outcomes.

When the data foundation is coherent, validation changes character. Instead of assembling evidence after the fact, you generate it as data moves. Types and units are checked as messages enter the system, state models are verified at the edge, and data lineage is captured as part of normal operation.

What Happens When the Data is Not AI-Ready? 

Let’s take an example: In a packaging hall on the afternoon shift, the counter at the end of Line 3 ticks up with every case that leaves the sealer, and a temperature probe on Machine 12 records process heat. At this site, PackageCount is written as PackageCount and stored as an integer with a unit of pieces, Temp is a float in °C, and the namespace follows Line→Cell→Machine with states reported as RUNNING/IDLE/FAULT. 

A sister plant sends what should be the same signals but with small differences that compound fast, the count arrives as pkg_cnt or sometimes count_pkg, Temp is logged as an integer in °F, the structure collapses to Line→Machine, and the state model reads ACTIVE/STANDBY/ERROR. 

When the team tries to roll out a shared model and a common dashboard, KPIs are no longer comparable, the model trained in one namespace will not travel without brittle translation, and audits balloon because you cannot prove the same control and the same outcome across sites without bespoke evidence. 

Every difference forces retesting, remapping, and new evidence. Project plans absorb it quietly as “engineering,” “validation,” or “UAT,” but it is the same cost wearing different labels. That is the data inconsistency tax, and it scales with the number of sites, models, and KPIs you want to share.

The table below summarizes the differences in the data models in Site A vs. Site B.

What Breaks Isn’t the Model, It’s the Meaning of the Data

Once a predictive maintenance model is trained at Site A, it expects the data to behave a certain way—temperatures rising in Celsius, specific state transitions like FAULT, and a consistent structure for identifying machines. But when that same model is deployed at Site B, the signals no longer mean the same thing. The model doesn’t fail because its logic is flawed but because the underlying assumptions about the data no longer hold.

You can rewrite the model to accommodate these differences—or you can fix the foundation so those differences don’t exist in the first place. AI doesn't scale when every rollout needs a translation layer. It scales when the data carries consistent meaning from site to site.

The Foundation for AI in Industry: A Unified Namespace with MQTT at Its Core

An increasingly adopted approach for building this foundation is the Unified Namespace (UNS). It organizes data from machines, MES, and enterprise systems into structured, contextual hierarchies that both OT and IT can trust. When tags, units, and states live in a single model, AI, digital twins, and analytics no longer waste time resolving upstream inconsistencies. Instead, they start compounding value from clean, contextualized inputs. 

IoT Analytics reports that data readiness now sits at the heart of manufacturing digital strategies, with unified data architectures ranked among the top priorities for industrial leaders navigating AI transformation. 

Building such a namespace requires a protocol built for scale. MQTT has become the de facto standard for UNS architectures, a lightweight, interoperable, event-driven protocol that reliably moves data across heterogeneous systems while preserving structure and context.

Name the Blocker, Not the Algorithm

The primary blocker to industrial AI is not the algorithm, but data that cannot be trusted beyond the line where it was born. Most manufacturers are not held back by weak models but by data that is not AI-ready. Counts arrive under different names, temperatures change types and units, structures differ between Line to Cell to Machine and Line to Machine, and states use inconsistent labels across sites.

The result is predictable: KPIs do not roll up cleanly, models trained in one namespace cannot travel without brittle translation, and validation work resurfaces with every rollout. What should be scalable becomes bespoke.

Make readiness the default. When the data foundation is sound, models, KPIs, and validation stop behaving like isolated efforts and start functioning as reusable components. A Unified Namespace gives signals shared meaning and context, turning scattered tags into trustworthy inputs.