Litmus has been named a Leader in the 2026 Gartner® Magic Quadrant™ for Global Industrial AIoT Platforms. We believe this recognition reflects the growing importance of a unified industrial data foundation as manufacturers move from AI pilots to enterprise-scale deployment. Here is how we see the opportunity in front of the industry, and the problem standing between manufacturers and it.
Hyperscalers are investing billions of dollars to build out the AI infrastructure that will power the next wave of manufacturing. Compute, models, and services are being purpose-built for industrial workloads at a pace nobody predicted three years ago.
That investment creates a real opportunity, but only for manufacturers able to deliver industrial data to those platforms at scale. Factory data has to be managed, contextualized, and governed in a way that ensures AI outcomes are actually realized, not just demonstrated.
This is where most industrial AI programs are stuck today. Not on the AI, but on what sits underneath it.
The core difficulty is structural, and it does not go away with a better model. No two factories are the same. The equipment differs. The context differs—what a tag means, what a normal shift looks like, which of three sensors is the one that matters. And the way each site is deployed and managed differs, because plants were built in different decades by different teams under different constraints, and several arrived through acquisition.
Then the effect compounds. A pilot succeeds at one plant. The team moves it to the second, and the equipment is newer, the naming convention lives in the head of a controls engineer who has been there nineteen years, and the integrator who built the first version has finished the engagement and moved on. What was supposed to be a rollout becomes a second integration project. Then a fortieth.
That inconsistency is what prevents standardization, stalls pilots, and produces AI outcomes nobody trusts enough to run operations on.
The common response is to absorb the variability downstream: send everything to the cloud and let data engineering reconcile it on arrival.
Enterprise AI platforms are extraordinary at what they were built for. They were not built to live on a factory floor. They do not sit where the data is generated, which means they inherit whatever arrives—and what arrives from a normalization layer built after the fact is technically complete and practically thin.
Two costs follow. Replication: every new site means new mappings and new reconciliation jobs, so the pipeline estate grows faster than the deployment does. And context loss: the metadata that made the data meaningful is exactly what is hardest to carry through a cleanup layer downstream.
You cannot reconstruct context that was never captured. If AI outcomes are only as good as the data beneath them, the data has to be right where it is produced.
Litmus bridges the gap between factory systems and cloud systems at scale.
Litmus provides a unified Industrial Data Foundation across all sites: one common data layer to connect, contextualize, govern, and deliver factory data to enterprise AI, without rebuilding the architecture plant by plant. It runs on the plant floor, where the data is generated, so what leaves each site is already in a form the next system can use—and the form is the same at every site.
It is one platform covering six capabilities, because leaving any one of them out pushes the problem downstream again:
Data Connectivity to any asset regardless of protocol or age
Industrial DataOps to contextualize data at the source
Edge Intelligence to run analytics and AI where security, latency, or bandwidth rule out a round trip to the cloud
Data Security with controlled access across the OT and IT boundary
Data Governance to keep data trusted as deployments grow
Central Management to deploy and operate edge instances from one place
Delivered together, they turn fragmented industrial data into a repeatable system for deploying AI — so the work you do once is the work you do everywhere.
At one global manufacturer, our team completed implementation across more than 90 production sites in under six months. That number is the argument. Not because ninety is large, but because the distance between the first site and the ninetieth was small enough to make the deployment a schedule rather than a program. When every plant produces data in the same shape, a use case proven in one factory reaches the rest of the network as a rollout instead of forty consecutive integration projects. That is the difference between an AI initiative and an AI capability.
Strategic partnerships with Microsoft, Google Cloud, AWS, Databricks, Oracle, Dell, and others bring factory data directly into the analytics and AI services manufacturers already use. This matters more than a list of logos suggests. The hyperscaler investment only reaches the plant floor if industrial data can travel to it in a governed, consistent form—and it only creates value if manufacturers can use the platforms they have already standardized on rather than building a parallel estate beside them. Those partnerships are how the foundation connects to everything above it.
"Being recognized as a Leader is a tremendous honor. We did not pivot to AI. We planned for it. We have been building products designed to solve factory data challenges at scale from day one." — Vatsal Shah, Co-Founder and CEO, Litmus
When Litmus started in 2014, the industry was still arguing about whether factory data belonged in the cloud at all. We were convinced the harder question would arrive later: once everyone had the data, would any of it be usable across a network of plants that were never built to match?
That is the question in front of every manufacturer now. It is the one we have been building for the entire time, and we are grateful to the customers who have been working through it alongside us.
If you are planning AI across a network of plants, this is the part worth getting right first.
Download the full Gartner report to see why Litmus was named a Leader.
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Gartner, Magic Quadrant for Global Industrial AIoT Platforms, By Scot Kim, Sudip Pattanayak et al., 15 September 2026
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