Most Industrial AI platform evaluations are scored on the wrong axis. The shortlist gets assembled around model capability—what the platform can predict, detect, or generate—and the data questions get treated as implementation detail. Then the pilot succeeds, the second site starts, and the project discovers that the data the model needs does not exist there in the same shape.
The most common failure point in Industrial AI projects is not the AI. It is getting the data. So the evaluation should start there. This guide covers twelve criteria, the questions that test each one, and what a weak answer sounds like.
"Industrial AI platform" covers two different products. Some supply models and applications. Some supply and govern the operational data those models run on. Vendors from both layers will appear on the same shortlist and answer the same RFP.
Decide which one you are short of before you score anyone. A platform that scores badly on model breadth may be the right purchase if what you lack is the data layer, and the reverse is equally true.
See: What is an Industrial AI platform? →
Ask directly whether the platform collects data from your equipment or analyses data that something else collected. Platforms that only consume leave you owning the integration project.
Ask: Which of our systems can you connect to natively, without custom development? What happens with the ones you cannot?
Weak answer: "We integrate with any data source via API." That describes an interface, not connectivity.
Coverage claims are only meaningful against the equipment you actually run — including the oldest line, which is usually the one that decides the timeline.
Ask: Bring your asset inventory. Which of these are supported out of the box, which need configuration, and which need development?
Benchmark: Litmus ships more than 250 native OT connectors covering PLCs, SCADA, historians, MES, robotics, and other industrial assets, with automated device and signal discovery.
A tag value is not data a model can use. The platform has to attach what the tag means, which asset it belongs to, what shift it was recorded on, and how it relates to other signals on the line.
Ask: Show me the asset model for a line like ours. How is it built, how long does it take, and how much of it is reusable at the next plant?
Weak answer: anything where contextualization is described as a mapping exercise performed per project.
This is the criterion that separates a pilot platform from an enterprise platform, and it is the one least often tested during evaluation.
Ask: If we build this use case at plant one, what exactly do we rebuild at plant two? Ask for the answer as a list of artifacts, not a reassurance.
Benchmark: reusable data models across machines, lines, cells, and plants, deployed through template-based rollout. One published reference point: 95 global sites in 18 weeks.
Industrial AI runs in two places for different work, and a platform that only supports one constrains your use case list permanently.
| Edge: inside the plant | Cloud: across sites |
Best suited to | Latency-sensitive, localized, air-gapped decisions | Continuous, high-compute analysis and cross-site comparison |
Latency | Milliseconds to sub-second | Minutes to hours |
Typical workloads | Computer vision, closed-loop control, local SLMs, operator troubleshooting | Model training, cross-plant pattern detection, enterprise benchmarking |
Data movement | Stays on site; works offline and air-gapped | Contextualized data forwarded through governed gateways |
Cost profile | Predictable; does not scale with tag volume | Scales with data volume and compute consumed |
Ask: Can we run vision inference locally at line speed? Can we run a language model inside an air-gapped plant? What is the hardware requirement for each?
The cost row above is the one most evaluations miss, because pilot pricing does not reveal it. Cloud-side cost grows with every tag you forward, which means a data-selection decision made in month two sets the run rate in year two.
Ask: Model our cost at 10 sites and at 100 sites, with the tag volume we actually have. Which line items scale with tag count, which with site count, and which are flat?
An AI output nobody can trace back to a source signal is an AI output nobody will act on. Lineage is not a compliance formality in Industrial AI — it is the mechanism by which an operator decides whether to believe a recommendation.
Ask: Take a number on a dashboard. Show me every transformation between the sensor and that number, and tell me who owns each one.
Look for: a metadata catalog of searchable, standardized data assets and models; end-to-end lineage across pipelines and models; documented ownership.
Ask: How does the platform operate in a segmented network? In an air-gapped plant? What crosses the OT-to-enterprise boundary, and what controls that gateway?
Look for: secure operation in air-gapped and restricted networks, controlled data gateways between OT and enterprise systems, role-based access control with least-privilege enforcement, and certificate-based encrypted communication.
Newer than the rest of this list, and increasingly the differentiator. Agents fail in industrial settings for one reason: they invent answers. Without grounded access to live operational data, an agent asked about a stalled filler describes what a filler generally does.
Ask: How does an LLM or agent get access to live tags, history, and the asset model? Is that a documented interface or a professional services engagement?
Look for: structured tool access to devices, tags, telemetry, history, and data models — with agents reasoning over asset models and hierarchies rather than raw tag names. Litmus provides this through Litmus MCP Server, an open-source Model Context Protocol server that runs in Docker inside the OT network.
Ask: What does this run on? Can it run on hardware we already own? Does choosing this platform commit us to one cloud, one model vendor, or one automation vendor's ecosystem?
Lock-in in this category is usually indirect: not a contractual restriction but a data model that only makes sense inside one vendor's stack.
Ask: From contract signature, how long until a real prediction runs on our equipment in production — not in a demo environment?
Benchmark: traditional Industrial AI deployments take 12 to 18 months to reach production. Where a standardized data foundation is already in place, individual use cases can deploy in days.
Anything over a few months for a first production result deserves a follow-up question about what specifically consumes the time.
Evaluations that run entirely on vendor demos test the vendor's environment, not yours.
Ask: Can our engineers connect one of our own machines without a sales process?
Benchmark: Litmus Edge Developer Edition provides full access to the platform in a free self-serve environment, without feature restrictions.
Weight the criteria by which layer you are short of, then treat criteria 3, 4, and 7 as gates rather than scores. A platform that fails contextualization, reusability, or lineage does not become acceptable by scoring well elsewhere—those three determine whether anything you build at the first site survives the second.
Start with the data layer: native connectivity to your actual equipment, contextualization into reusable asset models, and lineage from source signal to model output. Then execution flexibility, multi-site deployment, and cost behaviour at scale.
The three most revealing are: which of our systems do you connect to natively without development, what exactly do we rebuild at the second plant, and how long from signature to a real prediction running in production.
It has to standardize across sites — common data models, template-based rollout, centralized model and version management, and governance that holds across plants. Single-site success does not predict multi-site success.
Building the application layer on top of a standardized data foundation is common and often sensible. Building the data layer means rebuilding connectivity, contextualization, governance, and multi-site deployment — the work that consumes most of the 12 to 18 months.
Long enough to test against a second site. An evaluation that only proves one plant has not tested the criterion that most often decides the outcome.