Industrial AI: from a convincing demo to daily use.

Choose one job worth improving.

“We should use AI” gives a pilot little direction. A specific problem gives it a purpose: finding the current machine documentation, preparing a first review of an RFQ, or assembling information for a technical handover.

Define who does the work, what triggers it and what a useful result looks like. Observe the current approach before deciding what the tool should improve. A difficult task that happens rarely may call for a different product than a small frustration repeated every day.

This emphasis on the problem, people and process also appears in NIST MEP's discussion of AI adoption in manufacturing, which recommends starting with a limited pilot before expanding.

Bring the everyday conditions into the trial.

A prepared demonstration establishes what a tool can do with the material selected for it. A trial should explore the conditions the team will actually encounter.

This is a hypothetical example, not a FACTREN customer case. It shows how the value of an AI capability depends on the surrounding work.

Document ownership, access permissions and update responsibilities matter here. Someone must know which records are authoritative and how changes reach the tool. That work belongs in the adoption plan, alongside the software itself.

Measure the effort around the answer.

A fast response is useful evidence, but it does not describe the complete job. A team may spend time preparing inputs, checking sources, correcting the result and transferring it into the system where the work is recorded.

For the document assistant, a practical comparison could examine:

• Time to a verified result: include searching, checking and correcting, using representative tasks with and without the tool.
• Quality: assess whether the answer uses the relevant revision, covers the question and exposes missing information.
• Fit with the job: observe whether users can use the result in their normal tools, with their normal access rights.
• Ongoing effort: record the work needed to maintain documents, support users and investigate problems.

Agree which measures matter before the trial. Record initial setup and learning effort separately from repeated use. Quality and workload may improve differently; a useful result should make both visible.

NIST's AI Risk Management Framework 1.0 provides relevant evaluation guidance: measure performance under conditions similar to deployment and involve domain experts and users. The industrial measures above are our practical application to this example, not a NIST certification scheme.

Design the pilot around a decision.

A pilot should leave the team better able to decide what happens next. Before it begins, the industrial company and technology provider should agree:

1. The task and users. Name the work being tested, who will use the tool and who owns the decision.
2. The permitted material. Define the documents and systems it can access, who authorises that access and who maintains the inputs.
3. The review boundary. Decide which outputs need human approval and what users should do when information is missing or the tool is unavailable.
4. The comparison. Select representative tasks, the current method and the evidence needed to judge usefulness.
5. The next step. Set a review date and decide what would justify extending the trial, revising it or stopping.

The scope should match the tool. Testing document retrieval is different from allowing software to change a machine program. Any connection to physical equipment needs the site's agreed engineering, access and validation arrangements.

Build the next step with the people doing the work.

For AI builders: watch how an industrial user verifies and applies the output. Their feedback may reveal a need for better source handling, revision control, terminology or integration. Those observations can become concrete product improvements and evaluation tasks.

For industrial companies: bring a defined problem and make time for the people who know it. Involve the relevant engineering, operational and IT roles early enough to resolve practical constraints. An internal sponsor needs to know who will support the tool if the trial continues.

An encouraging pilot can justify a wider trial. It does not establish that every machine, site or team will see the same result. Expand with a clear view of what has been demonstrated and what still needs to be learned.

A useful industrial AI tool earns a place in the working day. Connecting technical capability with that daily reality is the work FACTREN aims to support.