Turn expert workflows into a practical starting point for AI

Start smaller than a job title

“Automate operations” is too broad to evaluate. A recurring case type with a recognizable beginning and end is a better starting point. It might involve gathering information, checking a record and preparing an update across existing applications. Agree which decisions belong to the system and which remain with the team.

This makes the pilot concrete. Process owners can identify a valid outcome, the evidence needed to support it and the circumstances that require escalation. It also gives the team a way to judge a result without being distracted by how impressive the demonstration looks.

A recording needs to explain the action

A screen recording shows what happened visually, but it does not automatically identify the control used or the meaning of a typed entry. Neurologic’s capture research addresses this by connecting interface observations with structured interaction records. The aim is to create clearer examples of the action that followed a particular screen.

Capture also needs a deliberate boundary. Agree the purpose with participating experts, select the applications involved and review what can appear on screen. Application scoping is not the same as redacting everything visible inside a captured frame. Appropriate source selection and review remain part of preparing the material.

Teach the decision, as well as the click

Useful examples connect individual actions to a case, a task and a process guide. The public data-preparation methodology describes how recordings can be organized into complementary training tasks: deciding the next action, understanding the current goal, interpreting the screen and answering questions about the procedure.

This is also a chance to examine the guide itself. If recorded work and written instructions disagree, the process owner needs to resolve the difference. Training on a polished but incorrect explanation would teach the wrong behavior with greater consistency. The examples should therefore be reviewed for business correctness, not merely for technical completeness.

Keep the test separate from the lesson

A convincing replay of a familiar example says little about unfamiliar work. Reserve complete cases for evaluation, rather than mixing adjacent steps from the same case between training and testing. Include exceptions and incomplete information, so the system is judged on its response when the happy path no longer applies.

The execution research also makes failures and outcomes visible in a record of the work. For a pilot, use that visibility to distinguish an incomplete task from a correct decision to stop. A missing input, a conflicting record and a completed case are different business outcomes; they should not collapse into a single success counter.

• Can the specialist reach a valid outcome on unseen cases?
• Does it preserve the required checks when the sequence changes?
• Does it stop or hand over when the evidence is insufficient?
• Can a reviewer reconstruct the actions and assess the result?

Build a learning loop with the people who do the work

Keep experts involved after the first capture session. Their review can reveal an unusual but legitimate sequence, an ambiguous instruction or an exception missing from the examples. These findings improve the understanding of the process as well as the material available for training.

A useful first deployment decision rests on that bounded evidence: which cases are handled well, what remains uncertain and what safeguards the team needs. Broader automation can then be considered from a measured starting point, with responsibility for the workflow still clearly assigned.