Innoscript Technologies··5 min read
The most reliable AI automation projects start with a specific, repetitive process: data entry, document classification, ticket triage, or report generation are common starting points.
A useful pattern is combining an LLM for judgment (classifying, summarizing, drafting) with deterministic code for the parts that need to be exact — data validation, calculations, and system updates.
Automations should include monitoring and a clear fallback path for cases the system isn't confident about, rather than assuming full automation from day one.
Starting narrow and expanding scope as the automation proves reliable tends to work better than automating an entire workflow at once.