Many lab workflow problems are not caused by a missing core platform. They appear between platforms: a result exported from one system, reshaped in a spreadsheet, pasted into another record, then summarized again for a team update. Workflow automation is often about making those handoffs explicit and reliable.
Map the handoff, not just the application
Start with one object moving through the lab: a sample, request, result, study update, deviation, or inventory item. Write down every system it touches and every moment a person has to copy, rename, reformat, chase, or reconcile information.
The resulting map usually exposes a smaller problem than “modernize our lab software.” A specific handoff may need an API integration. Another may need a validation screen. A third may only need a scheduled job that creates a consistent file and alerts the right person when something is missing.
Use one canonical identifier through the workflow
Automation becomes fragile when the same sample, project, or request is named differently in every system. Before adding AI, decide which identifier follows the object across the workflow and how records are matched when older systems cannot store it directly.
This sounds mundane, but identity resolution is often the difference between a workflow that can run unattended and one that still needs somebody to inspect every row.
Add AI only where ambiguity exists
Deterministic integrations should stay deterministic. If a field maps directly from one API to another, a language model adds cost and uncertainty without adding value. AI becomes useful where the input is messy: free-text requests, inconsistent documents, notes, PDFs, or a knowledge search that requires understanding meaning rather than matching an exact field.
A strong workflow can therefore mix ordinary software, rules, and AI. The user should not have to care which technique handled each step as long as the result is reliable and reviewable.
Make failure visible
Every automated handoff needs a visible exception path. When an API is down, a record cannot be matched, or an extracted value is uncertain, the system should create a review task rather than silently guessing. This keeps automation from turning a small data issue into a downstream scientific or operational problem.