Start Here
Define What You 
Want to Solve
Work through these questions:
What decision are you trying to improve? Specificity matters. “Improve
data quality” is too broad to build toward. A good example is: “Catch
instrument drift earlier in plate-based assays.”
Where does your team’s time go? The most tedious, repetitive parts of
your workflow are often the best candidates for automation. Think
beyond the obvious. There's likely an AI application for more of it than
you'd expect.
Where does variability introduce risk? Inconsistency costs you
confidence, reproducibility, and downstream decision quality. AI
excels at standardizing judgments that currently depend on 
who happens to be reviewing.
What will people actually use? A tool that requires
someone to export a file, run a script, and interpret
raw output will sit unused. A tool that surfaces a 
recommendation inside your LIMS or ELN —
right where the decision already happens —
will become part of how work gets done. 
Involve end users early and
design around their workflow,
not around the model.
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