Establish Governance
Before You Need It
As AI tools become
embedded in regulated
workflows, questions of
ownership and
accountability become
unavoidable. Who
approves updates to a
model that influences a
regulated decision?
What happens when a
model is retired? How
are changes shared
with the people relying
on it? These questions
are far easier to answer
before a problem
surfaces than after.
Involve your regulatory
affairs team early.
Heads Up
There’s a lot of software involved in developing confidence
indicators and connecting the model to internal systems. 
Yahara can help. Reach out to us for assistance. 
Monitor Performance
After Deployment
A model that was
accurate at launch
won't necessarily stay
that way. Processes
evolve, reagent lots
change, and the data
the model encounters
in production will
gradually diverge from
what it was trained on.
Define a performance
baseline at deployment,
track against it over
time, and establish the
conditions that would
trigger a retraining
cycle. A model with no
monitoring plan is a
model on a slow path
toward silent failure.
Connect to The Systems
Your Team Already Uses
For a model to become
part of how a lab
operates, its outputs
need to land
somewhere familiar
and actionable —
inside your LIMS, your
ELN, your existing
review workflow. If
using the model
requires someone to
leave the system
they're already
working in, most
people won't do it
consistently.
Integration is crucial
for adoption.
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