10808_AI_Bussiness_School_ExtraVid_FINAL_v3
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[AI Business School]
>>Norm Judah: One of the
interesting elements that comes
out of the maturity model
work that we did was
what is the right organization
model as you set up a
center of excellence.
And, then obviously a
different model, one is very
centralized, one is heavily
decentralized with some hybrid
in the middle and so
the centralized model would be
reporting in possibly to
the chief digital officer or
the chief information officer.
The center for excellence for AI
that does strategy governance
but also executions
so because that is a scientist
so hard to define, we cluster
them into one place and do it
centralized but the projects
actually still happen in the
business silos or in the
business divisions and these
people sort of pause down.
There is this other model
which is completely
decentralized which says you
have no centralization
and governance, but each one
of the business units actually
has their own mini groups so
you have a set of local
standards for each one and
local data scientists that happen
there as well.
In that case, there is the
propensity of course to develop
different standards and
different silos and so when I
actually prefer at this point
is the hybrid model which says
from a central perspective
you have strategy and
governance, model management
the ownership of the
maturity model, the activities
of that, but then execution
actually sits in the individual
silo, so the application
architects, the data scientists
all sit in a silo in a business
silo wrapped around a
particular problem, but the
only way that that is
successful in reality
is if we still form the
community.
The community of the central
governance but also the
architects on the individual
team bringing those people
together to make sure
that you are sharing data
models to make sure
that you are sharing data
itself, that you have
understanding of the ML
models, you have understanding
of the problems that that things
solve because one of the things
that AI will do is you will get
insight in a big data
science from one cluster of
data that becomes useful
in another cluster.
This thing that you learn about
credit card processing in a
bank that might be useful
in retail customer, so
this has to be point of
understanding of the breadth
of data and the breadth of the
model, so today, the model
that seems to be most
effective is this partially
centralized decentralized,
we have governance and
strategy and operational
technique coming from a
central org, but then the
applications being developed
by AI experts sitting in the
business as close as they can
be to actually get into that
[INDISCERNIBLE] agile mechanism
of developing great solutions
today.
[Microsoft]