Integrating AI into Asset Performance Management: It’s all about the data

Imagine
a
future
where
artificial
intelligence
(AI)
seamlessly
collaborates
with
existing
supply
chain
solutions,
redefining
how
organizations
manage
their
assets.
If
you’re
currently
using
traditional
AI,
advanced
analytics,
and
intelligent
automation,
aren’t
you
already
getting
deep
insights
into
asset
performance?

Undoubtedly.
But
what
if
you
could
optimize
even
further?
That’s
the
transformative
promise
of
generative
AI,
which
is
beginning
to
revolutionize
business
operations
in
game-changing
ways.
It
may
be
the
solution
that
finally
breaks
through
dysfunctional
silos
of
business
units,
applications,
data
and
people,
and
moves
beyond
the
constraints
that
have
cost
companies
dearly.  

Still,
as
with
any
emerging
technology,
early
adopters
will
incur
learning
costs,
and
there
are
challenges
to
preparing
and
integrating
existing
applications
and
data
into
newer
technologies
that
enable
these
emerging
technologies.
Let’s
look
at
some
of
those
challenges
to
generative
AI
for
asset
performance
management.

Challenge
1:
Orchestrate
relevant
data

The
journey
to
generative
AI
begins
with
data
management.
According
to the
Rethink
Data
Report,
68%
of
data
available
to
businesses
goes
unleveraged.
Here’s
your
opportunity
to
take
that
abundant
information
you’re
collecting
in
and
around
your
assets
and
put
it
to
good
use. 

Enterprise
applications
serve
as
repositories
for
extensive
data
models,
encompassing
historical
and
operational
data
in
diverse
databases.
Generative
AI
foundational
models
train
on
massive
amounts
of
unstructured
and
structured
data,
but
the
orchestration
is
critical
to
success.
You
need
mature
data
governance
plans,
incorporation
of
legacy
systems
into
current
strategies,
and
cooperation
across
business
units.
 

Challenge
2:
Prepare
data
for
AI
models

AI
is
only
as
trusted
as
the
data
that
fuels
it.
Data
preparation
for
any
analytical
model
is
a
skill-
and
resource-intensive
endeavor,
requiring
the
meticulous
attention
of
(often)
large
teams
with
both
technology
and
business-unit
knowledge.
 

Critical
issues
to
resolve
include
operational
asset
hierarchy,
reliability
standards,
meter
and
sensor
data,
and
maintenance
standards.
It
takes
a
collaborative
effort
to
lay
the
foundation
for
effective
AI
integration
in
APM
and
a
deep
understanding
of
the
intricate
relationships
within
your
organization’s
data
landscape.

Challenge
3:
Design
and
deploy
intelligent
workflows

Integrating
generative
AI
into
existing
processes
requires
a
paradigm
shift
in
how
many
organizations
operate.
This
shift
includes
embedding
AI
advisors
and
digital
workers—fundamentally
different
from
chatbots
or
robots—to
help
you
scale
and
accelerate
the
impact
of
AI
with
trusted
data
across
your
business
and
your
applications.
And
it’s
not
just
a
technology
change.

Your
AI
workflows
should
support
responsibility,
transparency,
and
“explainability.”

To
fully
leverage
the
potential
of
AI
in
APM
requires
a
cultural
and
organizational
shift.
Fusing
human
expertise
with
AI
capabilities
becomes
the
cornerstone
of
intelligent
workflows,
promising
increased
efficiency
and
effectiveness.

Challenge
4:
Build
sustainment
and
resiliency

The
initial
deployment
of
AI
in
APM
isn’t
the
last
stop
on
the
road.
A
holistic
approach
helps
you
build
sustainment
and
resiliency
into
the
new
enterprise
AI
ecosystem.
Increasing
managed
services
contracts
across
the
enterprise
becomes
a
proactive
measure,
ensuring
continuous
support
for
evolving
systems.

With
their
wealth
of
knowledge,
the
transition
of
the
aging
asset
reliability
workforce
presents
both
a
challenge
and
an
opportunity.
Maintaining
the
effective
deployment
of
embedded
technologies
may
require
your
organization
to
“think
outside
the
box”
when
managing
new
talent
models.

As
generative
AI
evolves,
you’ll
want
to
stay
vigilant
to
changing
regulatory
guidelines
and
stay
in
tune
with
local
and
global
ethical,
data
privacy
and
sustainability
standards.

Prepared
for
the
journey

Generative
AI
will
impact
your
organization
across
most
of
your
business
capabilities
and
imperatives.
So,
consider
these
challenges
as
interconnected
milestones,
each
harnessing
capabilities
to
streamline
processes,
enhance
decision-making,
and
drive
APM
efficiencies.
 

Reinvent
how
your
business
works
with
AI

Read
The
CEO’s
Guide
to
Generative
AI

Reimagine
Supply
Chain
Ops
with
Generative
AI