AI in commerce: Essential use cases for B2B and B2C

• Four
  AI
  in
  commerce
  use
  cases
  are
  already
  transforming
  the
  customer
  journey:
  modernization
  and
  business
  model
  expansion;
  dynamic
  product
  experience
  management
  (PXM);
  order
  intelligence;
  and
  payments
  and
  security. 
• By
  implementing
  effective
  solutions
  for
  AI
  in
  commerce,
  brands
  can
  create
  seamless,
  personalized
  buying
  experiences
  that
  increase
  customer
  loyalty,
  customer
  engagement,
  retention
  and
  share
  of
  wallet
  across
  B2B
  and
  B2C
  channels. 
• Poorly
  run
  implementations
  of
  traditional
  or
  generative
  AI
  in
  commerce—such
  as
  models
  trained
  on
  inadequate
  or
  inappropriate
  data—lead
  to
  bad
  experiences
  that
  alienate
  consumers
  and
  businesses.
• Successful
  integration
  of
  AI
  in
  commerce
  depends
  on
  earning
  and
  keeping
  consumer
  trust.
  This
  includes
  trust
  in
  the
  data,
  the
  security,
  the
  brand
  and
  the
  people
  behind
  the
  AI.

---

Recent
advancements
in

artificial
intelligence
(AI)
are
transforming

commerce
at
an
exponential
pace.
As
these
innovations
are
dynamically
reshaping
the
commerce
journey,
it
is
crucial
for
leaders
to
anticipate
and
future-proof
their
enterprises
to
embrace
the
new
paradigm.  

In
the
context
of
this
rapid
advancement,

generative
AI
and

automation
have
the
capacity
to
create
more
fundamentally
relevant
and
contextually
appropriate
buying
experiences.
They
can
simplify
and
accelerate
workflows
throughout
the
commerce
journey,
from
discovery
to
the
successful
completion
of
a
transaction.
To
take
one
example,
AI-facilitated
tools
like
voice
navigation
promise
to
upend
the
way
users
fundamentally
interact
with
a
system.
And
these
technologies
provide
brands
with
intelligent
tools,
enabling
more
productivity
and
efficiency
than
was
possible
even
five
years
ago. 

AI
models
analyze
vast
amounts
of
data
quickly,
and
get
more
accurate
by
the
day.
They
can
provide
valuable
insights
and
forecasts
to
inform
organizational
decision-making
in
omnichannel
commerce,
enabling
businesses
to
make
more
informed
and
data-driven
decisions.
By
implementing
effective
AI
solutions—using
traditional
and
generative
AI—brands
can
create
seamless
and
personalized
buying
experiences.
These
experiences
result
in
increased
customer
loyalty,
customer
engagement,
retention,
and
increased
share
of
wallet
across
both

business-to-business
(B2B)
and
business-to-consumer
(B2C)
channels.
Ultimately,
they
drive
significant
increases
in
conversions
driving
meaningful
revenue
growth
from
the
transformed
commerce
experience.  

Explore
commerce
consulting
services

Creating
seamless
experiences
for
skeptical
users

It’s
been
a
swift
shift
toward
a
ubiquitous
use
of
AI.
Early
iterations
of
e-commerce
used
traditional
AI

largely
to
create
dynamic
marketing
campaigns,
improve
the
online
shopping
experience,
or
triage
customer
requests.
Today
the
technology’s
advanced
capabilities
encourage
widespread
adoption.
AI
can
be
integrated
into
every
touchpoint
across
the
commerce
journey.
According
to
a

recent
report
from
the
IBM
Institute
for
Business
Value,
half
of
CEOs
are
integrating
generative
AI
into
products
and
services.
Meanwhile,
43%
are
using
the
technology
to
inform
strategic
decisions. 

But
customers
aren’t
yet
completely
on
board.
Fluency
with
AI
has
grown
along
with
the
rollout
of
ChatGPT
and

virtual
assistants
like
Amazon’s
Alexa.
But
as
businesses
around
the
globe
rapidly
adopt
the
technology
to
augment
processes
from
merchandising
to
order
management,
there
is
some
risk.
High-profile
failures
and
expensive
litigation
threatens
to
sour
public
opinion
and
cripple
the
promise
of
generative
AI-powered
commerce
technology.  

Generative
AI’s
impact
on
the
social
media
landscape

garners
occasional
bad
press.
Disapproval
of
brands
or
retailers
that
use
AI
is
as
high
as
38%
among
older
generations,
requiring
businesses
to
work
harder
to
gain
their
trust. 

A

report
from
the
IBM
Institute
of
Business
Value
found
that
there’s
enormous
room
for
improvement
in
the

customer
experience.
Only
14%
of
surveyed
consumers
described
themselves
as
“satisfied”
with
their
experience
purchasing
goods
online.
A
full
one-third
of
consumers
found
their
early
customer
support
and

chatbot
experiences
that
use

natural
language
processing
(NLP)
so
disappointing
that
they
didn’t
want
to
engage
with
the
technology
again. And
the
centrality
of
these
experiences
isn’t
limited
to
B2C
vendors.
Over
90%
of
business

buyers
say
a
company’s
customer
experience
is
as
important
as
what
it
sells.   

Poorly
run
implementations
of
traditional
or
generative
AI
technology
in
commerce—such
as
deploying
deep
learning
models
trained
on
inadequate
or
inappropriate
data—lead
to
bad
experiences
that
alienate
both
consumers
and
businesses. 

To
avoid
this,
it’s
crucial
for
businesses
to
carefully
plan
and
design

intelligent
automation
initiatives
that
prioritize
the
needs
and
preferences
of
their
customers,
whether
they
are
consumers
or
B2B
buyers.
By
doing
so,
brands
can
create
contextually
relevant
personalized
buying
experiences,
seamless
and
friction-free,
which
foster
customer
loyalty
and
trust. 

This
article
explores

four
transformative
use
cases
for
AI
in
commerce
that
are
already
enhancing
the
customer
journey,
especially
in
the
e-commerce
business
and
e-commerce
platform
components
of
the
overall
omnichannel
experience.
It
also
discusses
how
forward-thinking
companies
can
effectively
integrate
AI
algorithms
to
usher
in
a
new
era
of
intelligent
commerce
experiences
for
both
consumers
and
brands.
But
none
of
these
use
cases
exist
in
a
vacuum.
As
the
future
of
commerce
unfolds,
each
use
case
interacts
holistically
to
transform
the
customer
journey
from
end-to-end–for
customers,
for
employees,
and
for
their
partners.
  

Use
case
1:
AI
for
modernization
and
business
model
expansion

AI-powered
tools
can
be
incredibly
valuable
in
optimizing
and
modernizing
business
operations
throughout
the
customer
journey,
but
it
is
critical
in
the
commerce
continuum.
By
using

machine
learning
algorithms
and
big
data
analytics,
AI
can
uncover
patterns,
correlations
and
trends
that
might
escape
human
analysts.
These
capabilities
can
help
businesses
make
informed
decisions,
improve
operational
efficiencies,
and
identify
opportunities
for
growth. The
applications
of
AI
in
commerce
are
vast
and
varied.
They
include:

Dynamic
content

Traditional
AI
fuels
recommendation
engines
that
suggest
products
based
on
customer
purchase
history
and
customer
preferences,
creating
personalized
experiences
that
result
in
increased
customer
satisfaction
and
loyalty.
Experience
building
strategies
like
these

have
been
 used
by
online
retailers
for
years.
Today,
generative
AI
enables
dynamic
customer
segmentation
and
profiling.
This
segmentation
activates
personalized
product
recommendations
and
suggestions,
such
as
product
bundles
and
upsells,
that
adapt
to
individual
customer
behavior
and
preferences,
resulting
in
higher
engagement
and
conversion
rates. 

Commerce
operations

Traditional
AI
allows
for
the
automation
of
routine
tasks
such
as
inventory
management,
order
processing
and
fulfillment
optimization,
resulting
in
increased
efficiency
and
cost
savings.
Generative
AI
activates
predictive
analytics
and
forecasting,
enabling
businesses
to
anticipate
and
respond
to
changes
in
demand,
reducing
stockouts
and
overstocking,
and
improving
supply
chain
resilience.
It
can
also
significantly
impact
real-time
fraud
detection
and
prevention,
minimizing
financial
losses
and
improving
customer
trust.  

Business
model
expansion

Both
traditional
and
generative
AI
have
pivotal
and
functions
that
can
redefine
business
models.
They
can,
for
example,
enable
the
seamless
integration
of
a
marketplace
platform
where
AI-driven
algorithms
match
supply
with
demand,
effectively
connecting
sellers
and
buyers
across
different
geographic
areas
and
market
segments.
Generative
AI
can
also
enable
new
forms
of
commerce—such
as
voice
commerce,
social
commerce
and
experiential
commerce—that
provide
customers
with
seamless
and
personalized
shopping
experiences.

Traditional
AI
can
enhance
international
purchasing
by
automating
tasks
such
as
currency
conversions
and
tax
calculations.
It
can
also
facilitate
compliance
with
local
regulations,
streamlining
the
logistics
of
cross-border
transactions.

However,
generative
AI
can
create
value
by
generating
multilingual
support
and
personalized
marketing
content.
These
tools
adapt
content
to
the
cultural
and
linguistic
nuances
of
different
regions,
offering
a
more
contextually
relevant
experience
for
international
customers
and
consumers. 

Use
case
2:
AI
for
dynamic
product
experience
management
(PXM)

Using
the
power
of
AI,
brands
can
revolutionize
their
product
experience
management
and
user
experience
by
delivering
personalized,
engaging
and
seamless
experiences
at
every
touchpoint
in
commerce.
These
tools
can
manage
content,
standardize
product
information,
and
drive
personalization.
With
AI,
brands
can
create
a
product
experience
that
informs,
validates
and
builds
the
confidence
necessary
for
conversion.
Some
ways
to
use
relevant
personalization
by
transforming
product
experience
management
include: 

Intelligent
content
management

Generative
AI
can
revolutionize
content
management
by
automating
the
creation,
classification
and
optimization
of
product
content.
Unlike
traditional
AI,
which
analyzes
and
categorizes
existing
content,
generative
AI
can
create
new
content
tailored
to
individual
customers.
This
content
includes
product
descriptions,
images,
videos
and
even
interactive
experiences.
By
using
generative
AI,
brands
can
save
time
and
resources
while
simultaneously
delivering
high-quality,
engaging
content
that
resonates
with
their
target
audience.
Generative
AI
can
also
help
brands
maintain
consistency
across
all
touchpoints,
ensuring
that
product
information
is
accurate,
up-to-date
and
optimized
for
conversions. 

Hyperpersonalization

Generative
AI
can
take
personalization
to
the
next
level
by
creating
customized
experiences
that
are
tailored
to
individual
customers.
By
analyzing
customer
data
and
customer
queries,
generative
AI
can
create
personalized
product
recommendations,
offers
and
content
that
are
more
likely
to
drive
conversions.

Unlike
traditional
AI,
which
can
only
segment
customers
based
on
predefined
criteria,
generative
AI
can
create
unique
experiences
for
each
customer,
considering
their
preferences,
behavior
and
interests.
Such
personalization
is
crucial
as
organizations
adopt
software-as-a-service
(SaaS)
models
more
frequently:
Global
subscription-model
billing
is
expected
to
double
over
the
next
six
years,

and
most
consumers
say
those
models
help
them
feel
more
connected
to
a
business.
With
AI’s
potential
for
hyperpersonalization,
those
subscription-based
consumer
experiences
can
vastly
improve.
These
experiences
result
in
higher
engagement,
increased
customer
satisfaction,
and
ultimately,
higher
sales. 

Experiential
product
information

Al
tools
allow
individuals
to
learn
more
about
products
through
processes
like
visual
search,
taking
a
photograph
of
an
item
to
learn
more
about
it.
Generative
AI
takes
these
capabilities
further,
transforming
product
information
by
creating
interactive,
immersive
experiences
that
help
customers
better
understand
products
and
make
informed
purchasing
decisions.
For
example,
generative
AI
can
create
360-degree
product
views,
interactive
product
demos,
and
virtual
try-on
capabilities.
These
experiences
provide
a
richer
product
understanding
and
help
brands
differentiate
themselves
from
competitors
and
build
trust
with
potential
customers.
Unlike
traditional
AI,
which
provides
static
product
information,
generative
AI
can
create
engaging,
memorable
experiences
that
drive
conversions
and
build
brand
loyalty.  

Smart
search
and
recommendations

Generative
AI
can
revolutionize
search
engines
and
recommendations
by
providing
customers
with
personalized,
contextualized
results
that
match
their
intent
and
preferences.
Unlike
traditional
AI,
which
relies
on
keyword
matching,
generative
AI
can
understand
natural
language
and
intent,
providing
customers
with
relevant
results
that
are
more
likely
to
match
their
search
queries.
Generative
AI
can
also
create
recommendations
that
are
based
on
individual
customer
behavior,
preferences
and
interests,
resulting
in
higher
engagement
and
increased
sales.
By
using
generative
AI,
brands
can
deliver
intelligent
search
and
recommendation
capabilities
that
enhance
the
overall
product
experience
and
drive
conversions. 

Use
case
3:
AI
for
order
intelligence 

Generative
AI
and
automation
can
allow
businesses
to
make
data-driven
decisions
to
streamline
processes
across
the
supply
chain,
reducing
inefficiency
and
waste.
For
example,

a
recent
analysis
from
McKinsey
found
that
nearly
20%
of
logistics
costs
could
stem
from
“blind
handoffs”—the
moment
a
shipment
is
dropped
at
some
point
between
the
manufacturer
and
its
intended
location.
According
to
the
McKinsey
report,
these
inefficient
interactions
might
amount
to
as
much
as
$95
billion
in
losses
in
the
United
States
every
year.
AI-powered
order
intelligence
can
reduce
some
of
these
inefficiencies
by
using: 

Order
orchestration
and
fulfillment
optimization

By
considering
factors
such
as
inventory
availability,
location
proximity,
shipping
costs
and
delivery
preferences,
AI
tools
can
dynamically
select
the
most
cost-effective
and
efficient
fulfillment
options
for
an
individual
order.
These
tools
might
dictate
the
priority
of
deliveries,
predict
order
routing,
or
dispatch
deliveries
to
comply
with
sustainability
requirements.  

Demand
forecasting

By
analyzing
historical
data,
AI
can
predict
demand
and
help
businesses
optimize
their
inventory
levels
and
minimize
excess,
reducing
costs
and
improving
efficiency.
Real-time
inventory
updates
allow
businesses
to
adapt
quickly
to
changing
conditions,
allowing
for
effective
resource
allocation.

Inventory
transparency
and
order
accuracy

AI-powered
order
management
systems
provide
real-time
visibility
into
all
aspects
of
the
critical
order
management
workflow.
These
tools
enable
companies
to
proactively
identify
potential
disruptions
and
mitigate
risks.
This
visibility
helps
customers
and
consumers
trust
that
their
orders
will
be
delivered
exactly
when
and
how
they
were
promised. 

Use
case
4:
AI
for
payments
and
security 

Intelligent
payments
enhance
the
payment
and
security
process,
improving
efficiency
and
accuracy.
Such
technologies
can
help
process,
manage
and
secure
digital
transactions—and
provide
advance
warning
of
potential
risks
and
the
possibility
of
fraud. 

Intelligent
payments

Traditional
and
generative
AI
both
enhance
transaction
processes
for
B2C
and
B2B
customers
making
purchases
in
online
stores.
Traditional
AI
optimizes
POS
systems,
automates
new
payment
methods,
and
facilitates
multiple
payment
solutions
across
channels,
streamlining
operations
and
improving
consumer
experiences.
Generative
AI
creates
dynamic
payment
models
for
B2B
customers,
addressing
their
complex
transactions
with
customized
invoicing
and
predictive
behaviors.
The
technology
can
also
provide
strategic
and
personalized
financial
solutions.
Also,
generative
AI
can
enhance
B2C
customer
payments
by
creating
personalized
and
dynamic
pricing
strategies. 

Risk
management
and
fraud
detection

Traditional
AI
and
machine
learning
excel
in
processing
vast
volumes
of
B2C
and
B2B
payments,
enabling
businesses
to
identify
and
respond
to
suspicious
trends
swiftly.
Traditional
AI
automates
the
detection
of
irregular
patterns
and
potential
fraud,
reducing
the
need
for
costly
human
analysis.
Meanwhile,
generative
AI
contributes
by
simulating
various
fraud
scenarios
to
predict
and
prevent
new
types
of
fraudulent
activities
before
they
occur,
enhancing
the
overall
security
of
payment
systems. 

Compliance
and
data
privacy

In
the
commerce
journey,
traditional
AI
helps
secure
transaction
data
and
automates
compliance
with
payment
regulations,
enabling
businesses
to
quickly
adapt
to
new
financial
laws
and
conduct
ongoing
audits
of
payment
processes.
Generative
AI
further
enhances
these
capabilities
by
developing
predictive
models
that
anticipate
changes
in
payment
regulations.
It
can
also
automate
intricate
data
privacy
measures,
helping
businesses
to
maintain
compliance
and
protect
customer
data
efficiently. 

The
future
of
AI
in
commerce
is
based
on
trust 

Today’s
commercial
landscape
is
swiftly
transforming
into
a
digitally
interconnected
ecosystem.
In
this
reality,
the
integration
of
generative
AI
across
omnichannel
commerce—both
B2B
and
B2C—is
essential.
However,
for
this
integration
to
be
successful,

trust
must
be
at
the
core
of
its
implementation.
Identifying
the
right
moments
in
the
commerce
journey
for
AI
integration
is
also
crucial.
Companies
need
to
conduct
comprehensive
audits
of
their
existing
workflows
to
make
sure
AI
innovations
are
both
effective
and
sensitive
to
consumer
expectations.
Introducing
AI
solutions
transparently
and
with
robust
data
security
measures
is
imperative.  

Businesses
must
approach
the
introduction
of
trusted
generative
AI
as
an
opportunity
to
enhance
the
customer
experience
by
making
it
more
personalized,
conversational
and
responsive.
This
requires
a
clear
strategy
that
prioritizes
human-centric
values
and
builds
trust
through
consistent,
observable
interactions
that
demonstrate
the
value
and
reliability
of
AI
enhancements.  

Looking
forward,
trusted
AI
redefines
customer
interactions,
enabling
businesses
to
meet
their
clients
precisely
where
they
are,
with
a
level
of
personalization
previously
unattainable.
By
working
with
AI
systems
that
are
reliable,
secure
and
aligned
with
customer
needs
and
business
outcomes,
companies
can
forge
deeper,
trust-based
relationships.
These
relationships
are
essential
for
long-term
engagement
and
will
be
essential
to
every
business’s
future
commerce
success,
growth
and,
ultimately,
their
viability.

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