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Table of contents
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Table of contents

0:00
Intro
0:47
Agenda of the webinar
1:10
About ACBaltica
2:13
About the solution
3:07
Planning with SAP Analytics Cloud
3:41
Overview of AI and predictive capabilities in SAP Analytics Cloud
4:42
Natural language query feature
6:29
Smart Predict
8:30
Predictive techniques and scenarios
9:49
Compass
10:35
Demo agenda
11:20
High-level overview of sales planning and analysis in SAC
14:14
Pricing options for SAC
16:17
How much data do we need to make a good prediction?
17:30
Model training and applying of AI-generated forecast scenario
19:46
Compass demo
29:20
Key benefits of AI in SAC
30:50
QA session
Video tags
|

Video tags

AI
SAP
SAC
SAP Analytics Cloud
Machine Learning
Prediction
Analytics
Subtitles
|

Subtitles

00:00:03
so dear guest I believe that most of our
00:00:06
guests are now connected and so we can
00:00:09
start thank you again for joining us
00:00:12
today today's topic is the power of
00:00:15
artificial intelligence in sap analytics
00:00:18
cloud my name is Margarita and I'm sales
00:00:21
and account manager at AC baltica and i'
00:00:24
I'd like to introduce my colleague it's
00:00:26
pav romanovski pav is a head of our sap
00:00:30
by unit he is our main technical expert
00:00:34
today if uh you have any questions
00:00:37
during our webinar please feel free to
00:00:40
write them in the chat or ask them
00:00:42
during our Q&A session after the
00:00:46
presentation now let me start with our
00:00:49
today's agenda first I will provide a
00:00:52
brief overview of AC baltica is a
00:00:54
company then we will talk about sa
00:00:57
capabilities features and steps in
00:01:00
planning cycle after that pav will
00:01:03
present a demo and finally you will have
00:01:06
a chance to ask your
00:01:11
questions now let me say a few words
00:01:13
about our company so you will get an
00:01:16
idea who we are and what we do AC
00:01:18
baltica is an sap Platinum partner with
00:01:21
the main office in Lithuania our comp
00:01:24
company is a member of United Wars
00:01:26
association with overall experience inp
00:01:29
area for more than 20 years we have a
00:01:32
team of 140 employees both consultants
00:01:35
and developers and our customers and
00:01:38
partners are located in more than 10
00:01:40
countries across Europe and in the
00:01:42
United
00:01:44
States we have expertise in many sap
00:01:47
areas I have divided them into four main
00:01:51
streams first it's customer experience
00:01:54
all connected with sales service and
00:01:56
marketing clouds CRM and so on we also
00:01:59
have here P and S for H Department that
00:02:01
covers the whole range of modules and we
00:02:05
have extensive experience in BW by bpc
00:02:08
and analytics cloud and of course we
00:02:11
have our own team of
00:02:14
developers now we will briefly review
00:02:17
all the technical all the functional
00:02:19
blocks of sa and we will start with
00:02:23
reporting reporting in in sap analytics
00:02:26
cloud is available on three levels first
00:02:30
it's operational level operational
00:02:32
reporting and data mining are available
00:02:35
for the company's
00:02:36
analysts second level is a decision
00:02:39
making level here we mean dashboards and
00:02:41
single points for entry for business
00:02:44
users and the third it's board level uh
00:02:48
there is an sap digital boardroom
00:02:50
powered by sa you can enhance reporting
00:02:53
on all these levels with AI capabilities
00:02:56
like embedded forecasting predicted data
00:02:59
analys is and more and we will explain
00:03:03
all these in
00:03:07
details if you have a standalone sap
00:03:10
analytics Cloud you have analysis
00:03:12
planning forecasting and Reporting in
00:03:15
one application this allows you to make
00:03:17
decision fast based on the systems data
00:03:21
using sa planning tools you can create
00:03:23
and share private versions of plans and
00:03:26
discuss what if analysis scenarios
00:03:28
before publication these planning tools
00:03:30
are modern and browser based and allow
00:03:33
you planning and modeling with various
00:03:35
level of details for any number of
00:03:42
users generative AI is already helping
00:03:45
organization to unlock the full
00:03:47
potential of the data and Achieve higher
00:03:49
value from planning and analytics sap
00:03:53
analytics Cloud complements it but by
00:03:55
providing a complete planning and
00:03:58
analytic solution argument by AI to
00:04:01
transform data driven driven decision
00:04:03
making using the data and semantics
00:04:06
provided business users can access
00:04:09
insights on demand generate more
00:04:11
accurate plans and make faster
00:04:14
fact-based decision to drive planning
00:04:16
and analytic
00:04:19
success and now I would like to hand it
00:04:21
over to pav who will dive deeper into
00:04:25
technical details and present a demo P
00:04:28
please take the floor
00:04:32
hello
00:04:34
everyone my name is p thanks
00:04:37
Margarita and I will explain which AI
00:04:40
feature is Avail
00:04:43
available you should see my screen
00:04:46
yeah yes okay nice I will explain some
00:04:51
main artificial intelligence
00:04:55
feature available in and how to use it
00:04:58
and after that I will switch to demo to
00:05:01
show one of the use cases that can be
00:05:03
interested for you or yeah interested
00:05:08
for you the first feature is natural
00:05:11
language query currently it named just
00:05:15
ask from the second half of this year it
00:05:18
will be replaced with Jewel assistant
00:05:20
integrated in
00:05:22
sa this feature allow you to ask
00:05:25
question about your data such as to talk
00:05:28
to someone you can see an example from
00:05:31
person type net sales actual versus
00:05:34
public budget in numeric point chart and
00:05:37
the system get info from your data and
00:05:44
play answer and comparison actual and
00:05:47
public version answer can be provided in
00:05:50
tables in charts in some text Fields but
00:05:54
with Jewel from the second half of this
00:05:57
year it's something from future but
00:06:00
still it will be available to perform
00:06:04
some kind of conversation based on your
00:06:07
data not only one question one answer
00:06:10
but maintain dialogue and also juwel
00:06:15
will be able
00:06:17
to explain some hidden data
00:06:20
interdependency and answer on question
00:06:23
like why am my sales going
00:06:27
down the second feature it's smart
00:06:30
predict smart predict use machine
00:06:33
learning to help you predict the future
00:06:35
by analyzing patterns in your data why
00:06:39
we should use it it helps you make
00:06:41
trusted prediction based on your
00:06:43
historical data available in a lot of
00:06:46
your
00:06:47
systems focused on what you can
00:06:50
predict it make decision making easier
00:06:53
with artificial intelligence it's you
00:06:55
can use it without coding without deep
00:06:58
knowledge of python or any
00:07:02
data
00:07:03
scientist uh knowledge specific to this
00:07:06
area area and also you can turn your
00:07:10
data into a story you can see the
00:07:12
results in visuals set explanations and
00:07:15
share provided prediction with your team
00:07:19
and and inside your
00:07:22
company you can
00:07:26
use Smart predict to predict pay
00:07:29
payments figured out if customer will
00:07:32
pay on
00:07:33
time supports campaign predictions for
00:07:36
marketing helping you decide which ads
00:07:39
will work best based on historical data
00:07:41
based on your customer
00:07:43
profile and finance at off for Revenue
00:07:46
cash flow forecasting based on
00:07:48
forecasting of some Ross pricing or
00:07:53
Market
00:07:54
Behavior you can default fraud detection
00:07:58
risk management
00:08:00
based based again on your real data and
00:08:04
your current
00:08:06
cases and with smart predictor can get
00:08:09
reliable prediction more intelligent
00:08:11
decision and straightforward story from
00:08:13
your data can um you can build your plan
00:08:19
based on prediction you can use it just
00:08:22
for analytical purposes or insights or
00:08:25
data idea
00:08:30
there's three available models for
00:08:32
prediction in sap analytic Cloud it's
00:08:35
classification
00:08:36
analysis this model helps you grow group
00:08:39
new data into categories based on
00:08:42
historical examples they sort your
00:08:44
customers from those who buy the least
00:08:46
to those who buy the most or you can
00:08:50
predict
00:08:53
yeah you or you can predict customer CH
00:08:57
this customer will CH with hyp ability
00:08:59
and this customer are more more stable
00:09:03
ones classification looks at historical
00:09:06
data and breaks down the customer into
00:09:07
groups as I
00:09:08
said we have a regression model the one
00:09:11
is used to find relationship between
00:09:13
numbers it's about how S one sync One
00:09:17
driver affects another how many sales
00:09:20
increase if you increase your
00:09:21
advertising
00:09:23
budget helps to predict number based on
00:09:26
patterns and trying to find data
00:09:30
dependencies and the S one is time
00:09:32
serious forecast model looks at data
00:09:35
over time to find Trend
00:09:38
patterns Define Main influencers and
00:09:41
forecast data in Futures that allow you
00:09:43
to plan more
00:09:47
confident one more feature is a new one
00:09:50
feature released in this year it's
00:09:53
called Compass it's a tool for
00:09:55
simulating different business outcomes
00:09:58
especially when you're B on some
00:10:00
uncertainties we can combine several
00:10:03
what if scenarios in one big
00:10:06
simulation based on your data create
00:10:08
pessimistic realistic and optimistic
00:10:11
scenario for outcomes and you can see
00:10:13
various possibilities and be more sure
00:10:17
less sure that your plan will become a
00:10:20
reality it's very easy to use you don't
00:10:23
need to be a technical expert on no
00:10:24
complicated things like holding or
00:10:27
complicated math I will show show how to
00:10:29
work with it on during my demo
00:10:35
part during my demo I will provide a
00:10:39
high level in few words a review of
00:10:42
sales planning analysis and SEC because
00:10:45
our use case will be built around sales
00:10:49
planning content provided by sa we'll
00:10:53
train our predictive
00:10:55
model applying predicted result to our
00:10:58
plan
00:11:01
we will calculate revenue and gross
00:11:04
margin based on this predicted data and
00:11:06
simulate probability of a new plan
00:11:09
taking consideration different ranges
00:11:11
different drivers and adores in
00:11:15
it now I will share my system
00:11:22
screen do it
00:11:30
yes we can see y okay okay nice
00:11:33
thanks s provide a lot of predefined
00:11:38
planning
00:11:40
layouts plan sales Workforce Finance cxs
00:11:45
perform project planning perform
00:11:48
marketing activities planning and so
00:11:51
on it can be integrated as with your sap
00:11:55
accounting systems with success factors
00:11:58
ibp for Han Earp and also you can
00:12:01
integrate your nonsp systems to sa and
00:12:05
use this data as a basis for
00:12:08
planning here I can see predefined
00:12:11
template for volume planning you can
00:12:14
perform volume Planning by products
00:12:17
customer profit Center and
00:12:20
plant we can see actual data plan dat
00:12:24
and let's click on generate AI plan
00:12:28
button let's
00:12:30
select time frame which want or
00:12:35
forecast and after that we will go
00:12:40
through this
00:12:45
layout I selected first quarter of 2024
00:12:49
for example January 2025 because I have
00:12:52
enough data to do it and click run on
00:12:56
the ground during this time we can go
00:12:59
prepared lay out it will take some time
00:13:03
to proceed all your data you have and
00:13:05
provide the forecast vales we will wait
00:13:09
a little
00:13:10
bit here you can see actual data coming
00:13:13
from your accounting
00:13:17
system we have dedicated page to plan
00:13:22
gross prices sales deduction perc cost
00:13:25
of good sold per one base unit of
00:13:27
measure
00:13:29
we have calculated actual values from
00:13:32
based on your actual data revenue and
00:13:35
volume it's average prices average for
00:13:38
each product product group for each
00:13:43
customer you can droll
00:13:46
down analyze and adjust it if it's
00:13:50
needed the simple sales deduction
00:13:52
percent it's driver based
00:13:55
planning based on your real data from
00:13:59
your production accounting
00:14:03
system while data are being loaded
00:14:07
probably we can answer some questions
00:14:09
that we have got we can we can yeah the
00:14:12
first one is what are the pricing
00:14:14
options for all those AI Tools in uh sap
00:14:19
analytics
00:14:20
Cloud
00:14:22
okay uh if you have generally as see can
00:14:27
be provided by two
00:14:30
versions let's say yeah and by S and by
00:14:34
mean that you have when you have S4
00:14:37
public Cloud success factors or c4c you
00:14:41
already have embedded product on your
00:14:44
board free to use yeah it also contains
00:14:49
some ready to use reports and you can
00:14:51
build your own reports if it's needed
00:14:53
but embedded as
00:14:55
AC uh contain only reporting possibility
00:15:00
is another version of is
00:15:04
Enterprise that CA that licensed by
00:15:08
users you can license planning user but
00:15:12
separate price and bi user separate
00:15:15
price and the prices AC see can provide
00:15:19
ability of reporting planning and
00:15:23
artificial intelligence feature like
00:15:25
predictions
00:15:26
simulations and Jewel
00:15:29
potentially
00:15:31
tool can be additionally
00:15:35
charged but uh it's an open question for
00:15:39
now and we expect that if you want to
00:15:42
use it in intensively ask a lot of
00:15:46
questions
00:15:48
um and juw it may be charged
00:15:51
additionally but this information will
00:15:53
be
00:15:55
communicated near the June or July when
00:15:58
this
00:16:00
Dr will be integrated
00:16:03
in yeah so if you have
00:16:06
Enterprise you can use predictions
00:16:09
functionality except D Point free of
00:16:14
charge thank you p and I have uh one
00:16:17
more question how much data do we need
00:16:20
to have a better prediction yeah it's a
00:16:24
good question it's a good question it's
00:16:28
recommended required have five periods
00:16:31
of data to forecast one periods period
00:16:36
period five period of historical data to
00:16:39
forecast one period of future
00:16:42
data and it should be also depends on
00:16:46
level of details that you have because
00:16:48
when you forecast your data you select
00:16:52
which categories your forecast we look
00:16:55
forecast revenue for whole company or
00:16:59
you want to forast revenue for dedicated
00:17:02
product line or for certain
00:17:04
Customer because it's depends fully
00:17:07
depends yeah uh it's much easier to have
00:17:13
five months of Revenue on level on the
00:17:16
company level then five months of
00:17:19
historical data on the product Plus
00:17:21
customer level but the answer is five to
00:17:24
one five historical to one
00:17:27
forecasted thank than
00:17:29
you uh here we have some calculated data
00:17:33
it's backgrounded in
00:17:36
yellow we can go to
00:17:40
rates we can adjust it is it to buy for
00:17:45
example let's
00:17:50
adjust sales deduction percent for
00:17:53
racing for our racing bikes for large
00:17:57
customer let let drop down it to
00:18:02
5% yeah I
00:18:05
can I and drop
00:18:11
it average sales deduction per
00:18:16
calculates let's switch to gross price
00:18:18
per best unit of measure and let's
00:18:21
increase all the prices by 5% plus 5%
00:18:33
all the data was increased
00:18:39
5%
00:18:43
yeah let's finish
00:18:48
it after that we can switch to planning
00:18:52
trigger TP and Trigger our calculation
00:18:55
we have finalized our rates planning
00:18:58
volume planning and now we calculate
00:19:02
revenue and gross
00:19:05
margin it's fin
00:19:08
analyzed and now we can analyze gross
00:19:11
margin
00:19:12
report here we can see the gross margin
00:19:15
net revenue cost of good salt and sales
00:19:17
deduction and absolute
00:19:20
values for product product groups
00:19:24
customer and customer groups you can
00:19:26
filter out profit Center company
00:19:29
and also you can switch to gross margin
00:19:32
per
00:19:33
unit to see the related
00:19:38
figures now let's have a look on
00:19:43
question or our prediction model how it
00:19:46
works and after that switch to Compass
00:19:49
to simulate our gross margin
00:19:53
plan publish data
00:20:00
switch to our predictive
00:20:10
scenario here we
00:20:12
have is a configuration you should
00:20:15
select your data source where your data
00:20:18
is your historical data is which version
00:20:22
of data considered should be considered
00:20:24
as historical let's say
00:20:27
actuals what you want
00:20:30
forecast volume in base unit of
00:20:34
measure I can set up some
00:20:38
filters and click train and forecast if
00:20:41
you want to introduce some influencers
00:20:43
like
00:20:44
price you can add it
00:20:47
here and the model will take in consider
00:20:51
into consideration not only the
00:20:53
historical data for volume but also in
00:20:56
dependency of on price
00:21:02
after the training and
00:21:04
forecasting you'll get over viw page is
00:21:07
top and bottom entities in terms of map
00:21:10
map is average median average percentage
00:21:14
error it's a Delta different difference
00:21:17
between actual and predicted value how
00:21:19
prediction model works it takes 75% of
00:21:23
your historical data to forecast L
00:21:27
latest 25 % of historical data and rify
00:21:32
itself Remodel and select the most
00:21:38
accurate forecasting model and provide
00:21:40
your with
00:21:43
results some entities entities
00:21:46
combination of what we forecasted now
00:21:49
it's
00:21:50
company Plant profit Center and
00:21:55
product some entities we have B they
00:21:58
enough expected M like 7
00:22:02
177% but some of
00:22:05
them forecasted very good less than 1%
00:22:10
of
00:22:14
map switch to
00:22:18
forecast T here we can
00:22:22
select set of parameters from your
00:22:24
entity
00:22:30
and analyze
00:22:34
influencers should wait several
00:22:42
seconds yeah this selected entity
00:22:45
expected map is
00:22:47
2.63% that is good
00:22:50
enough here is your actual
00:22:52
data
00:22:54
in direct blow your forecasted data and
00:22:58
prediction minimum and maximum
00:23:05
interval you can see it month by
00:23:07
month and also you can switch on
00:23:11
explanation
00:23:13
tab but you can see that mostly your
00:23:17
forecast depends on trend for this
00:23:21
combination yeah no Cycles in
00:23:26
here yeah
00:23:29
98% is strength and some corrections
00:23:32
adjustments it's only
00:23:35
1.8% but for different entity situation
00:23:38
can be
00:23:40
different after that you can save
00:23:42
forecast from here to your plan or use
00:23:46
as I showed just one click from the
00:23:49
planning layout and ready to use data
00:23:51
you
00:23:55
have and let's go to Compass
00:24:02
here I have different scenarios already
00:24:07
created we'll have a look on gross
00:24:09
margin simulation
00:24:11
scenario I select gross margin and
00:24:14
system will provide me with list of
00:24:18
drivers which is driven myross
00:24:23
margin I can filter gross price per
00:24:28
groups for example and define different
00:24:33
range of potential price for bikes and
00:24:37
for tires because it's completely
00:24:39
different product
00:24:40
groups we can adjust it here's our
00:24:44
Baseline Val is average price for per
00:24:47
for whole product group during my
00:24:53
planning here let's increas
00:24:57
it my cost of good sold I assume it will
00:25:02
be let divided by product groups let's
00:25:05
click on ADD restrict to
00:25:07
driver and select filter
00:25:13
product
00:25:18
bikes and tires
00:25:23
separately because it's
00:25:25
completely different kind of product
00:25:28
products it depends on each other for
00:25:30
sure but it's still
00:25:32
different let's say 25 to 40 and here
00:25:39
2,000 to 2 and a
00:25:44
half comply with it at additional
00:25:48
filters by locations where produ product
00:25:51
is produced for example if we have such
00:25:54
Dimension such analytic in our model
00:25:58
set up different kind of ranges and
00:26:00
click on run scenario now I will click
00:26:03
on fastest
00:26:05
run just to show you how it
00:26:10
works will select the medium or the
00:26:15
highest precising will take more
00:26:19
time but not so
00:26:21
much now we have our basine PL like
00:26:26
35,000 of dollars
00:26:29
we have pessimistic case in this range
00:26:32
of gross margin realistic case of gross
00:26:34
margin and optimistic case of gross
00:26:40
margin and we can analyze only not only
00:26:44
where my Bas line is in realistic case
00:26:46
pessimistic optimistic
00:26:49
one but also we can analyze probability
00:26:52
of
00:26:54
it the bigger speak the more realistic
00:26:57
gross Marin
00:27:01
this
00:27:02
okay can switch on
00:27:06
different pessimistic and optimistic
00:27:09
case
00:27:10
borders let place a 10 10% and here
00:27:14
should be
00:27:15
18 you can switch color if it's
00:27:19
needed yeah this recalculates let's
00:27:28
change our
00:27:33
drivers let's
00:27:36
decrease gross price or decrease volume
00:27:39
let's decrease
00:27:42
volume 5,000 to let's say
00:27:47
8,000 and
00:27:54
here this range round preview
00:28:03
our potential gross margin will be
00:28:04
recalculate based on this combination
00:28:09
provided
00:28:15
yeah even more
00:28:19
realistic so you can model um list of
00:28:22
drivers fully depend on your data model
00:28:26
and inter dependencies you can set up
00:28:29
more drivers for gross margin you can
00:28:31
model not only gross margin but also
00:28:34
Mayda or gross profit or whatever you
00:28:40
want depends on data that you have on
00:28:42
your
00:28:43
system and also it's not only tool to
00:28:47
double verification your plan generated
00:28:51
by AI by predicted by predicted data but
00:28:54
also simulation tools that can be used
00:28:57
together with prediction model or
00:29:01
parallel
00:29:05
independently from the demo point of
00:29:07
view that's it from my side marar please
00:29:10
take your floor Marita will explain
00:29:13
shortly the key benefits of sa in terms
00:29:17
of artificial intelligence
00:29:20
features thank you P yes before uh we
00:29:24
start our Q&A session I would like to
00:29:26
tell some words about benefits of AI and
00:29:29
sap analytics
00:29:32
Cloud
00:29:33
uh to make it sure short ai ai in sap
00:29:38
analytics cloud makes you work easier
00:29:40
and smarter it helps make more accurate
00:29:44
plans AI remove human errors giving you
00:29:47
precise data driven predictions
00:29:50
instantly it helps take faster decisions
00:29:53
generative AI analyzes data and
00:29:56
automates task saving time and efforts
00:30:00
with AI you can discover smarter
00:30:02
insights it connect connects data points
00:30:06
to uncover gross opportunities and no
00:30:08
coding is needed you can get instant
00:30:11
access um real time insights with a
00:30:15
click uh and no data specialist is
00:30:18
required and finally you can do
00:30:21
automation AI handles repetitive tasks
00:30:24
so you can focus on
00:30:26
strategy as a result
00:30:28
with AI in Sac you work faster smarter
00:30:32
and more
00:30:35
efficiently so thank you for your
00:30:38
attention and uh now I think we can uh
00:30:42
ask uh answer your questions please feel
00:30:45
free to write them in the
00:30:50
chat I see one question the question is
00:30:54
um what is preferable to use Predictive
00:30:57
Analytics or Compass how do they differ
00:31:00
from each
00:31:03
other a good question I face this
00:31:07
question often
00:31:09
enough and I will answer like this and
00:31:13
predict in machine learning and PR smart
00:31:15
predict feature you have time
00:31:19
forecast time serious forecast it allow
00:31:21
you to predict future based on your
00:31:24
historical
00:31:26
data and Compass is like other
00:31:29
simulation tool in prediction one that
00:31:34
um we calculate a lot of time your data
00:31:38
based on your data model and based on
00:31:41
your assumption it's more like what if
00:31:45
analysis or simulation tool rather than
00:31:47
prediction prediction of
00:31:50
somewhat
00:31:52
uh you can even it works in different
00:31:56
way and compass based on your currently
00:32:00
planned or actual data or actual you
00:32:03
also can build Compass
00:32:06
simulation um based on
00:32:09
your driver ranges and combine a lot of
00:32:13
time different set of values and
00:32:15
recalculate a lot of time well
00:32:17
prediction make find some data patterns
00:32:21
in your historical data and provide some
00:32:25
future generative uh
00:32:29
forecast generative
00:32:36
figures okay thank you very
00:32:39
much so please raise your hand or just
00:32:43
write the questions if you have any um
00:32:47
and we will try to answer
00:32:58
so if uh there are no questions anymore
00:33:02
then I think we can um finish H oh I see
00:33:07
one question how about the integration
00:33:09
of just ask or Jew into
00:33:16
stories just ask is a slightly different
00:33:20
tool yeah and justask is not integrated
00:33:24
and I not expect that it will be
00:33:26
integrated directly into
00:33:28
stories but
00:33:31
Jew uh currently expected that will be
00:33:35
integrated yeah if you worked with SEC
00:33:38
before or work currently with classic
00:33:41
design experience story you can be
00:33:44
familiar with smart inside
00:33:47
feature and Jewel will be something
00:33:51
similar but better yeah so you can click
00:33:54
on data on certain chart or on certain
00:33:58
table St right click and ask Jewel about
00:34:03
this certain number and this certain
00:34:05
area and
00:34:07
um J will try to explain this
00:34:11
number highlight some data dependency
00:34:15
and
00:34:19
yeah yeah I'm
00:34:21
also exciting to know it
00:34:25
and wait a lot
00:34:28
of this feature to be released and
00:34:30
integrated fully in sa currently uh just
00:34:35
one more point you can have Jewel inside
00:34:38
your sexcess factors or any other tool
00:34:41
where jewel is already
00:34:43
integrated you can integrate it with
00:34:45
your as well it's not needed to wait you
00:34:49
can use it right now it's released
00:34:52
restricted uh released to customers who
00:34:55
have Jewel and SA
00:34:58
as well but also you can wait one
00:35:01
quarter and be waiting for this
00:35:04
additional General release ju integrated
00:35:07
in Enterprises I
00:35:12
see okay thank
00:35:15
you so thank you dear guest for coming
00:35:19
today to our webinar I'd like to uh
00:35:22
mention that uh we would be happy to
00:35:25
stay in touch with all of you and in
00:35:28
case if you uh will need any support um
00:35:33
any help with the sap products
00:35:35
implementation or support please feel
00:35:38
free to contact us and we will be happy
00:35:41
to assist you to help you we will be
00:35:43
very happy to work with
00:35:46
you thank you
00:35:48
all there are no any more questions then
00:35:53
I think we can um close our session
00:35:56
today and uh we will be happy to see you
00:36:01
uh on our next
00:36:12
webinars so thank you

Description:

🚀 AI-Driven Business Planning – See It in Action! 🚀 What if you could make faster, smarter decisions with AI doing the heavy job? With SAP Analytics Cloud (SAC), that’s not just a vision anymore – it’s reality! AI-powered forecasting, automated insights, and predictive analytics are already transforming how businesses plan and execute strategies – and you can do it, too! Watch our webinar and explore how Generative AI, Machine Learning, and Predictive Analytics in SAC help you: ✅ Turn data into decisions. Let AI handle complex analysis so you can focus on strategy. ✅ Predict future trends. Use Smart Predict to make accurate, data-driven forecasts. ✅ Test business scenarios. With a Compass tool, stress-test your strategies before making big moves. ✅ Automate reporting. Generate AI-powered insights and reports in seconds. 💡 Pavel Ramanouski, Head of BI Practice at ACBaltica, showcase SAC’s AI capabilities with a live demo, real-world examples, and expert insights. Timecodes: 00:00 Intro 00:47 Agenda of the webinar 01:10 About ACBaltica 02:13 About the solution 03:07 Planning with SAP Analytics Cloud 03:41 Overview of AI and predictive capabilities in SAP Analytics Cloud 04:42 Natural language query feature 06:29 Smart Predict 08:30 Predictive techniques and scenarios 09:49 Compass 10:35 Demo agenda 11:20 High-level overview of sales planning and analysis in SAC 14:14 Pricing options for SAC 16:17 How much data do we need to make a good prediction? 17:30 Model training and applying of AI-generated forecast scenario 19:46 Compass demo 29:20 Key benefits of AI in SAC 30:50 QA session And remember that you can ask us for a personal demo of this solution based on your company's specific and unique needs. To book a demo, please get in touch with us at https://acbaltica.com/about/ You can also find more information about our company and provided services here: ACBaltica site – https://acbaltica.com/ LinkedIn – https://www.linkedin.com/company/acbaltica/

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