WEBVTT

47d78de8-c66a-4ab6-a5c6-41ecaa2f187f-0
00:00:01.600 --> 00:00:02.480
Hi everyone.

b4f30286-b644-4602-a384-1f4d563a918c-0
00:00:02.800 --> 00:00:08.000
Welcome to our webinar prepared in
collaboration with SAP and Nagaro.

7144d8cb-eb7e-430a-846f-daf79a5f9ebe-0
00:00:09.480 --> 00:00:11.760
This is Kavan Choktash from Nagaro.

be520a71-4d95-4589-aced-9ff7f6c0f1db-0
00:00:11.760 --> 00:00:18.280
I'm group manager to Analytics and
Performance Management in Nagaro, Turkey.

716227eb-579c-4fe9-8327-1af16850beef-0
00:00:18.800 --> 00:00:25.005
So today with my colleagues,
we are going to discover a shortcut to

716227eb-579c-4fe9-8327-1af16850beef-1
00:00:25.005 --> 00:00:27.560
AAI transformation with SAP.

a3d4ad68-6967-4ca2-87dc-5af9818bd5a6-0
00:00:28.520 --> 00:00:34.060
So today with me,
Harris Kentali and Roberto Urban from SAP,

a3d4ad68-6967-4ca2-87dc-5af9818bd5a6-1
00:00:34.060 --> 00:00:40.599
MENA and Eraneskin from Nagaro,
they are both sharing their experiences

a3d4ad68-6967-4ca2-87dc-5af9818bd5a6-2
00:00:40.599 --> 00:00:47.775
in this topic and our agenda will be
covering starting with the AI strategy by

a3d4ad68-6967-4ca2-87dc-5af9818bd5a6-3
00:00:47.775 --> 00:00:48.320
Harry.

94364637-ebb3-450c-81fd-677ac653b883-0
00:00:48.600 --> 00:00:54.584
He's going to inform us about the all of
the box solutions delivered by SAP and

94364637-ebb3-450c-81fd-677ac653b883-1
00:00:54.584 --> 00:01:00.120
then we are going to discover how custom
AI is implemented on top of SAP.

e7d524f6-97e2-48c9-9965-1f38b7657119-0
00:01:00.920 --> 00:01:05.534
And in the third part,
I will be delivering some information how

e7d524f6-97e2-48c9-9965-1f38b7657119-1
00:01:05.534 --> 00:01:08.800
AI can utilize on top of Business Data
Cloud.

5dedc895-e082-4c18-af2f-9d2001279eb6-0
00:01:09.200 --> 00:01:15.219
Beside this, in the last part,
LNA scheme will be delivering Nagara

5dedc895-e082-4c18-af2f-9d2001279eb6-1
00:01:15.219 --> 00:01:22.036
methodology on AI and to workshops to
address the problems or address the AI

5dedc895-e082-4c18-af2f-9d2001279eb6-2
00:01:22.036 --> 00:01:25.399
possibilities within the organization.

6a81ad8e-4aa2-44d0-ad89-acea5736bf2e-0
00:01:26.800 --> 00:01:32.632
So this webinar is hosted by Nagara,
which is the global collective of

6a81ad8e-4aa2-44d0-ad89-acea5736bf2e-1
00:01:32.632 --> 00:01:37.480
technology experts with an
entrepreneurial caring mindset.

f409a978-080c-4f16-b8f7-78528e12c1d8-0
00:01:38.040 --> 00:01:42.520
And it is founded 28 years ago,
listed in Deutsche Birthday.

c48cddad-f064-48f4-acf2-9e6be4c19f83-0
00:01:42.880 --> 00:01:49.456
And the global presence is over 27
countries with 60 countries with customer

c48cddad-f064-48f4-acf2-9e6be4c19f83-1
00:01:49.456 --> 00:01:52.360
presence and more than 18 experts.

61c21ecb-578c-4a69-91fb-d30ef100566d-0
00:01:52.360 --> 00:01:55.200
They are delivering technology solutions
everywhere.

3414d7fd-a4f9-4b4d-91c6-6b0d8dad0164-0
00:01:55.760 --> 00:02:00.388
So today let's jump into the today's
topic,

3414d7fd-a4f9-4b4d-91c6-6b0d8dad0164-1
00:02:00.388 --> 00:02:04.280
which is a really a hype is about AI.

c6c70778-9a69-40e7-aa91-c2175c81bab3-0
00:02:04.720 --> 00:02:07.320
But I will start with the shocking facts.

bea36e5e-0f73-486a-91aa-7ec0afb81737-0
00:02:07.720 --> 00:02:12.200
Did you know that 95% of AI projects
failed?

2d869f41-05b3-42f2-a40d-e5bffa0723c1-0
00:02:13.360 --> 00:02:17.702
So this fact from a brand new paper from
MIT,

2d869f41-05b3-42f2-a40d-e5bffa0723c1-1
00:02:17.702 --> 00:02:25.161
which we will also refer a lot in this
webinar has also main reasons shared in

2d869f41-05b3-42f2-a40d-e5bffa0723c1-2
00:02:25.161 --> 00:02:26.200
this study.

c66e2ff1-d260-4d58-8804-5f940e3e153f-0
00:02:26.760 --> 00:02:32.286
So one of them is like the company is
still seeing AI as an algorithm,

c66e2ff1-d260-4d58-8804-5f940e3e153f-1
00:02:32.286 --> 00:02:36.722
only an algorithm and it's also maybe
only a technology,

c66e2ff1-d260-4d58-8804-5f940e3e153f-2
00:02:36.722 --> 00:02:42.715
but they're seeing it independent from
the business workflow or the business

c66e2ff1-d260-4d58-8804-5f940e3e153f-3
00:02:42.715 --> 00:02:45.440
work, let's say business processes.

f8491fb3-1b78-4fbb-8093-838f9d73bf86-0
00:02:46.200 --> 00:02:52.624
And also second important topic on this
is also complication by in house

f8491fb3-1b78-4fbb-8093-838f9d73bf86-1
00:02:52.624 --> 00:02:58.960
development of AI is also causing this
impact in the AI developments of

f8491fb3-1b78-4fbb-8093-838f9d73bf86-2
00:02:58.960 --> 00:02:59.840
companies.

d57360f5-019b-43ae-bd6a-21522bf51385-0
00:03:00.360 --> 00:03:03.200
But we do have a great expert here Harris
today.

7ab9bfa5-e18a-47b4-85ba-ef9f7c3f8b99-0
00:03:03.480 --> 00:03:05.200
So I will hand over to him.

441064c7-cb48-4281-a7e5-89b421a53c3c-0
00:03:05.520 --> 00:03:12.552
But before jumping to his session,
I will also forward him a question that

441064c7-cb48-4281-a7e5-89b421a53c3c-1
00:03:12.552 --> 00:03:19.021
how can a statement could reverse this
ratio with like by helping AI

441064c7-cb48-4281-a7e5-89b421a53c3c-2
00:03:19.021 --> 00:03:22.960
capitalities in SAP Harris stage of yours.

a3d2f9f6-9ca6-4010-a5d1-0d70d4bc4dfc-0
00:03:23.600 --> 00:03:25.400
Thank you Kewank for the introduction.

45dbebaf-475f-45d6-b91c-395effbc1696-0
00:03:25.800 --> 00:03:26.200
Hello.

7fd06d51-e930-463b-92af-fbe2fe154a18-0
00:03:26.200 --> 00:03:26.880
Hi, everyone.

6ce5c102-5a7c-4ca9-92a9-e9feaaf922b7-0
00:03:27.600 --> 00:03:29.040
Pleasure to be with you today.

cd3fc7b0-9b22-4bd4-86f9-105b81caeec2-0
00:03:29.840 --> 00:03:31.720
That was an interesting insight.

1e4e90c7-8c30-4c68-9989-698efc738888-0
00:03:31.960 --> 00:03:32.520
Kewank.

d486cf38-d0a9-4524-8d7e-6771cafe0818-0
00:03:33.520 --> 00:03:39.914
95% of the AI pilots remain in the pilot
stage and not able to deliver the ROI on

d486cf38-d0a9-4524-8d7e-6771cafe0818-1
00:03:39.914 --> 00:03:42.800
their investments from the customers.

6030a721-3e41-49cd-b6c6-b2749bea0e28-0
00:03:42.800 --> 00:03:47.229
And this is a recent study from MIT,
but similar studies have been published

6030a721-3e41-49cd-b6c6-b2749bea0e28-1
00:03:47.229 --> 00:03:48.840
in the last couple of weeks.

e93e4b88-d509-4c17-b16a-7ec51d2f09f6-0
00:03:49.200 --> 00:03:54.538
And this has been the key reason why many
of the AI company stocks have been

e93e4b88-d509-4c17-b16a-7ec51d2f09f6-1
00:03:54.538 --> 00:03:56.480
dropping or plunging, right.

3c634e42-80ef-4acc-8bd8-71b44879ea1c-0
00:03:56.960 --> 00:04:00.000
So there are two key outcomes that came
out of the study.

94c6ac9a-53d2-4f46-b4fc-eff6e6d233ac-0
00:04:00.280 --> 00:04:03.200
Number one, buy versus build strategy.

7af76805-d548-43bf-a96a-0488033cc8a6-0
00:04:03.600 --> 00:04:11.924
It is observed after several surveys that
67% success has been seen when customers

7af76805-d548-43bf-a96a-0488033cc8a6-1
00:04:11.924 --> 00:04:17.240
adopted a buy strategy rather than a
build strategy.

e017f7c6-6458-4b3f-bb30-96c28c71db6a-0
00:04:17.240 --> 00:04:21.023
So rather than building your own large
language models,

e017f7c6-6458-4b3f-bb30-96c28c71db6a-1
00:04:21.023 --> 00:04:25.347
rather than trying to build an
applications from scratch on AI,

e017f7c6-6458-4b3f-bb30-96c28c71db6a-2
00:04:25.347 --> 00:04:30.414
it's better to lookout for what is
available out-of-the-box right from the

e017f7c6-6458-4b3f-bb30-96c28c71db6a-3
00:04:30.414 --> 00:04:35.684
different ERP vendors #2 implementing AI
and the back end process rather than

e017f7c6-6458-4b3f-bb30-96c28c71db6a-4
00:04:35.684 --> 00:04:36.360
front end.

9d66fe24-5aaf-4de5-ab8d-490083d6e653-0
00:04:36.680 --> 00:04:40.613
So most of these companies who try to do
this, POC's,

9d66fe24-5aaf-4de5-ab8d-490083d6e653-1
00:04:40.613 --> 00:04:46.369
most of them try to infuse AI into the
front end processes in marketing rather

9d66fe24-5aaf-4de5-ab8d-490083d6e653-2
00:04:46.369 --> 00:04:51.031
than looking at, you know,
infusing AI into their core back end

9d66fe24-5aaf-4de5-ab8d-490083d6e653-3
00:04:51.031 --> 00:04:51.760
processes.

18321aee-f72b-4119-b9ab-4de5ae73f71e-0
00:04:52.080 --> 00:04:53.320
So these are the 2 outcomes.

f7cd3008-aff6-41fa-809a-1ac3c53027d6-0
00:04:53.320 --> 00:04:59.446
And now we will see how SAP delivers the
AI strategy and also the out-of-the-box

f7cd3008-aff6-41fa-809a-1ac3c53027d6-1
00:04:59.446 --> 00:05:05.194
AI capabilities to bridge this gap and
make sure the customers are adopting

f7cd3008-aff6-41fa-809a-1ac3c53027d6-2
00:05:05.194 --> 00:05:05.800
further.

5148b798-ba38-43a9-8c30-8fd3ef9a8993-0
00:05:06.240 --> 00:05:11.749
So what you see here is the three layer
strategy of SAP on the top what you see

5148b798-ba38-43a9-8c30-8fd3ef9a8993-1
00:05:11.749 --> 00:05:16.707
is the conversational layer which is
nothing but your copilot but fully

5148b798-ba38-43a9-8c30-8fd3ef9a8993-2
00:05:16.707 --> 00:05:19.600
grounded and answering from your SAP data.

b611e1cc-2c88-49f9-bfba-d14f0ef1bb38-0
00:05:20.200 --> 00:05:24.453
So any response,
any question you you ask Julie is going

b611e1cc-2c88-49f9-bfba-d14f0ef1bb38-1
00:05:24.453 --> 00:05:29.080
to take the data in consideration from
your SAP applications.

b973eb62-fad4-44d4-96c2-d82ee78667d4-0
00:05:29.560 --> 00:05:32.880
Also in the same first layer we offer
agents.

4249b953-9b2e-4726-bbb7-3dfed350c3fe-0
00:05:33.520 --> 00:05:37.040
As you see there are number of agents
across different lines of business.

fe8cd10f-ce59-4776-8d2f-1d36b3f2fc09-0
00:05:37.320 --> 00:05:40.240
So we plan to deliver about 40 agents by
end of the year.

e44c3c5b-ee28-4e3f-b528-f5dc0f8568da-0
00:05:40.280 --> 00:05:44.646
We already have almost 12 to 14 agents
already out there and there will be more

e44c3c5b-ee28-4e3f-b528-f5dc0f8568da-1
00:05:44.646 --> 00:05:46.720
coming in the next couple of quarters.

5ec7ddde-a82d-4845-a48c-ae9b550285f5-0
00:05:47.360 --> 00:05:50.480
Now we move on to the next layer which is
the embedded AI layer.

347a2597-1abc-422d-b1e4-88b966c489d5-0
00:05:50.800 --> 00:05:56.360
As you see across the various lines of
business and processes, we infuse AI.

e0d723e1-4f1a-4d8e-bb69-f84333339892-0
00:05:56.600 --> 00:05:59.637
For example,
if you have been creating your goals,

e0d723e1-4f1a-4d8e-bb69-f84333339892-1
00:05:59.637 --> 00:06:04.104
performance goals and SuccessFactors in
the past where you might have been

e0d723e1-4f1a-4d8e-bb69-f84333339892-2
00:06:04.104 --> 00:06:07.440
spending like a couple of hours or
sometimes even days.

ef6144d9-69ce-4e4c-b499-7b8441bb6d0b-0
00:06:07.800 --> 00:06:11.787
Now with infusing AI into your goal
creation process,

ef6144d9-69ce-4e4c-b499-7b8441bb6d0b-1
00:06:11.787 --> 00:06:15.480
you can do that in in less than one to
two hours.

91a39285-c94b-4282-b04a-d1b973826520-0
00:06:15.840 --> 00:06:21.925
So basically embedded AI is just
enhancing and improvising your existing

91a39285-c94b-4282-b04a-d1b973826520-1
00:06:21.925 --> 00:06:23.760
process infused by AI.

b8ef4383-78e5-4889-849a-9129f6918e10-0
00:06:24.360 --> 00:06:28.067
Now the third pillar,
which is the customized AI, of course,

b8ef4383-78e5-4889-849a-9129f6918e10-1
00:06:28.067 --> 00:06:32.989
you would like to differentiate from your
competition and you would like to have

b8ef4383-78e5-4889-849a-9129f6918e10-2
00:06:32.989 --> 00:06:35.359
something very niche for your industry.

2dcd1dac-e343-43ec-bdc3-99c89df81ef4-0
00:06:35.600 --> 00:06:40.320
This is where we also offer you the
strong AI foundation powered by business

2dcd1dac-e343-43ec-bdc3-99c89df81ef4-1
00:06:40.320 --> 00:06:43.080
data cloud and business technology
platform.

1b14c1a9-06f3-4a8e-9f3f-b288c1339167-0
00:06:43.480 --> 00:06:47.554
And why this combination is really
powerful is especially because of the

1b14c1a9-06f3-4a8e-9f3f-b288c1339167-1
00:06:47.554 --> 00:06:48.280
partnerships.

34bdd1d4-cb89-4656-8d10-292eb3af37ac-0
00:06:48.280 --> 00:06:50.840
We have different large language models.

fd39c863-8e86-4317-ba5e-ad9fe2257322-0
00:06:51.840 --> 00:06:58.168
For example what you see here IBM is is a
very often used large language model for

fd39c863-8e86-4317-ba5e-ad9fe2257322-1
00:06:58.168 --> 00:06:59.160
HR processes.

02e93cd4-d72b-438f-830d-c9aa251e0007-0
00:06:59.440 --> 00:07:04.280
Similarly for Anthropic,
we have seen customers preferring supply

02e93cd4-d72b-438f-830d-c9aa251e0007-1
00:07:04.280 --> 00:07:06.480
chain use cases for Anthropic.

ad97230a-91b4-454c-a7e2-2127e6350397-0
00:07:06.720 --> 00:07:09.040
So you have the full flexibility and
choice.

a02ed071-522b-415c-96cc-d0c5f9d2da43-0
00:07:09.160 --> 00:07:14.269
You can decide what model works for you
best based on the full range of models

a02ed071-522b-415c-96cc-d0c5f9d2da43-1
00:07:14.269 --> 00:07:16.080
that we offer if we move on.

4f6933b3-c9fb-4c0d-832f-97391b3bb735-0
00:07:19.880 --> 00:07:22.400
So one last strategy slide.

b0b2b847-3783-4dbf-9f99-17f09c56b33e-0
00:07:22.400 --> 00:07:28.200
So what you see on the left is how an
ideal data and AI platform should be.

fb060767-f993-4a54-b01e-b5dce57c961c-0
00:07:28.200 --> 00:07:33.299
So basically in order to build AI out of
or get insights out of your data, first,

fb060767-f993-4a54-b01e-b5dce57c961c-1
00:07:33.299 --> 00:07:36.720
you need a harmonized and strong data
platform, right?

899d6341-7595-42c3-b8a8-b4b46f5250aa-0
00:07:37.040 --> 00:07:38.360
So this is the ideal situation.

6a8abc05-dc84-4dc8-8beb-e6a585d89372-0
00:07:38.360 --> 00:07:43.412
But what happens in the reality is you
have a combination of various ERP systems,

6a8abc05-dc84-4dc8-8beb-e6a585d89372-1
00:07:43.412 --> 00:07:47.110
non SAP applications,
LOB applications and a number of data

6a8abc05-dc84-4dc8-8beb-e6a585d89372-2
00:07:47.110 --> 00:07:47.480
leaks.

09ce6164-501e-443a-9f18-eae2c29a81ab-0
00:07:47.800 --> 00:07:51.357
And when you try to pull this data into
these database,

09ce6164-501e-443a-9f18-eae2c29a81ab-1
00:07:51.357 --> 00:07:53.200
try to rebuild the semantics.

6f7d47fe-4327-4ec8-a72e-0dac2d0d994c-0
00:07:53.720 --> 00:07:58.820
It's not just the technological issue,
rather it is the expertise you try to

6f7d47fe-4327-4ec8-a72e-0dac2d0d994c-1
00:07:58.820 --> 00:08:03.456
bring together from the ERP vendor,
from the non SAP vendors and also

6f7d47fe-4327-4ec8-a72e-0dac2d0d994c-2
00:08:03.456 --> 00:08:06.040
bringing these data engineers together.

c277fbcd-508d-4a1b-8b57-66417b3bd3ad-0
00:08:06.040 --> 00:08:09.640
And trying to rebuild those semantics is
the most complex thing.

e35dd39b-c73a-492c-b6a5-6861647fc435-0
00:08:09.960 --> 00:08:12.876
And when you try to build such semantics
again,

e35dd39b-c73a-492c-b6a5-6861647fc435-1
00:08:12.876 --> 00:08:16.280
you have to build AI on top of that
derailed semantics.

12229df0-aadf-46c6-8766-e04ae585c60b-0
00:08:16.600 --> 00:08:20.720
This is where the AI projects are,
you know, bound to fail.

286aaf57-a3de-4b81-a0a5-8e9044297bad-0
00:08:21.560 --> 00:08:27.125
If we move on,
how SAP Knowledge Graph will give you

286aaf57-a3de-4b81-a0a5-8e9044297bad-1
00:08:27.125 --> 00:08:29.960
exceptional results, right?

a8441622-4174-4f65-820f-11d4e60b14e8-0
00:08:29.960 --> 00:08:32.960
Which a traditional large language model
cannot deliver.

bd401067-aa90-44ed-9970-52295af75a1c-0
00:08:33.480 --> 00:08:36.547
For example,
if you look at how do I know if my boots

bd401067-aa90-44ed-9970-52295af75a1c-1
00:08:36.547 --> 00:08:39.160
movement is posted for a certain sales
order.

d01b9713-d351-4dda-9580-3cbf1cb79215-0
00:08:39.520 --> 00:08:43.861
If you ask this to a large language model,
it will look at information for your

d01b9713-d351-4dda-9580-3cbf1cb79215-1
00:08:43.861 --> 00:08:46.520
sales order,
it will look at your boots movement

d01b9713-d351-4dda-9580-3cbf1cb79215-2
00:08:46.520 --> 00:08:48.040
document number, That's all.

bb2297f3-ada7-4dc2-900e-b632d614302d-0
00:08:48.560 --> 00:08:54.800
When we talk about Knowledge graph,
so it is grounded on half a million about

bb2297f3-ada7-4dc2-900e-b632d614302d-1
00:08:54.800 --> 00:08:58.880
tables and thousands of CDs views in SAP
Espohana.

95f7c704-1178-4ce2-9e62-239ff85f9afd-0
00:08:58.880 --> 00:09:02.628
Especially right now when you ask a
question about your sales order,

95f7c704-1178-4ce2-9e62-239ff85f9afd-1
00:09:02.628 --> 00:09:05.670
goods movement,
Knowledge Graph has an understanding of

95f7c704-1178-4ce2-9e62-239ff85f9afd-2
00:09:05.670 --> 00:09:06.920
the end to end process.

80f5dbc8-1611-4510-8a45-9aae9980200b-0
00:09:07.240 --> 00:09:11.261
Starting from the quote to the invoice,
all the documents and the relationships

80f5dbc8-1611-4510-8a45-9aae9980200b-1
00:09:11.261 --> 00:09:12.920
are known to the Knowledge graph.

b606bc41-de3a-49c1-a315-e3cad4548fbd-0
00:09:13.360 --> 00:09:18.862
And when you ask such a question in June,
which is grounded on the Knowledge Graph,

b606bc41-de3a-49c1-a315-e3cad4548fbd-1
00:09:18.862 --> 00:09:23.840
it can give you a very relevant response
looking at the end to end process.

b98fc066-af2d-4886-8978-ffe1acacceeb-0
00:09:25.120 --> 00:09:29.813
Now if we move on how this is embedded,
so knowledge graph is embedded of course

b98fc066-af2d-4886-8978-ffe1acacceeb-1
00:09:29.813 --> 00:09:33.926
in Espohana beside behind Jewel,
you see that, behind agents, you see,

b98fc066-af2d-4886-8978-ffe1acacceeb-2
00:09:33.926 --> 00:09:34.680
you see that.

5311a4c2-6e31-4209-9974-521f7dd91570-0
00:09:35.040 --> 00:09:39.680
And now we are enrolling that rolling out
that across other applications as well.

e694c11e-38a5-4dfc-b2e2-1d9e89471aa0-0
00:09:40.080 --> 00:09:44.931
And what you see here is the number of
skills we offer across various business

e694c11e-38a5-4dfc-b2e2-1d9e89471aa0-1
00:09:44.931 --> 00:09:46.160
applications of SAP.

5091d7f0-5fb4-45bc-9600-ee1283f28aae-0
00:09:46.560 --> 00:09:50.345
So 1600 skills is what we offer
out-of-the-box and of course,

5091d7f0-5fb4-45bc-9600-ee1283f28aae-1
00:09:50.345 --> 00:09:53.520
you have the capabilities to build your
own skills.

2a6ead24-a031-48e3-8ec3-367b620fe30a-0
00:09:54.160 --> 00:09:58.158
But if you move on,
I can show you quickly how do you

2a6ead24-a031-48e3-8ec3-367b620fe30a-1
00:09:58.158 --> 00:10:01.120
interact with your new UI which is June.

e09de11d-60b6-4756-a16c-5b1fc0a348c0-0
00:10:02.320 --> 00:10:06.600
So we have 4 key patterns here,
informational pattern,

e09de11d-60b6-4756-a16c-5b1fc0a348c0-1
00:10:06.600 --> 00:10:12.280
the number one which is to get you
information from SAP help or your own

e09de11d-60b6-4756-a16c-5b1fc0a348c0-2
00:10:12.280 --> 00:10:15.160
company policies or documents, right.

0af67a3f-dc27-45e4-a180-5255543b0a5d-0
00:10:15.480 --> 00:10:19.025
For example,
you can ask a question about what is your

0af67a3f-dc27-45e4-a180-5255543b0a5d-1
00:10:19.025 --> 00:10:23.215
allowance if you have to travel from UAE
to Germany for example,

0af67a3f-dc27-45e4-a180-5255543b0a5d-2
00:10:23.215 --> 00:10:24.440
on a business trip.

5356629c-65c1-4d8d-b7fc-cd64ea7b0d24-0
00:10:24.720 --> 00:10:28.606
So this could be something documented in
your policies within your SharePoint and

5356629c-65c1-4d8d-b7fc-cd64ea7b0d24-1
00:10:28.606 --> 00:10:30.360
Jul can get that information for you.

a82da6d9-6097-43f1-a1c6-68d062439f40-0
00:10:30.960 --> 00:10:34.906
Then we have the navigational pattern,
especially useful in your Esfahana

a82da6d9-6097-43f1-a1c6-68d062439f40-1
00:10:34.906 --> 00:10:38.000
application where you have hundreds of
Fury applications.

34fa1533-155d-445f-97c1-99f48557c582-0
00:10:38.360 --> 00:10:42.573
All you have to do is I would like to
create my purchase requisition and Julie

34fa1533-155d-445f-97c1-99f48557c582-1
00:10:42.573 --> 00:10:46.360
will help you navigate to that
application to conduct the transaction.

94752d7f-c550-4de5-aec2-63feb13c3cd8-0
00:10:46.800 --> 00:10:49.805
The key pattern which is the
transactional pattern,

94752d7f-c550-4de5-aec2-63feb13c3cd8-1
00:10:49.805 --> 00:10:52.406
creating a leave request and
SuccessFactors,

94752d7f-c550-4de5-aec2-63feb13c3cd8-2
00:10:52.406 --> 00:10:56.800
creating a purchase order named S4,
looking at your invoice postings in S4.

df8f5d69-d6b7-4e51-aa3d-7765fff84724-0
00:10:57.000 --> 00:11:01.774
These are typical examples of
transactional pattern which are basically

df8f5d69-d6b7-4e51-aa3d-7765fff84724-1
00:11:01.774 --> 00:11:04.560
interacting with your database underneath.

64d94a90-5e48-41f3-ac57-a0901fc093ea-0
00:11:05.240 --> 00:11:10.067
Then we have the last pattern which is
the analytical pattern basically to

64d94a90-5e48-41f3-ac57-a0901fc093ea-1
00:11:10.067 --> 00:11:15.023
represent your numbers or numerical
information and more pictorial format as

64d94a90-5e48-41f3-ac57-a0901fc093ea-2
00:11:15.023 --> 00:11:17.920
where analytical pattern comes into
picture.

33126b73-2864-46f8-9bd5-92bc1b868de4-0
00:11:17.920 --> 00:11:22.264
For example,
can you compare my Q3 revenue versus Q2

33126b73-2864-46f8-9bd5-92bc1b868de4-1
00:11:22.264 --> 00:11:22.920
revenue?

12a187df-ea40-451f-a8b7-0c7494988587-0
00:11:23.240 --> 00:11:28.440
This will be represented as a graphical
pattern and and true then we move on.

5db1dd49-bab8-4fba-86c1-beb6f06cdaef-0
00:11:30.280 --> 00:11:38.380
This is one example of how a cash manager
can benefit from June with an SO and if

5db1dd49-bab8-4fba-86c1-beb6f06cdaef-1
00:11:38.380 --> 00:11:41.640
we move further, let's play this.

a8fffe03-6eea-42d9-8cae-81dba490e623-0
00:11:42.800 --> 00:11:48.082
So all you have to do here is you ask for
the cash management overview and within

a8fffe03-6eea-42d9-8cae-81dba490e623-1
00:11:48.082 --> 00:11:53.300
just a click you will see what are all
the bank accounts which are active versus

a8fffe03-6eea-42d9-8cae-81dba490e623-2
00:11:53.300 --> 00:11:53.880
inactive.

2e4f7915-1353-4195-82ee-003fd79b6aa8-0
00:11:54.360 --> 00:11:59.392
Then you can look at the top three
upflows and also you can look at the top

2e4f7915-1353-4195-82ee-003fd79b6aa8-1
00:11:59.392 --> 00:12:00.320
three inflows.

d6b62020-9f6b-4a8d-bcf3-b065535e916c-0
00:12:02.120 --> 00:12:06.803
And once you have this information,
you can also have a detailed look at one

d6b62020-9f6b-4a8d-bcf3-b065535e916c-1
00:12:06.803 --> 00:12:11.669
of these transactions just within June
and it can also help you navigate in the

d6b62020-9f6b-4a8d-bcf3-b065535e916c-2
00:12:11.669 --> 00:12:13.920
background to that particular detail.

653c1c99-b414-4313-8d31-41ee3936ab07-0
00:12:14.440 --> 00:12:17.960
Now you can also look at your processing
status of a certain transaction.

c4f56b51-9768-40ea-8f6b-29bfe28c85d8-0
00:12:18.480 --> 00:12:21.520
You will see if it has been processed,
any warnings, errors.

8f5de127-9e7a-4d7b-8a0f-f75612359aec-0
00:12:21.640 --> 00:12:23.920
All this information is at your
fingertips again.

6749655c-5dea-4aa0-985d-44e41db4f5a0-0
00:12:25.080 --> 00:12:30.018
Now you can look at the cash shortages
within your company for a certain

6749655c-5dea-4aa0-985d-44e41db4f5a0-1
00:12:30.018 --> 00:12:35.361
currency or a certain date and it would
give you the specific information with

6749655c-5dea-4aa0-985d-44e41db4f5a0-2
00:12:35.361 --> 00:12:38.000
the accounts which are short of amount.

276bbfeb-4a4a-4b8a-80f7-d7e459434584-0
00:12:38.760 --> 00:12:44.212
And what you can also ask June,
is to identify accounts which have

276bbfeb-4a4a-4b8a-80f7-d7e459434584-1
00:12:44.212 --> 00:12:50.560
overage of funds to transfer part of the
funds to the accounts with shortage.

a84f630f-d0ed-48d7-a167-c1b4c1fc5cb7-0
00:12:52.400 --> 00:12:56.510
So as you can see everything end to end
with few clicks,

a84f630f-d0ed-48d7-a167-c1b4c1fc5cb7-1
00:12:56.510 --> 00:13:00.477
you're able to manage a typical day of a
cash manager,

a84f630f-d0ed-48d7-a167-c1b4c1fc5cb7-2
00:13:00.477 --> 00:13:05.742
which usually takes a number of
navigations to various fury applications

a84f630f-d0ed-48d7-a167-c1b4c1fc5cb7-3
00:13:05.742 --> 00:13:10.863
and clicking on so many things,
so many transactions to to get to this

a84f630f-d0ed-48d7-a167-c1b4c1fc5cb7-4
00:13:10.863 --> 00:13:11.440
outcome.

18ae56d7-ca50-4a13-b49e-28de24a150fc-0
00:13:13.200 --> 00:13:15.200
OK, let's move.

cbdeb351-ae7f-42d7-bd73-76fcabb7b75f-0
00:13:15.840 --> 00:13:20.149
So just in a nutshell,
these are the innovations in June where

cbdeb351-ae7f-42d7-bd73-76fcabb7b75f-1
00:13:20.149 --> 00:13:22.680
SAP is investing and keeps investing.

2c57c509-957c-4727-ac31-c9ae05f3cc49-0
00:13:23.360 --> 00:13:28.436
We have tool not only for business users,
but we also have tool for consultants,

2c57c509-957c-4727-ac31-c9ae05f3cc49-1
00:13:28.436 --> 00:13:32.009
for developers,
basically helping them to accelerate the

2c57c509-957c-4727-ac31-c9ae05f3cc49-2
00:13:32.009 --> 00:13:33.200
project deployment.

494f1e3b-70a9-4214-984f-5a74f6af7641-0
00:13:33.720 --> 00:13:37.360
And we also offer Joule Studio to extend
the standard skills.

c81be8d6-ccb4-42f4-9e94-f11c8133b256-0
00:13:37.360 --> 00:13:41.772
So if you need specific skills to
interact with your non SAP applications,

c81be8d6-ccb4-42f4-9e94-f11c8133b256-1
00:13:41.772 --> 00:13:45.832
for example like ServiceNow,
then you can use Joule Studio to create

c81be8d6-ccb4-42f4-9e94-f11c8133b256-2
00:13:45.832 --> 00:13:47.480
your own custom Joule skill.

5ddac093-8288-402b-8e1c-bb5b7847afc2-0
00:13:48.320 --> 00:13:54.014
We also offer bi directional integration
between Microsoft and Joule besides the

5ddac093-8288-402b-8e1c-bb5b7847afc2-1
00:13:54.014 --> 00:13:55.280
other innovations.

8d28368b-525b-46f8-b024-04508de3bdbe-0
00:13:57.280 --> 00:14:01.999
Now just quickly in a nutshell,
the key fundamental difference between

8d28368b-525b-46f8-b024-04508de3bdbe-1
00:14:01.999 --> 00:14:06.520
the Joule skill and agent Joule skill is
something straightforward.

ad28d19d-d301-4e77-a065-43901926f6e5-0
00:14:06.560 --> 00:14:10.960
You ask to create a leave request to
check to check the status of my order.

65c365af-50de-44a1-af6c-8a37cc5391c0-0
00:14:10.960 --> 00:14:11.880
Joule can do that.

91385798-5480-401d-957f-55376d3fc444-0
00:14:11.880 --> 00:14:13.120
It's a direct instruction.

85787bb3-e499-463f-aefc-830e2a0f8686-0
00:14:13.760 --> 00:14:16.280
Agents on the other hand or objective
based.

4e50524f-5a96-43f4-acfa-bc1e923f53d5-0
00:14:16.520 --> 00:14:19.672
So if you take the example of order
delays,

4e50524f-5a96-43f4-acfa-bc1e923f53d5-1
00:14:19.672 --> 00:14:23.040
then Joule agent can analyze the order
delays.

71fb08db-9596-4193-a6d1-14c457cc10a7-0
00:14:23.040 --> 00:14:27.297
So just alternative shipping routes and
notify the customers when their order

71fb08db-9596-4193-a6d1-14c457cc10a7-1
00:14:27.297 --> 00:14:28.280
will be delivered.

5a39e271-57e2-46c5-99f5-63a660023728-0
00:14:28.640 --> 00:14:31.520
So you see there are 4 main steps
involved.

3d896010-e2e3-4e50-9c34-228feba2cdf7-0
00:14:31.800 --> 00:14:35.720
First it plans the task or the objective
that has to be met.

fc2c59a0-9f1d-4486-a2ba-82cae21f0aa3-0
00:14:36.200 --> 00:14:40.080
Then it reflects upon the tasks,
the subtasks basically.

24574252-8138-4b9a-b1a0-1ad12c072400-0
00:14:40.520 --> 00:14:44.400
Then if the objective has not been met,
it will reason.

18242ce5-22c0-408d-908b-06840bee4840-0
00:14:44.640 --> 00:14:48.706
The agent will reason utilizing the
latest large language models,

18242ce5-22c0-408d-908b-06840bee4840-1
00:14:48.706 --> 00:14:53.511
reasoning models basically which can
think like humans before it executes the

18242ce5-22c0-408d-908b-06840bee4840-2
00:14:53.511 --> 00:14:55.360
task to achieve the objective.

e359e8db-4c38-4fe9-9c63-9b12a0148ead-0
00:14:57.840 --> 00:15:05.356
More so basically what you see here is we
embed knowledge graph across the agent,

e359e8db-4c38-4fe9-9c63-9b12a0148ead-1
00:15:05.356 --> 00:15:08.840
agent process starting from grounding.

8744a7fd-f810-40de-8dd9-053dd4eb3633-0
00:15:09.200 --> 00:15:11.640
We have a knowledge graph utilized there.

3182d94e-c5eb-40f0-b480-c120cabb9b58-0
00:15:11.960 --> 00:15:14.948
Similarly,
we have knowledge graph for planning the

3182d94e-c5eb-40f0-b480-c120cabb9b58-1
00:15:14.948 --> 00:15:18.800
execution of a certain task and finally
also to execute the tasks.

996557a4-5ef0-4a99-8231-7af4349ba77c-0
00:15:18.800 --> 00:15:21.880
So this is a really powerful combination.

29b803d0-4f66-487a-909b-304b943896a8-0
00:15:21.880 --> 00:15:28.002
Trying to replicate that outside the SAP
landscape or ecosystem is going to take

29b803d0-4f66-487a-909b-304b943896a8-1
00:15:28.002 --> 00:15:32.160
mammoth amount of time and also could be
very complex.

503b8825-bff9-4aee-add5-21304da0a2e6-0
00:15:32.960 --> 00:15:35.560
Now if you move on.

e27f9230-3796-4dce-953b-3a48e8110eda-0
00:15:39.400 --> 00:15:42.337
Yeah,
last but last slide on the Joule and

e27f9230-3796-4dce-953b-3a48e8110eda-1
00:15:42.337 --> 00:15:43.840
especially the agents.

e71e860a-dce5-47d6-80b9-380382512c42-0
00:15:44.080 --> 00:15:48.832
So what you see here is first of all the
Joule and the agents are powered by the

e71e860a-dce5-47d6-80b9-380382512c42-1
00:15:48.832 --> 00:15:52.117
knowledge graph,
but then you also need perfect data or

e71e860a-dce5-47d6-80b9-380382512c42-2
00:15:52.117 --> 00:15:55.520
harmonised data in order to empower that
knowledge graph.

eadaa2e3-9a25-47eb-a10b-41a50259b1b7-0
00:15:55.560 --> 00:15:59.098
So this is where Business Data Cloud
comes into picture,

eadaa2e3-9a25-47eb-a10b-41a50259b1b7-1
00:15:59.098 --> 00:16:03.320
giving access to those specific data
products for SAP applications.

a73ca8af-daeb-4901-9382-c7a5d03bd4ae-0
00:16:03.640 --> 00:16:08.828
And then you can also infuse your non SAP
data through Business Data Cloud,

a73ca8af-daeb-4901-9382-c7a5d03bd4ae-1
00:16:08.828 --> 00:16:14.222
build those data products and empower
your agents to have a end to end view of

a73ca8af-daeb-4901-9382-c7a5d03bd4ae-2
00:16:14.222 --> 00:16:15.520
your business data.

2adf7b97-f6ad-49f7-aea4-d58547a6ee23-0
00:16:18.600 --> 00:16:23.485
Now let's look at an example of one of
the out-of-the-box AI agents that we

2adf7b97-f6ad-49f7-aea4-d58547a6ee23-1
00:16:23.485 --> 00:16:24.000
deliver.

1ad76716-209d-4ef0-8e97-e0122efc1ee2-0
00:16:24.360 --> 00:16:28.850
So this is for the accounts receivable
expert trying to mimic the actions of a

1ad76716-209d-4ef0-8e97-e0122efc1ee2-1
00:16:28.850 --> 00:16:30.840
typical accounts receivable expert.

aa5274be-0b87-44e7-967b-deb45270274c-0
00:16:31.000 --> 00:16:35.697
So what the dual agent is doing here is
looking at the over dues of the

aa5274be-0b87-44e7-967b-deb45270274c-1
00:16:35.697 --> 00:16:36.480
receivables.

be00be3c-10e9-468a-9206-f51a26d64825-0
00:16:36.880 --> 00:16:40.880
Then it looks at the dispute cases that
have been open and for how long.

67ad3b44-f07a-4551-b1cd-2b6bc932b89c-0
00:16:41.000 --> 00:16:42.760
It also looks at the turning history.

565d28f0-6a16-4d02-9a08-29a79baebfed-0
00:16:43.320 --> 00:16:47.815
Now you can ask the agent to recommend
the actions to, you know,

565d28f0-6a16-4d02-9a08-29a79baebfed-1
00:16:47.815 --> 00:16:49.960
bring your money faster, right?

07d60702-1088-4afa-94cc-f567eb4d3fc7-0
00:16:49.960 --> 00:16:52.160
So it gives you specific instructions.

a1bb0a86-6f5b-4b66-bd62-945a8a33bc2e-0
00:16:52.480 --> 00:16:54.460
Hey,
these are the disputes which have been

a1bb0a86-6f5b-4b66-bd62-945a8a33bc2e-1
00:16:54.460 --> 00:16:55.360
pending for so long.

1b8ee8c2-94bf-4657-86f8-24896a58e5c0-0
00:16:55.800 --> 00:16:59.600
Please expedite those and you can get
your cash faster.

23dbcfab-2995-4f8c-8066-c21653a9245e-0
00:17:00.040 --> 00:17:04.111
Then you can also ask can you explain me
what is the reason behind this

23dbcfab-2995-4f8c-8066-c21653a9245e-1
00:17:04.111 --> 00:17:04.960
recommendation?

cce040e7-9eb6-4e82-a3f3-4927c104609d-0
00:17:04.960 --> 00:17:10.402
So you get the transparency behind those
suggestions and then you can also look at

cce040e7-9eb6-4e82-a3f3-4927c104609d-1
00:17:10.402 --> 00:17:14.600
further details of those certain disputes
which have been open.

3cecb28d-9dab-45f5-9a7f-b663f963d8dd-0
00:17:15.240 --> 00:17:18.509
Finally,
you can also navigate to the dispute and

3cecb28d-9dab-45f5-9a7f-b663f963d8dd-1
00:17:18.509 --> 00:17:22.040
not only that,
you can also create a new dispute from

3cecb28d-9dab-45f5-9a7f-b663f963d8dd-2
00:17:22.040 --> 00:17:22.759
your agent.

edae1f05-84e4-4e2e-a4f9-df58284fdf31-0
00:17:23.720 --> 00:17:29.371
So this is how the life of an AI expert
would be information at his or her

edae1f05-84e4-4e2e-a4f9-df58284fdf31-1
00:17:29.371 --> 00:17:30.200
fingertips.

a6f3e50b-a7d2-4e7d-a46d-4815bde8dfd2-0
00:17:31.800 --> 00:17:35.215
Let's go now moving on to the next layer
of AI,

a6f3e50b-a7d2-4e7d-a46d-4815bde8dfd2-1
00:17:35.215 --> 00:17:37.920
which is the embedded AI capabilities.

fdf8a351-fe92-404a-bc2e-4fab05c0ac53-0
00:17:38.320 --> 00:17:42.400
As you can see across the different lines
of business, we offer the AI capabilities.

4ddc5640-3b72-4c41-8c2e-137561195565-0
00:17:42.840 --> 00:17:47.425
Now if we move on,
we can just look at an overview of some

4ddc5640-3b72-4c41-8c2e-137561195565-1
00:17:47.425 --> 00:17:51.467
examples,
what we offer as out-of-the-box AI within

4ddc5640-3b72-4c41-8c2e-137561195565-2
00:17:51.467 --> 00:17:52.400
the finance.

e7492fb8-97ae-4f83-b31e-b8a305d9bd0a-0
00:17:52.720 --> 00:17:56.254
So as you see,
starting from financial planning to the

e7492fb8-97ae-4f83-b31e-b8a305d9bd0a-1
00:17:56.254 --> 00:18:00.688
court to cash process and also the
government's risk and compliance,

e7492fb8-97ae-4f83-b31e-b8a305d9bd0a-2
00:18:00.688 --> 00:18:02.680
these are a couple of examples.

4c75b397-16f0-4481-8918-5324c1947506-0
00:18:02.800 --> 00:18:06.484
If we move on,
we can look at some examples in the HR

4c75b397-16f0-4481-8918-5324c1947506-1
00:18:06.484 --> 00:18:11.602
space in in SuccessFactors especially,
you have the full employee journey,

4c75b397-16f0-4481-8918-5324c1947506-2
00:18:11.602 --> 00:18:15.560
end to end employee journey enhanced and
empowered by AI.

f67314f3-426e-4e62-a5a8-44846cb730f3-0
00:18:16.320 --> 00:18:22.016
And we can look at one example of how an
embedded AI use case out-of-the-box looks

f67314f3-426e-4e62-a5a8-44846cb730f3-1
00:18:22.016 --> 00:18:22.360
like.

c69f922e-7b6d-4d9b-b6bd-ee9c0655374e-0
00:18:23.080 --> 00:18:28.120
So this is the performance goal creation
and SuccessFactors.

e1e076fb-0711-4242-abbd-276814655b4f-0
00:18:28.480 --> 00:18:31.480
This is something we at SAP utilize today.

b7ce4de8-b753-42dd-b8b0-f94c227bebe8-0
00:18:31.800 --> 00:18:36.895
And I remember those days before this
feature has been rolled out,

b7ce4de8-b753-42dd-b8b0-f94c227bebe8-1
00:18:36.895 --> 00:18:43.055
I used to spend like 2 days and sometimes
my weekend to plan my goals for for my

b7ce4de8-b753-42dd-b8b0-f94c227bebe8-2
00:18:43.055 --> 00:18:43.360
ear.

207ce84b-571d-45a0-b5ce-b2a09d5564e2-0
00:18:43.480 --> 00:18:48.371
Right now with the AI enhanced or infused
goal creation,

207ce84b-571d-45a0-b5ce-b2a09d5564e2-1
00:18:48.371 --> 00:18:52.920
all I need to know is what is my goal for
this year?

32d79fb9-9377-4b9d-a258-80c15a3da426-0
00:18:52.960 --> 00:18:54.240
Where do I want to go?

783aaf99-c71c-4946-b522-579c45b9eac2-0
00:18:54.800 --> 00:18:55.080
Now?

29333302-7883-4ede-a96d-297f335aed9a-0
00:18:55.240 --> 00:18:58.700
As you see here,
when you take the help of AI,

29333302-7883-4ede-a96d-297f335aed9a-1
00:18:58.700 --> 00:19:04.590
AI will breakdown your goal and also give
you suggestions how you can breakdown

29333302-7883-4ede-a96d-297f335aed9a-2
00:19:04.590 --> 00:19:09.817
that goal into sub goals or tasks to
achieve across the next couple of

29333302-7883-4ede-a96d-297f335aed9a-3
00:19:09.817 --> 00:19:10.480
quarters.

fe0bae42-ebb7-4a52-b054-9622b0c1e9c4-0
00:19:10.480 --> 00:19:15.385
So within the different milestones,
you can see how your core execution can

fe0bae42-ebb7-4a52-b054-9622b0c1e9c4-1
00:19:15.385 --> 00:19:16.160
be achieved.

572bc948-5246-43b8-8f67-581de8d76f9d-0
00:19:17.360 --> 00:19:22.760
You can accept the suggestion by AI or
you can also enhance this further.

afe68e2a-ea74-4a7c-a4ae-2fefc4c7f267-0
00:19:22.760 --> 00:19:29.120
And if you if you look at again,
you can click on generate AI.

06e14cab-43af-4b45-90ae-4e6cde975b5d-0
00:19:29.400 --> 00:19:34.560
So we generate the goal and then you can
change the goal planning.

7b9a4bcd-9e53-40a9-823c-4cfec412489a-0
00:19:34.760 --> 00:19:38.927
So in this case,
we change this instead of a three quarter

7b9a4bcd-9e53-40a9-823c-4cfec412489a-1
00:19:38.927 --> 00:19:42.177
plan,
give me a four quarter plan and AI will

7b9a4bcd-9e53-40a9-823c-4cfec412489a-2
00:19:42.177 --> 00:19:44.720
enhance the goal generation process.

cc53ecd5-a678-4075-a769-fc7285335def-0
00:19:45.320 --> 00:19:50.641
What you see on the right is what is
proposed by AI and you're free to choose

cc53ecd5-a678-4075-a769-fc7285335def-1
00:19:50.641 --> 00:19:55.280
whether to accept or reject the proposal
and create your own goals.

d77581e1-b673-4f99-9f25-c59dfeaa6698-0
00:19:55.560 --> 00:19:59.816
So likewise,
we have embedded AI delivered across

d77581e1-b673-4f99-9f25-c59dfeaa6698-1
00:19:59.816 --> 00:20:06.200
various processes in HR and procurement,
Arriba, supply chain, everywhere.

f8581c80-4459-47af-8646-a3b1628d5439-0
00:20:06.640 --> 00:20:09.960
Of course,
we will see in the next sessions how you

f8581c80-4459-47af-8646-a3b1628d5439-1
00:20:09.960 --> 00:20:13.920
can adopt those AI capabilities within
your own organization.

705ee394-158f-4f5a-a9c9-e827ea8388eb-0
00:20:14.800 --> 00:20:15.880
Thank you, Kiwan.

82371807-fa6e-46b7-94b0-6bf286150b8a-0
00:20:17.520 --> 00:20:18.360
Thank you, Arish.

0e2151da-23a3-4f16-b54f-8419e64376d4-0
00:20:19.920 --> 00:20:20.560
Thank you, Arish.

86c9883d-d362-4871-92d3-098c6af6e5cc-0
00:20:21.400 --> 00:20:28.200
This is Roberto from SAP as as Harish in
in this section.

f718248c-c7b9-4979-bb4c-fcae0b3d959c-0
00:20:28.200 --> 00:20:32.090
In this section of the presentation,
you will see how to manage custom AI

f718248c-c7b9-4979-bb4c-fcae0b3d959c-1
00:20:32.090 --> 00:20:33.720
implement a custom AI scenario.

e4ddb32d-ecc5-4517-9944-7813fa1465f2-0
00:20:33.720 --> 00:20:37.984
That means as soon as you identify a
scenario that can be potentially

e4ddb32d-ecc5-4517-9944-7813fa1465f2-1
00:20:37.984 --> 00:20:42.127
augmented by AII capabilities,
First and foremost you have to check

e4ddb32d-ecc5-4517-9944-7813fa1465f2-2
00:20:42.127 --> 00:20:45.600
whether this scenario is part of our
embedded solutions.

f5103e7b-3352-4355-82ad-db3b4c18893a-0
00:20:45.680 --> 00:20:51.611
They're the ones that Harish show if this
scenario is not part of the embedded

f5103e7b-3352-4355-82ad-db3b4c18893a-1
00:20:51.611 --> 00:20:57.618
solution and or you want to extend this
scenario with the the standard scenario

f5103e7b-3352-4355-82ad-db3b4c18893a-2
00:20:57.618 --> 00:21:03.400
with your own to fill your own gaps,
you can work with the custom AI option.

a4bf796e-77ae-414c-af3c-098c5a48d864-0
00:21:03.640 --> 00:21:08.966
The custom AI option as you see here,
it's founding its own capability on the

a4bf796e-77ae-414c-af3c-098c5a48d864-1
00:21:08.966 --> 00:21:12.040
AI foundation services available in the
BTP.

82be064f-35ea-4eff-83ff-021b18bdf833-0
00:21:16.640 --> 00:21:20.660
In this slide,
we see that the I Foundation capability

82be064f-35ea-4eff-83ff-021b18bdf833-1
00:21:20.660 --> 00:21:23.000
are splitted in two main groups.

5cd029b9-b34d-4fab-a771-63d5c277fbc6-0
00:21:23.360 --> 00:21:29.308
The first group is so-called SAPAI
services and these services are basically

5cd029b9-b34d-4fab-a771-63d5c277fbc6-1
00:21:29.308 --> 00:21:34.561
AI services ready to be used as a fast
start for approaching the AI

5cd029b9-b34d-4fab-a771-63d5c277fbc6-2
00:21:34.561 --> 00:21:35.720
implementation.

b7b46ca8-7bc2-44ba-8e56-a43dcc0580d7-0
00:21:36.120 --> 00:21:40.649
What it means,
it means that you can start using the AI

b7b46ca8-7bc2-44ba-8e56-a43dcc0580d7-1
00:21:40.649 --> 00:21:43.400
services without develop anything.

11a143b2-a0db-48f6-8170-971f93971247-0
00:21:44.440 --> 00:21:46.360
It's just matter of configuration.

7c042e0b-8a02-4a4f-aecf-2e8486a459e6-0
00:21:46.360 --> 00:21:52.595
It's just matter of training the standard
AI services and consuming the standard AI

7c042e0b-8a02-4a4f-aecf-2e8486a459e6-1
00:21:52.595 --> 00:21:55.120
services out of the provided APIs.

b85fb71b-2635-4f2d-8510-466e6426a7bf-0
00:21:55.560 --> 00:22:00.400
The next slide we will see an example of
these services.

ee571723-5693-4b2c-b9a3-b5072337d0a3-0
00:22:01.200 --> 00:22:03.880
These are four services we are offering
out of the platform.

da8d4200-d4e4-41ad-9219-64c26f68d011-0
00:22:04.360 --> 00:22:08.667
The main 2 ones are the two in the middle,
the document information extraction and

da8d4200-d4e4-41ad-9219-64c26f68d011-1
00:22:08.667 --> 00:22:10.120
personalized recommendation.

a9dccb90-c2c1-4418-8395-530c415639c9-0
00:22:10.680 --> 00:22:15.676
Document Information Extraction is a
service that allows you to extract

a9dccb90-c2c1-4418-8395-530c415639c9-1
00:22:15.676 --> 00:22:18.800
structured data out of unstructured
content.

6d8c4cde-53be-45b1-95ff-e835b1ea1200-0
00:22:18.800 --> 00:22:21.400
So typically you'll receive APDF.

5258e0b6-8046-4fab-996b-aa3241e446e8-0
00:22:21.840 --> 00:22:25.850
Typical example,
you receive APDF with an invoice data,

5258e0b6-8046-4fab-996b-aa3241e446e8-1
00:22:25.850 --> 00:22:31.220
with some invoices invoice data,
and then you want to extract this invoice

5258e0b6-8046-4fab-996b-aa3241e446e8-2
00:22:31.220 --> 00:22:35.803
data to, for example,
create a corresponding invoice within the

5258e0b6-8046-4fab-996b-aa3241e446e8-3
00:22:35.803 --> 00:22:36.520
AP system.

13a4e56d-3573-4c94-8e18-759a1ec635c6-0
00:22:36.520 --> 00:22:39.040
This is something you can achieve using
this service.

2ecf47a4-2104-44ef-88aa-1986bf43ec3f-0
00:22:39.440 --> 00:22:43.427
The beauty of this service is that you
can use this service as it is because the

2ecf47a4-2104-44ef-88aa-1986bf43ec3f-1
00:22:43.427 --> 00:22:46.480
service is already strained by SAP with
millions of invoices.

d933dce8-ea1f-437a-9e12-e6b8672280a0-0
00:22:46.920 --> 00:22:49.760
So you can use as it is in order to extra
the invoices data.

2befd73f-2724-42b9-bccf-530cc81444c5-0
00:22:50.160 --> 00:22:53.825
Otherwise,
you can use the premium capability of the

2befd73f-2724-42b9-bccf-530cc81444c5-1
00:22:53.825 --> 00:22:57.560
service where you can create your own
invoice schema,

2befd73f-2724-42b9-bccf-530cc81444c5-2
00:22:57.560 --> 00:23:03.162
data schema and then instruct the service
in order to find and extract these the

2befd73f-2724-42b9-bccf-530cc81444c5-3
00:23:03.162 --> 00:23:08.280
property of your of your own data schema
through the usage of NLLM model.

f73bfa55-546a-49f1-904b-cbf7acd3f123-0
00:23:08.880 --> 00:23:13.712
And this way the service is much more
performant and can be much more flexible

f73bfa55-546a-49f1-904b-cbf7acd3f123-1
00:23:13.712 --> 00:23:17.200
than of course the basic option provided
out-of-the-box.

5e70127a-f943-4400-85e7-9ac6ef57e88f-0
00:23:17.680 --> 00:23:21.311
The personalized recommendation is
another service that you can train with

5e70127a-f943-4400-85e7-9ac6ef57e88f-1
00:23:21.311 --> 00:23:22.280
the historical data.

5d344418-949d-48b2-bbff-66bbb4386b53-0
00:23:22.560 --> 00:23:28.409
For example, typical use cases,
you train the service with historical

5d344418-949d-48b2-bbff-66bbb4386b53-1
00:23:28.409 --> 00:23:35.094
orders data of a user and then you can
use the service in order to promote some

5d344418-949d-48b2-bbff-66bbb4386b53-2
00:23:35.094 --> 00:23:41.445
goods to be to be selected by the the
current user to be put in the cart in

5d344418-949d-48b2-bbff-66bbb4386b53-3
00:23:41.445 --> 00:23:44.120
order to enrich the order value.

6e79cda6-fa61-4262-983c-b54e2f759ed4-0
00:23:44.520 --> 00:23:50.066
And the other two ones instead are
services related to getting business

6e79cda6-fa61-4262-983c-b54e2f759ed4-1
00:23:50.066 --> 00:23:52.840
entity out of unstructured document.

2c96edf2-532b-4ab7-a0b5-cc5532e1dbfe-0
00:23:58.000 --> 00:24:00.767
In this slide,
you see an example of the document

2c96edf2-532b-4ab7-a0b5-cc5532e1dbfe-1
00:24:00.767 --> 00:24:02.040
information extraction.

b501e9bd-9782-4340-a2f6-3e2f9bc23e0b-0
00:24:02.640 --> 00:24:05.249
In the picture,
you see on the on the right side on the

b501e9bd-9782-4340-a2f6-3e2f9bc23e0b-1
00:24:05.249 --> 00:24:07.440
structure document,
basically it's a contract.

147cd749-0cd1-48e3-9d32-6f123e353d06-0
00:24:07.880 --> 00:24:13.040
And then on the left side you see a form
that basically contains structure data.

e7e4e708-1d5a-4c48-ae3b-3019bda38334-0
00:24:13.120 --> 00:24:17.520
And these structure data are extracted
out of the PDF you see in the right.

d8300e66-cd3d-4a5d-a14d-73e500e67798-0
00:24:17.760 --> 00:24:20.509
So out of as I said,
out of an unstructured content,

d8300e66-cd3d-4a5d-a14d-73e500e67798-1
00:24:20.509 --> 00:24:24.658
you see you can extract structure data
and then you can use this structure data

d8300e66-cd3d-4a5d-a14d-73e500e67798-2
00:24:24.658 --> 00:24:25.800
whatever way you want.

38d99e0c-1203-48d5-bc40-769a10e3e634-0
00:24:26.280 --> 00:24:29.605
You can you,
you have to remind that these services

38d99e0c-1203-48d5-bc40-769a10e3e634-1
00:24:29.605 --> 00:24:34.338
are available both via API and via a
dedicated UIS and this is an just an

38d99e0c-1203-48d5-bc40-769a10e3e634-2
00:24:34.338 --> 00:24:37.920
example of the document information
extraction service.

48d86252-1413-4282-80cc-209aa9477617-0
00:24:41.120 --> 00:24:45.800
Then in the lower part of the of this
feature of the AI Foundation services,

48d86252-1413-4282-80cc-209aa9477617-1
00:24:45.800 --> 00:24:48.779
we have,
we have the I life cycle management and

48d86252-1413-4282-80cc-209aa9477617-2
00:24:48.779 --> 00:24:50.360
the business data context.

1f832091-1314-42ec-b4bc-c8cf9e402b68-0
00:24:50.360 --> 00:24:53.960
These two services are the heavy lift
services.

8de19a44-114d-4ccb-8424-653328ba68a7-0
00:24:54.200 --> 00:25:00.623
So are the services that you can use for
really create any custom scenario you

8de19a44-114d-4ccb-8424-653328ba68a7-1
00:25:00.623 --> 00:25:01.680
have in mind.

a1233372-a9a3-4578-9616-abc996307e81-0
00:25:02.000 --> 00:25:05.640
Are there services that allow you to
create them to use?

ecf730ee-8942-478a-abbd-41c896422d8e-0
00:25:05.840 --> 00:25:09.800
Sorry to use the latest and greatest LLM
models.

035e2a8d-9d16-442b-aebd-6ae7d5eb8ab1-0
00:25:09.800 --> 00:25:17.225
So these services are basically the ones
that gives you the 100% freedom of

035e2a8d-9d16-442b-aebd-6ae7d5eb8ab1-1
00:25:17.225 --> 00:25:20.840
generating your own custom scenarios.

bf3b6bc1-a632-4569-8159-2653eb54f22f-0
00:25:21.320 --> 00:25:24.440
And then let's see what is contained in
these services.

9966b27e-555b-44ad-a4f7-ab0cbc154c55-0
00:25:28.400 --> 00:25:32.490
The first and foremost capability
containing this service is the model

9966b27e-555b-44ad-a4f7-ab0cbc154c55-1
00:25:32.490 --> 00:25:33.240
availability.

9b75c300-971c-41cd-a0fd-5d5a2b6abb3d-0
00:25:33.640 --> 00:25:39.472
So through the generative AI Hub that's
basically a service contained within the

9b75c300-971c-41cd-a0fd-5d5a2b6abb3d-1
00:25:39.472 --> 00:25:43.000
I Foundation,
you can access more than 50 AILLM.

8d5bfe3b-1d42-4a23-a9bb-6ae22936ba8b-0
00:25:43.800 --> 00:25:50.302
These AILLM are the ones provided by the
you can find in in the in the market are

8d5bfe3b-1d42-4a23-a9bb-6ae22936ba8b-1
00:25:50.302 --> 00:25:56.805
provided by our own partners and you can
leverage on these AILLM services through

8d5bfe3b-1d42-4a23-a9bb-6ae22936ba8b-2
00:25:56.805 --> 00:25:57.440
the BTP.

56c23f50-e033-46c7-86b6-1df3c9207568-0
00:25:57.920 --> 00:26:01.986
Why it's interesting to have this
capability available because in this way,

56c23f50-e033-46c7-86b6-1df3c9207568-1
00:26:01.986 --> 00:26:06.320
you don't have to subscribe by yourself
to each and every model you want to use.

39e7ab61-b4b2-4af5-b4c1-65f7a20283a8-0
00:26:06.640 --> 00:26:10.832
You are automatically subscribed to all
the models and then as soon as you decide

39e7ab61-b4b2-4af5-b4c1-65f7a20283a8-1
00:26:10.832 --> 00:26:13.440
to use one of them,
you will pay for such a usage.

b4a7daa3-1011-4aee-8c4d-e7439ff2ed0b-0
00:26:14.360 --> 00:26:16.880
This gives you gives you the advantage of
choice.

e0434f54-c045-42dd-8774-212e0909924b-0
00:26:16.880 --> 00:26:20.772
So that means let's say that you want to
create a scenario where you want to

e0434f54-c045-42dd-8774-212e0909924b-1
00:26:20.772 --> 00:26:21.480
generate text.

169eb8a1-f1db-4a41-b403-66f2ab749d83-0
00:26:21.720 --> 00:26:27.437
Probably GPT model are the best instead
if you want to work on a scenario to

169eb8a1-f1db-4a41-b403-66f2ab749d83-1
00:26:27.437 --> 00:26:31.521
extra data out of an image,
typically an OCR scenario,

169eb8a1-f1db-4a41-b403-66f2ab749d83-2
00:26:31.521 --> 00:26:33.600
probably Gemini is the best.

1fd28daa-63df-4fb0-94b2-b6b53364c6fb-0
00:26:33.880 --> 00:26:37.640
So you can accordingly your needs your
specific implementation need.

40658d26-7f1f-4309-8dbe-fcf6e63028b8-0
00:26:37.640 --> 00:26:44.101
You can decide which one of the model you
want to use and then this model can of

40658d26-7f1f-4309-8dbe-fcf6e63028b8-1
00:26:44.101 --> 00:26:50.323
course be the most performance one for
the purpose you want to achieve in the

40658d26-7f1f-4309-8dbe-fcf6e63028b8-2
00:26:50.323 --> 00:26:51.360
next picture.

1d11fc74-d39e-4338-a593-e4b5120a1327-0
00:26:51.360 --> 00:26:54.976
In this picture,
you see how the platform shows you the

1d11fc74-d39e-4338-a593-e4b5120a1327-1
00:26:54.976 --> 00:26:55.880
model library.

47776bf5-c997-4bd9-9292-7b50eaf76919-0
00:26:56.120 --> 00:27:01.091
So this is the model library where you
can access all the models available in

47776bf5-c997-4bd9-9292-7b50eaf76919-1
00:27:01.091 --> 00:27:03.960
the BTP and subscribe with the with the
BTP.

8bdbd66c-8fed-47fb-82f0-9d556cdc4656-0
00:27:04.120 --> 00:27:08.360
And then here you can find all the
information related to the models it's on.

4d59809e-be4d-4c07-8c5a-1a5d2b3fcc2d-0
00:27:08.640 --> 00:27:13.385
KP is if the model is better for doing
something than something else,

4d59809e-be4d-4c07-8c5a-1a5d2b3fcc2d-1
00:27:13.385 --> 00:27:16.640
whether a model is deprecated or not and
so on.

8d54df6b-a28f-4817-92df-49431b11f1f1-0
00:27:16.640 --> 00:27:21.808
So here is the entry point where you can
really checking this periment on all the

8d54df6b-a28f-4817-92df-49431b11f1f1-1
00:27:21.808 --> 00:27:22.880
models available.

662c0e8c-585d-407f-a9d8-c0b264e04acd-0
00:27:26.320 --> 00:27:30.720
Another important capability provided by
the general TVA hub is the orchestration.

c3444fb2-ef07-4674-991f-63aa56869aa3-0
00:27:31.120 --> 00:27:37.215
The orchestration capability basically
allows you to work and manage a a request

c3444fb2-ef07-4674-991f-63aa56869aa3-1
00:27:37.215 --> 00:27:38.720
a response scenario.

1489fa75-15e3-40b0-9cfa-9d76888172c1-0
00:27:38.720 --> 00:27:45.298
So let's say that the the user sends a
prompt and then this prompt should be

1489fa75-15e3-40b0-9cfa-9d76888172c1-1
00:27:45.298 --> 00:27:50.680
executed that should be managed by an LLM
to get the response.

89f7bd05-28c5-43bc-bf1a-3ef390f9de52-0
00:27:51.160 --> 00:27:55.508
The generative I have and the
orchestration capability,

89f7bd05-28c5-43bc-bf1a-3ef390f9de52-1
00:27:55.508 --> 00:28:00.710
as we can see in the next slide,
gives you the freedom to define a

89f7bd05-28c5-43bc-bf1a-3ef390f9de52-2
00:28:00.710 --> 00:28:04.360
pipeline of action to work on the user
prompt.

d44d61fd-a02f-4658-b306-13895a05f283-0
00:28:04.680 --> 00:28:05.360
What does it mean?

0052870a-3467-49c2-9913-24f9ede9f171-0
00:28:05.360 --> 00:28:08.272
For example,
the user sensor prompt and then you can

0052870a-3467-49c2-9913-24f9ede9f171-1
00:28:08.272 --> 00:28:12.120
define via the data masking capability
provided by the orchestration.

9a436a88-3024-4778-a028-df4cf2441dcd-0
00:28:12.520 --> 00:28:18.878
You can ask to mask the sensible,
the sensitive data provider within the

9a436a88-3024-4778-a028-df4cf2441dcd-1
00:28:18.878 --> 00:28:26.108
prompt or you can ask to filter any kind
of not relevant data within the prompt or

9a436a88-3024-4778-a028-df4cf2441dcd-2
00:28:26.108 --> 00:28:32.815
any kind content that is related to that
it's aiming to hate or or any other

9a436a88-3024-4778-a028-df4cf2441dcd-3
00:28:32.815 --> 00:28:35.079
racism or religious topic.

6bdbf772-6dcb-4852-a14f-4a5232e6244b-0
00:28:35.320 --> 00:28:40.571
So you can ask to filter the prompt in
the proper way that just the information

6bdbf772-6dcb-4852-a14f-4a5232e6244b-1
00:28:40.571 --> 00:28:45.954
that you need and that you want will be
collected by the LLM in order to generate

6bdbf772-6dcb-4852-a14f-4a5232e6244b-2
00:28:45.954 --> 00:28:49.040
the response are effectively going to the
LLM.

781b41f5-cd0f-42fd-a378-efa3b810b93e-0
00:28:49.600 --> 00:28:55.417
And this is a way that the orchestration
allows you to, let's say,

781b41f5-cd0f-42fd-a378-efa3b810b93e-1
00:28:55.417 --> 00:29:00.800
better manage and optimize the prompts
provided by the users.

789500f2-a702-4bfc-9d8c-e83881a4f504-0
00:29:03.960 --> 00:29:07.994
In this picture,
you see an example of a customer scenario

789500f2-a702-4bfc-9d8c-e83881a4f504-1
00:29:07.994 --> 00:29:13.669
where a customer was asking us to have a
chat about experience in order to let its

789500f2-a702-4bfc-9d8c-e83881a4f504-2
00:29:13.669 --> 00:29:16.200
own user to create warranty requests.

39f58e98-53a7-4ef0-8915-7be420399890-0
00:29:16.200 --> 00:29:17.800
So this is a retail customer.

0507741d-2412-4432-aa0d-af02eb745821-0
00:29:18.080 --> 00:29:23.166
This customer sells technology products
and then it happened that the user that

0507741d-2412-4432-aa0d-af02eb745821-1
00:29:23.166 --> 00:29:28.380
bought the product the the customer that
bought the product it it wants to create

0507741d-2412-4432-aa0d-af02eb745821-2
00:29:28.380 --> 00:29:31.560
a warranty because because the product is
broken.

597657ea-2abe-4a33-b651-d7ff0f128176-0
00:29:31.920 --> 00:29:33.720
So the idea is to create a Chatwood.

f9e708d3-31ab-4f31-8f7d-131c3bfd2abd-0
00:29:33.800 --> 00:29:38.292
That's the one you will see in the next
slide to create a chat mode that

f9e708d3-31ab-4f31-8f7d-131c3bfd2abd-1
00:29:38.292 --> 00:29:42.600
basically allows the user to interact
with the the post sale support.

73c37af2-01ba-4d27-bdd0-f9aede188f9f-0
00:29:42.920 --> 00:29:48.341
And then the user here for can see its
own chat history, its own message history,

73c37af2-01ba-4d27-bdd0-f9aede188f9f-1
00:29:48.341 --> 00:29:50.920
can send pictures of the broken device.

2c822af6-df17-4d31-bd73-5a6c69e24a4b-0
00:29:51.240 --> 00:29:56.201
And then the AI behind the scene takes
this picture and identify whether

2c822af6-df17-4d31-bd73-5a6c69e24a4b-1
00:29:56.201 --> 00:29:58.920
effectively the device is broken or not.

86972401-d633-43d8-86ae-416d5412e0bf-0
00:29:58.960 --> 00:30:02.680
If it's broken automatically,
the warranty request is created.

c5afc180-6dcc-4434-9dbf-0f0fd617aac6-0
00:30:03.040 --> 00:30:07.895
If the AI that identifies that the the
device cannot be broken,

c5afc180-6dcc-4434-9dbf-0f0fd617aac6-1
00:30:07.895 --> 00:30:14.117
it moves the request to a user that has
the freedom to better manage the warranty

c5afc180-6dcc-4434-9dbf-0f0fd617aac6-2
00:30:14.117 --> 00:30:20.263
request generation creation or deny the
warranty request because effectively the

c5afc180-6dcc-4434-9dbf-0f0fd617aac6-3
00:30:20.263 --> 00:30:22.160
the device is not broken.

8648416b-5276-4518-8741-18439eaf8b72-0
00:30:22.720 --> 00:30:28.559
And with this slide,
I conclude my presentation and I leave

8648416b-5276-4518-8741-18439eaf8b72-1
00:30:28.559 --> 00:30:32.160
the stage to give thank you, Roberto.

e7b64f14-b34e-4752-bad1-63719f06063c-0
00:30:32.400 --> 00:30:34.640
So grateful information here.

267aa683-4553-4235-91a7-1fe585cfa582-0
00:30:35.240 --> 00:30:40.961
Before jumping into the BDC part,
I just want to remind everyone like

267aa683-4553-4235-91a7-1fe585cfa582-1
00:30:40.961 --> 00:30:44.640
please do not hesitate to ask any
questions.

dcbaf8ce-10f4-43ae-95df-897d8f7e7a2c-0
00:30:44.840 --> 00:30:47.400
It will be all answered in the end of the
session.

faad5d8d-54da-4eb4-8088-94f494d8d3e2-0
00:30:47.440 --> 00:30:48.920
We will have a time for that.

0796933d-51f6-4749-9852-76b0655f3104-0
00:30:49.400 --> 00:30:55.720
So like all the experts are here,
fire your hardest question till the end.

60c5d8ca-9d2d-4435-901b-dfaf69f0834c-0
00:30:56.240 --> 00:31:04.142
So starting with the AI and how we can
utilize the AI within business data

60c5d8ca-9d2d-4435-901b-dfaf69f0834c-1
00:31:04.142 --> 00:31:04.880
clouds.

1efabdc2-fb87-4a50-ab6b-865687ff38f2-0
00:31:05.120 --> 00:31:10.395
But before jumping to session,
I just want to explain Business Data

1efabdc2-fb87-4a50-ab6b-865687ff38f2-1
00:31:10.395 --> 00:31:16.834
Cloud for the audience maybe who does not
know very well about Business Data Cloud

1efabdc2-fb87-4a50-ab6b-865687ff38f2-2
00:31:16.834 --> 00:31:22.885
is a framework like working as a business
data fabric which combines the data

1efabdc2-fb87-4a50-ab6b-865687ff38f2-3
00:31:22.885 --> 00:31:26.687
management,
analytics and visualization planning

1efabdc2-fb87-4a50-ab6b-865687ff38f2-4
00:31:26.687 --> 00:31:29.480
capabilities of SAP in one umbrella.

84c8a09a-21d8-4d11-af76-8b80a54b4209-0
00:31:29.480 --> 00:31:37.075
So you will do the your data management
orchestration under Business Data Cloud

84c8a09a-21d8-4d11-af76-8b80a54b4209-1
00:31:37.075 --> 00:31:43.436
to understand this deeper,
the SAP stronger solutions here such as

84c8a09a-21d8-4d11-af76-8b80a54b4209-2
00:31:43.436 --> 00:31:50.842
Data spare for business semantics or
maybe like the Data warehousing usage or

84c8a09a-21d8-4d11-af76-8b80a54b4209-3
00:31:50.842 --> 00:31:55.400
Analytics Cloud for the analytics and
planning.

d94a19ec-b557-423c-96ab-1a84ba6c1a72-0
00:31:55.640 --> 00:32:01.464
Or if you are a legacy SAP user and still
on the business data warehouse PW,

d94a19ec-b557-423c-96ab-1a84ba6c1a72-1
00:32:01.464 --> 00:32:07.590
there's also an option to modernize this
and put it in the under the umbrella of

d94a19ec-b557-423c-96ab-1a84ba6c1a72-2
00:32:07.590 --> 00:32:08.120
the PW.

139eda50-ea8f-4127-b94b-614b8b292317-0
00:32:08.520 --> 00:32:11.855
Sorry,
Business Data and of course for the AI

139eda50-ea8f-4127-b94b-614b8b292317-1
00:32:11.855 --> 00:32:15.697
machine learning or like for the advanced
analytics,

139eda50-ea8f-4127-b94b-614b8b292317-2
00:32:15.697 --> 00:32:21.570
it has the capabilities of data bricks
and it is also used for AI foundation for

139eda50-ea8f-4127-b94b-614b8b292317-3
00:32:21.570 --> 00:32:22.440
AI purposes.

0fc90953-0137-4528-92bd-c5e0e9162af7-0
00:32:22.440 --> 00:32:30.520
So all of these powerful products are
working in the corporation and they are

0fc90953-0137-4528-92bd-c5e0e9162af7-1
00:32:30.520 --> 00:32:38.600
seamlessly integrated to each other in
your like on top of your applications.

a50b7072-7d7c-4c11-b08a-987278ecc554-0
00:32:39.000 --> 00:32:43.029
For example,
if you're an SAP managed system such as

a50b7072-7d7c-4c11-b08a-987278ecc554-1
00:32:43.029 --> 00:32:48.960
maybe public cloud or As for HANA or like
2 SuccessFactors, Arriba, whatever.

f3038ae4-b917-4e01-997d-e7475e2df6f9-0
00:32:49.360 --> 00:32:55.290
We also business Data Cloud delivers some
pretty delivered scenarios called data

f3038ae4-b917-4e01-997d-e7475e2df6f9-1
00:32:55.290 --> 00:32:58.000
products or intelligent applications.

221ba7a9-86bc-4a03-8a0c-7c0169360aef-0
00:32:58.560 --> 00:33:04.922
The data products are pretty delivered
data models that will activate it by you

221ba7a9-86bc-4a03-8a0c-7c0169360aef-1
00:33:04.922 --> 00:33:10.330
and once they are activated,
this data models start to extract data

221ba7a9-86bc-4a03-8a0c-7c0169360aef-2
00:33:10.330 --> 00:33:16.215
and ready to use by third party
consumption or any like the visualization

221ba7a9-86bc-4a03-8a0c-7c0169360aef-3
00:33:16.215 --> 00:33:18.840
or like any kind of purpose here.

65c2d5d6-09ab-4633-be5e-b33414c31c6c-0
00:33:19.120 --> 00:33:25.960
So it's a very fast implementation
approach and a really cool shortcut here.

1fc9390e-2ce5-4860-ae7e-ee3c2c946c97-0
00:33:26.400 --> 00:33:31.376
We can also don't miss out the
intelligent applications here because

1fc9390e-2ce5-4860-ae7e-ee3c2c946c97-1
00:33:31.376 --> 00:33:37.002
those are the applications not only for
analytic purposes such as dashboards,

1fc9390e-2ce5-4860-ae7e-ee3c2c946c97-2
00:33:37.002 --> 00:33:40.320
reports but can be also used for AI
purposes.

b9236a15-b4f5-41db-bdd5-4f8cdd18b2f4-0
00:33:40.600 --> 00:33:46.947
So once you create or let's say activate
an app which is also again the pre

b9236a15-b4f5-41db-bdd5-4f8cdd18b2f4-1
00:33:46.947 --> 00:33:50.705
delivered by STP or by the partner
contents,

b9236a15-b4f5-41db-bdd5-4f8cdd18b2f4-2
00:33:50.705 --> 00:33:55.800
it will automatically activate the
underlying data products.

7c380b74-b2f7-4e76-9705-bd3e56b6001c-0
00:33:55.800 --> 00:34:03.227
So this means that not spending months of
time for implementing an AI project or

7c380b74-b2f7-4e76-9705-bd3e56b6001c-1
00:34:03.227 --> 00:34:08.179
analytics project,
it will be activated by within the

7c380b74-b2f7-4e76-9705-bd3e56b6001c-2
00:34:08.179 --> 00:34:15.791
experience of SAP on top of this SAP data
and will be ready to use in maybe couple

7c380b74-b2f7-4e76-9705-bd3e56b6001c-3
00:34:15.791 --> 00:34:16.800
of minutes.

3619d22b-5ca7-49f3-a5f4-ce2d921b7c00-0
00:34:17.120 --> 00:34:21.520
So it's very,
I really find this handy and important

3619d22b-5ca7-49f3-a5f4-ce2d921b7c00-1
00:34:21.520 --> 00:34:27.997
because this enables us to ensure the
governance and also scalability through

3619d22b-5ca7-49f3-a5f4-ce2d921b7c00-2
00:34:27.997 --> 00:34:29.160
the ecosystem.

dce7ad11-f5f7-4cbe-9793-d3feb9145d8c-0
00:34:29.800 --> 00:34:35.131
And those applications are context aware
because like has already explained

dce7ad11-f5f7-4cbe-9793-d3feb9145d8c-1
00:34:35.131 --> 00:34:40.323
working on top of knowledge Graph,
they do have the context awareness and

dce7ad11-f5f7-4cbe-9793-d3feb9145d8c-2
00:34:40.323 --> 00:34:45.163
these are, as I mentioned,
business rated data products and they are

dce7ad11-f5f7-4cbe-9793-d3feb9145d8c-3
00:34:45.163 --> 00:34:48.040
seamlessly integration within each other.

4addeb75-efc6-4419-b8dc-c11a3ccfe4b0-0
00:34:48.520 --> 00:34:57.530
This also enables us a unified unified
data platform which also helps us on

4addeb75-efc6-4419-b8dc-c11a3ccfe4b0-1
00:34:57.530 --> 00:35:01.680
decision making with AI inside PDC.

f7f3ef07-b321-47ae-9d9a-54c7cb95b35c-0
00:35:01.720 --> 00:35:07.094
There are also like a possibilities or
tools working together to help us to

f7f3ef07-b321-47ae-9d9a-54c7cb95b35c-1
00:35:07.094 --> 00:35:09.640
power and drive AI, which is the AI.

fc1e1d61-e12d-44f7-8481-99621235d770-0
00:35:09.960 --> 00:35:15.918
We can like account starting with AI
engineering which provides P directional

fc1e1d61-e12d-44f7-8481-99621235d770-1
00:35:15.918 --> 00:35:21.417
integration with data products,
state of Fair and also with the help of

fc1e1d61-e12d-44f7-8481-99621235d770-2
00:35:21.417 --> 00:35:22.640
the data bricks.

b267202e-be59-4ff8-8b0f-f81b8128962d-0
00:35:23.000 --> 00:35:28.936
You can create your own machine learning
workbench on scenarios to utilize your

b267202e-be59-4ff8-8b0f-f81b8128962d-1
00:35:28.936 --> 00:35:33.240
data and queue decisions with help of AI
Knowledge graph.

071bce11-e6eb-4285-8834-174beffbead4-0
00:35:33.240 --> 00:35:39.773
My colleague Harris already explained
will help us to drive us to context

071bce11-e6eb-4285-8834-174beffbead4-1
00:35:39.773 --> 00:35:46.747
survey results, such as when we save,
when we say sales or like when we search

071bce11-e6eb-4285-8834-174beffbead4-2
00:35:46.747 --> 00:35:48.160
about the sales.

1f5acd4e-9d1d-423c-b16a-a1cc561ac2e0-0
00:35:49.360 --> 00:35:54.795
It can also understand from the context,
from the sentence if it is positioning

1f5acd4e-9d1d-423c-b16a-a1cc561ac2e0-1
00:35:54.795 --> 00:35:59.280
the TNL with the goods sales or the
revenue from the sales model.

045911cd-721c-40aa-a892-1c48b46b6344-0
00:35:59.280 --> 00:36:07.960
And this also helps end users to have
accurate answers from their prompts.

66d9659f-d1e9-48ae-8893-22d40cece794-0
00:36:08.440 --> 00:36:11.960
June is there and also working inside the
analytic apps.

1a5c4a60-edea-4b7b-b137-721d5e5257f2-0
00:36:11.960 --> 00:36:17.285
So you can ask questions with your
natural language and get the answers from

1a5c4a60-edea-4b7b-b137-721d5e5257f2-1
00:36:17.285 --> 00:36:18.600
the analytic tools.

69873341-a5d3-4398-b618-d1591f2657f8-0
00:36:19.000 --> 00:36:24.376
And also it is embedding this AI,
generative AI to use features which I

69873341-a5d3-4398-b618-d1591f2657f8-1
00:36:24.376 --> 00:36:26.840
will also show in the next slide.

8b547fd0-65aa-4a0e-810a-a61079b5e232-0
00:36:27.160 --> 00:36:33.706
And also agents can work to deploy
complex duties such as flows with inside

8b547fd0-65aa-4a0e-810a-a61079b5e232-1
00:36:33.706 --> 00:36:39.305
the financial planning,
doing the data modelling or maybe fixing

8b547fd0-65aa-4a0e-810a-a61079b5e232-2
00:36:39.305 --> 00:36:41.200
it back in the system.

211594ac-ed0a-4b4e-8f00-dfda26135364-0
00:36:41.440 --> 00:36:49.225
They are all available there to also
materialize the example with the usage of

211594ac-ed0a-4b4e-8f00-dfda26135364-1
00:36:49.225 --> 00:36:52.280
AI within a business data club.

d5370101-5ffc-46e3-9a71-c50310a2b12b-0
00:36:52.280 --> 00:36:58.786
This is an analytics club example and
with help of AI here you can create all

d5370101-5ffc-46e3-9a71-c50310a2b12b-1
00:36:58.786 --> 00:37:04.960
the shorts by with only giving some
prompts with the generative approach.

00e817e7-d424-4a4b-b6f9-da667270fcc8-0
00:37:05.280 --> 00:37:12.263
Let's as in the example you only select
maybe the target audience of the task

00e817e7-d424-4a4b-b6f9-da667270fcc8-1
00:37:12.263 --> 00:37:18.800
force or story you are going to create
and prompt the requirements goal.

5e86de66-7ccc-4f53-a8ed-ca71eaa32ba3-0
00:37:18.800 --> 00:37:22.760
Define the goal and if you have a
template, select them.

7aeac101-b5e2-4461-a001-9b677ec5e382-0
00:37:23.040 --> 00:37:27.999
And once you generate it,
what a dashboard is there so you don't

7aeac101-b5e2-4461-a001-9b677ec5e382-1
00:37:27.999 --> 00:37:32.120
need to spend time to create everything
from scratch.

1a5d1488-f8f8-4157-8e69-f1204066647f-0
00:37:32.480 --> 00:37:35.600
It is also helpful and handy in the
situation.

077e984b-f81d-425b-916f-fe83747d5486-0
00:37:36.320 --> 00:37:40.818
But one of the important thing here is
the use cases,

077e984b-f81d-425b-916f-fe83747d5486-1
00:37:40.818 --> 00:37:47.400
because we do have a lot of handy tools
explain starting from the this webinar

077e984b-f81d-425b-916f-fe83747d5486-2
00:37:47.400 --> 00:37:50.399
also within the Business Data Cloud.

dbd256d5-0b7c-4f0d-a6e2-92696f909316-0
00:37:50.760 --> 00:37:56.224
But how can we become And also we can
really emphasize that this isn't hype and

dbd256d5-0b7c-4f0d-a6e2-92696f909316-1
00:37:56.224 --> 00:38:01.280
also everyone wants to do and address
some problem solve problem with AI.

d17e42ac-99f3-44b0-a170-b8a89ce82ffe-0
00:38:01.680 --> 00:38:07.578
But for inspiration,
let's look at the example of the use

d17e42ac-99f3-44b0-a170-b8a89ce82ffe-1
00:38:07.578 --> 00:38:11.240
cases can be implemented within PDC.

a4150259-455f-4187-8ac1-f957d73de03a-0
00:38:11.520 --> 00:38:17.541
So using Arriba and it's 400 at the same
time is the common case here we face

a4150259-455f-4187-8ac1-f957d73de03a-1
00:38:17.541 --> 00:38:23.563
which is on Arriba we keep the suppliers
or if these are accused and like the

a4150259-455f-4187-8ac1-f957d73de03a-2
00:38:23.563 --> 00:38:25.880
sourcing document information.

01240f22-6638-4405-a64d-a960b1df051a-0
00:38:26.400 --> 00:38:30.753
But on the other hand,
there is invoices or like the inventory,

01240f22-6638-4405-a64d-a960b1df051a-1
00:38:30.753 --> 00:38:35.854
inventory information inside the SAP,
which needs to be combined to have a

01240f22-6638-4405-a64d-a960b1df051a-2
00:38:35.854 --> 00:38:41.160
deeper and accurate analysis on this
topic for the supply chain optimization.

93fab30b-05c4-4f79-bb4f-e567680eda23-0
00:38:41.520 --> 00:38:47.346
So with help of these data products
inside Business Data Cloud and the

93fab30b-05c4-4f79-bb4f-e567680eda23-1
00:38:47.346 --> 00:38:52.105
capabilities of Data Spare to combine
this data together,

93fab30b-05c4-4f79-bb4f-e567680eda23-2
00:38:52.105 --> 00:38:58.506
we can analyse the data combined from
different sources in data bricks and in

93fab30b-05c4-4f79-bb4f-e567680eda23-3
00:38:58.506 --> 00:39:00.640
supply chain optimization.

83569c26-ab2d-4735-8f90-4f33dfdbc792-0
00:39:00.960 --> 00:39:07.881
We can do analysis such as to understand
invoicing errors or to report the

83569c26-ab2d-4735-8f90-4f33dfdbc792-1
00:39:07.881 --> 00:39:12.680
suspicious overbilling or to alert late
deliveries.

cb370acf-8c0d-4943-8b24-79648ea1fd09-0
00:39:12.680 --> 00:39:19.271
And also we can visualize it with the
help of analytics collapse another case

cb370acf-8c0d-4943-8b24-79648ea1fd09-1
00:39:19.271 --> 00:39:26.031
and which is also here again a common
case for us in the people intelligence by

cb370acf-8c0d-4943-8b24-79648ea1fd09-2
00:39:26.031 --> 00:39:32.960
like the using different tools such as
SuccessFactors and it's for the same time.

cb2244a4-2ed3-48b2-85b7-62de58aa714e-0
00:39:33.440 --> 00:39:38.472
In this scenario,
success factor keeps the employee data or

cb2244a4-2ed3-48b2-85b7-62de58aa714e-1
00:39:38.472 --> 00:39:44.762
like the performance survey information
from the employees but some of the

cb2244a4-2ed3-48b2-85b7-62de58aa714e-2
00:39:44.762 --> 00:39:51.556
confidential information kept maybe on
Prem or that separate separate parts such

cb2244a4-2ed3-48b2-85b7-62de58aa714e-3
00:39:51.556 --> 00:39:56.840
as maybe a test for payrolls
compensations take it separately.

5bedfd9a-16ea-4bf3-93ea-81b7466d1199-0
00:39:57.240 --> 00:40:01.183
So again here the help of Business Data
Cloud,

5bedfd9a-16ea-4bf3-93ea-81b7466d1199-1
00:40:01.183 --> 00:40:07.726
these data can be combined and analyse to
use the and understand the employee

5bedfd9a-16ea-4bf3-93ea-81b7466d1199-2
00:40:07.726 --> 00:40:14.438
engagements or retention risk or we can
also analyse the performance trends and

5bedfd9a-16ea-4bf3-93ea-81b7466d1199-3
00:40:14.438 --> 00:40:16.199
visualise it to help.

c64129b0-c5c9-480b-b36c-b1aa4676f29c-0
00:40:17.760 --> 00:40:19.440
To get decisions.

529cb222-1371-4501-8940-488f2fe63ad1-0
00:40:20.040 --> 00:40:23.710
So here as we refer to the research from
MIT,

529cb222-1371-4501-8940-488f2fe63ad1-1
00:40:23.710 --> 00:40:29.615
one of the important topic here to
address problem because there are like

529cb222-1371-4501-8940-488f2fe63ad1-2
00:40:29.615 --> 00:40:35.520
many use cases that we can get into live,
but it's also we can empathize.

4f04b22a-f89b-4d17-b90d-d87e74a7b779-0
00:40:35.840 --> 00:40:41.587
It's very hard for you to decide which
one is to decide and which one is to move

4f04b22a-f89b-4d17-b90d-d87e74a7b779-1
00:40:41.587 --> 00:40:41.800
on.

cc86fb55-feba-4898-8ebe-f96c96e7f70c-0
00:40:42.080 --> 00:40:45.360
So there's an expert here with us,
doctor and his kid.

21af71c9-638f-4475-89d9-c3248e4591ff-0
00:40:45.720 --> 00:40:50.827
So Nagaro within Nagaro,
he do have a methodology to address for

21af71c9-638f-4475-89d9-c3248e4591ff-1
00:40:50.827 --> 00:40:57.191
specific before your organization which
problems can be solved with AI and which

21af71c9-638f-4475-89d9-c3248e4591ff-2
00:40:57.191 --> 00:40:58.920
can be utilized first.

df772222-11ff-4cee-b77e-af55838b864c-0
00:40:59.320 --> 00:41:02.840
So let's stages on ULM.

717d5e05-175f-4442-9c01-14b6604c20e6-0
00:41:03.200 --> 00:41:08.783
We are also really curious about the
strategy and the products you are

717d5e05-175f-4442-9c01-14b6604c20e6-1
00:41:08.783 --> 00:41:12.480
delivering within the Nagar role
organization.

51960478-d6e9-4c3f-a4eb-8e985838a963-0
00:41:12.800 --> 00:41:14.080
Well, thank you so much.

a3ec70f3-1b7e-4eaa-9eeb-608ec1d5ea9d-0
00:41:15.240 --> 00:41:17.280
It's a pleasure for me to be here with
you.

908649b6-ce3d-4625-a08c-9dce156dc9d1-0
00:41:17.840 --> 00:41:19.480
Indeed, I had a bad for you.

7f9ac2c4-4342-4f92-b71f-55d3ce6b8539-0
00:41:19.520 --> 00:41:22.320
Sorry about my fantastic sounds for this
reason.

58731150-7a76-4af4-a28a-2ddddb8cf818-0
00:41:23.200 --> 00:41:23.800
Sound all good.

fad86ba3-e01a-4b76-9a18-4e9c3356a5a7-0
00:41:24.280 --> 00:41:25.160
Yeah, thank you.

68141514-0986-44cd-9517-53302696b66b-0
00:41:25.520 --> 00:41:30.000
Let us start with the fantastic report,
MIT report, the Gen.

021e5e18-a7e0-4a1c-a3ec-64b76d0e41b6-0
00:41:30.000 --> 00:41:35.505
AI Dwight report Indeed,
Harish and you also strongly highlight

021e5e18-a7e0-4a1c-a3ec-64b76d0e41b6-1
00:41:35.505 --> 00:41:39.720
the major takeaways,
major outcomes and results.

bcaf7d38-42d4-43b8-8659-b7663e1aaf1d-0
00:41:40.080 --> 00:41:44.808
And also the attendees can easily reach
out to reports and download it from

bcaf7d38-42d4-43b8-8659-b7663e1aaf1d-1
00:41:44.808 --> 00:41:47.360
resources section at the right hand side.

76ae3f83-6a6d-41c0-8990-56c9c5467ebd-0
00:41:48.000 --> 00:41:50.560
And here are the major takeaways.

1628675b-3171-408d-b49f-a7bee0c73153-0
00:41:51.200 --> 00:41:56.745
And to sum up,
indeed we have according to these figures,

1628675b-3171-408d-b49f-a7bee0c73153-1
00:41:56.745 --> 00:42:03.629
according to these statistics and
shocking facts, let me say to sum up,

1628675b-3171-408d-b49f-a7bee0c73153-2
00:42:03.629 --> 00:42:10.800
we have to orchestrate workflow,
memory and learning in a sensible manner.

68826611-b7b3-486e-a3db-29356f665b56-0
00:42:11.280 --> 00:42:16.817
So in it,
it's a interesting coinciding because if

68826611-b7b3-486e-a3db-29356f665b56-1
00:42:16.817 --> 00:42:22.571
we jumped over our motto or Nagaro AI
strategy, yes,

68826611-b7b3-486e-a3db-29356f665b56-2
00:42:22.571 --> 00:42:28.000
we are looking at state point within this
report.

8b88b65d-df43-4217-a15e-f5981f269a54-0
00:42:28.000 --> 00:42:33.959
Let me say because as the organizations,
we believe that artificial intelligence

8b88b65d-df43-4217-a15e-f5981f269a54-1
00:42:33.959 --> 00:42:36.240
is a magical wand in our hands.

a3b22188-b5bd-4bf9-a31c-ac824fac282d-0
00:42:36.280 --> 00:42:37.160
You know, one touch.

0ae8eb24-8882-4d2a-85e9-2a9f13c8ddcd-0
00:42:37.440 --> 00:42:42.023
We we believe that we can change
everything in a minute,

0ae8eb24-8882-4d2a-85e9-2a9f13c8ddcd-1
00:42:42.023 --> 00:42:45.240
but the reality is AI is not an alchemy.

4a5d8903-4f35-4a52-ba1d-ed81f35e828d-0
00:42:45.240 --> 00:42:51.260
We cannot change every pieces of our
business processes into gold,

4a5d8903-4f35-4a52-ba1d-ed81f35e828d-1
00:42:51.260 --> 00:42:57.819
but it's a chemistry and what we mean by
chemistry is the companies over

4a5d8903-4f35-4a52-ba1d-ed81f35e828d-2
00:42:57.819 --> 00:43:02.581
organizational DNA,
organization DNA or one business

4a5d8903-4f35-4a52-ba1d-ed81f35e828d-3
00:43:02.581 --> 00:43:03.480
processes.

d6aac294-d8cf-47e9-8785-051ef273ffda-0
00:43:03.920 --> 00:43:09.362
And when we explain our strategy,
AI strategy and our motto or mantra,

d6aac294-d8cf-47e9-8785-051ef273ffda-1
00:43:09.362 --> 00:43:14.650
let me say it resembles Karyung saying
Karyung quotation as you see,

d6aac294-d8cf-47e9-8785-051ef273ffda-2
00:43:14.650 --> 00:43:18.559
who looks outside dreams,
who looks inside awakes.

141e9a57-a4c9-477a-b7a8-8f1c6fbd3659-0
00:43:18.800 --> 00:43:23.571
So rather than making or having sweet
dreams,

141e9a57-a4c9-477a-b7a8-8f1c6fbd3659-1
00:43:23.571 --> 00:43:30.625
we have to get up and we have to get
awareness about our strengths,

141e9a57-a4c9-477a-b7a8-8f1c6fbd3659-2
00:43:30.625 --> 00:43:34.360
strong points and also our weakness.

0048013f-d0e9-45da-b860-daab31b0eaf7-0
00:43:34.600 --> 00:43:41.038
So in that mindset,
we designated a Nagara AI Evidence

0048013f-d0e9-45da-b860-daab31b0eaf7-1
00:43:41.038 --> 00:43:47.360
workshop and let us jumped with the
objectives first.

db5f9621-7cb6-47bd-9bf2-d766afa89810-0
00:43:47.360 --> 00:43:51.746
First,
I think the most crucial objective is

db5f9621-7cb6-47bd-9bf2-d766afa89810-1
00:43:51.746 --> 00:43:58.861
strongly alignment with SAP business AI
strategy and also organizations,

db5f9621-7cb6-47bd-9bf2-d766afa89810-2
00:43:58.861 --> 00:44:04.320
business processes,
technologies and data repositories.

b8137eb6-324c-42b3-958f-ab8c3dce5b2c-0
00:44:04.600 --> 00:44:11.887
Also while while analysing and while
making this alignment with at this manner,

b8137eb6-324c-42b3-958f-ab8c3dce5b2c-1
00:44:11.887 --> 00:44:18.720
we have to have a good understanding of
the underlying sector or industry.

11e8081b-cbcc-444f-ab1f-c7cf4255fd08-0
00:44:18.920 --> 00:44:22.987
So we have get the snapshot about major
drivers,

11e8081b-cbcc-444f-ab1f-c7cf4255fd08-1
00:44:22.987 --> 00:44:26.640
major challenges occurring at the
industry.

9775a81e-dcb3-4522-ac6b-b84468136dd8-0
00:44:26.880 --> 00:44:32.747
And secondly, after making such alignment,
we will come up with sensible and

9775a81e-dcb3-4522-ac6b-b84468136dd8-1
00:44:32.747 --> 00:44:37.929
applicable and also feasible AI
capabilities and current industrial

9775a81e-dcb3-4522-ac6b-b84468136dd8-2
00:44:37.929 --> 00:44:42.120
trends throughout this selected AI
business scenarios.

27623e60-7783-488a-b590-90622d5d6162-0
00:44:43.040 --> 00:44:46.997
Afterwards,
we will arrange them the we will give

27623e60-7783-488a-b590-90622d5d6162-1
00:44:46.997 --> 00:44:53.488
priorities to this AI business scenarios
with respect to business values or value

27623e60-7783-488a-b590-90622d5d6162-2
00:44:53.488 --> 00:44:58.080
propositions and also with respect to
their complexities.

a3fb93a0-bb51-4ea0-863d-5d6283f65f1f-0
00:44:58.480 --> 00:45:02.760
Within these two dimensions,
we come up with AAI road map.

4b05a6e0-49c2-47d8-8872-b3ddac7dd662-0
00:45:03.120 --> 00:45:10.458
And finally, according to this road map,
we have a consensus with our stakeholders,

4b05a6e0-49c2-47d8-8872-b3ddac7dd662-1
00:45:10.458 --> 00:45:17.360
with our clients and we define clear big
next steps within this AI adaptation.

40c7c94e-396d-4278-b4b7-90e331a01c9e-0
00:45:17.640 --> 00:45:21.608
Sometimes these big steps may be AI
trainings,

40c7c94e-396d-4278-b4b7-90e331a01c9e-1
00:45:21.608 --> 00:45:26.084
sometimes it can be proof of concepts or
pilot runs,

40c7c94e-396d-4278-b4b7-90e331a01c9e-2
00:45:26.084 --> 00:45:29.040
or sometimes it can be AI projects.

eeabb032-2522-47dc-a72d-da91ebbeba0e-0
00:45:29.640 --> 00:45:35.880
And on the top of this objectives,
let me say much, we conclude outcome.

cd14b471-6d21-4da1-b37e-d6a353b59f7f-0
00:45:35.880 --> 00:45:37.840
As you see AI action plan.

fa202d7b-ca89-4e79-8b1c-1f73a5ceeec8-0
00:45:38.120 --> 00:45:45.048
It consists of AI opportunity road map
considering business AI components just

fa202d7b-ca89-4e79-8b1c-1f73a5ceeec8-1
00:45:45.048 --> 00:45:49.960
like Jewel and mid AI and custom specific
AI scenarios.

499f3715-afa5-4dfa-ac8d-e8180ca2fa21-0
00:45:50.280 --> 00:45:56.268
And the second one, as I said,
actionable next steps and ownerships for

499f3715-afa5-4dfa-ac8d-e8180ca2fa21-1
00:45:56.268 --> 00:45:58.680
identified steps and classes.

9b2a68fb-e10e-48de-abdd-536e4da4bd55-0
00:45:59.920 --> 00:46:04.107
And yes,
I think that makes the objectives and the

9b2a68fb-e10e-48de-abdd-536e4da4bd55-1
00:46:04.107 --> 00:46:06.160
outcomes are pretty good.

ccbf93a9-3b8c-4dd6-9f01-1c272df440e7-0
00:46:06.160 --> 00:46:09.863
But how about the customer journey,
the road map,

ccbf93a9-3b8c-4dd6-9f01-1c272df440e7-1
00:46:09.863 --> 00:46:15.640
let me say Kumach and our customer
journey consists of four major milestones.

7f39eff3-c1da-4db5-9194-1120d608b4fc-0
00:46:15.920 --> 00:46:19.970
First thing first,
I think the inception phase is very

7f39eff3-c1da-4db5-9194-1120d608b4fc-1
00:46:19.970 --> 00:46:20.560
crucial.

4707f669-0cb5-44a5-8512-b8c207cfd49f-0
00:46:20.680 --> 00:46:25.642
What I mean that and also I titled this
inception phase as the power of what

4707f669-0cb5-44a5-8512-b8c207cfd49f-1
00:46:25.642 --> 00:46:28.800
question or the the art of asking what
question.

68ba540d-a0e4-4a24-b2d4-f078d5477531-0
00:46:28.800 --> 00:46:34.051
Let me say, as you know,
first thing first we make a brief call

68ba540d-a0e4-4a24-b2d4-f078d5477531-1
00:46:34.051 --> 00:46:40.369
with the with our clients and then we
share AI questionnaire including some,

68ba540d-a0e4-4a24-b2d4-f078d5477531-2
00:46:40.369 --> 00:46:46.852
some questions taking the snapshot or
taking the X-ray of the organizations in

68ba540d-a0e4-4a24-b2d4-f078d5477531-3
00:46:46.852 --> 00:46:52.760
terms of business process in terms of
technology and data repositories.

19e821c5-71e6-4873-827a-104f20a0fa70-0
00:46:53.720 --> 00:46:57.590
And after that,
after getting the responses from this AI

19e821c5-71e6-4873-827a-104f20a0fa70-1
00:46:57.590 --> 00:47:01.460
questionnaire,
let me say within one or two weeks later,

19e821c5-71e6-4873-827a-104f20a0fa70-2
00:47:01.460 --> 00:47:04.040
we take the elaboration step too much.

cfc96978-6414-4dc3-b9bd-ea650e0fd5e3-0
00:47:04.440 --> 00:47:08.280
So we try to prepare the workshop
contacts.

8d180152-3710-4c37-a520-1b1d433471e4-0
00:47:08.600 --> 00:47:14.884
So while making this workshop preparation,
first thing first we make a fit gap

8d180152-3710-4c37-a520-1b1d433471e4-1
00:47:14.884 --> 00:47:15.600
analysis.

34cbeb25-3d38-4709-b783-1402db19ca87-0
00:47:15.840 --> 00:47:20.752
What I mean that we got the responses,
we extract the major characteristics of

34cbeb25-3d38-4709-b783-1402db19ca87-1
00:47:20.752 --> 00:47:23.738
the clients,
the strength points or weaknesses,

34cbeb25-3d38-4709-b783-1402db19ca87-2
00:47:23.738 --> 00:47:24.360
let's say.

a6c48148-b08e-4a49-867b-bc46e6db4658-0
00:47:24.360 --> 00:47:28.457
And also we have a look at about the
benchmark use cases,

a6c48148-b08e-4a49-867b-bc46e6db4658-1
00:47:28.457 --> 00:47:33.332
about the best practices in AI,
especially in the underlying sector,

a6c48148-b08e-4a49-867b-bc46e6db4658-2
00:47:33.332 --> 00:47:35.240
in the underlying industry.

b774905d-8672-4ee7-af50-633c5e9b8468-0
00:47:35.600 --> 00:47:41.080
So we may come up with potential feasible
AI business scenarios.

793318c6-7c41-46de-b7aa-0883c2fc3a06-0
00:47:41.560 --> 00:47:48.555
Also while analysing the while preparing
the workshop content, let me say much,

793318c6-7c41-46de-b7aa-0883c2fc3a06-1
00:47:48.555 --> 00:47:52.840
we also get the get insights about the
industry.

226c2d9d-52d2-40c5-a763-b663d3efb462-0
00:47:52.840 --> 00:47:56.640
How about the state of art in the
industry?

836663a7-82cc-48cc-8aff-b82fbe051131-0
00:47:56.640 --> 00:47:58.120
How about the major drivers?

955cacad-4c6c-44f2-a0b9-a281161cb01a-0
00:47:58.120 --> 00:48:03.637
How about the major dramatic cases just
like carbon footprint or sustainability

955cacad-4c6c-44f2-a0b9-a281161cb01a-1
00:48:03.637 --> 00:48:04.120
issues?

63c94f74-bb28-41b0-b97c-6833ddc11ce5-0
00:48:04.640 --> 00:48:07.240
And as the third step, we get the action.

8483c87c-73cf-4d9c-8fb5-58644886de49-0
00:48:07.400 --> 00:48:11.089
We,
we arrange the Nagara AI Awareness

8483c87c-73cf-4d9c-8fb5-58644886de49-1
00:48:11.089 --> 00:48:13.360
workshop in that manner.

cf32b39e-e27a-4370-b517-a264ca69f612-0
00:48:13.360 --> 00:48:15.800
Our workshop consists of two major parts.

06111451-4f05-48dd-bafa-68cb6c67f3d6-0
00:48:15.800 --> 00:48:18.910
First thing first, we,
we come up with AI1O1,

06111451-4f05-48dd-bafa-68cb6c67f3d6-1
00:48:18.910 --> 00:48:21.480
let me say training or like a lecture.

da69fe67-84c8-4fc3-8fe6-1175eda260b3-0
00:48:21.840 --> 00:48:24.849
We,
we share our know how about machine

da69fe67-84c8-4fc3-8fe6-1175eda260b3-1
00:48:24.849 --> 00:48:29.440
learning and the future of art field
intelligence practices.

f627b99d-94c0-46bf-8d7a-50ae1bdc3308-0
00:48:29.720 --> 00:48:36.850
Also we we explain SAP business AI
technologies just like our colleagues

f627b99d-94c0-46bf-8d7a-50ae1bdc3308-1
00:48:36.850 --> 00:48:38.120
explain that.

26b8b69e-21a3-4765-804f-aa41ddb9f5fd-0
00:48:38.400 --> 00:48:43.016
And in the second part,
we come up with industrial specific AI

26b8b69e-21a3-4765-804f-aa41ddb9f5fd-1
00:48:43.016 --> 00:48:47.120
business scenario and we share AI
opportunity road map.

eba75433-89f8-417a-a3cb-3b24a0ccd3e7-0
00:48:47.240 --> 00:48:47.920
Also.

4e470ebf-16ad-419e-a6e8-a6f8d188c25c-0
00:48:48.680 --> 00:48:52.392
We arrange these opportunities in terms
of, as I said,

4e470ebf-16ad-419e-a6e8-a6f8d188c25c-1
00:48:52.392 --> 00:48:56.240
business value perspective and also
complex perspective.

a00efae6-73f8-450b-af1f-f909e640c1aa-0
00:48:57.160 --> 00:49:01.954
And according to this priorities business,
AI business scenario,

a00efae6-73f8-450b-af1f-f909e640c1aa-1
00:49:01.954 --> 00:49:07.116
visibilities and priorities,
we have a consensus with our clients and

a00efae6-73f8-450b-af1f-f909e640c1aa-2
00:49:07.116 --> 00:49:12.869
we review the outcomes and consolidated
our brainstorming session and come up

a00efae6-73f8-450b-af1f-f909e640c1aa-3
00:49:12.869 --> 00:49:14.640
with the big next steps.

f345f0eb-4beb-4fa5-b086-2e042f579df9-0
00:49:14.920 --> 00:49:19.691
And also we define the key stakeholders,
keep actors in both sides,

f345f0eb-4beb-4fa5-b086-2e042f579df9-1
00:49:19.691 --> 00:49:22.920
in client size and in vendor side,
let's say.

6e55c9ff-de6c-45e8-947c-3a4db4ae2afc-0
00:49:23.440 --> 00:49:30.924
And these are the four steps cooler and,
but can I jump in and like ask a question

6e55c9ff-de6c-45e8-947c-3a4db4ae2afc-1
00:49:30.924 --> 00:49:35.794
here because you know,
it sounds fantastic, but like,

6e55c9ff-de6c-45e8-947c-3a4db4ae2afc-2
00:49:35.794 --> 00:49:43.098
do you have a customer example that is
live and you can you show a case how it's

6e55c9ff-de6c-45e8-947c-3a4db4ae2afc-3
00:49:43.098 --> 00:49:44.000
done here?

63a6f601-6f5d-4aad-973e-9848615ee853-0
00:49:44.480 --> 00:49:46.480
Yes, exactly.

5709af27-8980-4392-9fff-412b659b4ff1-0
00:49:46.480 --> 00:49:46.840
Indeed.

df8fd6f5-0321-4156-81ee-1c6d0c7b13d8-0
00:49:46.840 --> 00:49:53.280
We have a a workshop with a client
operating in Summit Industry.

5434ea3c-c4ad-4b38-9a83-430064a30309-0
00:49:53.800 --> 00:49:59.120
And as you see,
I shared some screenshots about them.

54e65a9d-f267-458e-ba42-dec9c4fc008b-0
00:49:59.640 --> 00:50:05.874
Indeed, it consists of three major titles,
stages, or let me say first thing first,

54e65a9d-f267-458e-ba42-dec9c4fc008b-1
00:50:05.874 --> 00:50:11.514
the power of what question rather than
blessing her question the technology

54e65a9d-f267-458e-ba42-dec9c4fc008b-2
00:50:11.514 --> 00:50:11.960
stack.

173c9ee3-73f5-43b8-b749-b440a8169ad1-0
00:50:12.680 --> 00:50:15.520
I know that the technology is a bit
confusing.

0af5f497-e203-41fa-bec8-d32943e8db19-0
00:50:15.800 --> 00:50:21.784
We make it simple and we try to deep dive
the customer specific requirements and

0af5f497-e203-41fa-bec8-d32943e8db19-1
00:50:21.784 --> 00:50:23.040
their challenges.

88aad1cb-be0f-4e1a-9a9b-df83278299d1-0
00:50:23.320 --> 00:50:27.688
And in parallel,
we review the literature state of art in

88aad1cb-be0f-4e1a-9a9b-df83278299d1-1
00:50:27.688 --> 00:50:32.960
the industry for grasping the X-ray of
the industry and also company.

1d09eec9-46d0-40d9-9e70-eed1499945e0-0
00:50:33.240 --> 00:50:36.758
As you see, for instance,
in semantic industry,

1d09eec9-46d0-40d9-9e70-eed1499945e0-1
00:50:36.758 --> 00:50:42.696
we come up some state of art perspectives
and also main challenges in production

1d09eec9-46d0-40d9-9e70-eed1499945e0-2
00:50:42.696 --> 00:50:46.654
phases, for instance,
And as the following step quad,

1d09eec9-46d0-40d9-9e70-eed1499945e0-3
00:50:46.654 --> 00:50:50.760
we slightly move towards elaboration,
the half section.

8bb03032-ff48-46ba-a7a7-b7ed638c5bb0-0
00:50:51.160 --> 00:50:54.440
And I rename it just to as a rule of
thumb.

683e0a47-c970-44e3-b464-05b337476248-0
00:50:54.720 --> 00:51:03.325
What I mean that we try to keep it simple
this stage and we try to not to push lots

683e0a47-c970-44e3-b464-05b337476248-1
00:51:03.325 --> 00:51:08.960
of potential AI use cases and make
confuse our client.

93fdb9fb-1a2e-4983-849c-e529198b55b5-0
00:51:09.240 --> 00:51:15.208
Rather than this suggest 2 quick wins,
low hanging fruits, let me say use cases,

93fdb9fb-1a2e-4983-849c-e529198b55b5-1
00:51:15.208 --> 00:51:19.040
2 tactical and two strategic use cases,
as you see.

79c8141d-76ee-426e-8b01-01a87008daf0-0
00:51:19.680 --> 00:51:24.036
And finally,
we come up with a priority framework,

79c8141d-76ee-426e-8b01-01a87008daf0-1
00:51:24.036 --> 00:51:24.720
A prism.

2c57efe4-312b-414c-99d3-9b8028d4e5dc-0
00:51:24.720 --> 00:51:29.920
Let me say we arrange the these use cases
in terms of business value.

04d9da62-cdb4-41dd-9935-fc17d33b6daa-0
00:51:30.640 --> 00:51:36.405
They may sometimes cost saving potentials,
they may prove sometimes revenue

04d9da62-cdb4-41dd-9935-fc17d33b6daa-1
00:51:36.405 --> 00:51:37.240
generation.

f7cf0408-f0e3-4d14-bd99-3f6a8e44d485-0
00:51:37.520 --> 00:51:41.469
And in parallel we,
we we analyse these use cases in

f7cf0408-f0e3-4d14-bd99-3f6a8e44d485-1
00:51:41.469 --> 00:51:42.960
feasibility mindset.

3a7e87e7-d55d-475e-9cc1-4220761ddad8-0
00:51:43.160 --> 00:51:48.484
So come up with such a prism and also we
share our recommendations our and

3a7e87e7-d55d-475e-9cc1-4220761ddad8-1
00:51:48.484 --> 00:51:51.040
concluding remarks with our clients.

43e5afad-f1f2-44f2-ad78-266699c394fa-0
00:51:51.680 --> 00:52:00.225
So then and then finally we come up with
a road map and we we got ready for big

43e5afad-f1f2-44f2-ad78-266699c394fa-1
00:52:00.225 --> 00:52:01.400
next steps.

474af714-aa32-457e-b7d8-999cb019d1c6-0
00:52:01.920 --> 00:52:05.480
And finally it's full time.

abfde709-9d1a-40ce-8c05-06b84e7e4961-0
00:52:05.800 --> 00:52:09.760
Indeed, we shared a question at the poll.

5c19d211-1791-4542-87f2-7793e2d48e35-0
00:52:10.040 --> 00:52:14.568
Ladies and gentlemen,
according to your responses,

5c19d211-1791-4542-87f2-7793e2d48e35-1
00:52:14.568 --> 00:52:21.316
we are very glad to work both with you
and do not hesitate to ask questions

5c19d211-1791-4542-87f2-7793e2d48e35-2
00:52:21.316 --> 00:52:26.200
about our Nagara AI workshops and thank
you very much.

4a5db33a-b43a-4056-8e61-41f7dec2c452-0
00:52:27.240 --> 00:52:27.640
Thank you.

8dbe3e64-f4ed-498c-82f2-64bce372e3a8-0
00:52:28.280 --> 00:52:33.556
Now I understand like in the in this,
let's say maybe a product,

8dbe3e64-f4ed-498c-82f2-64bce372e3a8-1
00:52:33.556 --> 00:52:39.806
you will get deep into the industrial
knowledge and bring it to the customer

8dbe3e64-f4ed-498c-82f2-64bce372e3a8-2
00:52:39.806 --> 00:52:45.732
and also start to address the problems
with inside your organization and

8dbe3e64-f4ed-498c-82f2-64bce372e3a8-3
00:52:45.732 --> 00:52:50.440
prioritize them with also awareness with
realistic steps.

eda20b8d-244a-4987-8d8b-78b1cb53eab5-0
00:52:50.760 --> 00:52:54.360
Sounds cool, thank you, thank you.

3449b145-a84b-4023-a5b3-d6ef7e12f04d-0
00:52:54.520 --> 00:52:58.190
And while like the we are running the
poll,

3449b145-a84b-4023-a5b3-d6ef7e12f04d-1
00:52:58.190 --> 00:53:03.280
maybe also I can do a wrap up and go to
the Quran a station.

0d82df22-3f41-4db8-bdd6-5db73551d27a-0
00:53:03.360 --> 00:53:07.080
But thanks everyone for like being with
us so far.

5cb5777a-a979-4364-ac8e-df12597deb6a-0
00:53:07.560 --> 00:53:16.661
So today Harish explained us about the AI
strategy of SAP and also which tools are

5cb5777a-a979-4364-ac8e-df12597deb6a-1
00:53:16.661 --> 00:53:25.104
out-of-the-box for SAP like short,
great presentation and use cases from the

5cb5777a-a979-4364-ac8e-df12597deb6a-2
00:53:25.104 --> 00:53:26.640
huge scenario.

8340f1f4-5408-4f77-b296-da4a881edab4-0
00:53:27.040 --> 00:53:31.116
But beside that,
if you want to move forward further and

8340f1f4-5408-4f77-b296-da4a881edab4-1
00:53:31.116 --> 00:53:36.838
want to create some scenario maybe which
are not covered within all of the best

8340f1f4-5408-4f77-b296-da4a881edab4-2
00:53:36.838 --> 00:53:40.199
scenarios,
then comes the custom AI scenarios.

ab08ed22-5cd4-4784-80cc-ece7813a01be-0
00:53:41.200 --> 00:53:47.309
Roberto, thanks to Roberto's knowledge,
he also shared us how custom AI can be

ab08ed22-5cd4-4784-80cc-ece7813a01be-1
00:53:47.309 --> 00:53:51.640
utilized and implemented with SAP tools
are delivering.

e79d8d01-765f-47b6-9e22-fc6e7ee9f89d-0
00:53:52.120 --> 00:53:58.068
And I will I try to explain the
analytical aspects of this topic like

e79d8d01-765f-47b6-9e22-fc6e7ee9f89d-1
00:53:58.068 --> 00:54:02.573
this BDC is also used for the analytics
perspective,

e79d8d01-765f-47b6-9e22-fc6e7ee9f89d-2
00:54:02.573 --> 00:54:09.286
but also for the data management part and
how we can utilize that data already

e79d8d01-765f-47b6-9e22-fc6e7ee9f89d-3
00:54:09.286 --> 00:54:15.915
there in Business Data Cloud and the
tools under it and how we can utilize it

e79d8d01-765f-47b6-9e22-fc6e7ee9f89d-4
00:54:15.915 --> 00:54:19.400
with AI tools inside Business Data Cloud.

aecde406-43e1-48da-9617-f141151c75b7-0
00:54:19.400 --> 00:54:24.558
And thank you also Iran,
that you explained the workshop,

aecde406-43e1-48da-9617-f141151c75b7-1
00:54:24.558 --> 00:54:31.317
the methodology which helps our customers
to identify and address potential

aecde406-43e1-48da-9617-f141151c75b7-2
00:54:31.317 --> 00:54:35.320
potential problems that can be solved by
AI.

b24743be-9804-46fc-8fa4-6189fb36b884-0
00:54:35.880 --> 00:54:40.440
So this was our key to take away so far.

22f84f3b-2752-400c-ae74-063d939bec08-0
00:54:40.840 --> 00:54:45.163
But since we,
we do have last couple of minutes and

22f84f3b-2752-400c-ae74-063d939bec08-1
00:54:45.163 --> 00:54:49.320
questions,
I'm just jumping to the questions now.

25ba5c03-ef0a-4565-93b7-87a95e6b599e-0
00:54:51.160 --> 00:54:54.480
So there is a question and thanks for
that.

64183379-7c9e-43a8-b4a2-47563754e25a-0
00:54:54.640 --> 00:54:58.975
Well,
I I just want to summarize it that is

64183379-7c9e-43a8-b4a2-47563754e25a-1
00:54:58.975 --> 00:55:05.281
starting from data start,
like maybe also addressing the legacy

64183379-7c9e-43a8-b4a2-47563754e25a-2
00:55:05.281 --> 00:55:08.040
tools that is already there.

b17f4130-0581-48eb-ba69-e5c5171ef69c-0
00:55:09.360 --> 00:55:16.003
Like they already may be enlisted on the
data sphere and now they are facing some

b17f4130-0581-48eb-ba69-e5c5171ef69c-1
00:55:16.003 --> 00:55:21.998
different layers with different list
licensing And how SAP is 3 trying to

b17f4130-0581-48eb-ba69-e5c5171ef69c-2
00:55:21.998 --> 00:55:25.320
simplify this scenario, the complication.

9e0886ae-9611-4215-8410-05eae61b8bae-0
00:55:28.080 --> 00:55:32.890
So I just want to explain this and like
if anything missing,

9e0886ae-9611-4215-8410-05eae61b8bae-1
00:55:32.890 --> 00:55:36.360
maybe my colleagues can also add
something.

6049fef5-0841-46be-8c04-4eb8efb17165-0
00:55:36.360 --> 00:55:38.520
But I can understand that.

6a55bce1-9b68-41c5-82d5-518a3dc454be-0
00:55:38.520 --> 00:55:45.363
But like the data file was also announced
for the tool to, let's say operate,

6a55bce1-9b68-41c5-82d5-518a3dc454be-1
00:55:45.363 --> 00:55:50.451
orchestrate every data operation with
inside the company,

6a55bce1-9b68-41c5-82d5-518a3dc454be-2
00:55:50.451 --> 00:55:54.400
but it's still there and never been
changed.

33c7cb17-fdce-4762-9000-20f90b7515f6-0
00:55:54.720 --> 00:56:00.909
So the strategy in BDC is to create a
seamless integration within analytics

33c7cb17-fdce-4762-9000-20f90b7515f6-1
00:56:00.909 --> 00:56:06.935
tool and also create a framework which
the customers can easily implement

33c7cb17-fdce-4762-9000-20f90b7515f6-2
00:56:06.935 --> 00:56:10.600
scenarios on top of SAP or non SAP
solution.

6e11c54e-2098-4f16-b42f-08b4c5f57af3-0
00:56:11.000 --> 00:56:16.783
So we didn't BDC like status far is still
there or like the analytics cloud of

6e11c54e-2098-4f16-b42f-08b4c5f57af3-1
00:56:16.783 --> 00:56:19.200
every investment are still there.

9b91e06c-4932-4c97-8ca9-70f5352fd894-0
00:56:19.560 --> 00:56:24.714
And for example,
when you change your contract to BDC,

9b91e06c-4932-4c97-8ca9-70f5352fd894-1
00:56:24.714 --> 00:56:30.617
it's also possible to,
let's say to migrate your existing data

9b91e06c-4932-4c97-8ca9-70f5352fd894-2
00:56:30.617 --> 00:56:35.303
status fair investment to the under BDC
and also,

9b91e06c-4932-4c97-8ca9-70f5352fd894-3
00:56:35.303 --> 00:56:42.800
but it can give you a possibility to
let's say scale through this applications.

c174284d-64dd-4284-b808-e371726b10d9-0
00:56:42.840 --> 00:56:47.821
Maybe you can now reserve some of your if
you have some data fair,

c174284d-64dd-4284-b808-e371726b10d9-1
00:56:47.821 --> 00:56:49.160
let's say credits.

73138b02-40d7-4237-8858-df85ac668b1f-0
00:56:49.640 --> 00:56:56.075
You can maybe invest more phone stuck or
different for data bricks,

73138b02-40d7-4237-8858-df85ac668b1f-1
00:56:56.075 --> 00:57:00.240
utilize some AI or visualization
scenarios.

cb20a211-2f86-4934-8531-8b51b9fa6b4d-0
00:57:00.240 --> 00:57:07.447
It's all possible here and would be good
to discuss maybe like if any further

cb20a211-2f86-4934-8531-8b51b9fa6b4d-1
00:57:07.447 --> 00:57:12.160
discussion related with architecture or
licensing.

eb76e9aa-3d70-48a5-bedc-c637a2e6cf62-0
00:57:12.560 --> 00:57:17.463
We are more than happy and we are
absolutely welcomed here to analyse and

eb76e9aa-3d70-48a5-bedc-c637a2e6cf62-1
00:57:17.463 --> 00:57:19.120
understand your question.

c158c14b-65c1-422c-9922-273d1f9d4016-0
00:57:19.440 --> 00:57:24.440
You can also like the all of the
addresses and the information.

3c9e84e3-310a-4669-bac6-a1eac4e497f9-0
00:57:25.320 --> 00:57:32.079
The contact information here can be found
so there is one another questions like

3c9e84e3-310a-4669-bac6-a1eac4e497f9-1
00:57:32.079 --> 00:57:37.921
learning hub online resources provide
structured knowledge and guided

3c9e84e3-310a-4669-bac6-a1eac4e497f9-2
00:57:37.921 --> 00:57:43.680
experience on AI and datastare workshops
are positioned differently.

b29d327a-c8ef-454d-a5b7-ddb06d18d666-0
00:57:43.760 --> 00:57:47.200
Could about this is about the Nagara
workshops around.

f911b53d-f9df-46b1-91b9-b3fa559166c4-0
00:57:47.760 --> 00:57:52.666
Could you explain how the Nagara AI
workshops go beyond standard learning

f911b53d-f9df-46b1-91b9-b3fa559166c4-1
00:57:52.666 --> 00:57:56.778
content And there's also example like
customer specific data,

f911b53d-f9df-46b1-91b9-b3fa559166c4-2
00:57:56.778 --> 00:58:00.160
business use cases,
cross technology integrations.

420cb1c2-e811-4c89-843f-d55bf374bfbe-0
00:58:00.880 --> 00:58:02.440
What is different than learning?

8441437f-6100-4341-87b4-f9e8005b0f38-0
00:58:02.720 --> 00:58:03.760
Yeah, yeah.

23011ac7-15b6-45b0-8a8b-e6427d889dfd-0
00:58:04.760 --> 00:58:09.856
This trainings,
AI trainings is taking part of our AI

23011ac7-15b6-45b0-8a8b-e6427d889dfd-1
00:58:09.856 --> 00:58:13.160
workshops according to the outcome.

c29ca18c-b105-43b2-84fa-efae0e430490-0
00:58:13.160 --> 00:58:18.600
Maybe we can increase the intellectual
not know how of the clients.

35d19224-25fc-47ed-9aca-ad89c67e1b6a-0
00:58:18.600 --> 00:58:22.920
Let me say in the yes,
SAP Learning Hub is a good starting point.

63aad135-ced7-46d0-81ba-fa319f6425ed-0
00:58:22.920 --> 00:58:28.928
But as Nagara, we,
we have our own training repositories and

63aad135-ced7-46d0-81ba-fa319f6425ed-1
00:58:28.928 --> 00:58:30.800
also training hubs.

bc3fafd4-278c-4e45-9f1e-fee6c3d4aa85-0
00:58:31.000 --> 00:58:36.783
So during our according to the outcome,
we can share this content with the

bc3fafd4-278c-4e45-9f1e-fee6c3d4aa85-1
00:58:36.783 --> 00:58:37.400
clients.

9bda7f96-8eca-46b6-bbe0-f9369b76da67-0
00:58:37.760 --> 00:58:42.280
And also we can state that customs
specific data.

ce476428-8d79-4e4b-a170-d283b5bd49ff-0
00:58:42.280 --> 00:58:46.840
We we don't share such operation data
with other customers.

780b778d-569e-485c-a924-88077bb32a90-0
00:58:47.160 --> 00:58:51.536
But yes,
business case at the business case

780b778d-569e-485c-a924-88077bb32a90-1
00:58:51.536 --> 00:58:59.393
sharing and cross technology integration
there are potential no hop sharing in

780b778d-569e-485c-a924-88077bb32a90-2
00:58:59.393 --> 00:59:01.880
terms of our AI workshop.

d148cdf1-5e43-4444-91bc-61dc26cb3aea-0
00:59:02.680 --> 00:59:08.983
Also, as I said before,
we have such a literature review and also

d148cdf1-5e43-4444-91bc-61dc26cb3aea-1
00:59:08.983 --> 00:59:15.000
we review our best practices,
our period practices in AI site.

5a6550ac-23b3-484e-a1a6-070d6d00c5e4-0
00:59:15.240 --> 00:59:20.375
So we make such a, let me say checking,
we check the similarity,

5a6550ac-23b3-484e-a1a6-070d6d00c5e4-1
00:59:20.375 --> 00:59:26.063
we check the neighborhood of the
underlying use cases and we also share

5a6550ac-23b3-484e-a1a6-070d6d00c5e4-2
00:59:26.063 --> 00:59:27.960
our best best practices.

f00ad6cf-73a8-4d54-83aa-ef2531900bf6-0
00:59:27.960 --> 00:59:35.469
But sometimes it can be a customer
specific AI use case in in that manner,

f00ad6cf-73a8-4d54-83aa-ef2531900bf6-1
00:59:35.469 --> 00:59:43.280
we we look around and look to benchmark
use cases and how the period studies.

2473a2ca-b19c-4b40-a416-c7710488d146-0
00:59:43.280 --> 00:59:44.480
Applications.

f64405d0-1a1b-4680-b3c5-bb3242990f73-0
00:59:45.120 --> 00:59:48.440
Initiatives handled the underlying use
case.

f84e2621-9d43-41cf-bb4a-a720121dbcc5-0
00:59:48.760 --> 00:59:54.680
And we,
we try to move align with this practices.

db57551a-8f05-42f8-866a-6b0d316497e7-0
00:59:54.680 --> 00:59:55.240
Let me see.

b5f4d297-2718-45a0-846c-c1744efc623a-0
00:59:59.000 --> 00:59:59.680
Thank you, Eran.

dc59e39e-0494-4aa3-8766-af36862c3227-0
01:00:00.160 --> 01:00:00.760
You're welcome.

a5f7bc15-8284-4af2-b924-20d6d6776df9-0
01:00:01.080 --> 01:00:04.160
And now we reach to one hour.

d9baaaf6-bf2d-4eea-bbaf-ecdec990feab-0
01:00:05.120 --> 01:00:08.920
So maybe like if anyone with further
questions,

d9baaaf6-bf2d-4eea-bbaf-ecdec990feab-1
01:00:08.920 --> 01:00:12.880
please do not hesitate to get in contact
with us.

5bd7e611-0edf-4bd6-95ea-ebeaac9e0250-0
01:00:14.680 --> 01:00:19.400
We will like answer anything really
responsibly.

ebf56b48-4232-4c25-bdcc-40eafc961f1d-0
01:00:19.640 --> 01:00:25.469
But I will like to thank you again for
Roberto and Harish to joining us in this

ebf56b48-4232-4c25-bdcc-40eafc961f1d-1
01:00:25.469 --> 01:00:29.040
webinar and for the fruitful information
around.

09475774-7aa8-48e1-b551-cf8258ace16a-0
01:00:29.040 --> 01:00:29.800
Thank you again.

749bc2f1-8ece-45bb-ad5b-fa4bc7cd821a-0
01:00:31.880 --> 01:00:33.200
Hope to see you soon.

92cdc953-6131-4b68-836b-7731e756309a-0
01:00:35.480 --> 01:00:36.080
See you again.

63c473c2-4d12-41ce-a47c-a0a0f8e2da19-0
01:00:36.840 --> 01:00:37.200
Bye bye.