Hi everyone.
Welcome to our webinar prepared in collaboration with SAP and Nagaro.
This is Kavan Choktash from Nagaro.
I'm group manager to Analytics and Performance Management in Nagaro, Turkey.
So today with my colleagues, we are going to discover a shortcut to
AAI transformation with SAP.
So today with me, Harris Kentali and Roberto Urban from SAP,
MENA and Eraneskin from Nagaro, they are both sharing their experiences
in this topic and our agenda will be covering starting with the AI strategy by
Harry.
He's going to inform us about the all of the box solutions delivered by SAP and
then we are going to discover how custom AI is implemented on top of SAP.
And in the third part, I will be delivering some information how
AI can utilize on top of Business Data Cloud.
Beside this, in the last part, LNA scheme will be delivering Nagara
methodology on AI and to workshops to address the problems or address the AI
possibilities within the organization.
So this webinar is hosted by Nagara, which is the global collective of
technology experts with an entrepreneurial caring mindset.
And it is founded 28 years ago, listed in Deutsche Birthday.
And the global presence is over 27 countries with 60 countries with customer
presence and more than 18 experts.
They are delivering technology solutions everywhere.
So today let's jump into the today's topic,
which is a really a hype is about AI.
But I will start with the shocking facts.
Did you know that 95% of AI projects failed?
So this fact from a brand new paper from MIT,
which we will also refer a lot in this webinar has also main reasons shared in
this study.
So one of them is like the company is still seeing AI as an algorithm,
only an algorithm and it's also maybe only a technology,
but they're seeing it independent from the business workflow or the business
work, let's say business processes.
And also second important topic on this is also complication by in house
development of AI is also causing this impact in the AI developments of
companies.
But we do have a great expert here Harris today.
So I will hand over to him.
But before jumping to his session, I will also forward him a question that
how can a statement could reverse this ratio with like by helping AI
capitalities in SAP Harris stage of yours.
Thank you Kewank for the introduction.
Hello.
Hi, everyone.
Pleasure to be with you today.
That was an interesting insight.
Kewank.
95% of the AI pilots remain in the pilot stage and not able to deliver the ROI on
their investments from the customers.
And this is a recent study from MIT, but similar studies have been published
in the last couple of weeks.
And this has been the key reason why many of the AI company stocks have been
dropping or plunging, right.
So there are two key outcomes that came out of the study.
Number one, buy versus build strategy.
It is observed after several surveys that 67% success has been seen when customers
adopted a buy strategy rather than a build strategy.
So rather than building your own large language models,
rather than trying to build an applications from scratch on AI,
it's better to lookout for what is available out-of-the-box right from the
different ERP vendors #2 implementing AI and the back end process rather than
front end.
So most of these companies who try to do this, POC's,
most of them try to infuse AI into the front end processes in marketing rather
than looking at, you know, infusing AI into their core back end
processes.
So these are the 2 outcomes.
And now we will see how SAP delivers the AI strategy and also the out-of-the-box
AI capabilities to bridge this gap and make sure the customers are adopting
further.
So what you see here is the three layer strategy of SAP on the top what you see
is the conversational layer which is nothing but your copilot but fully
grounded and answering from your SAP data.
So any response, any question you you ask Julie is going
to take the data in consideration from your SAP applications.
Also in the same first layer we offer agents.
As you see there are number of agents across different lines of business.
So we plan to deliver about 40 agents by end of the year.
We already have almost 12 to 14 agents already out there and there will be more
coming in the next couple of quarters.
Now we move on to the next layer which is the embedded AI layer.
As you see across the various lines of business and processes, we infuse AI.
For example, if you have been creating your goals,
performance goals and SuccessFactors in the past where you might have been
spending like a couple of hours or sometimes even days.
Now with infusing AI into your goal creation process,
you can do that in in less than one to two hours.
So basically embedded AI is just enhancing and improvising your existing
process infused by AI.
Now the third pillar, which is the customized AI, of course,
you would like to differentiate from your competition and you would like to have
something very niche for your industry.
This is where we also offer you the strong AI foundation powered by business
data cloud and business technology platform.
And why this combination is really powerful is especially because of the
partnerships.
We have different large language models.
For example what you see here IBM is is a very often used large language model for
HR processes.
Similarly for Anthropic, we have seen customers preferring supply
chain use cases for Anthropic.
So you have the full flexibility and choice.
You can decide what model works for you best based on the full range of models
that we offer if we move on.
So one last strategy slide.
So what you see on the left is how an ideal data and AI platform should be.
So basically in order to build AI out of or get insights out of your data, first,
you need a harmonized and strong data platform, right?
So this is the ideal situation.
But what happens in the reality is you have a combination of various ERP systems,
non SAP applications, LOB applications and a number of data
leaks.
And when you try to pull this data into these database,
try to rebuild the semantics.
It's not just the technological issue, rather it is the expertise you try to
bring together from the ERP vendor, from the non SAP vendors and also
bringing these data engineers together.
And trying to rebuild those semantics is the most complex thing.
And when you try to build such semantics again,
you have to build AI on top of that derailed semantics.
This is where the AI projects are, you know, bound to fail.
If we move on, how SAP Knowledge Graph will give you
exceptional results, right?
Which a traditional large language model cannot deliver.
For example, if you look at how do I know if my boots
movement is posted for a certain sales order.
If you ask this to a large language model, it will look at information for your
sales order, it will look at your boots movement
document number, That's all.
When we talk about Knowledge graph, so it is grounded on half a million about
tables and thousands of CDs views in SAP Espohana.
Especially right now when you ask a question about your sales order,
goods movement, Knowledge Graph has an understanding of
the end to end process.
Starting from the quote to the invoice, all the documents and the relationships
are known to the Knowledge graph.
And when you ask such a question in June, which is grounded on the Knowledge Graph,
it can give you a very relevant response looking at the end to end process.
Now if we move on how this is embedded, so knowledge graph is embedded of course
in Espohana beside behind Jewel, you see that, behind agents, you see,
you see that.
And now we are enrolling that rolling out that across other applications as well.
And what you see here is the number of skills we offer across various business
applications of SAP.
So 1600 skills is what we offer out-of-the-box and of course,
you have the capabilities to build your own skills.
But if you move on, I can show you quickly how do you
interact with your new UI which is June.
So we have 4 key patterns here, informational pattern,
the number one which is to get you information from SAP help or your own
company policies or documents, right.
For example, you can ask a question about what is your
allowance if you have to travel from UAE to Germany for example,
on a business trip.
So this could be something documented in your policies within your SharePoint and
Jul can get that information for you.
Then we have the navigational pattern, especially useful in your Esfahana
application where you have hundreds of Fury applications.
All you have to do is I would like to create my purchase requisition and Julie
will help you navigate to that application to conduct the transaction.
The key pattern which is the transactional pattern,
creating a leave request and SuccessFactors,
creating a purchase order named S4, looking at your invoice postings in S4.
These are typical examples of transactional pattern which are basically
interacting with your database underneath.
Then we have the last pattern which is the analytical pattern basically to
represent your numbers or numerical information and more pictorial format as
where analytical pattern comes into picture.
For example, can you compare my Q3 revenue versus Q2
revenue?
This will be represented as a graphical pattern and and true then we move on.
This is one example of how a cash manager can benefit from June with an SO and if
we move further, let's play this.
So all you have to do here is you ask for the cash management overview and within
just a click you will see what are all the bank accounts which are active versus
inactive.
Then you can look at the top three upflows and also you can look at the top
three inflows.
And once you have this information, you can also have a detailed look at one
of these transactions just within June and it can also help you navigate in the
background to that particular detail.
Now you can also look at your processing status of a certain transaction.
You will see if it has been processed, any warnings, errors.
All this information is at your fingertips again.
Now you can look at the cash shortages within your company for a certain
currency or a certain date and it would give you the specific information with
the accounts which are short of amount.
And what you can also ask June, is to identify accounts which have
overage of funds to transfer part of the funds to the accounts with shortage.
So as you can see everything end to end with few clicks,
you're able to manage a typical day of a cash manager,
which usually takes a number of navigations to various fury applications
and clicking on so many things, so many transactions to to get to this
outcome.
OK, let's move.
So just in a nutshell, these are the innovations in June where
SAP is investing and keeps investing.
We have tool not only for business users, but we also have tool for consultants,
for developers, basically helping them to accelerate the
project deployment.
And we also offer Joule Studio to extend the standard skills.
So if you need specific skills to interact with your non SAP applications,
for example like ServiceNow, then you can use Joule Studio to create
your own custom Joule skill.
We also offer bi directional integration between Microsoft and Joule besides the
other innovations.
Now just quickly in a nutshell, the key fundamental difference between
the Joule skill and agent Joule skill is something straightforward.
You ask to create a leave request to check to check the status of my order.
Joule can do that.
It's a direct instruction.
Agents on the other hand or objective based.
So if you take the example of order delays,
then Joule agent can analyze the order delays.
So just alternative shipping routes and notify the customers when their order
will be delivered.
So you see there are 4 main steps involved.
First it plans the task or the objective that has to be met.
Then it reflects upon the tasks, the subtasks basically.
Then if the objective has not been met, it will reason.
The agent will reason utilizing the latest large language models,
reasoning models basically which can think like humans before it executes the
task to achieve the objective.
More so basically what you see here is we embed knowledge graph across the agent,
agent process starting from grounding.
We have a knowledge graph utilized there.
Similarly, we have knowledge graph for planning the
execution of a certain task and finally also to execute the tasks.
So this is a really powerful combination.
Trying to replicate that outside the SAP landscape or ecosystem is going to take
mammoth amount of time and also could be very complex.
Now if you move on.
Yeah, last but last slide on the Joule and
especially the agents.
So what you see here is first of all the Joule and the agents are powered by the
knowledge graph, but then you also need perfect data or
harmonised data in order to empower that knowledge graph.
So this is where Business Data Cloud comes into picture,
giving access to those specific data products for SAP applications.
And then you can also infuse your non SAP data through Business Data Cloud,
build those data products and empower your agents to have a end to end view of
your business data.
Now let's look at an example of one of the out-of-the-box AI agents that we
deliver.
So this is for the accounts receivable expert trying to mimic the actions of a
typical accounts receivable expert.
So what the dual agent is doing here is looking at the over dues of the
receivables.
Then it looks at the dispute cases that have been open and for how long.
It also looks at the turning history.
Now you can ask the agent to recommend the actions to, you know,
bring your money faster, right?
So it gives you specific instructions.
Hey, these are the disputes which have been
pending for so long.
Please expedite those and you can get your cash faster.
Then you can also ask can you explain me what is the reason behind this
recommendation?
So you get the transparency behind those suggestions and then you can also look at
further details of those certain disputes which have been open.
Finally, you can also navigate to the dispute and
not only that, you can also create a new dispute from
your agent.
So this is how the life of an AI expert would be information at his or her
fingertips.
Let's go now moving on to the next layer of AI,
which is the embedded AI capabilities.
As you can see across the different lines of business, we offer the AI capabilities.
Now if we move on, we can just look at an overview of some
examples, what we offer as out-of-the-box AI within
the finance.
So as you see, starting from financial planning to the
court to cash process and also the government's risk and compliance,
these are a couple of examples.
If we move on, we can look at some examples in the HR
space in in SuccessFactors especially, you have the full employee journey,
end to end employee journey enhanced and empowered by AI.
And we can look at one example of how an embedded AI use case out-of-the-box looks
like.
So this is the performance goal creation and SuccessFactors.
This is something we at SAP utilize today.
And I remember those days before this feature has been rolled out,
I used to spend like 2 days and sometimes my weekend to plan my goals for for my
ear.
Right now with the AI enhanced or infused goal creation,
all I need to know is what is my goal for this year?
Where do I want to go?
Now?
As you see here, when you take the help of AI,
AI will breakdown your goal and also give you suggestions how you can breakdown
that goal into sub goals or tasks to achieve across the next couple of
quarters.
So within the different milestones, you can see how your core execution can
be achieved.
You can accept the suggestion by AI or you can also enhance this further.
And if you if you look at again, you can click on generate AI.
So we generate the goal and then you can change the goal planning.
So in this case, we change this instead of a three quarter
plan, give me a four quarter plan and AI will
enhance the goal generation process.
What you see on the right is what is proposed by AI and you're free to choose
whether to accept or reject the proposal and create your own goals.
So likewise, we have embedded AI delivered across
various processes in HR and procurement, Arriba, supply chain, everywhere.
Of course, we will see in the next sessions how you
can adopt those AI capabilities within your own organization.
Thank you, Kiwan.
Thank you, Arish.
This is Roberto from SAP as as Harish in in this section.
In this section of the presentation, you will see how to manage custom AI
implement a custom AI scenario.
That means as soon as you identify a scenario that can be potentially
augmented by AII capabilities, First and foremost you have to check
whether this scenario is part of our embedded solutions.
They're the ones that Harish show if this scenario is not part of the embedded
solution and or you want to extend this scenario with the the standard scenario
with your own to fill your own gaps, you can work with the custom AI option.
The custom AI option as you see here, it's founding its own capability on the
AI foundation services available in the BTP.
In this slide, we see that the I Foundation capability
are splitted in two main groups.
The first group is so-called SAPAI services and these services are basically
AI services ready to be used as a fast start for approaching the AI
implementation.
What it means, it means that you can start using the AI
services without develop anything.
It's just matter of configuration.
It's just matter of training the standard AI services and consuming the standard AI
services out of the provided APIs.
The next slide we will see an example of these services.
These are four services we are offering out of the platform.
The main 2 ones are the two in the middle, the document information extraction and
personalized recommendation.
Document Information Extraction is a service that allows you to extract
structured data out of unstructured content.
So typically you'll receive APDF.
Typical example, you receive APDF with an invoice data,
with some invoices invoice data, and then you want to extract this invoice
data to, for example, create a corresponding invoice within the
AP system.
This is something you can achieve using this service.
The beauty of this service is that you can use this service as it is because the
service is already strained by SAP with millions of invoices.
So you can use as it is in order to extra the invoices data.
Otherwise, you can use the premium capability of the
service where you can create your own invoice schema,
data schema and then instruct the service in order to find and extract these the
property of your of your own data schema through the usage of NLLM model.
And this way the service is much more performant and can be much more flexible
than of course the basic option provided out-of-the-box.
The personalized recommendation is another service that you can train with
the historical data.
For example, typical use cases, you train the service with historical
orders data of a user and then you can use the service in order to promote some
goods to be to be selected by the the current user to be put in the cart in
order to enrich the order value.
And the other two ones instead are services related to getting business
entity out of unstructured document.
In this slide, you see an example of the document
information extraction.
In the picture, you see on the on the right side on the
structure document, basically it's a contract.
And then on the left side you see a form that basically contains structure data.
And these structure data are extracted out of the PDF you see in the right.
So out of as I said, out of an unstructured content,
you see you can extract structure data and then you can use this structure data
whatever way you want.
You can you, you have to remind that these services
are available both via API and via a dedicated UIS and this is an just an
example of the document information extraction service.
Then in the lower part of the of this feature of the AI Foundation services,
we have, we have the I life cycle management and
the business data context.
These two services are the heavy lift services.
So are the services that you can use for really create any custom scenario you
have in mind.
Are there services that allow you to create them to use?
Sorry to use the latest and greatest LLM models.
So these services are basically the ones that gives you the 100% freedom of
generating your own custom scenarios.
And then let's see what is contained in these services.
The first and foremost capability containing this service is the model
availability.
So through the generative AI Hub that's basically a service contained within the
I Foundation, you can access more than 50 AILLM.
These AILLM are the ones provided by the you can find in in the in the market are
provided by our own partners and you can leverage on these AILLM services through
the BTP.
Why it's interesting to have this capability available because in this way,
you don't have to subscribe by yourself to each and every model you want to use.
You are automatically subscribed to all the models and then as soon as you decide
to use one of them, you will pay for such a usage.
This gives you gives you the advantage of choice.
So that means let's say that you want to create a scenario where you want to
generate text.
Probably GPT model are the best instead if you want to work on a scenario to
extra data out of an image, typically an OCR scenario,
probably Gemini is the best.
So you can accordingly your needs your specific implementation need.
You can decide which one of the model you want to use and then this model can of
course be the most performance one for the purpose you want to achieve in the
next picture.
In this picture, you see how the platform shows you the
model library.
So this is the model library where you can access all the models available in
the BTP and subscribe with the with the BTP.
And then here you can find all the information related to the models it's on.
KP is if the model is better for doing something than something else,
whether a model is deprecated or not and so on.
So here is the entry point where you can really checking this periment on all the
models available.
Another important capability provided by the general TVA hub is the orchestration.
The orchestration capability basically allows you to work and manage a a request
a response scenario.
So let's say that the the user sends a prompt and then this prompt should be
executed that should be managed by an LLM to get the response.
The generative I have and the orchestration capability,
as we can see in the next slide, gives you the freedom to define a
pipeline of action to work on the user prompt.
What does it mean?
For example, the user sensor prompt and then you can
define via the data masking capability provided by the orchestration.
You can ask to mask the sensible, the sensitive data provider within the
prompt or you can ask to filter any kind of not relevant data within the prompt or
any kind content that is related to that it's aiming to hate or or any other
racism or religious topic.
So you can ask to filter the prompt in the proper way that just the information
that you need and that you want will be collected by the LLM in order to generate
the response are effectively going to the LLM.
And this is a way that the orchestration allows you to, let's say,
better manage and optimize the prompts provided by the users.
In this picture, you see an example of a customer scenario
where a customer was asking us to have a chat about experience in order to let its
own user to create warranty requests.
So this is a retail customer.
This customer sells technology products and then it happened that the user that
bought the product the the customer that bought the product it it wants to create
a warranty because because the product is broken.
So the idea is to create a Chatwood.
That's the one you will see in the next slide to create a chat mode that
basically allows the user to interact with the the post sale support.
And then the user here for can see its own chat history, its own message history,
can send pictures of the broken device.
And then the AI behind the scene takes this picture and identify whether
effectively the device is broken or not.
If it's broken automatically, the warranty request is created.
If the AI that identifies that the the device cannot be broken,
it moves the request to a user that has the freedom to better manage the warranty
request generation creation or deny the warranty request because effectively the
the device is not broken.
And with this slide, I conclude my presentation and I leave
the stage to give thank you, Roberto.
So grateful information here.
Before jumping into the BDC part, I just want to remind everyone like
please do not hesitate to ask any questions.
It will be all answered in the end of the session.
We will have a time for that.
So like all the experts are here, fire your hardest question till the end.
So starting with the AI and how we can utilize the AI within business data
clouds.
But before jumping to session, I just want to explain Business Data
Cloud for the audience maybe who does not know very well about Business Data Cloud
is a framework like working as a business data fabric which combines the data
management, analytics and visualization planning
capabilities of SAP in one umbrella.
So you will do the your data management orchestration under Business Data Cloud
to understand this deeper, the SAP stronger solutions here such as
Data spare for business semantics or maybe like the Data warehousing usage or
Analytics Cloud for the analytics and planning.
Or if you are a legacy SAP user and still on the business data warehouse PW,
there's also an option to modernize this and put it in the under the umbrella of
the PW.
Sorry, Business Data and of course for the AI
machine learning or like for the advanced analytics,
it has the capabilities of data bricks and it is also used for AI foundation for
AI purposes.
So all of these powerful products are working in the corporation and they are
seamlessly integrated to each other in your like on top of your applications.
For example, if you're an SAP managed system such as
maybe public cloud or As for HANA or like 2 SuccessFactors, Arriba, whatever.
We also business Data Cloud delivers some pretty delivered scenarios called data
products or intelligent applications.
The data products are pretty delivered data models that will activate it by you
and once they are activated, this data models start to extract data
and ready to use by third party consumption or any like the visualization
or like any kind of purpose here.
So it's a very fast implementation approach and a really cool shortcut here.
We can also don't miss out the intelligent applications here because
those are the applications not only for analytic purposes such as dashboards,
reports but can be also used for AI purposes.
So once you create or let's say activate an app which is also again the pre
delivered by STP or by the partner contents,
it will automatically activate the underlying data products.
So this means that not spending months of time for implementing an AI project or
analytics project, it will be activated by within the
experience of SAP on top of this SAP data and will be ready to use in maybe couple
of minutes.
So it's very, I really find this handy and important
because this enables us to ensure the governance and also scalability through
the ecosystem.
And those applications are context aware because like has already explained
working on top of knowledge Graph, they do have the context awareness and
these are, as I mentioned, business rated data products and they are
seamlessly integration within each other.
This also enables us a unified unified data platform which also helps us on
decision making with AI inside PDC.
There are also like a possibilities or tools working together to help us to
power and drive AI, which is the AI.
We can like account starting with AI engineering which provides P directional
integration with data products, state of Fair and also with the help of
the data bricks.
You can create your own machine learning workbench on scenarios to utilize your
data and queue decisions with help of AI Knowledge graph.
My colleague Harris already explained will help us to drive us to context
survey results, such as when we save, when we say sales or like when we search
about the sales.
It can also understand from the context, from the sentence if it is positioning
the TNL with the goods sales or the revenue from the sales model.
And this also helps end users to have accurate answers from their prompts.
June is there and also working inside the analytic apps.
So you can ask questions with your natural language and get the answers from
the analytic tools.
And also it is embedding this AI, generative AI to use features which I
will also show in the next slide.
And also agents can work to deploy complex duties such as flows with inside
the financial planning, doing the data modelling or maybe fixing
it back in the system.
They are all available there to also materialize the example with the usage of
AI within a business data club.
This is an analytics club example and with help of AI here you can create all
the shorts by with only giving some prompts with the generative approach.
Let's as in the example you only select maybe the target audience of the task
force or story you are going to create and prompt the requirements goal.
Define the goal and if you have a template, select them.
And once you generate it, what a dashboard is there so you don't
need to spend time to create everything from scratch.
It is also helpful and handy in the situation.
But one of the important thing here is the use cases,
because we do have a lot of handy tools explain starting from the this webinar
also within the Business Data Cloud.
But how can we become And also we can really emphasize that this isn't hype and
also everyone wants to do and address some problem solve problem with AI.
But for inspiration, let's look at the example of the use
cases can be implemented within PDC.
So using Arriba and it's 400 at the same time is the common case here we face
which is on Arriba we keep the suppliers or if these are accused and like the
sourcing document information.
But on the other hand, there is invoices or like the inventory,
inventory information inside the SAP, which needs to be combined to have a
deeper and accurate analysis on this topic for the supply chain optimization.
So with help of these data products inside Business Data Cloud and the
capabilities of Data Spare to combine this data together,
we can analyse the data combined from different sources in data bricks and in
supply chain optimization.
We can do analysis such as to understand invoicing errors or to report the
suspicious overbilling or to alert late deliveries.
And also we can visualize it with the help of analytics collapse another case
and which is also here again a common case for us in the people intelligence by
like the using different tools such as SuccessFactors and it's for the same time.
In this scenario, success factor keeps the employee data or
like the performance survey information from the employees but some of the
confidential information kept maybe on Prem or that separate separate parts such
as maybe a test for payrolls compensations take it separately.
So again here the help of Business Data Cloud,
these data can be combined and analyse to use the and understand the employee
engagements or retention risk or we can also analyse the performance trends and
visualise it to help.
To get decisions.
So here as we refer to the research from MIT,
one of the important topic here to address problem because there are like
many use cases that we can get into live, but it's also we can empathize.
It's very hard for you to decide which one is to decide and which one is to move
on.
So there's an expert here with us, doctor and his kid.
So Nagaro within Nagaro, he do have a methodology to address for
specific before your organization which problems can be solved with AI and which
can be utilized first.
So let's stages on ULM.
We are also really curious about the strategy and the products you are
delivering within the Nagar role organization.
Well, thank you so much.
It's a pleasure for me to be here with you.
Indeed, I had a bad for you.
Sorry about my fantastic sounds for this reason.
Sound all good.
Yeah, thank you.
Let us start with the fantastic report, MIT report, the Gen.
AI Dwight report Indeed, Harish and you also strongly highlight
the major takeaways, major outcomes and results.
And also the attendees can easily reach out to reports and download it from
resources section at the right hand side.
And here are the major takeaways.
And to sum up, indeed we have according to these figures,
according to these statistics and shocking facts, let me say to sum up,
we have to orchestrate workflow, memory and learning in a sensible manner.
So in it, it's a interesting coinciding because if
we jumped over our motto or Nagaro AI strategy, yes,
we are looking at state point within this report.
Let me say because as the organizations, we believe that artificial intelligence
is a magical wand in our hands.
You know, one touch.
We we believe that we can change everything in a minute,
but the reality is AI is not an alchemy.
We cannot change every pieces of our business processes into gold,
but it's a chemistry and what we mean by chemistry is the companies over
organizational DNA, organization DNA or one business
processes.
And when we explain our strategy, AI strategy and our motto or mantra,
let me say it resembles Karyung saying Karyung quotation as you see,
who looks outside dreams, who looks inside awakes.
So rather than making or having sweet dreams,
we have to get up and we have to get awareness about our strengths,
strong points and also our weakness.
So in that mindset, we designated a Nagara AI Evidence
workshop and let us jumped with the objectives first.
First, I think the most crucial objective is
strongly alignment with SAP business AI strategy and also organizations,
business processes, technologies and data repositories.
Also while while analysing and while making this alignment with at this manner,
we have to have a good understanding of the underlying sector or industry.
So we have get the snapshot about major drivers,
major challenges occurring at the industry.
And secondly, after making such alignment, we will come up with sensible and
applicable and also feasible AI capabilities and current industrial
trends throughout this selected AI business scenarios.
Afterwards, we will arrange them the we will give
priorities to this AI business scenarios with respect to business values or value
propositions and also with respect to their complexities.
Within these two dimensions, we come up with AAI road map.
And finally, according to this road map, we have a consensus with our stakeholders,
with our clients and we define clear big next steps within this AI adaptation.
Sometimes these big steps may be AI trainings,
sometimes it can be proof of concepts or pilot runs,
or sometimes it can be AI projects.
And on the top of this objectives, let me say much, we conclude outcome.
As you see AI action plan.
It consists of AI opportunity road map considering business AI components just
like Jewel and mid AI and custom specific AI scenarios.
And the second one, as I said, actionable next steps and ownerships for
identified steps and classes.
And yes, I think that makes the objectives and the
outcomes are pretty good.
But how about the customer journey, the road map,
let me say Kumach and our customer journey consists of four major milestones.
First thing first, I think the inception phase is very
crucial.
What I mean that and also I titled this inception phase as the power of what
question or the the art of asking what question.
Let me say, as you know, first thing first we make a brief call
with the with our clients and then we share AI questionnaire including some,
some questions taking the snapshot or taking the X-ray of the organizations in
terms of business process in terms of technology and data repositories.
And after that, after getting the responses from this AI
questionnaire, let me say within one or two weeks later,
we take the elaboration step too much.
So we try to prepare the workshop contacts.
So while making this workshop preparation, first thing first we make a fit gap
analysis.
What I mean that we got the responses, we extract the major characteristics of
the clients, the strength points or weaknesses,
let's say.
And also we have a look at about the benchmark use cases,
about the best practices in AI, especially in the underlying sector,
in the underlying industry.
So we may come up with potential feasible AI business scenarios.
Also while analysing the while preparing the workshop content, let me say much,
we also get the get insights about the industry.
How about the state of art in the industry?
How about the major drivers?
How about the major dramatic cases just like carbon footprint or sustainability
issues?
And as the third step, we get the action.
We, we arrange the Nagara AI Awareness
workshop in that manner.
Our workshop consists of two major parts.
First thing first, we, we come up with AI1O1,
let me say training or like a lecture.
We, we share our know how about machine
learning and the future of art field intelligence practices.
Also we we explain SAP business AI technologies just like our colleagues
explain that.
And in the second part, we come up with industrial specific AI
business scenario and we share AI opportunity road map.
Also.
We arrange these opportunities in terms of, as I said,
business value perspective and also complex perspective.
And according to this priorities business, AI business scenario,
visibilities and priorities, we have a consensus with our clients and
we review the outcomes and consolidated our brainstorming session and come up
with the big next steps.
And also we define the key stakeholders, keep actors in both sides,
in client size and in vendor side, let's say.
And these are the four steps cooler and, but can I jump in and like ask a question
here because you know, it sounds fantastic, but like,
do you have a customer example that is live and you can you show a case how it's
done here?
Yes, exactly.
Indeed.
We have a a workshop with a client operating in Summit Industry.
And as you see, I shared some screenshots about them.
Indeed, it consists of three major titles, stages, or let me say first thing first,
the power of what question rather than blessing her question the technology
stack.
I know that the technology is a bit confusing.
We make it simple and we try to deep dive the customer specific requirements and
their challenges.
And in parallel, we review the literature state of art in
the industry for grasping the X-ray of the industry and also company.
As you see, for instance, in semantic industry,
we come up some state of art perspectives and also main challenges in production
phases, for instance, And as the following step quad,
we slightly move towards elaboration, the half section.
And I rename it just to as a rule of thumb.
What I mean that we try to keep it simple this stage and we try to not to push lots
of potential AI use cases and make confuse our client.
Rather than this suggest 2 quick wins, low hanging fruits, let me say use cases,
2 tactical and two strategic use cases, as you see.
And finally, we come up with a priority framework,
A prism.
Let me say we arrange the these use cases in terms of business value.
They may sometimes cost saving potentials, they may prove sometimes revenue
generation.
And in parallel we, we we analyse these use cases in
feasibility mindset.
So come up with such a prism and also we share our recommendations our and
concluding remarks with our clients.
So then and then finally we come up with a road map and we we got ready for big
next steps.
And finally it's full time.
Indeed, we shared a question at the poll.
Ladies and gentlemen, according to your responses,
we are very glad to work both with you and do not hesitate to ask questions
about our Nagara AI workshops and thank you very much.
Thank you.
Now I understand like in the in this, let's say maybe a product,
you will get deep into the industrial knowledge and bring it to the customer
and also start to address the problems with inside your organization and
prioritize them with also awareness with realistic steps.
Sounds cool, thank you, thank you.
And while like the we are running the poll,
maybe also I can do a wrap up and go to the Quran a station.
But thanks everyone for like being with us so far.
So today Harish explained us about the AI strategy of SAP and also which tools are
out-of-the-box for SAP like short, great presentation and use cases from the
huge scenario.
But beside that, if you want to move forward further and
want to create some scenario maybe which are not covered within all of the best
scenarios, then comes the custom AI scenarios.
Roberto, thanks to Roberto's knowledge, he also shared us how custom AI can be
utilized and implemented with SAP tools are delivering.
And I will I try to explain the analytical aspects of this topic like
this BDC is also used for the analytics perspective,
but also for the data management part and how we can utilize that data already
there in Business Data Cloud and the tools under it and how we can utilize it
with AI tools inside Business Data Cloud.
And thank you also Iran, that you explained the workshop,
the methodology which helps our customers to identify and address potential
potential problems that can be solved by AI.
So this was our key to take away so far.
But since we, we do have last couple of minutes and
questions, I'm just jumping to the questions now.
So there is a question and thanks for that.
Well, I I just want to summarize it that is
starting from data start, like maybe also addressing the legacy
tools that is already there.
Like they already may be enlisted on the data sphere and now they are facing some
different layers with different list licensing And how SAP is 3 trying to
simplify this scenario, the complication.
So I just want to explain this and like if anything missing,
maybe my colleagues can also add something.
But I can understand that.
But like the data file was also announced for the tool to, let's say operate,
orchestrate every data operation with inside the company,
but it's still there and never been changed.
So the strategy in BDC is to create a seamless integration within analytics
tool and also create a framework which the customers can easily implement
scenarios on top of SAP or non SAP solution.
So we didn't BDC like status far is still there or like the analytics cloud of
every investment are still there.
And for example, when you change your contract to BDC,
it's also possible to, let's say to migrate your existing data
status fair investment to the under BDC and also,
but it can give you a possibility to let's say scale through this applications.
Maybe you can now reserve some of your if you have some data fair,
let's say credits.
You can maybe invest more phone stuck or different for data bricks,
utilize some AI or visualization scenarios.
It's all possible here and would be good to discuss maybe like if any further
discussion related with architecture or licensing.
We are more than happy and we are absolutely welcomed here to analyse and
understand your question.
You can also like the all of the addresses and the information.
The contact information here can be found so there is one another questions like
learning hub online resources provide structured knowledge and guided
experience on AI and datastare workshops are positioned differently.
Could about this is about the Nagara workshops around.
Could you explain how the Nagara AI workshops go beyond standard learning
content And there's also example like customer specific data,
business use cases, cross technology integrations.
What is different than learning?
Yeah, yeah.
This trainings, AI trainings is taking part of our AI
workshops according to the outcome.
Maybe we can increase the intellectual not know how of the clients.
Let me say in the yes, SAP Learning Hub is a good starting point.
But as Nagara, we, we have our own training repositories and
also training hubs.
So during our according to the outcome, we can share this content with the
clients.
And also we can state that customs specific data.
We we don't share such operation data with other customers.
But yes, business case at the business case
sharing and cross technology integration there are potential no hop sharing in
terms of our AI workshop.
Also, as I said before, we have such a literature review and also
we review our best practices, our period practices in AI site.
So we make such a, let me say checking, we check the similarity,
we check the neighborhood of the underlying use cases and we also share
our best best practices.
But sometimes it can be a customer specific AI use case in in that manner,
we we look around and look to benchmark use cases and how the period studies.
Applications.
Initiatives handled the underlying use case.
And we, we try to move align with this practices.
Let me see.
Thank you, Eran.
You're welcome.
And now we reach to one hour.
So maybe like if anyone with further questions,
please do not hesitate to get in contact with us.
We will like answer anything really responsibly.
But I will like to thank you again for Roberto and Harish to joining us in this
webinar and for the fruitful information around.
Thank you again.
Hope to see you soon.
See you again.
Bye bye.