August 29, 2025 • 30 mins
AI in the enterprise has moved from theoretical discussion to real deployments, with measurable business impact.
In this episode, host Andreas Welsch sits down with Bob Sakalas, Innovation Strategist at SAP, to explore how leading organizations are moving past the hype and actually delivering business value with AI.
Together, they discuss:
- How can you move your organization from a “what could go wrong” to a “what’s going right” mindset?
- Why does good AI start with good data, and does your business data really move the needle?
- What’s the bottom-line impact industry leaders in retail and transportation achieve with built-in AI?
- How does a collaborative approach fuel AI success, and what does it look like?
Whether you are an IT leader to hear how others are doing AI or tasked with maximizing the value from your SAP deployment, this episode offers insights straight from the source that you can apply right away. Are you ready to hear more?
Join SAP's Platform & Data Summit series in North America this September and October to hear from IT leaders and practitioners how they are getting value from AI in the SAP applications they use every day: https://events.sap.com/us-2025-sap-data-summit-home/en_us/home.html?source=Andreas3
Thank you, SAP, for sponsoring this episode.
Questions or suggestions? Send me a Text Message.
***********
Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Andreas Welsch (00:00):
Thank you to SAP for sponsoring this episode.
(00:02):
Today we'll talk about howleading enterprises are adopting
AI to drive value, and whobetter to talk about it than
someone who's actively workingon that Bob Sakalas.
Hey, Bob, thank you so much forjoining.
Bob Sakalas (00:14):
Hey Andreas, how are you doing?
Andreas Welsch (00:15):
Doing well.
It's so great to have you on theshow, but I know not everybody
might know you and what you do.
Why don't you tell us a littlebit about yourself?
Bob Sakalas (00:24):
Oh yeah, sure.
I'm from North America.
I'm part of the solutionadvisory, which is part of our
customer advisory group.
But we basically help customersget past the hype.
And I know it's part of youropening here, right?
But how do you take this greattechnology that's changing so
quickly?
How do you actually startapplying it to your business
(00:46):
processes?
How do you become moreefficient, more effective?
And of course, we're all talkingabout AI because AI is
absolutely the next big thing.
Andreas Welsch (00:55):
That's wonderful.
That's exciting.
I know you work at SAP andthere's been a lot of
conversation, a lot of buzz onAI as well, and you've been in,
in the industry for quite sometime.
What's getting you excited thesedays?
Bob Sakalas (01:10):
It's becoming real.
I guess that's where I'm gonnastart, right?
If you think about it, two yearsago, every presentation I saw on
AI was talking about what couldgo wrong.
Oh my goodness.
This Canadian airline soldtickets for a dollar, or
somebody bought a Chevy Tahoefor a dollar because they
tricked the chatbot into doingthat.
(01:31):
So two years ago we were talkingabout how this thing goes off
the rails.
Today we've got customers, likefor example, we've got these BTP
summit events.
Actually they're now called theplatform and Data Summits.
We renamed it of course but,five different cities where
we're gonna have customerspresenting how they're using AI
and other advanced technology toactually deliver outcomes and
(01:55):
results for their business.
So we've gone from what could gowrong and everybody was worried
about it.
To what can go right and howwe're actually able to use this
technology.
And it's not SAP doing thetalking, it's customers showing
up and saying, this is whatwe're doing with the technology.
This is delivering results forus.
Andreas Welsch (02:13):
That's awesome.
And I think especially this partabout practitioners and leaders
talking about how they are usingit in their business is so
incredibly important becausethere is so much talk, there is
so much noise in the market.
And for me personally as wellthis year, events that I've
attended where you heard from,real people, how have they
approached it?
What worked really well?
(02:33):
What were some of the strugglesthey went through and what are
they achieving at the end of theday?
I think that's the mostmeaningful and the most valuable
time that, that you can spend.
Learning.
Bob Sakalas (02:42):
Yeah.
And the demand's incredible.
By the way.
We went from three years ago wehad two cities.
Last year we had three cities.
This year we have five cities.
So we're gonna be in SanFrancisco, New York, Chicago,
Toronto, and Atlanta, all inSeptember and October.
It's gonna be a great event.
I hope you can make it.
Andreas Welsch (03:03):
Yeah, I hope I get to be there for, one of the
events and, definitely hearingwhat your customers are doing
and sharing.
But, we've, heard so much in, inrecent years ever since we've
talked about big data, you notonly can gather the data, but
you almost need it, or youalready and, always needed if
(03:23):
you want to do AI and if youwant to do good AI.
And to me, that was one of thebig realizations that, I saw
many leaders go through earlyon.
ChatGPT, LLMs everybody said,yes.
Great.
Now we can do all these thingswe haven't been able to do
before and write new languageand summarize information and
write code and whatnot.
But very quickly, people said,it's actually pretty bland.
It's pretty generic.
(03:44):
It doesn't know anything aboutmy business.
It doesn't know anything aboutmy products, it doesn't know
anything about my services andwhat I do.
And we quickly came back to therealization that, hey, guess
what?
We need data, we need businessdata, we need good data and
quality data and things that arespecific to combined data.
Yeah.
Yeah.
And so we've, been talking aboutthis for, years, and I know you,
(04:08):
you've been working with some ofthe largest brands on the
planet.
How are you seeing companiesactually use this and what makes
a difference for them in theiradoption?
Bob Sakalas (04:16):
It's really a big wake up call.
If you take a, and I don't carewhich LLM we're talking about,
if you take any of the LLMs andyou ask it a question about SAP
tables, for example, what wediscovered very quickly is that
sure, the l LMS great at theconversation, but when you ask
it a very specific question,you're gonna find out that it's
(04:39):
maybe 70% right.
And 70% right might be goodenough for hand grenades, but
it's not good enough when you'retrying to figure out when
something's gonna deliver toyour customer.
And so finding a way to take aknowledge graph and make sure
that you absolutely have yourcurrent data combined with
understanding the businessprocess under, with having that
(04:59):
conversational I, I don't wannadiscount, by the way, I don't
wanna discount the importance ofthe LLM.
But we always had a usabilityproblem, right?
We didn't, we knew AI had greatpotential, but we had that
little department in the cornerthat worked on data science and
they worked on one problem at atime.
Suddenly, it's now available toeverybody.
So there's a great big bangmoment that large, language
(05:21):
models have done.
The usability factorsincredible, but we also need it
to be accurate.
The thing that I think we allhave seen, and by the way, it's
not just when I say customershave now realized.
They have to get the data right,if they're gonna have great
possibilities with AI full stop,bottom line.
And so what's great for us asSAP is that now the customer is
(05:44):
realistic.
The customer realizes, oh, Ican't get the great outcomes
where I'm getting 95% plusaccurate answers on everything
without having the underlyingdata be right as well.
And incredibly I hate to say itthis way, but the planets have
aligned.
We've come out with sometechnology recently it's called
(06:06):
Business Data Cloud, butBusiness Data Cloud helps us get
that data right and break downthose silos so suddenly you can
layer AI on top of great dataand accomplish great outcomes.
Andreas Welsch (06:18):
Great to have all of that is, is coming
together and how are you seeingyour customers first of all,
make the decision what to moveforward with and what are the.
What are they building on top ofit?
It's, one thing to, to have lotsof business data in your system
or maybe to, to some extentlocked away now that you can
(06:40):
unlock it and that you can useit.
What are your customers using itfor?
Bob Sakalas (06:46):
Everything.
Let, lemme start with a verysimple example just because I
think it, it communicates thepower of using AI in such a way
that it not only.
Helps your own employees, whichis something we all wanna do,
but it also helps the customerget a better experience as well.
(07:07):
Syntax is a company out ofMontreal.
They're a consultancy that workson a lot of ERP projects.
And so they had a it's gonnasound mundane, but I think it's
an incredibly important story.
They wanted to have theirconsultants use AI on their
phone.
They basically report theirtime.
(07:27):
Let's say I just spent fourhours working on my Andreas
project for the Andreas company.
Okay.
And the AI would say, okay, Iknow that you have worked on
this pro, these two projects inthe past, and might ask me one
additional question that says,are you working on project A or
Project B?
But I'm gonna now book fourhours of time against the
(07:48):
Andreas project.
And so it's suddenly it turnsout consultants might use pieces
of paper or they may use allkinds of different things to jot
their notes on what they'reworking on.
They wind up putting it in laterinto the system.
And of course there's,inconsistencies, there's billing
problems, all that kind ofstuff.
So now the consultant isn'thappy'cause, he or she's gotta
(08:09):
go back and do it later.
And the customer may not behappy if they think their
billing's not straight.
And so in this case, an AIinterface was created by using
SAP App House, by the way, isthis design group that helps you
design cool new things.
I've probably been talking tothe users, and so they designed
the interface to connect to S/4Public Cloud through APIs.
(08:34):
So S/4 Public Cloud stayspristine exactly the way that
SAP shifted into the cloud,right?
It can continue to upgrade, butbecause it's using an API
between the AI and S/4 PublicCloud, suddenly it just works
and it works well.
So a great example of somethingthat you might think is small,
but when you multiply thatacross thousands of consultants
(08:55):
working on thousands of projectsand everybody's happy, the
customer's happy, they get greatbilling or correct billing, I
should say, the consultant'shappy because this become as
easy as talking to your phone.
A, great outcome and just oneexample of how AI is really
making a difference.
Andreas Welsch (09:14):
That's really powerful.
And, I remember for years we'vealso talked about it's these
parts of a business processthese tasks that people like in
this case, consultants gothrough on a daily basis that
are mundane before you innovateand before you change and they
can be accelerated andsignificantly improved
(09:36):
afterwards.
So to me that's a great exampleof how AI connected with SAP can
drive a real impact.
Bob Sakalas (09:43):
And one of the things we're talking about is,
of course SAP is acceleratingour innovation.
We're doing that all in thecloud.
So things like Joule andembedded applications are all
gonna be part of our newest S/4stuff.
But we still have a lot ofcustomers that are still on ECC
that are looking to make thatjourney to the cloud soon, but
they don't wanna wait foranother two years while they
make that journey.
(10:05):
I'll give you another story, butit's a, it is a powerful one.
I think Louisiana Pacific, Idunno if you're familiar with
them, but they have buildingproducts, lumber, all that kind
of stuff, big bulky stuff thatthey ship to build your house,
basically.
They, their system was still onECC, but they wanted to take
advantage of an optimizer tooptimize their shipping.
(10:25):
It turns out that when you'vegot.
A dozen manufacturing plants andlots of their warehouses are
actually yards with huge lumberand things like that.
Optimizing the shipping the,fulfill an order that may have a
thousand line items is reallyimportant to them because that's
where all the profitability is.
So low margin business, it'sreally big, bulky stuff and they
(10:47):
has to get there on time orconstruction gets delayed.
So they used, in this case, ourbusiness technology platform to
do the plumbing.
They said, okay, we're gonnatake these orders multiple times
a day out of the older ERP, theECC system, use BTP to do the
plumbing, to go to this thirdparty optimizer that kind of did
(11:08):
three, three dimensional chess.
It wasn't an if then, if, then,if, then, like we used to do
back in the nineties.
It looked at all the orders andsaid, what's the most efficient
way to build these orders,package these orders, shift
these orders, and get'em to theright place at the right time so
that the customer is happy andwe've optimized the cost of that
in our low margin business.
That matters a lot.
(11:29):
Riner, who's the, main leaderthere that put this project
together?
He said, we got it done in sixweeks.
Wow.
And it contributed 2% to ourprofitability.
An incredible success.
And what's interesting aboutthis project is as they move
toward S/4, all thisinfrastructure that was built in
(11:49):
a loosely coupled fashion withbusiness technology platform,
we'll be able to plug into theirS/4 system then too.
So there, it's not, it's noregrets work.
It's work that they've done thatcontributes to profitability
today, and it'll work in thefuture when they're now in the
cloud with S/4.
Andreas Welsch (12:05):
That sounds like another great example and
especially the part about beingable to continue using what
they've built and just connectit to their new SAP S/4HANA
system.
I think it's really there.
Bob Sakalas (12:17):
There's no reason to wait.
SAP would love for you toupgrade the S/4 in the cloud
right now because you'll get thebest possible innovation and
there's no doubt that you will.
All our Juul which is our chatbot, that's the smart chat bot
that knows not only theinformation around the process,
but how to run transactions andall that stuff that all runs
within.
(12:38):
S/4 on top of Joule.
We also have embeddedapplications that we're creating
to make sure that processes runbetter.
But BTP and gener generative AIhub will help you apply AI today
if you're not there yet.
So you don't have to, you knowhow many cus how many companies
wait two years to start doinginnovation that their users
feel?
(12:58):
That's not the right way to go.
The right way to go is toloosely couple that and start
innovating today.
Andreas Welsch (13:03):
Awesome.
The, part about the urgency I,think is important because
things are moving so quickly.
There's hardly a week that goesby where you're not seeing any
kind of big AI announcements ornew models, new capabilities,
new companies on one hand beingformed, the ones that might be
competing with you that youdon't even have on the radar
(13:25):
yet, or the ones that areincorporating it and that are
becoming so much more efficientand effective into that point
about.
Connecting data that you have inyour business systems with
something that you might buildas an extension or as an
addition to to that core systemand, data.
I think there's a lot ofopportunity, and I know for the
(13:45):
last 10 years companies and thelargest companies on the planet
have been looking at thingslike, how can we get better
insights from our customers?
I remember working with retailcustomers and
Bob Sakalas (13:59):
I think you're bringing up a very important
point, which is.
In the old days, all roads ledto Rome and today all roads lead
to the data.
We have to make our decisionsfaster, but when I ask customers
are, you, do your decisionmakers get their data in real
time today?
The answer is no.
(14:20):
Yeah I then ask them, do theywant it in real time?
And it it's unanimous.
Everybody's absolutely.
We're being asked that all thetime.
Guess what?
We start talking about thefuture and AI agents and agents
working together.
An AI agent's not gonna beeffective unless it's got a real
time or very close to real timeview of your business.
(14:40):
If you're working on a batchprocess that ran last week, that
AI agent can't make thoserecommendations or decisions for
you in time for you to impactyour earnings.
And so when you start thinkingabout the, what's I've been
asked this question a bunch oftimes, but what's different
about SAP's approach to AI andmost other companies?
(15:03):
And so it's easy to compare,like one of our closest
partners, by the way, isMicrosoft, but Microsoft and
Copilot, which I have on my owndesk.
It's really great at personalproductivity stuff.
I can write a long email, I canthrow it in a copilot.
I can say, Hey, can you makethis email harder?
Hitting professional and short.
Instantaneously I get the answerit's a personal productivity
(15:24):
thing.
SAP is not focused on as muchthat personal productivity in
its own little microcosm of mewriting emails or me creating a
PowerPoint.
What is, what we're focused onis this idea of how do we make
your business process reallyhum?
How do we make your businessprocess ship the goods?
Half a day faster.
(15:45):
How do we make that businessprocess make recruiting people
and landing them so we get thebest talent better, right?
So we're always focused on thebusiness process, and we think
that's the area that's gonnacontribute the earnings the
most.
So if you want to deliver foryour shareholders and show that
your company's becoming moreprofitable and using AI in a
(16:06):
very substantive way, I thinkthe a, the AI focus that SAP
has, which is on your businessprocess.
Every company, I don't care.
I used to work on Walmart as anexample, and even at Walmart
when I actually made thesentence or I said the sentence
out loud in the meeting, I saidno matter how large you are, you
really are the sum of yourbusiness processes.
(16:28):
Everybody in the room would notat the end of the day, it's not
the one-off thing that you didthat makes a difference.
It's the thing that you do everysingle day, and you want to do
that really well.
And I don't care if you'reWalmart or if you're Bank of
America, or if you're Home Depotor any of these companies.
Doing the every single dayprocess is what's gonna make you
more profitable, is what's gonnadrive your stock Price is what's
(16:50):
gonna drive growth, it's what'sgonna drive opportunities for
employees.
It's not that I wrote an emailtwice as well, and now I can go
stand at the coffee machine andchat about last weekend a little
bit longer.
Andreas Welsch (17:03):
And I think that's the important part,
right?
How do you use it to drivemeasurable business results and
business value and, being ableto do this in new ways now.
Coming back to the retailexample, I know customers and
companies have been using bigdata and big data platforms for
a long time.
They put analytics on top.
(17:23):
You could analyze your SKUs anddemand on a store level on a
city regional.
State level and what have you,how are you seeing that changing
now?
In, in terms of requirements,but also in, in terms of what
the, underlying capabilitieslook like when you bring in data
(17:44):
from different systems, when youlayer AI, generative AI, agentic
AI and these things on top.
How do things change and howdoes that drive the, value
discussion in, the also specific
Bob Sakalas (17:56):
Yeah, specifically to retail.
It's always been a struggle overthe volume of the data, right?
So you, let's say you're acategory manager and you're
trying to improve the milkcategory in your store.
You would, try to get the rightproduct mix to drive to make
milk as a category, the mostprofitable that it could be in
(18:18):
your store.
There was a mathematicaldisaster of a problem to do
great category management.
And why?
Because it took a lot of humanpower to analyze that, to fiddle
with the different parameters,to see, oh, I need to stock more
quarts and I need to stock more,half and half.
And all of a sudden people aredown.
Now everybody's lactoseintolerant.
(18:39):
When did that happen?
But but the whole point is thatit was a very difficult thing
for the category manager to makesure that he had the right mix
to maximize the profit of that.
And of course, this is happeningwith detergents, is happening
with cereals, is happening witheverything else.
So now if you think about the AIage, we've always been drowning
(19:01):
in too much data.
We've always not had enough timeto analyze it, and the people
doing the analysis often spent80% of their time just gathering
and cleaning and making surethat their data was clean enough
to do the analysis, which theyonly did 20% of the time.
What we're trying to do withthis, I think the term is data
(19:22):
product economy, but the, basicidea is you define.
Great data products that haveall the metadata around it.
So now you're doing less of yourtime stuck trying to massage the
data, and you're spending a lotmore time analyzing it.
That's the first step.
Now, AI agents can often do partof our job.
Not all of it.
(19:43):
Let's face facts.
AI is absolutely not curious asa technology.
There's no curiosity, right?
It's not very good at whitespace.
Because it's very good at doingtasks that it's seen before, but
when you give it, what's missingin this equation?
AI struggles, right?
The curiosity is not there.
The understanding of white spaceis not there, but now it doesn't
(20:06):
tire at a lot of these topicsthat we just talked about.
So as a category manager, whatthat, let's just pretend I'm at
Walmart, that milk might be verydifferent in Denver than it is
in Phoenix, than it is inCanada, as an example.
But I would get tired of thethousands and thousands of
stores and the thousands ofsituations that I had that might
(20:28):
be a little bit different.
The AI agent never gets tired.
So when it comes to that, I'mgonna gather the data for this
specific market area.
I'm competing against theseother grocers that are using
milk maybe as a loss leader,right?
So there's unique competition bymarket space as well.
The AI agents don't get tired.
So all of a sudden, the thingsthat we can do in a repetitive
(20:51):
fashion, I become a much bettercategory manager because I can
now get recommendations that arevery specific, the specific
markets, the specificcompetitive conditions, the
specific regional differences, Iguess would be a great way to
put it.
I'm able to do a much moregranular job because that AI
agent's not getting tired.
The data is cleaner, so I'mspending less time there.
(21:13):
The agent's not getting tired,and guess what?
I actually get to spend time onthe white space.
I get to spend time.
I don't.
Wow, that's interesting.
One of the things that SAP'sdoing right now that's really
cool is that we've got thesesmart insights that are part of
our analytical tools where it'llgo find mathematical or data
correlations, I guess the rightway to put it, that are
different that I may not neverthought of.
(21:35):
I don't have to go ask thequestion, do you see something
funny in the data?
It can, it'll come back to meand say, Hey, we spot an
interesting trend in the data.
So then, wow.
I, I'm excited because I justgot a new insight that I didn't
necessarily generate.
So AI is absolutely gonna make adifference at the granularity
level.
Things like business Data Cloudare gonna get us out of this
(21:56):
data, moving smushing,transforming, eing, whatever you
wanna call it, and just deliverus good quality data so we can
spend more time on the stuffthat really hits a home run.
Andreas Welsch (22:07):
Beautiful.
That's, the kind of thing we, weshould all be striving for in,
in our businesses to, to lookfor these kinds of
opportunities.
Yeah.
Now you already mentioned a,couple examples with Louisiana
Pacific and, others.
Who else are you seeing doingthis really well?
Bringing AI into the business,driving more value from AI in
(22:28):
their business processes andwith their business systems.
Bob Sakalas (22:31):
I don't have a retailer that's gone public yet,
because I think they're alllooking at this as a competitive
advantage moment.
And they're all working reallyhard to use AI in these kind of
areas I just talked about.
But I do have a CPG examplethat's really interesting.
CPGs consumer products in casenot everybody's a CPG person on
this call, but constellationbrands.
(22:55):
If you're familiar with them,they've got Modelo as a beer,
they've got Corona as a beer.
They've got other spirits andvodkas and things like that, but
they're one of the fastestgrowing CPG companies in the
world, and they have been forthe last 10 years.
And I don't know if you saw, butModelo actually, I think's now
the number one beer in NorthAmerica, all that beer gets
shipped from the south to thenorth on trains.
(23:19):
Turns out that they're shipping400 million cases of beer.
Per year via train to NorthAmerica.
So I guess we're drinking a lotof beer.
That's a lot of beer.
That's a lot of, it's a lot ofbeer.
When you consider, we got 350million people and you're
shipping four oh million cases,and that's only one company.
We're drinking a lot of beer inNorth America, but that's a
(23:39):
different topic anyway, turnsout when you're shipping so much
beer, there's a lot of damage onthese trainings to the tune,
believe it or not, of about athousand insurance damage claims
per week.
So Constellation would have tothe, way this used to work is it
(23:59):
would take the person that didthe receiving would take their
phone.
They would have to take 15 or 20pictures of the damage, they
would then have to create hisdamage claim.
They would have to send it in.
It was like a 20 to 30 minuteprocess.
Every single time it wasdamaged.
And this happened a thousandtimes a week, 52 weeks a year.
They worked with us and said,look actually this started out,
(24:22):
they weren't even thinking AI,but it led to AI, right?
How can we make this processbetter?
And it turns out that thesereceiving areas where they
unload the beer already had alot of cameras.
And so they could gatherpictures, attach'em to the AI
created insurance claim.
(24:43):
They would have a person look itover to make sure that it looked
right.
They might have to add a littlebit to it, but they went down
from 20 to 30 minutes to everyclaim is now below five minutes,
and so that process, if youthink about it a thousand times,
has 52,000 times a year.
They now have a much betterinsurance claims process that's
(25:04):
worth millions of dollars.
This is not small change.
So another example of SAPworking with the customer to
ensure that the customer has abetter running business process.
I can't stress enough.
That's the SAP difference whenthe business process.
I love that.
Yeah.
When the business process runsgreat, it drops to your bottom
(25:26):
line.
It's not just a matter offreeing people up so they can
jump in the traffic 15 minutesearly to go home.
Andreas Welsch (25:33):
And a lot of times I feel these initiatives
might start as we need to figureout something that we can do
with AI.
But I think once you've reallystart with the business
strategy, the business processand, you look at where
opportunities to, to drive morevalue to, to automate things to.
Also measure what the outcome isbefore we even start.
(25:53):
Be clear about what is our KPIthat we want to drive?
What are we looking to improve?
And you attach that AI projectand the value to it.
All of a sudden it's measurableand it's tangible and it's
aligned with your businessgoals.
And it's much easierconversation than saying, let's
boil the ocean and look for somelow hanging fruit and quick
ones.
Whatever buzzword.
Bob Sakalas (26:13):
The best way to start, no doubt, is a lot of
these started with a workshop.
A simple workshop that had thesupply chain people and the IT
people that worked on supplychain together with SAP to just
have a conversation, to justhave a design thinking session
that what are the top threethings we could, really make
better around here?
It turns out that AI is almostalways an answer that can help.
(26:36):
And unlike the old days, by theway, we, oh, we're gonna create
a custom application.
It's gonna take us two years toget it done.
Because the AI is somultipurpose now.
It's the AI helps us get to thefinish line quickly.
SAP's jobs to make sure that itstays relevant, reliable, and
responsible.
Because if it gets you to thefinish line quickly, but it's
(27:00):
only right 80% of the time,that's not a good outcome.
And so starting with theworkshop's a great way these
events that we talked about atthe beginning, by the way.
These are gonna be customerstalking about innovative
problems that they are solvingin these five cities.
What a great way to start yourthinking.
You can go to a session abouthow did you guys remarkably
(27:21):
change your financial planning?
Same basic concept, right?
Getting inspired by what anothercustomer did is a great way to
start.
Andreas Welsch (27:29):
Absolutely.
And like I said, hearing fromothers how they have approached
it and why they even came upwith the idea and what it drives
in terms of meaningful value.
I think that's one of the big,benefits.
I'm definitely looking forwardto attending some of these
events.
You said there were five in, inNorth America in September, in,
(27:50):
in October hitting all majorcities and hubs.
We'll make sure to put it in, inthe description as well.
So please do take a look at...
Bob Sakalas (27:58):
they missed Dallas.
They missed Dallas where I live,so I'm upset about that.
So I would argue that theymissed one location.
But yes, we got five greatlocations this year.
Andreas Welsch (28:08):
You said they were so popular that SAP has
been expanding them over thelast couple years there's always
a chance that 2026 Dallas willbe on the list.
Bob Sakalas (28:17):
We can only hope, but but yeah I would very much
invite, and by the way, this isa free event if you're a
customer.
A lot of events, people arecharging you entrance fees and
everything else.
This is a free event if you'rean SAP customer.
Worth attending, there's nodoubt.
But it's a great way to startyour AI journey to think about
it, right?
(28:38):
I would also encourage people toreach out to their SAP account
team and just say, Hey, I'd liketo have a workshop with my
finance team, or I'd like tohave a workshop with my supply
chain team, or my HR team.
We, have been doing these halfday workshops that have really
been.
I hate to let make it sound thisway, but they dropped to the
bottom line this year.
(28:59):
How many times in business do wework on something that doesn't?
Get us the attaboy this year,but you can be a hero at your
company this year with AI
Andreas Welsch (29:08):
I think that's a great point to close on.
You can be the hero of yourcompany with AI this year if you
work with SAP and you look forthese meaningful examples that
derive value.
Bob, it's been a pleasure havingyou on and, hearing from you how
you and how SAP are addressingthese big challenges, how
companies can drive innovationand try drive real value with AI
(29:28):
today.
So thank you so much forjoining.
Bob Sakalas (29:31):
Great seeing you, Andreas.
Thank you to SAP for sponsoring this episode.
(00:02):
Today we'll talk about howleading enterprises are adopting
AI to drive value, and whobetter to talk about it than
someone who's actively workingon that Bob Sakalas.
Hey, Bob, thank you so much forjoining.
Bob Sakalas (00:14):
Hey Andreas, how are you doing?
Andreas Welsch (00:15):
Doing well.
It's so great to have you on theshow, but I know not everybody
might know you and what you do.
Why don't you tell us a littlebit about yourself?
Bob Sakalas (00:24):
Oh yeah, sure.
I'm from North America.
I'm part of the solutionadvisory, which is part of our
customer advisory group.
But we basically help customersget past the hype.
And I know it's part of youropening here, right?
But how do you take this greattechnology that's changing so
quickly?
How do you actually startapplying it to your business
(00:46):
processes?
How do you become moreefficient, more effective?
And of course, we're all talkingabout AI because AI is
absolutely the next big thing.
Andreas Welsch (00:55):
That's wonderful.
That's exciting.
I know you work at SAP andthere's been a lot of
conversation, a lot of buzz onAI as well, and you've been in,
in the industry for quite sometime.
What's getting you excited thesedays?
Bob Sakalas (01:10):
It's becoming real.
I guess that's where I'm gonnastart, right?
If you think about it, two yearsago, every presentation I saw on
AI was talking about what couldgo wrong.
Oh my goodness.
This Canadian airline soldtickets for a dollar, or
somebody bought a Chevy Tahoefor a dollar because they
tricked the chatbot into doingthat.
(01:31):
So two years ago we were talkingabout how this thing goes off
the rails.
Today we've got customers, likefor example, we've got these BTP
summit events.
Actually they're now called theplatform and Data Summits.
We renamed it of course but,five different cities where
we're gonna have customerspresenting how they're using AI
and other advanced technology toactually deliver outcomes and
(01:55):
results for their business.
So we've gone from what could gowrong and everybody was worried
about it.
To what can go right and howwe're actually able to use this
technology.
And it's not SAP doing thetalking, it's customers showing
up and saying, this is whatwe're doing with the technology.
This is delivering results forus.
Andreas Welsch (02:13):
That's awesome.
And I think especially this partabout practitioners and leaders
talking about how they are usingit in their business is so
incredibly important becausethere is so much talk, there is
so much noise in the market.
And for me personally as wellthis year, events that I've
attended where you heard from,real people, how have they
approached it?
What worked really well?
(02:33):
What were some of the strugglesthey went through and what are
they achieving at the end of theday?
I think that's the mostmeaningful and the most valuable
time that, that you can spend.
Learning.
Bob Sakalas (02:42):
Yeah.
And the demand's incredible.
By the way.
We went from three years ago wehad two cities.
Last year we had three cities.
This year we have five cities.
So we're gonna be in SanFrancisco, New York, Chicago,
Toronto, and Atlanta, all inSeptember and October.
It's gonna be a great event.
I hope you can make it.
Andreas Welsch (03:03):
Yeah, I hope I get to be there for, one of the
events and, definitely hearingwhat your customers are doing
and sharing.
But, we've, heard so much in, inrecent years ever since we've
talked about big data, you notonly can gather the data, but
you almost need it, or youalready and, always needed if
(03:23):
you want to do AI and if youwant to do good AI.
And to me, that was one of thebig realizations that, I saw
many leaders go through earlyon.
ChatGPT, LLMs everybody said,yes.
Great.
Now we can do all these thingswe haven't been able to do
before and write new languageand summarize information and
write code and whatnot.
But very quickly, people said,it's actually pretty bland.
It's pretty generic.
(03:44):
It doesn't know anything aboutmy business.
It doesn't know anything aboutmy products, it doesn't know
anything about my services andwhat I do.
And we quickly came back to therealization that, hey, guess
what?
We need data, we need businessdata, we need good data and
quality data and things that arespecific to combined data.
Yeah.
Yeah.
And so we've, been talking aboutthis for, years, and I know you,
(04:08):
you've been working with some ofthe largest brands on the
planet.
How are you seeing companiesactually use this and what makes
a difference for them in theiradoption?
Bob Sakalas (04:16):
It's really a big wake up call.
If you take a, and I don't carewhich LLM we're talking about,
if you take any of the LLMs andyou ask it a question about SAP
tables, for example, what wediscovered very quickly is that
sure, the l LMS great at theconversation, but when you ask
it a very specific question,you're gonna find out that it's
(04:39):
maybe 70% right.
And 70% right might be goodenough for hand grenades, but
it's not good enough when you'retrying to figure out when
something's gonna deliver toyour customer.
And so finding a way to take aknowledge graph and make sure
that you absolutely have yourcurrent data combined with
understanding the businessprocess under, with having that
(04:59):
conversational I, I don't wannadiscount, by the way, I don't
wanna discount the importance ofthe LLM.
But we always had a usabilityproblem, right?
We didn't, we knew AI had greatpotential, but we had that
little department in the cornerthat worked on data science and
they worked on one problem at atime.
Suddenly, it's now available toeverybody.
So there's a great big bangmoment that large, language
(05:21):
models have done.
The usability factorsincredible, but we also need it
to be accurate.
The thing that I think we allhave seen, and by the way, it's
not just when I say customershave now realized.
They have to get the data right,if they're gonna have great
possibilities with AI full stop,bottom line.
And so what's great for us asSAP is that now the customer is
(05:44):
realistic.
The customer realizes, oh, Ican't get the great outcomes
where I'm getting 95% plusaccurate answers on everything
without having the underlyingdata be right as well.
And incredibly I hate to say itthis way, but the planets have
aligned.
We've come out with sometechnology recently it's called
(06:06):
Business Data Cloud, butBusiness Data Cloud helps us get
that data right and break downthose silos so suddenly you can
layer AI on top of great dataand accomplish great outcomes.
Andreas Welsch (06:18):
Great to have all of that is, is coming
together and how are you seeingyour customers first of all,
make the decision what to moveforward with and what are the.
What are they building on top ofit?
It's, one thing to, to have lotsof business data in your system
or maybe to, to some extentlocked away now that you can
(06:40):
unlock it and that you can useit.
What are your customers using itfor?
Bob Sakalas (06:46):
Everything.
Let, lemme start with a verysimple example just because I
think it, it communicates thepower of using AI in such a way
that it not only.
Helps your own employees, whichis something we all wanna do,
but it also helps the customerget a better experience as well.
(07:07):
Syntax is a company out ofMontreal.
They're a consultancy that workson a lot of ERP projects.
And so they had a it's gonnasound mundane, but I think it's
an incredibly important story.
They wanted to have theirconsultants use AI on their
phone.
They basically report theirtime.
(07:27):
Let's say I just spent fourhours working on my Andreas
project for the Andreas company.
Okay.
And the AI would say, okay, Iknow that you have worked on
this pro, these two projects inthe past, and might ask me one
additional question that says,are you working on project A or
Project B?
But I'm gonna now book fourhours of time against the
(07:48):
Andreas project.
And so it's suddenly it turnsout consultants might use pieces
of paper or they may use allkinds of different things to jot
their notes on what they'reworking on.
They wind up putting it in laterinto the system.
And of course there's,inconsistencies, there's billing
problems, all that kind ofstuff.
So now the consultant isn'thappy'cause, he or she's gotta
(08:09):
go back and do it later.
And the customer may not behappy if they think their
billing's not straight.
And so in this case, an AIinterface was created by using
SAP App House, by the way, isthis design group that helps you
design cool new things.
I've probably been talking tothe users, and so they designed
the interface to connect to S/4Public Cloud through APIs.
(08:34):
So S/4 Public Cloud stayspristine exactly the way that
SAP shifted into the cloud,right?
It can continue to upgrade, butbecause it's using an API
between the AI and S/4 PublicCloud, suddenly it just works
and it works well.
So a great example of somethingthat you might think is small,
but when you multiply thatacross thousands of consultants
(08:55):
working on thousands of projectsand everybody's happy, the
customer's happy, they get greatbilling or correct billing, I
should say, the consultant'shappy because this become as
easy as talking to your phone.
A, great outcome and just oneexample of how AI is really
making a difference.
Andreas Welsch (09:14):
That's really powerful.
And, I remember for years we'vealso talked about it's these
parts of a business processthese tasks that people like in
this case, consultants gothrough on a daily basis that
are mundane before you innovateand before you change and they
can be accelerated andsignificantly improved
(09:36):
afterwards.
So to me that's a great exampleof how AI connected with SAP can
drive a real impact.
Bob Sakalas (09:43):
And one of the things we're talking about is,
of course SAP is acceleratingour innovation.
We're doing that all in thecloud.
So things like Joule andembedded applications are all
gonna be part of our newest S/4stuff.
But we still have a lot ofcustomers that are still on ECC
that are looking to make thatjourney to the cloud soon, but
they don't wanna wait foranother two years while they
make that journey.
(10:05):
I'll give you another story, butit's a, it is a powerful one.
I think Louisiana Pacific, Idunno if you're familiar with
them, but they have buildingproducts, lumber, all that kind
of stuff, big bulky stuff thatthey ship to build your house,
basically.
They, their system was still onECC, but they wanted to take
advantage of an optimizer tooptimize their shipping.
(10:25):
It turns out that when you'vegot.
A dozen manufacturing plants andlots of their warehouses are
actually yards with huge lumberand things like that.
Optimizing the shipping the,fulfill an order that may have a
thousand line items is reallyimportant to them because that's
where all the profitability is.
So low margin business, it'sreally big, bulky stuff and they
(10:47):
has to get there on time orconstruction gets delayed.
So they used, in this case, ourbusiness technology platform to
do the plumbing.
They said, okay, we're gonnatake these orders multiple times
a day out of the older ERP, theECC system, use BTP to do the
plumbing, to go to this thirdparty optimizer that kind of did
(11:08):
three, three dimensional chess.
It wasn't an if then, if, then,if, then, like we used to do
back in the nineties.
It looked at all the orders andsaid, what's the most efficient
way to build these orders,package these orders, shift
these orders, and get'em to theright place at the right time so
that the customer is happy andwe've optimized the cost of that
in our low margin business.
That matters a lot.
(11:29):
Riner, who's the, main leaderthere that put this project
together?
He said, we got it done in sixweeks.
Wow.
And it contributed 2% to ourprofitability.
An incredible success.
And what's interesting aboutthis project is as they move
toward S/4, all thisinfrastructure that was built in
(11:49):
a loosely coupled fashion withbusiness technology platform,
we'll be able to plug into theirS/4 system then too.
So there, it's not, it's noregrets work.
It's work that they've done thatcontributes to profitability
today, and it'll work in thefuture when they're now in the
cloud with S/4.
Andreas Welsch (12:05):
That sounds like another great example and
especially the part about beingable to continue using what
they've built and just connectit to their new SAP S/4HANA
system.
I think it's really there.
Bob Sakalas (12:17):
There's no reason to wait.
SAP would love for you toupgrade the S/4 in the cloud
right now because you'll get thebest possible innovation and
there's no doubt that you will.
All our Juul which is our chatbot, that's the smart chat bot
that knows not only theinformation around the process,
but how to run transactions andall that stuff that all runs
within.
(12:38):
S/4 on top of Joule.
We also have embeddedapplications that we're creating
to make sure that processes runbetter.
But BTP and gener generative AIhub will help you apply AI today
if you're not there yet.
So you don't have to, you knowhow many cus how many companies
wait two years to start doinginnovation that their users
feel?
(12:58):
That's not the right way to go.
The right way to go is toloosely couple that and start
innovating today.
Andreas Welsch (13:03):
Awesome.
The, part about the urgency I,think is important because
things are moving so quickly.
There's hardly a week that goesby where you're not seeing any
kind of big AI announcements ornew models, new capabilities,
new companies on one hand beingformed, the ones that might be
competing with you that youdon't even have on the radar
(13:25):
yet, or the ones that areincorporating it and that are
becoming so much more efficientand effective into that point
about.
Connecting data that you have inyour business systems with
something that you might buildas an extension or as an
addition to to that core systemand, data.
I think there's a lot ofopportunity, and I know for the
(13:45):
last 10 years companies and thelargest companies on the planet
have been looking at thingslike, how can we get better
insights from our customers?
I remember working with retailcustomers and
Bob Sakalas (13:59):
I think you're bringing up a very important
point, which is.
In the old days, all roads ledto Rome and today all roads lead
to the data.
We have to make our decisionsfaster, but when I ask customers
are, you, do your decisionmakers get their data in real
time today?
The answer is no.
(14:20):
Yeah I then ask them, do theywant it in real time?
And it it's unanimous.
Everybody's absolutely.
We're being asked that all thetime.
Guess what?
We start talking about thefuture and AI agents and agents
working together.
An AI agent's not gonna beeffective unless it's got a real
time or very close to real timeview of your business.
(14:40):
If you're working on a batchprocess that ran last week, that
AI agent can't make thoserecommendations or decisions for
you in time for you to impactyour earnings.
And so when you start thinkingabout the, what's I've been
asked this question a bunch oftimes, but what's different
about SAP's approach to AI andmost other companies?
(15:03):
And so it's easy to compare,like one of our closest
partners, by the way, isMicrosoft, but Microsoft and
Copilot, which I have on my owndesk.
It's really great at personalproductivity stuff.
I can write a long email, I canthrow it in a copilot.
I can say, Hey, can you makethis email harder?
Hitting professional and short.
Instantaneously I get the answerit's a personal productivity
(15:24):
thing.
SAP is not focused on as muchthat personal productivity in
its own little microcosm of mewriting emails or me creating a
PowerPoint.
What is, what we're focused onis this idea of how do we make
your business process reallyhum?
How do we make your businessprocess ship the goods?
Half a day faster.
(15:45):
How do we make that businessprocess make recruiting people
and landing them so we get thebest talent better, right?
So we're always focused on thebusiness process, and we think
that's the area that's gonnacontribute the earnings the
most.
So if you want to deliver foryour shareholders and show that
your company's becoming moreprofitable and using AI in a
(16:06):
very substantive way, I thinkthe a, the AI focus that SAP
has, which is on your businessprocess.
Every company, I don't care.
I used to work on Walmart as anexample, and even at Walmart
when I actually made thesentence or I said the sentence
out loud in the meeting, I saidno matter how large you are, you
really are the sum of yourbusiness processes.
(16:28):
Everybody in the room would notat the end of the day, it's not
the one-off thing that you didthat makes a difference.
It's the thing that you do everysingle day, and you want to do
that really well.
And I don't care if you'reWalmart or if you're Bank of
America, or if you're Home Depotor any of these companies.
Doing the every single dayprocess is what's gonna make you
more profitable, is what's gonnadrive your stock Price is what's
(16:50):
gonna drive growth, it's what'sgonna drive opportunities for
employees.
It's not that I wrote an emailtwice as well, and now I can go
stand at the coffee machine andchat about last weekend a little
bit longer.
Andreas Welsch (17:03):
And I think that's the important part,
right?
How do you use it to drivemeasurable business results and
business value and, being ableto do this in new ways now.
Coming back to the retailexample, I know customers and
companies have been using bigdata and big data platforms for
a long time.
They put analytics on top.
(17:23):
You could analyze your SKUs anddemand on a store level on a
city regional.
State level and what have you,how are you seeing that changing
now?
In, in terms of requirements,but also in, in terms of what
the, underlying capabilitieslook like when you bring in data
(17:44):
from different systems, when youlayer AI, generative AI, agentic
AI and these things on top.
How do things change and howdoes that drive the, value
discussion in, the also specific
Bob Sakalas (17:56):
Yeah, specifically to retail.
It's always been a struggle overthe volume of the data, right?
So you, let's say you're acategory manager and you're
trying to improve the milkcategory in your store.
You would, try to get the rightproduct mix to drive to make
milk as a category, the mostprofitable that it could be in
(18:18):
your store.
There was a mathematicaldisaster of a problem to do
great category management.
And why?
Because it took a lot of humanpower to analyze that, to fiddle
with the different parameters,to see, oh, I need to stock more
quarts and I need to stock more,half and half.
And all of a sudden people aredown.
Now everybody's lactoseintolerant.
(18:39):
When did that happen?
But but the whole point is thatit was a very difficult thing
for the category manager to makesure that he had the right mix
to maximize the profit of that.
And of course, this is happeningwith detergents, is happening
with cereals, is happening witheverything else.
So now if you think about the AIage, we've always been drowning
(19:01):
in too much data.
We've always not had enough timeto analyze it, and the people
doing the analysis often spent80% of their time just gathering
and cleaning and making surethat their data was clean enough
to do the analysis, which theyonly did 20% of the time.
What we're trying to do withthis, I think the term is data
(19:22):
product economy, but the, basicidea is you define.
Great data products that haveall the metadata around it.
So now you're doing less of yourtime stuck trying to massage the
data, and you're spending a lotmore time analyzing it.
That's the first step.
Now, AI agents can often do partof our job.
Not all of it.
(19:43):
Let's face facts.
AI is absolutely not curious asa technology.
There's no curiosity, right?
It's not very good at whitespace.
Because it's very good at doingtasks that it's seen before, but
when you give it, what's missingin this equation?
AI struggles, right?
The curiosity is not there.
The understanding of white spaceis not there, but now it doesn't
(20:06):
tire at a lot of these topicsthat we just talked about.
So as a category manager, whatthat, let's just pretend I'm at
Walmart, that milk might be verydifferent in Denver than it is
in Phoenix, than it is inCanada, as an example.
But I would get tired of thethousands and thousands of
stores and the thousands ofsituations that I had that might
(20:28):
be a little bit different.
The AI agent never gets tired.
So when it comes to that, I'mgonna gather the data for this
specific market area.
I'm competing against theseother grocers that are using
milk maybe as a loss leader,right?
So there's unique competition bymarket space as well.
The AI agents don't get tired.
So all of a sudden, the thingsthat we can do in a repetitive
(20:51):
fashion, I become a much bettercategory manager because I can
now get recommendations that arevery specific, the specific
markets, the specificcompetitive conditions, the
specific regional differences, Iguess would be a great way to
put it.
I'm able to do a much moregranular job because that AI
agent's not getting tired.
The data is cleaner, so I'mspending less time there.
(21:13):
The agent's not getting tired,and guess what?
I actually get to spend time onthe white space.
I get to spend time.
I don't.
Wow, that's interesting.
One of the things that SAP'sdoing right now that's really
cool is that we've got thesesmart insights that are part of
our analytical tools where it'llgo find mathematical or data
correlations, I guess the rightway to put it, that are
different that I may not neverthought of.
(21:35):
I don't have to go ask thequestion, do you see something
funny in the data?
It can, it'll come back to meand say, Hey, we spot an
interesting trend in the data.
So then, wow.
I, I'm excited because I justgot a new insight that I didn't
necessarily generate.
So AI is absolutely gonna make adifference at the granularity
level.
Things like business Data Cloudare gonna get us out of this
(21:56):
data, moving smushing,transforming, eing, whatever you
wanna call it, and just deliverus good quality data so we can
spend more time on the stuffthat really hits a home run.
Andreas Welsch (22:07):
Beautiful.
That's, the kind of thing we, weshould all be striving for in,
in our businesses to, to lookfor these kinds of
opportunities.
Yeah.
Now you already mentioned a,couple examples with Louisiana
Pacific and, others.
Who else are you seeing doingthis really well?
Bringing AI into the business,driving more value from AI in
(22:28):
their business processes andwith their business systems.
Bob Sakalas (22:31):
I don't have a retailer that's gone public yet,
because I think they're alllooking at this as a competitive
advantage moment.
And they're all working reallyhard to use AI in these kind of
areas I just talked about.
But I do have a CPG examplethat's really interesting.
CPGs consumer products in casenot everybody's a CPG person on
this call, but constellationbrands.
(22:55):
If you're familiar with them,they've got Modelo as a beer,
they've got Corona as a beer.
They've got other spirits andvodkas and things like that, but
they're one of the fastestgrowing CPG companies in the
world, and they have been forthe last 10 years.
And I don't know if you saw, butModelo actually, I think's now
the number one beer in NorthAmerica, all that beer gets
shipped from the south to thenorth on trains.
(23:19):
Turns out that they're shipping400 million cases of beer.
Per year via train to NorthAmerica.
So I guess we're drinking a lotof beer.
That's a lot of beer.
That's a lot of, it's a lot ofbeer.
When you consider, we got 350million people and you're
shipping four oh million cases,and that's only one company.
We're drinking a lot of beer inNorth America, but that's a
(23:39):
different topic anyway, turnsout when you're shipping so much
beer, there's a lot of damage onthese trainings to the tune,
believe it or not, of about athousand insurance damage claims
per week.
So Constellation would have tothe, way this used to work is it
(23:59):
would take the person that didthe receiving would take their
phone.
They would have to take 15 or 20pictures of the damage, they
would then have to create hisdamage claim.
They would have to send it in.
It was like a 20 to 30 minuteprocess.
Every single time it wasdamaged.
And this happened a thousandtimes a week, 52 weeks a year.
They worked with us and said,look actually this started out,
(24:22):
they weren't even thinking AI,but it led to AI, right?
How can we make this processbetter?
And it turns out that thesereceiving areas where they
unload the beer already had alot of cameras.
And so they could gatherpictures, attach'em to the AI
created insurance claim.
(24:43):
They would have a person look itover to make sure that it looked
right.
They might have to add a littlebit to it, but they went down
from 20 to 30 minutes to everyclaim is now below five minutes,
and so that process, if youthink about it a thousand times,
has 52,000 times a year.
They now have a much betterinsurance claims process that's
(25:04):
worth millions of dollars.
This is not small change.
So another example of SAPworking with the customer to
ensure that the customer has abetter running business process.
I can't stress enough.
That's the SAP difference whenthe business process.
I love that.
Yeah.
When the business process runsgreat, it drops to your bottom
(25:26):
line.
It's not just a matter offreeing people up so they can
jump in the traffic 15 minutesearly to go home.
Andreas Welsch (25:33):
And a lot of times I feel these initiatives
might start as we need to figureout something that we can do
with AI.
But I think once you've reallystart with the business
strategy, the business processand, you look at where
opportunities to, to drive morevalue to, to automate things to.
Also measure what the outcome isbefore we even start.
(25:53):
Be clear about what is our KPIthat we want to drive?
What are we looking to improve?
And you attach that AI projectand the value to it.
All of a sudden it's measurableand it's tangible and it's
aligned with your businessgoals.
And it's much easierconversation than saying, let's
boil the ocean and look for somelow hanging fruit and quick
ones.
Whatever buzzword.
Bob Sakalas (26:13):
The best way to start, no doubt, is a lot of
these started with a workshop.
A simple workshop that had thesupply chain people and the IT
people that worked on supplychain together with SAP to just
have a conversation, to justhave a design thinking session
that what are the top threethings we could, really make
better around here?
It turns out that AI is almostalways an answer that can help.
(26:36):
And unlike the old days, by theway, we, oh, we're gonna create
a custom application.
It's gonna take us two years toget it done.
Because the AI is somultipurpose now.
It's the AI helps us get to thefinish line quickly.
SAP's jobs to make sure that itstays relevant, reliable, and
responsible.
Because if it gets you to thefinish line quickly, but it's
(27:00):
only right 80% of the time,that's not a good outcome.
And so starting with theworkshop's a great way these
events that we talked about atthe beginning, by the way.
These are gonna be customerstalking about innovative
problems that they are solvingin these five cities.
What a great way to start yourthinking.
You can go to a session abouthow did you guys remarkably
(27:21):
change your financial planning?
Same basic concept, right?
Getting inspired by what anothercustomer did is a great way to
start.
Andreas Welsch (27:29):
Absolutely.
And like I said, hearing fromothers how they have approached
it and why they even came upwith the idea and what it drives
in terms of meaningful value.
I think that's one of the big,benefits.
I'm definitely looking forwardto attending some of these
events.
You said there were five in, inNorth America in September, in,
(27:50):
in October hitting all majorcities and hubs.
We'll make sure to put it in, inthe description as well.
So please do take a look at...
Bob Sakalas (27:58):
they missed Dallas.
They missed Dallas where I live,so I'm upset about that.
So I would argue that theymissed one location.
But yes, we got five greatlocations this year.
Andreas Welsch (28:08):
You said they were so popular that SAP has
been expanding them over thelast couple years there's always
a chance that 2026 Dallas willbe on the list.
Bob Sakalas (28:17):
We can only hope, but but yeah I would very much
invite, and by the way, this isa free event if you're a
customer.
A lot of events, people arecharging you entrance fees and
everything else.
This is a free event if you'rean SAP customer.
Worth attending, there's nodoubt.
But it's a great way to startyour AI journey to think about
it, right?
(28:38):
I would also encourage people toreach out to their SAP account
team and just say, Hey, I'd liketo have a workshop with my
finance team, or I'd like tohave a workshop with my supply
chain team, or my HR team.
We, have been doing these halfday workshops that have really
been.
I hate to let make it sound thisway, but they dropped to the
bottom line this year.
(28:59):
How many times in business do wework on something that doesn't?
Get us the attaboy this year,but you can be a hero at your
company this year with AI
Andreas Welsch (29:08):
I think that's a great point to close on.
You can be the hero of yourcompany with AI this year if you
work with SAP and you look forthese meaningful examples that
derive value.
Bob, it's been a pleasure havingyou on and, hearing from you how
you and how SAP are addressingthese big challenges, how
companies can drive innovationand try drive real value with AI
(29:28):
today.
So thank you so much forjoining.
Bob Sakalas (29:31):
Great seeing you, Andreas.
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