Hello and welcome to the SAP Learning Insights podcast.
I am David Chaviano today with Thomas Yenovine to talk about
everybody's favorite topic, Artificial intelligence type of
recording is 17th of September 2024.
I think a little bit of the hype has worn down, but I think it's
quite a solid topic to talk about in a more maybe rational
fashion. So let's talk about AI adoption,
specifically within organizations and business.
So the technologies out there, how do we get people to actually
start using it? Before we jump into it, we
always want to get our hero's origin story.
Thomas, take a moment. Please introduce yourself.
Where are you from? What have you studied and how
did you find yourself at SAP? Hey, David.
Hi, everyone. Thanks so much for having me as
a guest. Yeah, my hero's story.
So, OK, currently I'm living near Khalshwa.
Originally I'm from the South of Germany, Hybron Weinsburg, near
Stuttgart. So I didn't make it that far.
However, due my studies, I was in San Diego studying a little
bit in California, and I studied organizational psychology.
Yeah, so I think that's super fascinating.
And this actually even brought me to SAP.
So I started as a student very long ago, 1998.
I saw in the internal learning organizational development team
and yeah, I had a good time there.
But then I moved on to product management and now since eight
years I'm doing business development in Europe.
We have 4 topics around learning, change management and
also some cloudified services. All right, very cool.
So often I think most of our guests, they end up studying
something and then doing something completely different.
SAP but it seems like you're quite consistent in what you
study, organizational psychology, business
development, and in this topic in particular, adoption of AI.
So that involves a lot of psychology within the large
organizations and the change of management.
So I think you're the perfect person to be talking about this
topic today. So, starting from scratch, maybe
you can give us a a general introduction on the topic of AI
adoption and why it's important. So one of the challenges of
course, if the, if you look at the term of adoption, it's like
a buzzword, yeah. So people have different
meanings or project different meanings into it.
So for some it's just the consumption of whatever digital
tool. For some it's acceptance and
actually use it. Yeah.
So whatever I I think it's a mix and even the implementation or
or the transformation. So what I find interesting also
look at the different stakeholder interests.
Yeah. So like if management looks at
adoption, they want ROI, they want cost reduction, they want
growth, they want profit because they buy software and the goal
is to pick a management goal. Yeah, IT, but if you talk to
someone from IT, they perhaps they won low integration costs
or security. So I really other things or
perhaps even automation of their own processes.
And if you are someone in the line of business, like in HR, of
course they have their own goals like perhaps want to process
more recruits or whatever. And I think where you actually
should start is the actual user. What they usually want is less
frustration. If they have a new tool, I would
say they want to have some individual benefits that their
work is per simpler, fast, better.
Yeah. And I think that's that's
important to know. And, and also there are
different other, I would say words or narratives around
adoption. So one is adoption, which is the
actual usage, but you also have the adaption.
And I think that's, I would say almost equally important because
if you have a new software and with AI, it's really disruptive.
You can do very new things. You can automate knowledge work,
you can really improve tasks, like in text processing, for
example, up to 80% or so, for example, regards of timing.
But what, what does this mean? How do we adapt as an
organization? Adoption is one side of the
metal, but on the other side we need to adapt our jobs, our
tasks, our processes. Perhaps some tasks are not
needed anymore, but there are new tasks possible, for example.
Yeah. And of course, if you as an
organization do not adopt, the competition is certainly going
to be doing it. So it's quite an important topic
to stay ahead of the curve here at least, and if you're really
good, be a trendsetter. So adoption, I think in my mind
at least, it's focusing a lot on the human component.
So the technology is there, but there's a lot of, I guess,
psychology involved in getting humans to actually start
adopting new technology within an organization.
So you have people that do things a certain way, and
they're obviously going to have to change the way they do things
if you're going to adopt new technology.
So what are the key benefits to humans and an AI adoption
situation within the organization?
Of course, it depends on the use case because there are different
use cases around AI. Now, if you talk about AII think
recently, we often talk about generative AI, right, which
really is powered by large language models and so on.
And there, of course, it's working with text, for example,
like if we're a sales professional, marketing
professional or HR professional. And often you really can be much
more productive. So you can do much more things
shorter time or you can do additional things like just once
more example for our podcast, we also have a German speaking
podcast around education, the education Newscast.
We now do transcripts automated, which is voice to text.
And from that whole transcripts, so the pages we do summary also
with AI tool. I think we tried it once or
twice, but usually it takes at least two hours to transcribe
and summarise a podcast. Now if we have this AI chain,
let's say the transcription and the summary, perhaps it's down
to 20 minutes or so. So I think we're quite safe some
time for example, but also have an extra service because earlier
times we perhaps often didn't have to have the time to to to
do that to offer that additional service.
OK. So, so the the promise of of
increased productivity is certainly there.
What would be some change in management considerations that
an organization to take with this people when implementing
this technology? The productivity of course,
especially in, in our German area, that's the number one
thing. But I think you also can be more
creative, you know, because you have the word knowledge now in
let's say TCPT or in atrophic cloud or whatever you use and
you can research, you can brainstorm, you have a, let's
say, own body. And that's also something what
really makes you more creative. So often people look at the
productivity, but I've think it also can make your, for example,
your knowledge work also more creative.
Yeah, yeah. I, I can report from personal
experience. It's, it's always interesting to
give various prompts when you want to present and a large
language model a problem that you're facing.
And you can ask it like how would Jeff Bezos address it,
such as this situation, what would be Bill Gates strategy and
on solving this problem, stuff like that.
And it's interesting to sort of answers you get that you may not
have considered otherwise. Absolutely.
And going in into like the change of management.
So I imagine not everybody would be convinced in an organization
based on what we just said. So I managers have to go and put
a little bit more effort into implementing this technology
into an organization. So what considerations should
leadership take regarding its people and specifically when
during such an implementation? So I think first of all you and
I think that's really with every new technology pretty similar.
I think you should think about how do people react to something
new, to new innovation. And there you have the diffusion
model, which is the old model, I think many of us know it, which
says that just perhaps to to, I think 12% of people are
innovators or early adopters. They like every new offering,
every new tool, they like to change, they like to try out new
things. But then you have perhaps the
early maturity, yeah, you know, this typical curve, this normal
distribution, and then you have a late maturity and then you
have laggards. So perhaps just 12% of the whole
base of people of your target group, they will jump on it
absolutely motivated and then the first will question it and
look at the individual benefit, not just for the new or
technology sake. And then perhaps there's even a
larger portion also they are more let's say hesitant or
perhaps even this is critic. So I think this is one point to
look on that and perhaps in in the beginning work with the
innovators and earlier adopters to understand the use cases, how
they use it, how it works. And then, yeah, regarding change
management, I think, I think with every IT software
implementation also if it's a large as Farhana implementation,
there's certain things to consider.
So of course people may need to be aware, yeah, like why the
change is coming. So you need to have a strategy
and narrative, whole story. They need to have somehow a
desire, so something emotional. I think that's important to also
understand your own individual benefit.
Often this is something which is missed because managers perhaps
select the new software due to manager KPIs, but they not
certainly correlate to to, you know, less restoration
expectation, for example. So yeah, employees need to
understand what's in it for them and also let's say, get that
desire, that individual desire, then they need to build up
knowledge here. Is it like how to change or what
is changing in detail, like which of my tasks at my top are
now impacted of AI? And I think of course what's
similar, but it's still different.
They need to the possibility to develop abilities or
competencies. Yeah, like really in the daily
top really now do something different like not write a
resume in HR or write and own let's say block or so in
marketing or e-mail, but use AI tool to support you.
And I think this ability, of course, it's different than the
awareness knowledge or the desire.
And then I think this is very simplified.
Yeah. But I think then if people are
able and they know what's changing and so on, but also
it's always important this this reinforcement that that you
reinforce new behavior because something like what we have
experienced perhaps in in the corona pandemic.
So we tried out new things, but then suddenly if everything is
over, people want to go back to physical events.
They do some managers want 100% people back in the office.
But yeah, yeah, you don't you don't reinforce the good things
or the changes. I think that that's equally
important. Important, Yeah.
So that's very simple and there are different models of
frameworks. Perhaps we can put something in
the show notes, the different categories and and so on, but I
think pretty simplistic. These are some of the change
management considerations. OK.
Yeah, that, that'll sound great if we can get those into the
notes. And I think it first step, the
very first step was, is maybe a willingness to actually
communicate with one's organization.
I think that's often a complaint from many organizations that
management up there in the ivory tower make a decision and they
get the news very late and feel like they didn't really have any
sort of say in the whole thing as this is just happening to
them and it's done to them by the managers.
So best to avoid such situations with effective communication and
identifying all those people along the bell curve that you
mentioned and that communicate with them with a message that
resonates with them and their concerns.
So Speaking of concerns, at the top of the list, I would say for
most I would say anti AI people in the workforce are concerns
about job security. What would be your best way to
address concerns about job security?
I think that's not already going into the implementation
considerations. So I think this concerns of
fears, I think it's fear and resistance, they are often more
diffuse or generic, like even AI will kill us all.
That's super concern that this topic future.
But now some other thing perhaps depending on my job, there will
be some negative changes that I perhaps even will lose my job,
but others perhaps even will think and my job won't be as
important anymore. So they lose status, which is or
can be equally, let's say, you know, fearful.
So I think the best thing of course is, and that's already
key for the implementation that they really look on the use
cases of AI in the end. Yeah.
So it's not this black box strange thing.
But in the end, you need to look what the use cases are affected
or tasks in the end are affected by AI in my daily work.
And also how does this change and we know from earlier, let's
say automation waves, I would say we had I think 567 years
ago, we had already a first AI wave with more manual chat bots
with machine learning coming up. Also there people at fears and
already there what you saw it's not that full top which is
eliminated it's tasks which are perhaps not some I think some
are fully eliminated like typing text from somewhere else.
You now have text recognition and transcription and so on or
translation, but even translation in this example,
it's not fully, Yeah, now done automated you always.
And that's also inside how the AI tools work.
You need to yeah, review it. Yeah, so, but perhaps it's 8090%
some tasks, some tasks are perhaps just 10% impacted.
And I think this task and the lists and also depending on use
case to your daily top, I think this really needs to be done on
the top level or team level. OK.
And just basically letting people know that this is freeing
them up to do other, probably more interesting things
throughout the day and not necessarily replacing them
completely. Absolutely.
And what I said you can't replace many people anyhow
because it's individual tasks which are more or less
automated. And perhaps in some very special
like like what I mentioned, of course, a translator or show on
the list, depending if it's very standardised data, you can to a
high degree automate, but still you need to have the quality
control, then you perhaps can, yeah, translate more or create
more output. And would this also be
convincing to sorts of folks that would say, we've always
done it this way and it works just fine as it is?
So that that's one of the favorite things I've heard from
various parts of my professional experience thus far.
People that just figure why change, why improve?
Would the procedure of convincing them be similar to
the job security folks? I think in the end you need to.
So there are different aspects to that.
Of course. One thing is the individual
benefit that people understand that what's in it for them, what
is changing and how can they cope, how to, let's say, build
up the new tasks, the new skills and so on.
That's one thing. The other thing is, of course, a
change story for the company. Yeah.
What's the overall benefit? So not just perhaps some very,
let's say, rational stuff. I think important is that also
this is connected to somehow emotions person to the company
culture in the best way. For example, what I really
liked, it's not AI, but similar Hugo Boss.
Their, their slogan for the S4 implementation was it's for
hookah boss, like S for hookah boss.
So already it's connected to their, let's say company logo
and claim. And they had their own claim.
They created merch like T-shirts, like own, let's say
marketing again to build up that desire and this emotional thing.
Of course, perhaps I would say 10, 12% of people you anyhow
can't reach because they are negative.
And you need to think in the end, is it the skills or is it
the motivation? If it's the skills, you can help
them or yeah, somehow, if it's a motivation or if they even are
opponent, of course, then it's again a cultural topic.
You do, you just ignore them or you give them other jobs or you
fire them. I think that then, of course, in
every transformation, more cultural topic.
OK. And for new people coming into a
situation like this, so like digital natives, people who
maybe are not so institutionalized and the way
the organization used to be coming in with a fresh mind that
may be more open to an AI adoption situation, but
obviously more junior, maybe they don't have as much
influence. What can those new people do to
help an organization and their and the adoption process and how
can leadership identify those people and and get them to help?
I think in the in the end it's similar, yeah.
So I think they need to be aware, they need to build the
desire, they need to build the knowledge and abilities
regarding their tasks. And I wouldn't say that younger
people, Percy, are more digital fluent.
So that's also, I think a disbelief.
I think they're older people who are also, let's say pretty much
digital fluent or open to new technologies.
But yeah, I think in the end it's also, let's say
motivational topic. If someone you like, let's say a
new coworker is extremely passionate about that.
And even, yeah, builds own apps or whatever, tries regularly new
things then here see that it's perhaps one of the innovators
and you can think about giving them a special job like a change
agent or multiplier and training others.
And I think managers need to of course, recognise this.
I think Speaking of managers, I think for them it's very
important that they not just have some executive, let's say
presentations and high level, let's say cognitive info on what
AI is and what the business case is.
Of course, that's important. They need to understand the use
cases and need to perhaps also prioritize, let's say, which
have the highest value, which doable, let's say this triple
prioritisation criteria. But I think they also should
somehow hands on understand what AI does because otherwise you
know, they they try to talk to experts or to everyone and don't
understand the subjects. And AI, such a disruptive
technology I would say. I think you also should train
managers and they should be enabled.
And what I actually haven't seen yet often is use cases how
managers can improve their own knowledge work.
And honestly, I suspect many managers are still working
mainly on excellent PowerPoint, which is in an AI world, perhaps
not the go to tools anymore. Oh yeah, actually I've
experienced 2 very exciting, let's say fresher new new
formats. So one was a prompt hackathon
where you actually just get minimum input regarding let's
say theory and you have maximum hands on let's say work, for
example. There we formulated different
tasks and especially challenges. We did it for learning
professionals and they worked in five hours in small groups on
the different challenges and then also talk to each other and
share their their learnings. I think that's extremely
helpful. I know for example, SAP
internally, but other companies like Deutsche Telecom in BV,
whoever they do that in different formats, perhaps two
hours or only. But the, the key is really work
on challenges from your daily job and really maximum, let's
say hands on. I think that's, that's a very
cool format because it then let's experience, let's say the
new technology on how to handle it.
I think that's important, but I also did, but I also like over a
longer period of time to work on in a learning group.
I did this with my team and I used that their workbook based
approach like Germany. There's a whole movement of Leno
S guides, a lot of open source guides.
We can put that also in the show notes.
And there you have again, a little bit of input and quite
different kind of tasks or talent just again, and you do
that with a team and I always have some you do meet once a
week, then you do some homework and reflect and then you meet
again. So it's, let's say, over a
longer period of time. And this is, I would say
different iterations. You also very, very much build
up new competencies and yeah, really relate and reflect that
to your daily work. So I think those two elements
are also pretty helpful for hands on more fun learning
approach. OK.
And making it real for for the job and making it real for
everybody. Involved and relevant they're.
Very cool. And they're called Hack.
They're like a hackathon. Right, a hackathon or name, but
you can call it for the whole like OK.
Got it. Cool.
And you mentioned understanding managers and leadership,
understanding the technology a bit better.
Can we maybe go into some specific examples of how this
technology can be applied in an organization?
So we covered sort of like the marketing and communications
part, generating text and stuff like that.
But is there anything else maybe SAP process related that Jewel
is doing in the future that we could use as an example of how
I'm assuming a large language model can automate?
I think that the number one even above HR and the marketing
that's a top functions is everything with software, of
course, yeah, because they're also language models are very
capable. So if you have any task related
software, software development, software support and so on, of
course, there you you will if I have a high value because
there's a high portion or high degree of, let's say, possible
automation. And so like with the GitHub
Copilot or with other tools. And I think that's just the
beginning. Yeah.
So certainly that and then, well, I think there are certain
studies, I don't know it out of my head, of course, everything
around education. Yeah, I think they're special
generative AI is very helpful. Also everything around support,
let's say more the call centre work.
I think they're so, so so the the last generation of let's say
support ticketing and similar tools was very annoying, I would
say if it was AI based. But I think that's now really
getting better. And also the technology on the
one hand, you have the world knowledge in the large language
models. And then you can extend that,
yeah, with grounding and different kind of tools like
vector databases to feed or to. I don't say train because it's
not the training, but to ground your own knowledge into it.
Like, I think this is also very beneficial, yeah.
And can you comment on what SAP specifically is doing in terms
of learning and enablement for AI adoption?
Yeah. So we have different offerings.
We have these free offerings. So especially for SAP experts,
different learning journeys on more on the basics, perhaps we
can research that on some business AI.
They're very worthwhile. They're very good courses on
Essex. I think that's something also
what you need to reflect. So like also see that ties back
to your earlier questions that you have some Essex frameworks
which, for example, describe use cases, what you don't do or
which you usually expect always, for example, if AI is touching
somehow personal data or customer data and so on.
So I think, yeah, so there's this free offering.
Then we have the SAP Learning Hub, yeah, which is our, let's
say, premium learning platform that you have always live
sessions by experts. Also several ones are touching
AI topics. We have the learning systems
where you can try it out hands on.
I think that's important like what I mentioned, so not just
read or hear or see, but also try out, let's say if you and
the training systems, they have real data in in them to
practice. So you don't need to build up
everything by your own. So I think that's certainly
something to to upskill yourself.
I think the end users, I don't know, they perhaps don't need
that much upscaling. They need more, let's say
generic, yeah, skills like prompt engineering, for example,
like the actual tool handling there.
We also have different offerings like SAP Enable now a support
set, which also has no generative AI embedded, for
example, writing a system or you can summarize content and so on.
So that's on the enablement side.
What we also perhaps can put in the show notes.
We have also big toolbox in the road map navigator, which is,
let's say our central tool for everything around our
implementation methodology sub activate.
And that's their own cross topic page on organizational change
management with many different, for example, templates.
And the good thing is that's really aligned to a whole SAP.
Yeah, implementation project like for discover, prepare,
explore to realize, deploy and run.
And they have templates, let's say how to create the change
strategy and vision, communication, enablement and so
on. And monitoring.
I think that's also important. And of course we have next to
this free offerings, we have also services what you can order
if you like. OK, can you repeat the name of
that toolbox again? That'd be definitely should get
down to the show notes. Yeah, it's a road map viewer and
there you have a cost topic page on organizational change
management with mandate template.
What's also very good tool, which you really should think
about using in the very beginning is from the SAP App
house. It's our organization which
supports customers in let's say we are user research and
everything around design, syncing and so on.
And they have toolkit for generative AI exploration
workshop to really find out the use cases, priorise them and
this I think that's even free so.
And I know they are currently working on other offerings
actually already. OK.
Yeah, that's a lot of information that SAP is putting
out. And hopefully we can make the
show notes of this podcast a a central repository of all that
information if if one doesn't already exist out there.
All right, that that covers a lot.
And since we're approaching 30 minutes, I've taken plenty of
your valuable time. So I always want to close on
some final words of wisdom from you, Sir, if you have any words
of wisdom for new people who are entering the workforce in this
time of change and also for those who are maybe already
established and we'll be facing these these changes as they
come. All right.
So, so I think like what we already mentioned, I think it's
important to yeah, leverage change management and yeah,
organizational development, let's say, as part of AI
adoption, because let's say trust is so important there and
perhaps even fear and real assistance are also high to
think about ethics and trust and so on.
I think it's important to be used and human centered.
Yeah. So think about the end users
like leverage, for example, this different powers design thinking
tools. And perhaps to end with some
final words. So I'm a big fan of ESA
Molechia, who is professor at MIT and he just wrote the Co
intelligence and book living and working with AI.
And he has four key rules. Let's say 1 is always invite AI
to the table. I think that's important, yeah.
Like always, whatever you do, like new project, new task,
think about how can an AI help me if it's with pictures, video,
text, whatever or code. So always invite AI to the
table, but be the human in the loop.
I think that's also important. Yeah.
To use empathy, your own knowledge.
We know AI can hallucinate, so we need to double check on Fact
Check content. Then treat AI like a person but
tell it what kind of person it is.
That relates to the whole prompt engineering ways.
So it's important that you breathe their eye with a lot of
context knowledge. And in prompt engineering with
defining prompts you already can do that if you don't want to go
further. So like telling what Pezona you
are, what are the tasks, what does this output and so on.
So treat AI like a person, but tell it what kind of person it
is and assume that's the worst AI you will ever use.
So I think that's, we know now that we have the strawberry
model from open eye. So it's so much fast progressing
then even the entropics models suddenly have become better, at
least I I think then the ones from open my eyes.
So I think it's a real race always gets better and faster.
So I think everyone should start now to deal that and put it on
the table all. Right, some great guidance and
great tips and lots of good information.
Thank you so much for being on the podcast, Thomas.
Thank you. Thanks for the invitation.
Yeah, All the best, everyone. Bye.