March 24, 2023 • 25 mins
In this episode, Tom Davenport (Professor & Author) and Andreas Welsch discuss how companies can transform their business with AI. Tom shares his perspective on creating new business modes based on AI and provides valuable insights for listeners looking to set up their company for successful AI projects.
Key topics:
- Find out how AI drives change
- Achieve business outcomes from AI
- Transfer experience to Generative AI
Listen to the full episode to hear how you can:
- Draw parallels from examples
- Get stakeholder buy-in
- Use generative AI for enterprise knowledge management
Watch this episode on YouTube:
https://youtu.be/IZktwsbfO0E
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):
Today we'll talk about being all in with
generative AI and who better totalk to about it than someone
who's actually just published abook on that Tom Davenport.
Hey Tom, thanks for joining.
Tom Davenport (00:11):
Happy to be here, Andreas.
Thanks for having me.
Andreas Welsch (00:13):
Awesome.
Hey, I'm sure the majority ofour audience today already knows
who you are.
You've been doing this for quitesome time.
But why don't you tell us alittle bit about yourself, what
you've been up to lately beforewe get into our session today?
Tom Davenport (00:28):
Sure.
So I'm an academic I guess youmight call me a pracademic.
I try to be applied tobusinesses and the work that I
do I go back and forth betweenbusiness schools and consulting
firms, but I've been a professorat Babson College, which is a
(00:50):
business school in the Bostonarea for, I don't know, almost
20 years now, but a consultantrunning research centers for
places like Accenture andMcKinsey and EY.
And I write about how people andorganizations use information
and technology.
So initially focused on businessprocess re-engineering, wrote a
(01:12):
book on ERP at one point,knowledge management for quite a
while.
And for the last, I don't know,20 years analytics, big data and
AI which are all part of thesame family.
Andreas Welsch (01:28):
Awesome.
I'm gonna say I'm having a bitof a fanboy moment here.
We're talking about this in ourprep.
I came across your work back in,in 2016/17.
I remember you writing aboutfuture of work and how
automation and AI will shapethat.
Sounds a bit geeky, butespecially on March 14th- Pie
Day, like I said, having a bitof a fanboy moment with you for
(01:51):
our special episode.
Tom Davenport (01:52):
Yeah.
I should have brought a slice ofcherry pie or something myself,
but sorry, I forgot.
Andreas Welsch (01:58):
Always after the episode is enough time, right?
Hey folks in the audience, ifyou're just joining the stream,
drop a comment in the chat.
What do you've recently usedgenerative AI for?
With all the tools that areavailable, whether it's for text
or audio, video, images, there'sso many opportunities these
days.
I'm curious to hear what you'vebeen using it for.
Tom Davenport (02:21):
And you should reveal that I'm not actually on
this session.
I'm a deep fake.
Andreas Welsch (02:29):
Who knows?
It's almost like Schroedinger'scat.
But Tom, should we play a littlegame and kick things off?
What do you think?
Tom Davenport (02:37):
Sure.
Andreas Welsch (02:38):
Perfect.
So this game is called In YourOwn Words.
When I hit the buzzer, thewheels will start spinning.
And when they stop, you'll see asentence.
I'd like you to answer with thefirst thing that comes to mind
and why in your own.
(02:59):
And to make it even a littlemore interesting, you'll only
have 60 seconds for your answer.
And for those of you watching,drop your answer in the chat as
well and why.
Tom, are you ready for What'sthe BUZZ?
Tom Davenport (03:14):
Oh, I'm nervous.
Yes, I'm ready.
Andreas Welsch (03:17):
Okay.
Excellent.
Then let's get started here.
If AI were a movie genre, whatwould it be?
60 seconds on the clock go.
Tom Davenport (03:28):
I think it would be a combination of a
warmhearted love story with afew horror elements thrown in.
We don't know exactly how the AIstory is going to end.
There are a lot of peopleconcerned that it might have
some scary elements,.
(03:49):
And I recently saw this movieMegan about a very smart robot
who ends up doing all sorts ofdastardly things.
And I thought we're not that farfrom that really in terms of
what capabilities AI has.
It is certainly quite possiblethat it would be a combination
(04:11):
of outcomes.
Andreas Welsch (04:13):
Thanks for that answer on the spot.
So definitely a bit of sci-fi inthere.
It's good.
It seems like it resonates withthe audience as well than all
all different things fromhorror, comedy, action, and
killer I-Robot and some goodexamples.
Awesome.
So now with the icebreaker outof the way, why don't we jump in
(04:33):
into our questions?
You mentioned you've justpublished a book called"All In
on AI", and I wanted to takethat as the first question that
has at least been on my mind.
I'm sure in on the audiences aswell as they read the title of
today's episode.
What does it mean to be all inon ai?
What did you find out from thoseinterviews that you've done.
Tom Davenport (04:57):
Andreas, if you were to drive from your home in
Pennsylvania down to AtlanticCity, you go all in.
You put all your chips on thetable.
You make a big bet on aparticular gambling outcome.
And so going all in on AI ismaking a big bet on AI in your
business.
Not using it on the margins, notusing it to tinker with a few
(05:24):
business processes or run a fewproofs of concept, but really
dedicate yourself to someimportant production deployments
and to change somethingimportant about your business
model, your strategy keyprocesses end to end.
(05:46):
Even changing customer behavior,I think in a substantial way is
possible with AI.
So in this book, which I wrotewith Nitin Mittal of Deloitte,
we talk about companies thathave already gone all in to
greater or lesser degree anyway,and have addressed something
(06:08):
important about theirbusinesses.
And these were companies fromall around the world, from Asia,
Europe, United States, Canada,et cetera.
So if, you're not thinking aboutthat, you have a fairly
substantial chance of, I think,falling behind some of your
competitors.
Andreas Welsch (06:26):
I think that's an excellent point, that it
takes that kind of convictionand support throughout that.
This is really a priority.
Something you really want tomake happen.
And not just something thathappens in a dark corner
somewhere.
What do you see these companiesdo differently compared to those
that just play around with it orthat don't get it into a
(06:48):
scalable outcome or a scalableinfrastructure?
Tom Davenport (06:53):
I think to me, the most interesting thing is
actually changing your businessmodel or enabling a new business
model.
So we talked about companieslike Pingan in China, Airbus in
Europe, SAMO in Japan.
Big insurance company there thatare really using AI to enable
(07:17):
business ecosystems.
Pingan has five of them.
All of them are powered by AI.
One, the healthcare ecosystem,is powered by a system called
Good Doctor.
It's an intelligent telemedicinesystem.
Sadly, in the United States, wethought we were being advanced
by letting you actually talk toyour doctor over Zoom during
(07:41):
COVID.
But Good Doctor lets you use AIto triage whether you need to
see a doctor or not, to suggesta diagnosis to the doctor, and
to suggest a treatment strategy.
And 400 million people in Chinaand Southeast Asia are using
(08:05):
Good Doctors.
So well over the number ofpeople total in the United
States.
That's definitely all in on AIand healthcare.
In Airbus, they are using AI topower an ecosystem of all the
airlines around the world thatbuy and use Airbus commercial
(08:28):
aircraft.
They also have on their defenseside, they have a satellite
imagery business that theyformed another ecosystem with.
So I think we saw in theseplatform oriented companies that
are digital natives, Uber andAirbnb and so on, the
(08:51):
connections of buyers andsellers through platforms.
And you're seeing this in largeorganizations as well.
But you can also change yourproducts and services, your
strategy.
I wrote an article recently witha Oxford professor and the head
of AI at Shell about how AI isbringing back process
(09:18):
re-engineering, which was myfirst research focus in
business.
I wrote the first article in thefirst book on business process
re-engineering back in the early1990s.
And Shell is totally changinghow it does processes like
maintenance and exploration.
(09:39):
How it enables moving toward aless carbon oriented business
model with AI.
These are all companies doingsomething substantial.
Some are in process.
Some like Pingan have alreadyseen fantastic results.
It's the largest private sectorcompany in China and the 16th
(10:02):
largest company in the world interms of revenues, founded in
1988.
So that tells you somethingabout how rapidly they've grown.
Andreas Welsch (10:11):
Fantastic.
Thanks for sharing.
I think those are actually threeexcellent examples of where AI
can demonstrate a measurablebusiness value.
One of the questions that I seein the chat here is from
Michael.
Michael is asking (10:25):
Is the hype over AI just the hype over
blockchain, like a solutionlooking for a problem?
And I think that's an excellentquestion.
Because, in my perception, saybefore the end of November last
year, media and analysts werealmost talking up the next AI
winter.
Is there really going to be asustained investment?
(10:46):
Is this going anywhere?
Are we just flattening out here?
And then, now with generativeAI, certainly that's hit the
turbo and the booster.
So are we still just looking fora problem for the technology
that we have?
What do you think?
Tom Davenport (11:03):
We can look at companies that have already
gotten considerable businessadvantage out of AI.
Like some of the ones I wasmentioning.
Which was never really the casewith blockchain.
I think the only industry thatprospered with blockchain was
the cryptocurrency industry.
And I must say, I was always alittle suspicious.
(11:25):
If this is such a great and safeway to store information, then
why are there's so many fraudsand hacks in that particular
industry.
AI is always rated ahead of anyother technology in surveys of
business people for what is themost transformative technology
(11:47):
that we're looking at now?
Blockchain has droppedconsiderably, but AI hasn't
really dropped over the pastyear or so.
And as you suggest, Andreas,because of generative AI, I
think it's even gone up.
And we have still lots ofcompanies working to make AI
(12:07):
better.
Large vendors, small vendors.
The amount of venture capitalflowing into AI has dropped as
it has to any in any othertechnology domain.
But I don't think there's muchdoubt that AI is here to say.
Andreas Welsch (12:26):
Thanks for sharing.
I think that's very good andvery concise also where we
believe things will be headed inthe future.
Now, coming back to the topic ofbeing all in.
So you've shared examples fromPenan, from Airbus, from Shell.
What are the kinds of businessoutcomes that leaders and
(12:47):
businesses can expect when theyare all in, when they do go all
in?
What have you seen there?
Tom Davenport (12:53):
In some of these ecosystems, AI powered ecosystem
business models.
As one of the people weinterviewed at Pingan said it
creates sort of a Disneyland ofdata in the sense that every
participant in these ecosystemssupplies data.
(13:14):
To the central organization andthe ecosystem.
They all benefit from havingaccess to that data, but the
central organization uses it tocreate new products and services
that are even more valuable.
That enables more participantsin the ecosystem.
So you have this this virtuouscircle that enables much faster
(13:38):
growth and better profitabilityover time.
In the organizations we lookedat that are doing operational
transformation, Shell isinspecting its pipelines and its
refineries.
And used to be years that itwould take to get through an
(13:59):
entire refinery and inspect allthe piping and the valves and so
on.
Now they use drones and deeplearning based image recognition
models to see does this pipelook fine?
Or is there a potential problemthere that we need to have a
human go out and look at?
And it's gone down to a matterof days for an entire refinery.
(14:23):
So huge savings in terms ofoperations.
An interesting model is the onethat we have seen in a negative
way in social media companies.
It's changing the behavior ofcustomers.
And in social media, obviouslythat hasn't worked out terribly
well.
(14:44):
And I think it's responsible fora lot of the polarization that
we're experiencing in oursociety now.
Teenagers are getting depressed,et cetera.
I'm not saying that wasintentional, but that was a
behavior change outcome.
But a number of companies,mostly insurance companies, are
trying to change healthbehaviors to be more positive.
(15:07):
Companies like Progressive areusing metrics not just to charge
you more if you're an unsafedriver, but actually to tell you
when you're driving in an unsafefashion and to try to discourage
you from doing so.
So I think changing customerbehavior is a third possible
outcome, but not one that is aswell developed outside of social
(15:31):
media.
And as I say, the goal is verydifferent is to create healthier
people, better drivers, etcetera.
Andreas Welsch (15:39):
I think that's an especially interesting point.
And also when it comes to amoral, ethics type of
discussion.
To what extent is it nudging forthe person's own benefit or to
what extent is it optimizing thereward function or the
optimization function of themodel or of the company that
(16:01):
employs AI?
I think there's a fine balance,right?
Tom Davenport (16:05):
Yeah, that's a good point.
In health insurance for example,if your customers get healthier,
that typically helps your bottomline as well.
There's a pretty good alignmentof incentives there.
But as you suggest, there couldcertainly be cases, and I think
(16:26):
we've seen that in social mediawhere what helps the company is
not something that necessarilybenefits the user at all.
Andreas Welsch (16:35):
Very true.
Maybe to pick one more questionfrom the chat, and I'll,
paraphrase this, but myinterpretation of when Maya was
asking earlier was (16:43):
If you are digital native, cloud native
compared to a larger, incumbent,traditional organization what's
the effort, right?
To go all in with AI, toimplement AI?
Is the effort higher to do thechange management and to build
(17:04):
the AI and the models itself?
Tom Davenport (17:07):
I think in digital native companies, there
is much less culture changeneeded.
I have a friend who works forMeta in the analytics and AI
space, and he's been a chiefdata and analytics officer at
various legacy companies.
And he said the big differencebetween his current job and the
(17:28):
previous ones is he doesn'tspend all of his time persuading
people about the importance ofdata and analytics and AI.
The reason why we focus reallyon legacy companies is it is a
huge organizational change forthem.
They have an establishedbusiness, a strategy, a culture,
(17:48):
et cetera.
And so changing in the directionof being all in, or AI first or
AI fueled, whatever you want tocall it, is a dramatic
organizational change.
I, asked one of the people Iinterviewed at a large retailer.
Why he keeps taking these jobsin legacy companies as the head
(18:11):
of analytics and AI?
And he said, ah in those digitalnative companies, it's too easy.
There's no challenge there.
Now, I'm not sure that's true.
It's still challenging,certainly, but less.
And we hope that these legacycompanies will take some of
these ideas and use them totransform their own business.
Andreas Welsch (18:33):
I think that's, great.
And looking at something likegenerative AI, I feel we are
really just, starting to scratchthe surface with all the
opportunities and possibilitiesthat are upon us and before us.
And, maybe even to some extent,making it easier to communicate,
to understand, to also get somefirsthand experience with AI.
(18:56):
I feel it's getting a lot easierthese days to get some kind of
output where, you know, yes,there has been AI behind it.
How do you see this evolving andespecially again with a theme of
being all in?
What does it mean being all innow with generative AI?
To take that one step further.
Tom Davenport (19:14):
I think now it means large scale
experimentation both incorporate sponsored activities,
but also encouraging individualknowledge and creative workers
to explore generative ai.
(19:34):
Picking some tools that peoplemight explore and funding any
costs that they incur.
We still have a number ofproblematic issues with
generative ai.
Of course, the hallucinationsthat occur, the legal issues
(19:57):
involved in who actually ownsthe images that are used to
train them.
I think there are even issuesaround all of the carbon that we
burn up in generating thesemodels.
They're just fantastically largeand very expensive in terms of a
(20:18):
dollars and energy to create.
But I also think there's amassive amount of potential
value there, and, ultimately, Ithink if you are a knowledge or
creative worker and you're notusing these tools, you will be
at a substantial disadvantage topeople who are.
(20:39):
I mentioned earlier my work inknowledge management 20 or 25
years ago.
And I think this has fantasticpotential to make available all
the knowledge that's been lockedup within organizations.
I'd written in the past some ofthings about Morgan Stanley's
(21:01):
use of generative AI to try tocapture all of its knowledge and
make it easily available tofinancial advisors.
It's gonna be described on CNBCin an hour or two.
And I think that's one of thegreat potential advantages for
large organizations to beaddressing.
(21:22):
Even though it's still tricky.
You're gonna have to do a lot offine tune training and you may
end up with multiple levels ofgenerative models.
I was talking to someone in thelegal industry last week who
said yeah, there's a overalllarge language model like GPT-3
or whatever, and then there is alegal version of it that some
(21:45):
companies have already created.
But then you also need a UKversion and a US version, and
maybe you need one for realestate law in particular.
Maybe you need one for aparticular firm.
So I think we're gonna end upwith multiple different layers
of these models with increasingdetail about the content that's
(22:05):
in them.
And it's not gonna be easy todo, but I think it's going to be
potentially quite valuable forcompanies to explore that.
Andreas Welsch (22:13):
That's awesome, Tom.
I think we are really just atthe beginning and there's so
much more to learn and toexplore as we're making progress
in the industry.
Now, I was wondering if youcould summarize the three key
takeaways for our audience todayas we're getting close to the
end of the show.
Tom Davenport (22:30):
Sure.
Going all in on AI means makinga substantial commitment in
terms of money and people, andintellectual horsepower.
How are we gonna use thistechnology to change our
business?
There are substantial outcomesthat companies have achieved
either in enabling newstrategies and business models
(22:53):
or drastically improved productsand services.
Morgan Stanley is one that'sdone that with a next best
action system or changingcustomer behavior as well in
addition to operationaltransformation.
Generative AI is in its earlydays.
(23:14):
But I certainly, it's excitingenough so that companies need to
devote considerable attention toexploring it, to trying it out
to and the most important thingI suggested was think about how
can we manage all the vastknowledge we have within an
organization to create customersand employees who are armed with
(23:39):
everything the company knows.
Andreas Welsch (23:44):
Fantastic.
Thank you for that summary.
And thank you for joining ustoday, Tom.
Like I said it's been apleasure.
I'm so excited we've been ableto make this work.
Thank you for sharing yourexpertise with us and for
learning with us.
Tom Davenport (23:59):
Thanks.
I enjoyed it, Andreas.
Andreas Welsch (24:01):
I'm going to celebrating Pi Day.
Maybe you can have some of thatcherry pie as well.
And for the rest of you and theaudience, thank you so much for
joining.
Today we'll talk about being all in with
generative AI and who better totalk to about it than someone
who's actually just published abook on that Tom Davenport.
Hey Tom, thanks for joining.
Tom Davenport (00:11):
Happy to be here, Andreas.
Thanks for having me.
Andreas Welsch (00:13):
Awesome.
Hey, I'm sure the majority ofour audience today already knows
who you are.
You've been doing this for quitesome time.
But why don't you tell us alittle bit about yourself, what
you've been up to lately beforewe get into our session today?
Tom Davenport (00:28):
Sure.
So I'm an academic I guess youmight call me a pracademic.
I try to be applied tobusinesses and the work that I
do I go back and forth betweenbusiness schools and consulting
firms, but I've been a professorat Babson College, which is a
(00:50):
business school in the Bostonarea for, I don't know, almost
20 years now, but a consultantrunning research centers for
places like Accenture andMcKinsey and EY.
And I write about how people andorganizations use information
and technology.
So initially focused on businessprocess re-engineering, wrote a
(01:12):
book on ERP at one point,knowledge management for quite a
while.
And for the last, I don't know,20 years analytics, big data and
AI which are all part of thesame family.
Andreas Welsch (01:28):
Awesome.
I'm gonna say I'm having a bitof a fanboy moment here.
We're talking about this in ourprep.
I came across your work back in,in 2016/17.
I remember you writing aboutfuture of work and how
automation and AI will shapethat.
Sounds a bit geeky, butespecially on March 14th- Pie
Day, like I said, having a bitof a fanboy moment with you for
(01:51):
our special episode.
Tom Davenport (01:52):
Yeah.
I should have brought a slice ofcherry pie or something myself,
but sorry, I forgot.
Andreas Welsch (01:58):
Always after the episode is enough time, right?
Hey folks in the audience, ifyou're just joining the stream,
drop a comment in the chat.
What do you've recently usedgenerative AI for?
With all the tools that areavailable, whether it's for text
or audio, video, images, there'sso many opportunities these
days.
I'm curious to hear what you'vebeen using it for.
Tom Davenport (02:21):
And you should reveal that I'm not actually on
this session.
I'm a deep fake.
Andreas Welsch (02:29):
Who knows?
It's almost like Schroedinger'scat.
But Tom, should we play a littlegame and kick things off?
What do you think?
Tom Davenport (02:37):
Sure.
Andreas Welsch (02:38):
Perfect.
So this game is called In YourOwn Words.
When I hit the buzzer, thewheels will start spinning.
And when they stop, you'll see asentence.
I'd like you to answer with thefirst thing that comes to mind
and why in your own.
(02:59):
And to make it even a littlemore interesting, you'll only
have 60 seconds for your answer.
And for those of you watching,drop your answer in the chat as
well and why.
Tom, are you ready for What'sthe BUZZ?
Tom Davenport (03:14):
Oh, I'm nervous.
Yes, I'm ready.
Andreas Welsch (03:17):
Okay.
Excellent.
Then let's get started here.
If AI were a movie genre, whatwould it be?
60 seconds on the clock go.
Tom Davenport (03:28):
I think it would be a combination of a
warmhearted love story with afew horror elements thrown in.
We don't know exactly how the AIstory is going to end.
There are a lot of peopleconcerned that it might have
some scary elements,.
(03:49):
And I recently saw this movieMegan about a very smart robot
who ends up doing all sorts ofdastardly things.
And I thought we're not that farfrom that really in terms of
what capabilities AI has.
It is certainly quite possiblethat it would be a combination
(04:11):
of outcomes.
Andreas Welsch (04:13):
Thanks for that answer on the spot.
So definitely a bit of sci-fi inthere.
It's good.
It seems like it resonates withthe audience as well than all
all different things fromhorror, comedy, action, and
killer I-Robot and some goodexamples.
Awesome.
So now with the icebreaker outof the way, why don't we jump in
(04:33):
into our questions?
You mentioned you've justpublished a book called"All In
on AI", and I wanted to takethat as the first question that
has at least been on my mind.
I'm sure in on the audiences aswell as they read the title of
today's episode.
What does it mean to be all inon ai?
What did you find out from thoseinterviews that you've done.
Tom Davenport (04:57):
Andreas, if you were to drive from your home in
Pennsylvania down to AtlanticCity, you go all in.
You put all your chips on thetable.
You make a big bet on aparticular gambling outcome.
And so going all in on AI ismaking a big bet on AI in your
business.
Not using it on the margins, notusing it to tinker with a few
(05:24):
business processes or run a fewproofs of concept, but really
dedicate yourself to someimportant production deployments
and to change somethingimportant about your business
model, your strategy keyprocesses end to end.
(05:46):
Even changing customer behavior,I think in a substantial way is
possible with AI.
So in this book, which I wrotewith Nitin Mittal of Deloitte,
we talk about companies thathave already gone all in to
greater or lesser degree anyway,and have addressed something
(06:08):
important about theirbusinesses.
And these were companies fromall around the world, from Asia,
Europe, United States, Canada,et cetera.
So if, you're not thinking aboutthat, you have a fairly
substantial chance of, I think,falling behind some of your
competitors.
Andreas Welsch (06:26):
I think that's an excellent point, that it
takes that kind of convictionand support throughout that.
This is really a priority.
Something you really want tomake happen.
And not just something thathappens in a dark corner
somewhere.
What do you see these companiesdo differently compared to those
that just play around with it orthat don't get it into a
(06:48):
scalable outcome or a scalableinfrastructure?
Tom Davenport (06:53):
I think to me, the most interesting thing is
actually changing your businessmodel or enabling a new business
model.
So we talked about companieslike Pingan in China, Airbus in
Europe, SAMO in Japan.
Big insurance company there thatare really using AI to enable
(07:17):
business ecosystems.
Pingan has five of them.
All of them are powered by AI.
One, the healthcare ecosystem,is powered by a system called
Good Doctor.
It's an intelligent telemedicinesystem.
Sadly, in the United States, wethought we were being advanced
by letting you actually talk toyour doctor over Zoom during
(07:41):
COVID.
But Good Doctor lets you use AIto triage whether you need to
see a doctor or not, to suggesta diagnosis to the doctor, and
to suggest a treatment strategy.
And 400 million people in Chinaand Southeast Asia are using
(08:05):
Good Doctors.
So well over the number ofpeople total in the United
States.
That's definitely all in on AIand healthcare.
In Airbus, they are using AI topower an ecosystem of all the
airlines around the world thatbuy and use Airbus commercial
(08:28):
aircraft.
They also have on their defenseside, they have a satellite
imagery business that theyformed another ecosystem with.
So I think we saw in theseplatform oriented companies that
are digital natives, Uber andAirbnb and so on, the
(08:51):
connections of buyers andsellers through platforms.
And you're seeing this in largeorganizations as well.
But you can also change yourproducts and services, your
strategy.
I wrote an article recently witha Oxford professor and the head
of AI at Shell about how AI isbringing back process
(09:18):
re-engineering, which was myfirst research focus in
business.
I wrote the first article in thefirst book on business process
re-engineering back in the early1990s.
And Shell is totally changinghow it does processes like
maintenance and exploration.
(09:39):
How it enables moving toward aless carbon oriented business
model with AI.
These are all companies doingsomething substantial.
Some are in process.
Some like Pingan have alreadyseen fantastic results.
It's the largest private sectorcompany in China and the 16th
(10:02):
largest company in the world interms of revenues, founded in
1988.
So that tells you somethingabout how rapidly they've grown.
Andreas Welsch (10:11):
Fantastic.
Thanks for sharing.
I think those are actually threeexcellent examples of where AI
can demonstrate a measurablebusiness value.
One of the questions that I seein the chat here is from
Michael.
Michael is asking (10:25):
Is the hype over AI just the hype over
blockchain, like a solutionlooking for a problem?
And I think that's an excellentquestion.
Because, in my perception, saybefore the end of November last
year, media and analysts werealmost talking up the next AI
winter.
Is there really going to be asustained investment?
(10:46):
Is this going anywhere?
Are we just flattening out here?
And then, now with generativeAI, certainly that's hit the
turbo and the booster.
So are we still just looking fora problem for the technology
that we have?
What do you think?
Tom Davenport (11:03):
We can look at companies that have already
gotten considerable businessadvantage out of AI.
Like some of the ones I wasmentioning.
Which was never really the casewith blockchain.
I think the only industry thatprospered with blockchain was
the cryptocurrency industry.
And I must say, I was always alittle suspicious.
(11:25):
If this is such a great and safeway to store information, then
why are there's so many fraudsand hacks in that particular
industry.
AI is always rated ahead of anyother technology in surveys of
business people for what is themost transformative technology
(11:47):
that we're looking at now?
Blockchain has droppedconsiderably, but AI hasn't
really dropped over the pastyear or so.
And as you suggest, Andreas,because of generative AI, I
think it's even gone up.
And we have still lots ofcompanies working to make AI
(12:07):
better.
Large vendors, small vendors.
The amount of venture capitalflowing into AI has dropped as
it has to any in any othertechnology domain.
But I don't think there's muchdoubt that AI is here to say.
Andreas Welsch (12:26):
Thanks for sharing.
I think that's very good andvery concise also where we
believe things will be headed inthe future.
Now, coming back to the topic ofbeing all in.
So you've shared examples fromPenan, from Airbus, from Shell.
What are the kinds of businessoutcomes that leaders and
(12:47):
businesses can expect when theyare all in, when they do go all
in?
What have you seen there?
Tom Davenport (12:53):
In some of these ecosystems, AI powered ecosystem
business models.
As one of the people weinterviewed at Pingan said it
creates sort of a Disneyland ofdata in the sense that every
participant in these ecosystemssupplies data.
(13:14):
To the central organization andthe ecosystem.
They all benefit from havingaccess to that data, but the
central organization uses it tocreate new products and services
that are even more valuable.
That enables more participantsin the ecosystem.
So you have this this virtuouscircle that enables much faster
(13:38):
growth and better profitabilityover time.
In the organizations we lookedat that are doing operational
transformation, Shell isinspecting its pipelines and its
refineries.
And used to be years that itwould take to get through an
(13:59):
entire refinery and inspect allthe piping and the valves and so
on.
Now they use drones and deeplearning based image recognition
models to see does this pipelook fine?
Or is there a potential problemthere that we need to have a
human go out and look at?
And it's gone down to a matterof days for an entire refinery.
(14:23):
So huge savings in terms ofoperations.
An interesting model is the onethat we have seen in a negative
way in social media companies.
It's changing the behavior ofcustomers.
And in social media, obviouslythat hasn't worked out terribly
well.
(14:44):
And I think it's responsible fora lot of the polarization that
we're experiencing in oursociety now.
Teenagers are getting depressed,et cetera.
I'm not saying that wasintentional, but that was a
behavior change outcome.
But a number of companies,mostly insurance companies, are
trying to change healthbehaviors to be more positive.
(15:07):
Companies like Progressive areusing metrics not just to charge
you more if you're an unsafedriver, but actually to tell you
when you're driving in an unsafefashion and to try to discourage
you from doing so.
So I think changing customerbehavior is a third possible
outcome, but not one that is aswell developed outside of social
(15:31):
media.
And as I say, the goal is verydifferent is to create healthier
people, better drivers, etcetera.
Andreas Welsch (15:39):
I think that's an especially interesting point.
And also when it comes to amoral, ethics type of
discussion.
To what extent is it nudging forthe person's own benefit or to
what extent is it optimizing thereward function or the
optimization function of themodel or of the company that
(16:01):
employs AI?
I think there's a fine balance,right?
Tom Davenport (16:05):
Yeah, that's a good point.
In health insurance for example,if your customers get healthier,
that typically helps your bottomline as well.
There's a pretty good alignmentof incentives there.
But as you suggest, there couldcertainly be cases, and I think
(16:26):
we've seen that in social mediawhere what helps the company is
not something that necessarilybenefits the user at all.
Andreas Welsch (16:35):
Very true.
Maybe to pick one more questionfrom the chat, and I'll,
paraphrase this, but myinterpretation of when Maya was
asking earlier was (16:43):
If you are digital native, cloud native
compared to a larger, incumbent,traditional organization what's
the effort, right?
To go all in with AI, toimplement AI?
Is the effort higher to do thechange management and to build
(17:04):
the AI and the models itself?
Tom Davenport (17:07):
I think in digital native companies, there
is much less culture changeneeded.
I have a friend who works forMeta in the analytics and AI
space, and he's been a chiefdata and analytics officer at
various legacy companies.
And he said the big differencebetween his current job and the
(17:28):
previous ones is he doesn'tspend all of his time persuading
people about the importance ofdata and analytics and AI.
The reason why we focus reallyon legacy companies is it is a
huge organizational change forthem.
They have an establishedbusiness, a strategy, a culture,
(17:48):
et cetera.
And so changing in the directionof being all in, or AI first or
AI fueled, whatever you want tocall it, is a dramatic
organizational change.
I, asked one of the people Iinterviewed at a large retailer.
Why he keeps taking these jobsin legacy companies as the head
(18:11):
of analytics and AI?
And he said, ah in those digitalnative companies, it's too easy.
There's no challenge there.
Now, I'm not sure that's true.
It's still challenging,certainly, but less.
And we hope that these legacycompanies will take some of
these ideas and use them totransform their own business.
Andreas Welsch (18:33):
I think that's, great.
And looking at something likegenerative AI, I feel we are
really just, starting to scratchthe surface with all the
opportunities and possibilitiesthat are upon us and before us.
And, maybe even to some extent,making it easier to communicate,
to understand, to also get somefirsthand experience with AI.
(18:56):
I feel it's getting a lot easierthese days to get some kind of
output where, you know, yes,there has been AI behind it.
How do you see this evolving andespecially again with a theme of
being all in?
What does it mean being all innow with generative AI?
To take that one step further.
Tom Davenport (19:14):
I think now it means large scale
experimentation both incorporate sponsored activities,
but also encouraging individualknowledge and creative workers
to explore generative ai.
(19:34):
Picking some tools that peoplemight explore and funding any
costs that they incur.
We still have a number ofproblematic issues with
generative ai.
Of course, the hallucinationsthat occur, the legal issues
(19:57):
involved in who actually ownsthe images that are used to
train them.
I think there are even issuesaround all of the carbon that we
burn up in generating thesemodels.
They're just fantastically largeand very expensive in terms of a
(20:18):
dollars and energy to create.
But I also think there's amassive amount of potential
value there, and, ultimately, Ithink if you are a knowledge or
creative worker and you're notusing these tools, you will be
at a substantial disadvantage topeople who are.
(20:39):
I mentioned earlier my work inknowledge management 20 or 25
years ago.
And I think this has fantasticpotential to make available all
the knowledge that's been lockedup within organizations.
I'd written in the past some ofthings about Morgan Stanley's
(21:01):
use of generative AI to try tocapture all of its knowledge and
make it easily available tofinancial advisors.
It's gonna be described on CNBCin an hour or two.
And I think that's one of thegreat potential advantages for
large organizations to beaddressing.
(21:22):
Even though it's still tricky.
You're gonna have to do a lot offine tune training and you may
end up with multiple levels ofgenerative models.
I was talking to someone in thelegal industry last week who
said yeah, there's a overalllarge language model like GPT-3
or whatever, and then there is alegal version of it that some
(21:45):
companies have already created.
But then you also need a UKversion and a US version, and
maybe you need one for realestate law in particular.
Maybe you need one for aparticular firm.
So I think we're gonna end upwith multiple different layers
of these models with increasingdetail about the content that's
(22:05):
in them.
And it's not gonna be easy todo, but I think it's going to be
potentially quite valuable forcompanies to explore that.
Andreas Welsch (22:13):
That's awesome, Tom.
I think we are really just atthe beginning and there's so
much more to learn and toexplore as we're making progress
in the industry.
Now, I was wondering if youcould summarize the three key
takeaways for our audience todayas we're getting close to the
end of the show.
Tom Davenport (22:30):
Sure.
Going all in on AI means makinga substantial commitment in
terms of money and people, andintellectual horsepower.
How are we gonna use thistechnology to change our
business?
There are substantial outcomesthat companies have achieved
either in enabling newstrategies and business models
(22:53):
or drastically improved productsand services.
Morgan Stanley is one that'sdone that with a next best
action system or changingcustomer behavior as well in
addition to operationaltransformation.
Generative AI is in its earlydays.
(23:14):
But I certainly, it's excitingenough so that companies need to
devote considerable attention toexploring it, to trying it out
to and the most important thingI suggested was think about how
can we manage all the vastknowledge we have within an
organization to create customersand employees who are armed with
(23:39):
everything the company knows.
Andreas Welsch (23:44):
Fantastic.
Thank you for that summary.
And thank you for joining ustoday, Tom.
Like I said it's been apleasure.
I'm so excited we've been ableto make this work.
Thank you for sharing yourexpertise with us and for
learning with us.
Tom Davenport (23:59):
Thanks.
I enjoyed it, Andreas.
Andreas Welsch (24:01):
I'm going to celebrating Pi Day.
Maybe you can have some of thatcherry pie as well.
And for the rest of you and theaudience, thank you so much for
joining.
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