Today we're talking about generative AI and leadership
with Paul Doherty, the Global Chief Executive for Accenture
Technology. My guest cohost is Q Harrison
Terry, the Chief Growth Officer for the Mark Cuban Companies.
Thanks for having me, Mike, and it's exciting to be able to talk
with Paul on AI today. Paul, why don't we begin by
asking you to tell us about your work as the Chief Executive for
Technology at Accenture? Yeah.
Accenture is a large organization.
We're about 740,000 people over $60 billion in revenue.
And we help companies do amazing things with technology, You
know, that's what we're all about.
Do you want to give us to start a just kind of a brief overview
of generative AI? I think everybody in the
audience knows what it is, but in the context of business and
in our world, where does it fit today?
Talk about generative AI. You have to talk about AI 1st,
and AI has been around for a long time and all of us use AI
continuously. You know anybody you know?
Those of us, the three of us talking here and anybody
listening has used a a I dozens, if not hundreds of times today.
So it's because, you know, a I has become a pervasive part of
our life through the advances in machine learning and deep
learning and such that have come before.
And a I, as I'm sure you know, most of the audience knows it's
an old field if the term was invented, I believe in 1953 at a
conference in Dartmouth 70 years ago.
And it's gone through a lot of iterations over the years.
So we I like to think about 3 forms of a I.
Diagnostic A I which is using a I to diagnose things often you
know deep learning and the like to look at you know for example
using machine vision to look for manufacturing defects.
The other thing we do commonly or to unlock our phones, you
know as we do, as we do every every few minutes of every day
or assisted driving features in cars and then there's that's
diagnostic and then there's predictive.
A I such as a I we use to do retail forecasting for companies
often you know machine learning and optimization models those
are well established techniques. So we have, you know, lots of
people doing that work for lots of clouds around the world and
many, many companies use it. Generative A I now is the new
thing on the scene and it really is a massive breakthrough,
probably the biggest breakthrough in A I today.
And what we're really talking about with generative A I is
foundation models, which are really powerful models.
That can be reused in across many different use cases.
That's why they're called foundation models.
Large language models are are type of foundation models that
that really understand language and have mastered have allowed
us to really master language through artificial intelligence
and then the transformer technology added on top of that
allows us to to generate things. That's why, you know GPT is
generative pretrain transformer. It's the it's these large models
that then have transformer technologies they can create.
New sources of content. So that's really the
breakthrough of generative A, I models that are very powerful
and can be reused rather than bespoke data science projects
combined with foundation models which have tremendous reuse and
power combined with this creative capability to produce
language content, whether it be graphics, video, etcetera.
And it really is really transformational in terms of
what it allows us to do as it is individuals and what it allows
companies to do. But we're at the very early
stages still. Hey, Paul.
One of the things that I want to talk to you about today is the
whole concept of you thinking about this stuff.
I mean, almost a decade ago in your book, Human plus Machine
reimagining work in the Age of AI.
I don't, sorry, I don't have it in front of me, but I did read
it a while ago. And when I was looking back at
that book, one of the things that you talked about was how AI
would ultimately become the ultimate innovation machine.
And it's it's fascinating that it's 2023, a few, almost five
years later since you published that book.
What's your take like that? It seems like you were, it seems
like you're spot on, but what things happen in generative AI
that you didn't envision or forecast back in 2018?
I think that the premise and the all the precepts in human plus
machine really, really have stood up the the stood the test
of time well and the concepts we talked about the human plus
machine and the idea that a I gives humans superpowers to do
new things really has you know stood the test of time.
And we see generative A I as an even bigger step forward in
terms of you know, the the augmentation and enhancement of
what it can do, you know for for all of us in terms of giving us
the greater tools and productivity to do, you know, to
do new things. I think that the surprise we did
talk about, you know, all this technology in that book and then
our next book. And my coauthor and I wrote Jim
Wilson's, my coauthor, which was called Radically Human, that was
the 2nd book. But it did.
The pace of the advance is what surprised us more so than the
capability. We're anticipating that some of
these capabilities would come along, but with the, you know,
the pace of development of the foundation models, the rapid
growth as. You know the size and complexity
of the parameters and the weightings and everything and
you know the breakthroughs that came about with that.
We're probably the biggest surprise you know queue in terms
of what we saw and. Then one last thing on that is
when you talk about the timing and how like just fast
everything is is coming together, it's it's fascinating
to think that you know even Open a Eyes ChatGPT is still
relatively like 9 to 10 months old as we stand today.
And then when we Fast forward I mean like to just yesterday Elon
Musk announced ex A I which is another you know fascinating A I
company as a business leader and executive how should I think
about A I I mean it's happening fast but does that mean I take
you know the move fast and break things approach or should I wait
and see where things settle with our but but but on the flip side
of that our organization might you know be behind.
How should I think of that? Our belief is that this is a,
this is a generative AI is a participant sport.
You have to jump in and start using it and experience it and
and do some experimentation. So we're encouraging, you know
companies to do that and that's the approach we're taking in our
in our own organization. And but it's also it's very
early with the models that you just, you just highlighted that
with the, you know how young you know the GPT and ChatGPT models
are and a lot of a lot of companies still you know do not
have, you know have not reached GA, you know general
availability status of their models and products.
So this is is the early and evolving Elon Elon's company was
announced recently and there's new companies sprouting up
continuously. And so I think the key for for
companies is is first. Is first you look across your
business and decide where it's applicable.
Second is pick some use cases where you can you we can jump in
and experiment with the technology and manage some of
the complexity and risk. And then third, develop the the,
the foundational capabilities that you need to then scale it
faster. Those capabilities include
technology capabilities like understanding the models, how do
you, you know the prompt engineering, the pretraining and
other things that you might need to do and how to integrate these
models back into your business as well as the business skills
of of understanding how and where you apply it.
How do you develop a business case for it?
How much does it cost to do you know, to apply these models?
And that's really those three steps.
You know, looking, looking across the landscape,
experimenting and laying the foundation are what what we're
helping a lot of companies do today.
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Paul, you're describing this kind of open field of innovation
that's going to be happening. But everything around generative
AI right now seems so ambiguous. The technology is changing.
The implications for business are apparently amazing but
unclear. And so how should business
leaders navigate this intense ambiguity?
I think General Bay I is just a new ingredient into the mix.
We've been talking for a while. I've been talking for a while
about the the you know exponential advance in
technology that we're that we're living in.
And so organization you've been talking for a while about
organizations need to develop the ability to innovate it and
and and recognize that adapt technology faster And the the
three key technologies that are really I think will define
companies success in the next several years and decades are
cloud. Artificial intelligence and the
metaverse and those that you know 3 themes that I can talk
more about the you were talking about A I today happy to go to
others other directions as well. And you know, as you look at the
A I piece of it, you know those those things build on each other
to success be successful with A I.
We're finding and companies are finding they need to get to the
cloud. Those that are having advanced
foundation the cloud are better prepared on how to utilize A I
you know most of these models run in, you know in in the cloud
and you need to have your data foundation in place to have your
data. You know have the data to drive
the A I model successfully and a lot of organizations have
struggled with this over the years.
We did a recent survey and only 5 to 10% of the companies.
Really have maturity in terms of how they manage their data and
the and the corresponding A I capabilities that means 90% have
a long way to go. So start with the you know you
need to start with the cloud foundation of what you're
looking at. You need to look at your data
the governance around your data and your metadata and how you
pull that together so that you can you know support you know A
I in the right way and then it's the you know the A I capability
and skills that you build on top of that.
So it's a journey that we're on it but and it's going to
continue. That generative A I is amazing,
but it's not the last big breakthrough, it's.
Not the biggest breakthrough we'll see in technology as this
exponential advance continues. So this is kind of the the, the,
the muscle so to speak. The organizations need to
develop, to continuously anticipate and have the
flexibility in their systems, their architecture and their
business and their business processes and their talent to,
you know, continue to adapt as technology advances.
So from your perspective, AI is essentially another in chain in
a chain of technologies that's not necessarily all that
different from what's come before.
What's different about AI? It is the latest of the chain
and these things all build on each other.
It is this combinatorial effect of the technologies coming
together that really creates the power.
But what's different about AI is it allows us to create more
human like capability. I can communicate.
With with the large language models using natural language,
using voice interaction etcetera, I can get output
that's you know that's easier for me to interpret.
So that's the powerful breakthrough with with the with
generative A I and the more you know what I advocate is the more
human like the technology the more powerful and the more
exciting it is for us. We shouldn't view it as a threat
as technology acquires this capability allows us to really,
you know, to really leverage the technology and and give us you
know, kind of super. Powers is, you know, what we
talked about in our book around giving us new capability.
For example, I can be a customer service Rep and rather than just
what I know in my memory and from my my experience, I can
understand, I can, you know, have out my fingertips.
Every aspect of every technical manual on the product that I'm
answering questions on brought me a brought to the forefront
prioritize that I can answer the customer's question the right
way. This is the type of power you
know, that's that the technology's given us and, you
know, just to. You know to go at that a little
further, when we look at the real impact of a I while you
know cloud probably changed technology a lot and how we
built technology and supported technology.
A I changing work and the way we work because of this capability.
And one of this, the research studies we did recently showed
that 40% of working hours across companies globally.
Are impacted by generative AI 40%.
That doesn't mean 40% of jobs go away, far from it.
We actually see it enhancing jobs and enhancing productivity
capabilities that people have in in many ways and you'll have to
go into that in more detail. Q Harrison mentioned that your
book was called Human Plus Machine, and we have a really
interesting question from LinkedIn, and this is from
Melena Z. And, she says, how would you
describe the significance of incorporating human values into
the development of generative AI technology?
It's incredibly important. It's if if you don't have in
your organization a really strong responsible A I program
you're simply being irresponsible.
And at the core of responsible A I is you know counting for human
values and in the way that you do it.
Responsible A I in our view is about it's about things like the
you know accuracy and and coming up with the right answers,
avoiding the hallucination. It's about the ethical issues
that cut that you need to think about.
In terms of how you're applying the A I, it's about bias and
ensuring you that there's fair you know fair outcomes and fair
use of the technology and and you know where in in certain
cases the transparency and explainability that you need
around the technology and we're we encourage every organization
using a I to do is is really especially with the advance of
generative A I we've been talking about this for six years
but especially with generative A I, you need a responsible A I
program in place and if you can inventory every use of a I in
your company. And understand the risk of it
and know how you're mitigating those risks, then you're simply
going to get yourself in trouble with the with improper uses of a
I That's that's what the way we think about responsible a I it's
not just, you know, mushy values and principles.
It's execution, operations and compliance in terms of how
you're applying the technology. I mean, Paul, it's a great
point, but the question I have is like the theoretical version
of that and the actual application of that often times
look entirely different. For example, if I'm in a company
and let's say I'm experimenting with generative AI and it's just
in our R&D department. And then we quickly realized
that this could actually have some scale.
We just apply it to a whole another sector of our company,
or maybe we apply it to the whole company.
At what point do I actually stop and and say okay, there's a
legal component here and often times when we point to that
direction, I mean we're that's the big debate in a I today.
I mean even at the congressional level is you know, what do we
do? How do we regulate this stuff?
If I stop now, aren't I hindering my innovation?
And if I'm in charge of innovation and acceleration of
technological development within the company, what like I'm
caught in the catch 22, if you, if you understand what I'm
saying? I am not one of those that
supports stopping and banning or pausing on the technology.
I think it's about putting in place the right framework, in
the right guidelines that you know what you're doing and can
evaluate the risk of it. I would say not just at the end,
but every step along the way and before you even get started, you
should do an assessment. Of the risk.
You know, there's a lot of guidelines and and ways you can
do that. The EUEU is.
Is going through the stages of approval on the A I act, they
identify high risk you know different risk categories of a I
do you know does your team understand those and are you
assessing for any application of a I whether what risk category
you're fitting into and then how you you know mitigate that or
deal with that to make sure you're you're handling that.
And that's just one example of respective EU there's also the
White House guidelines, there's NIST and and other things that
are that are out there and they'll and they'll be more
coming because of the interest in in kind of setting some
guardrails around this which I think is a good thing but.
But I think, I think the teams need to be trained and
organizations need to have tools in place so that you see you are
assessing you know the use of a I and understand again
understand that the risk of it and make decisions accordingly
there. There's things we won't do and
things we've decided not to do. You know applications that we've
not pursued because of you know the the risk profile of more we
we did. We felt it was not aligned with
the the right values and that's an important consideration to
build into your process. It can't just be after the fact.
It's got to be, you know, as you're considering use cases and
starting out. AI It brings out either the best
in people or the worst in people, and the latter component
when it brings out the worst in people.
Traditionally what I'm seeing is people will try to hinder the
Ai's abilities or slow it down in fear of losing their job or
seeing other calamities ensue within their industry.
One of the questions I have for you is how do we get better at
communicating AI? Technology is neutral.
Like technology isn't good or bad.
There's yeah in in AI fit it was generative.
AI fits in fits into that description.
Gender value isn't good or bad. It's exactly as you said, Q.
It's how you use it and it can be used for bad purposes.
It could be used to spread misinformation at scale, deep
fakes and all sorts of things. But that's people using the
technology that I think people I think that's what some of the
communication we need to do around generative A I is that
the thing we really need to be looking out for and preventing
is bad uses of a I and people using a I in bad ways.
We need to educate, you know, the broader, you know, the
general population on what that means that they they can
recognize and understand if something you know has been
appropriate propagated and generated, you know.
You know, artificially at scale using gender of a I for some
illicit purpose whatever it might be.
So I I think that's like there there is a broad education that
that needs to happen that we're doing a lot of work on that
we're working with a lot of different organizations on that
governments and other bodies to you know to look at how we can
better. Educate, you know, the the
general population, as well as business leaders and
technologists and decision makers.
Around using the technology in the right way.
So I think that's an ongoing effort that we'll all need to
work together on. We have a bunch of questions
that are stacking up on LinkedIn and Twitter.
And I have to say you guys in the audience, you are so
intelligent, so smart and sophisticated and your questions
are absolutely great. And our next question comes from
Florin Rotar. He is the Chief Technology
Officer at Avanade. And I have to say that I did a
video with Florin years and years and years and years ago in
Seattle. So Florin, it's great to see you
pop up. And here's here's Florin's
question and I think it gets right to the heart of some of
the key issues. And he says, how will Generative
A I change the future of work? Can it also play a role to
enable people to realize their full potential to thrive and to
grow, not just to drive productivity?
Will it blur the lines between white color and blue color?
And I'll just add to that. To me, this question is also
getting to the point that Q Harrison just raised, which is
generative. A I brings out the the best and
brings out the worst in people. We talked in human plus machine
about the idea of no collar jobs and exactly what the foreign
highlights of eliminating this distinction between you know
kind of blue collar, white white collar.
As you look at it. I mean think about it a hands on
service technician, think about a plumber or an electrician that
now has access to large language models that that give them
tremendous amounts of additional information and potential.
It can give them tools to run their business more effectively.
Maybe they can be a a service provider to others in their
profession rather than. Than just you know, being rather
than just being you know the specialist at the the physical
trade that they have. I think that's the blurring of
capability that the the A I allows.
And think about it, a small business that now or at any part
of a larger business that wants to go international overnight,
they can start communicating in dozens of languages.
You know seamlessly and expanding their business, you
know it's the Super powers that are enabled that give people
more capability and that leads to a lot of you know, new
entrepreneurial activity and ideas.
I mean think of what GoDaddy did to the Internet and creating a
generation of entrepreneurs a lot of different ways or eBay
market marketplaces and such. We're going to see that you know
to the next, you know to the next exponential multiple with
generative AI creating all these new possibilities of what people
can do. So that's the kind of what what
we see happening there more specific around it, we we see
you know the new opportunities for jobs and white generative A
I impacts that falling into 5 categories.
The 1st is advising and this is kind of you know advisors or
assistants or copilots to help people do their jobs more
effectively. A large for example large
European service organization that we're working with.
We're using generative A I in the customer service
organization to allow them to answer questions a lot more
accuracy and quality because they can as I mentioned earlier,
they can, they can pull tremendous amounts of technical
information together to answer customers questions better,
faster with higher quality And they can cross sell more
effectively because they get the ideas and prompts and support On
on how to cross sell that's you know, advising.
Creating is another whole count is a second whole category.
Another category, A good example here is work we're doing with in
a in the Pharmaceutical industry where we're able to in the drug
discovery process and clinical trials process create some of
the regulatory and compliance documents they need to create.
So that then gets reviewed at the final stage by humans in the
loop avoiding all the road work and you know that a person would
normally do and allowing them to apply their judgment and
expertise in the final product. That's creative in addition to
you know applying it in marketing and other areas that I
can talk about which is super interesting right now.
So that's the creating side of it.
There's automating where you can use Gender VI to automate some
of the transaction processing. An example here is a
multinational bank. We're using generative A I in
their in their back office processing to correlate read and
correlate 10s of thousands of emails that come in with
transaction activity. Normally people need to sort
through all this to reconcile and do their you know their
their their post trade processing more effectively.
Again, you can do this with other technology, you can do it
with gender A I and you can make people's jobs that more
productive and effective. And you know take out some of
the the drudgery, the 4th category that is protecting,
which I think is super interesting.
An example here is we're working with a large energy company on a
safety application so that workers in real time can get all
the information on what's happening real time conditions
weather conditions and other things in a complex say refinery
and and then combine that with the all the information they
need to know from safety procedures and manuals and and
regulation and such that they can they can operate in a more
safe manner in real time. You know again couldn't do this
all put all this together for generative A, I and then the
final use case we're seeing a lot is in technology itself
using a I and software development and technology
development. I'm sure we'll find more
examples as we go. Those are five, they're kind of
standing out right now just to you know drill into you know
some of the ways that it's transforming work in the
response before into question. We've got another question
coming in from Twitter from Chris Peterson, and the question
is, one of the opportunities mentioned in Human Plus Machine
was the AI explainer role. Is that even possible for
something as complex as GPT 4 with billions of parameters and
almost unlimited training data in some industries and some
problems If you can't explain it, you can't do it.
That's part of that screening that I talked about earlier with
responsible A I. If you have a kind of a
regulatory or ethical or business need to explain exactly
how things something's happening.
You need to use the right type of approach where you can do
that and you can't do that with some to your point with some of
the the models that that are there.
But there's a lot of advance happening to explain ability.
There's ways to query the models to understand, you know, how
they're processing. There's areas like gender Gans,
gender adversarial networks we can use in different ways to get
some insight into how models are working and such.
There's a lot of different advances there and there are,
you know, there's new fields in addition, new fields like prompt
engineering that are cropping up because of gender of a I we're
also seeing, you know, demands the market for explainability
engineers or explainability specialists who can bring that
understanding in to help them, to help understand those kind of
conditions. And the other thing that's
sometimes important is that even if in some applications you
don't necessarily need to explain exactly how you got the
answer, you need to provide the transparency of what information
you're using, what data are you using, and and the process
itself for. So you need to differentiate
where do you really need to explain exactly all the math you
did and how you did it, so to speak?
And where do you just need to provide transparency to how
you're doing it and show that you're using information such in
the right way and distinguishing that can you know can help
organizations unlock some of the potential to.
And we have another question from Wayne Anderson.
And you can see we love taking questions from the audience and
and again the audience is amazing.
So, so this is from Wayne Anderson on Twitter and Wayne
also has a question coming up on LinkedIn.
So he's like sort of a multi tenanted multifaceted.
It's social media happening here and and Wayne says what is the
litmus test? Is there one?
A question set of questions that you use to quickly evaluate A
client's place on the operational maturity journey.
For AI and ML, we have a maturity framework We use to
assess for ourselves as well As for our clients that it there's
steps of maturity you know that that you go through in in
assessing it. There's there's assessing you
know talent and where you are with the with the talent and the
expertise that you have in the organization.
That's about the technology talent as well as you know the
skills you have in the business and the kind of trading programs
that you have around that. There's assessing the data
readiness that for it you know in terms of as we talked about
earlier, your data maturity and the maturity of platforms, data
platforms to support what you need to do.
There's that. There's then the maturity of how
you what do you, how you need to use the models and your
sophistication around that. And that depends on the strategy
that you have. Is your strategy to use, use you
know, proprietary, you know, pre trade public, you know available
models. There's your strategy to do some
of your own pre training or customization using your own
data that requires far different operational skills.
And therefore you you need to evaluate where you are on that,
on that spectrum. And then there's the, the
operate the operational skills around it.
So how do you, how do you put the A I in place and how do you
moderate on an ongoing basis for the right outcomes?
And then finally, the responsible AI dimension of it.
So those are kind of the dimensions.
There's more underneath that, but there's a, there's a process
that we used to go to, to go through it.
I think that's every organization.
Having an understanding of that and having a way to evaluate
their maturity is important to to know how you're making
progress. Wayne actually did ask another
question on LinkedIn. I'm looking at it right here and
he talks about the security and the risk of AI is not something
that is entirely a technical solution.
A lot of it is in the humans and the innovation back slash
development processes and what formal steps do you need to be
in order for that innovation to provide the kind of guide rails
and talking points on the future of machine learning projects.
So the way I the way I interpret that is, you know we've got a
lot of groups working together. How do you, how do you make sure
they're all working and and their energies are going the
right way? Right direction.
We think the right approach to use is a center of excellence
kind of approach given that the state of the technology where
you create a center of excellence, you know you have to
you know centralize in your organization that has those
capabilities in it. That's what we've done for
ourselves and we're helping a lot of our clients to, in fact
we have something called the Coe in a box that we're using to
help clients set up these kind of capabilities.
It requires the technology capability, the business
capability, the legal, you know, the legal teams and capability,
legal and commercial. And and you know talent, you
know, kind of you know talent HR kind of capability around it.
So you need to. Bring all that, all that
together. So the center of excellence,
where you can have that capability assembled, you have
representatives from all those different groups in your
organization is important. You can federate some of the
experimentation. Then it's really important to
bring it together. Security is a great angle.
I don't know if that was the primary thrust to that question.
But there's a lot of implications on security from
generative A I both in terms of new security challenges as well
as consideration about data privacy, ground grounding of
models, use of sovereign data depending on the jurisdictions
you're operating and that become really critical considerations
for companies. So having this built into you
know kind of a center of excellence that you know that
you're channeling this in the right way in companies, I think
it's critical for the stage of development that we're that
we're at right now. So Paul, giving your purview and
and some of your thesis around the future, one of the things
that I I'm wondering in your your realm is when I look at
technologies like the cloud and enterprise corporation is
probably best suited for that realization today, right.
Like the personal cloud computing it exists and you know
I think the strongest use case of that is probably video games
today. But beyond that, it doesn't make
that much sense for an individual person, or even a
small startup to to endeavor on a very complex.
Cloud implementation. However, I think that might
differ given your some of your comments you just specified when
it comes to AI. On the AI front, one of the
things that we're seeing is. Corporations that have a lot of
technical debt or have a lot of data that hasn't been digitized
or have you know very complex teams and org charts, they're
not well suited because it's going to take them some time to
get all these things in progress in place.
Now on the flip side, they have the most data so they'll
probably have some of the the stronger AI models.
But to what I to the to make the question that I have here is
like would it make more sense for a startup or even an
organization to think about, you know, creating an internal
startup and then going? And after it, I mean, that's
what Google did with DeepMind. And I mean we just saw some of
the new news related to DeepMind where they're bringing in demis
and to lead their actual AI practices at Google.
And even if there's countless examples where this is also true
in the AI industry, is that the right approach or do you think
that that ship has sailed to a long time ago?
For some companies, there's an example, a media organization
we're working with that sees an opportunity.
To really create a whole new part of their business using
gender of a I, they can use gender of a I to create a way to
generate coverage for things they couldn't cover before.
I can't get too too specific about it and and in that case
that's that's maybe more of a startup thing.
You actually are using gender of a I to branch out in a new
direction. But we think a lot of the a lot
of the gender of a I potential is going to be in the changing
the core of how you work as a company.
It's going to transform the way work is done.
That phrase UW is reimagining work.
That's what this is about, which means I think you do need to to
have a lot of capability at the heart of your organization
looking at how you, how you do this, how you do and drive the
transformation. So, so I think it could be a mix
for different types of use cases.
You know, some a company may spin out or have a little, you
know, separate projects you know, to pursue some initiatives
they're doing. But I think this gets to the
core of how companies are operating, which is why, you
know, companies need to embrace it broadly.
But another point that you're mentioning is.
I do think this that the gender they offers a lot of potential
for, for new startups and small companies because they can
access tremendous capability to build new businesses in addition
to the power it gives big businesses.
So I heard people ask does this, you know, are the big only, you
know big companies only going to get stronger with this or are
the did the new startups, you know new companies going to win
out? I think it's really a mix here
that we'll see going forward because of the power of the
models, the power for new organizations to leverage them
as well as, you know, the power that larger organizations have
to move faster. Well, let's shift gears a little
bit and talk about investment. Technology investment A I is
changing so rapidly. The capabilities are changing,
the models are changing. The implications for the
enterprise and for society at large remain very unclear.
Given this ambiguity, how should how do you recommend?
That organizations should be investing and I will mention
that Accenture recently announced a $3 billion
investment in this. So obviously it's something that
you're giving a lot of thought to.
As you said, we announced the $3 billion, billion with A B, we
don't do that too often, $3 billion investment in our in
data and artificial intelligence.
You know there's a good part of that is for generative A I, but
it's across data and artificial intelligence.
So we're we're doubling our workforce we have 40,000 people
that work in data and a I today we do a lot of work in the area.
We're going to double that over three years.
We're going to we're developing a new tool called a I Navigator
for enterprise to help companies apply a I more quickly including
generative A I tool self uses uses generative A I to help
companies understand the road map they need to to follow and
how they you know, industry by industry, how they can drive
value from. A I and and we we're creating a
Center for advanced A I where we're looking not just a
generative A I, but the next breakthroughs that will come as
as well. So we're excited about it.
We're putting, we're putting a lot of money and focus on it
because we do believe this is transformational for business
and this will this will this wave will build faster than
cloud and faster than some of the other technology waves that
we've that we've seen before. So yeah, so a big, big focus
and. And we see companies doing the
same. So we did a survey recently and
97% of executives that we surveyed, this is just a couple
weeks ago, 97% believe this is going to be strategic for their
companies and and it's going to change their business or their
industry. 97% that's basically everybody over 50% believe it's
game changing, you know not just change some change but game
changing for their industry or company.
About 46% are going to invest a significant part of their budget
and generative A I. In the next two years, this is a
fast build and maybe some of this is you know, companies
getting a little overexcited, but but we believe that that
that pattern will hold and companies will move and invest
in this technology, you know get more quickly than we've seen
other ways of technology built. But what about the risk
associated with investing in something where the end
trajectory is so unclear? You need to look at the horizon.
I think there's a lot of things that that are clear that you
can, you know that are clear. I think the key thing is to look
at this from 2 dimensions, business case, dimension and the
response responsible A I to mention which helps you balance
the risk. The business case helps you look
at the value responsible. A I helps you look at you know
applying with the human values and right risk profile.
If you take two those two lenses, I think you can find the
intersection of the right things.
You can start on now with no regrets, and obviously you have
to make sure that the use case you look at.
As you know can be supported with the technology that's
available today which is moving super fast.
So I think I think Michael we can you can identify no regrets
things to do. We believe in the near term this
is going to be human in the loop types of solutions for the for
the most part it's going to be solutions that bring in
tremendous new capabilities for people.
It's going to be, you know, new exciting capabilities for
consumers to use, you know, more directly like in in one case, a
retailer we're working with that's using generative A I to
create all sorts. Of new product configuration
capability for their customer, for their customers, it's going
to create new capability for employees etcetera.
This is all stuff that's doable today, I think, with no regrets,
without, you know, worrying too much about the risk and you can
you can apply the right principles to to do it in a
responsible way. From an industry specific
standpoint, it seems like each industry is dealing with a I at
it at its own speed. The two that I want to bring up
right now that I've had probably, I would say some of
the most impact is 1 education and two, the legal sector.
The funny thing about it is they dealt with this regulation in
entirely different ways. In the education sector,
everything is pretty much a chaotic mess.
You know, you have schools banning things, turning things
off, then re enabling and and there's a whole that we could
have a whole show. On this but on the legal side,
you've got which surprises me the most as a technologies
lawyers really embracing this technology.
There's obviously a little resentment but there's there's
legal LL Ms. and there's a lot of adoption as to how you can
integrate it and adopt it and and and make you know your law
firm or your practice move faster.
I would have never predicted that in 100 years but it's
happening now. On the flip side, Elizabeth Shaw
from Twitter has this really good question where a lot of
organizations and individuals. Have begun using generative AI
for work without any AI governance in place and she's
wondering how you can apply governance once the horses are
out of the barn and racing. The reason why I brought up the
the points earlier is you know education, that whole sector is
dealing with this, this, this whole dilemma right now.
And I'm curious your take just because you're seeing it on the
enterprise side where? If I input, you know, an e-mail
or consonants of a document, there's a true risk there,
whether it be IP or trade secrets.
Whereas with school, you know if I put my quiz test is quiz
questions and test questions in the program, you know it really
only impacts me and and and will have a lasting impact on the the
knowledge that I retain and gain.
We're seeing broad adoption across industries unlike other
any other technology I've seen which had very specific and
everything had specific industry patterns.
Client, server, ERP, mobility, Cloud SASS had very specific
industry patterns. Generative A I is super broad in
terms of the industry adoption we're seeing in the industry,
potential use cases we're seeing the two you mentioned are are
super interesting. CUE education I think will be
literally transformed and and you know through generative A I,
it enables truly personalized learning in ways that are
significantly different than our current educational system.
It'll take a while for that to work through, but yes, it's
going to be, you know, pervasive and powerful.
Legal I agree with you. The interesting thing about the
legal profession is it can help paralegals work more effectively
and do higher level work and it could allow experienced lawyers
to lever to themselves more effectively in terms of the work
they get done. So we're seeing it being adopted
across you know the different types of work in the in the
legal. Industry or legal profession
from that perspective but I think to the horse out of the
barn question. Yeah you can you can still apply
responsible A I you can go back through and and do it.
It's a matter of being systematic and rigorous.
It's about having C-Suite and CEO support.
We report on responsible A I to our board.
It's part of our formal compliance responsibility that
we do and we encourage organizations to do the same.
And and if you already have a I out there and most organizations
do. And most organizations don't
have enough response by a I in place.
We believe it's time to do that inventory the A I know where you
using it. Understand the risk level, know
the mediation techniques and tools and have them at at your
disposal and know if you've mediated the risks.
You have to go back right track to do that if you haven't done
it so that you know what your baseline is as you start to
apply more A I in generative A I going forward.
Given the impact of AI, we know that it will be pro it is
profound and will be profound. Where is this going?
And more importantly, how should businesses position themselves
to capitalize on this obvious sea change that's kind of
erupting all around us? I think this the simple answer
is you need to think big, start small and scale fast.
But think big is think about where this what the real
potential is and where this could you take your organization
where the the big threats the big opportunities that's
thinking big. Start small.
Small as the experiment with the human in the loop and the no
regrets use cases. Get some experience, understand
the models. Select the right partners, your
models and such and and and do something and get ready to scale
fast. This is the centers of
excellence. The the operational maturity
that's one of the good questions came in on and another
capabilities that the talent that you built around it to
scale fast. So that's say I think big start
small scale fast. It's a good advice on give.
Is scifi has shown us, you know, what the future has looked like.
I mean we see some of the gadgets and gizmos that are real
life objects from Star Trek. We see some of the unforeseen
and uncomfortable futures from Black Mirror start to arise.
One of the things that I'm wondering in your take is, I
mean you wrote the book Human and Plus Machine and then you've
got another one. Since then, I'm guessing you've
been thinking about this whole concept of transhumanism and
merging, kind of the the. The brain computer interfaces
that Elon talks about with some of these AI models, like how how
near do you think that is? Or do you think that that is
still fodder for science fiction novelists?
First of all, I'm a massive fan of science fiction, and I
believe most science fiction eventually becomes real.
It's a matter of the timeline and if you want to, if you want
to. Read about where technology is
going. You pick up, you know, somebody
like a Neil Stephenson and read his books where he, he's
anticipated coin, the term metaverse, among other things,
and his befall previewed where we are with technology right
now, you know, a number of years ago really well.
So science fiction can be incredibly illuminating into
where we're going in terms of transhumanism.
You know, I'm not a real expert per se in that field, but I
talked to a lot of friends and colleagues who are, and I I
believe it's quite far away. I mean, think about.
Blown away. We are by large language models
today. And ChatGPT and everything.
There is no intelligence inherent in these models.
These are, these are, these are statistical models.
So people ask me how intelligent these models are.
The models have no intelligence. The models are a bunch of data
with the technology that can you know, that can statistically
create results from them. There is no inherent knowledge.
Now, some of the breakthroughs we're looking for in AI, the
next generations of things like common sense AI, the way
knowledge graphs come in and can be combined with generative AI
that starts to create, you know. Systems that have more
intelligence, you know, inherent in the models along with the
generative capability. And I think that's where you see
some interesting advances. But truly getting to the human,
you know, human and and surpassing human level, you
know, I think, I think we're quite far away from it.
And I were multiple, you know, multiple of multiple
breakthroughs, you know, away from, I believe from from.
Seeing that, I think. Look, that discussion I think,
distracts us a little bit from what we need to do today, which
is some of the great questions that listeners have asked about
human values and ethics and of what?
You know, let's let's prevent people from using today's
technology in bad ways and and avoid getting a little bit too
distracted by the things that are that are pretty far down the
road. This is from Mike Prest.
He's a chief information officer on LinkedIn.
He says, as a business leader managing the risks of AI, how do
you? What advice can you offer on
sharing information to become good stewards of the technology
and dispel some of the dystopian conversations about generative
AI? And very quickly, please.
I think we should share more. I'm happy to on that front.
I'm happy to you know connect with anybody and share some
ideas. There's some various forms out
there where there's a lot of the sharing happening both in
business communities and different technology for them.
So I think that's how they'll all get better.
It's it's it's at the early stages that I'm I have a lot of
forms that I'm running with some of my peers and colleagues and
and other companies to to to share a lot because we're all
learning together in this fast moving technology.
And we have another question from Twitter another really good
one and again really quickly please.
And this is from James McGovern who says with Microsoft and
Oracle holding layoffs the talent for enterprise
architecture and sales professionals must be huge.
Who's hiring? Enterprise architecture.
You know, as much as the generative AI skills enterprise
architecture is. Immensely important Generative A
I is creating and and along with the Metaverse capabilities which
we didn't talk about in this this call it, it creates really
a rethink of your enterprise architecture what you need to
do. So those skills I think are
tremendous demand as we look at this going forward.
So I think a lot of companies are looking at hiring the right
talent to to build this out. I think enterprise architects in
in particular has been a shortage in the industry for a
while in our you know even even more demand as we go forward
with the every every new technology like generative A I
Paul let's shift gears here you're an avid sailor I've known
you for many years and and I see you sailing tell us why.
What do you tell us about your sailing and and why do you like
to sail so much? I've sailed my whole life, so
it's something that's been a lifelong passion and I find I
think I love the experience of it when you're out on the on the
water. And you're seeing the sunset.
You have a nice breeze behind you and you're and you're
powered only by the wind and it's sailing along and can hear
the little bubbling under the keel of your boat as you're as
you're moving through the water at a nice pace.
There's not a better feeling in the world than that.
There's a, there's a challenge aspect of it, which is.
Which is optimizing. How do you go a little faster?
How do you get the sales tuned a little bit better?
And I love the intellectual challenge of that.
There's a learning aspect I learned something about.
I've been selling my whole life. I learned something new, either
by making a mistake or just encountering something every
single time I'm on the boat. And it's as a continual,
continual learning experience. And finally, I just say it's
it's my happy place. You know the one, it's the one
place where I really don't think about anything else because from
a safety perspective and focusing on what what I'm doing
on the boat and everything else. When I'm when I'm on my boat,
that's that's where I am and that's where my whole focus in
my mind is that is on my, you know, on my boat and the the
guests and passengers that I that I have on it.
As an author, I'm sure your some of your pastime includes
reading. What books are you reading these
days, and and what's keeping you sane?
One of my favorite authors and heroes is Neil Stephenson, who's
wrote so many great science science fiction books, so I put
him out there. A great book that I read
recently is Cloud Atlas, which is a fantastic story that gets
into some of the top topics that we talked about.
It's a prize winning novel that covers everything from the fall
of the Ottoman Empire to to space travel in the future.
In through a series of parallel stories.
So it's a very interesting read. There's a book called Reality
Plus which is I'd recommend to anybody anyone that's interested
in it. Well first of all, transhumanism
topic. You mentioned the metaverse or
or related topics. Reality Plus is by a philosopher
from NYU is exploring the question of are we living in a
real world or simulation and how do you know the difference
between the two. It's a fascinating book and
super well written so. I I read a lot and it's it's
those give you a sense of the the realm of from fiction to
science fiction to you know kind of philosophy as well as
technology. You're the senior person for
technology at Accenture which employs about 740,000 people.
I mean, just that number in and of itself is almost
incomprehensible. How do you spread yourself over
740,000 people and manage the pressure and the expectations?
It's an amazing privilege, you know that to have a role like
this because we our mission is to.
Deliver on the promise of technology and human ingenuity.
And the human. Ingenuity that we have the
740,000 people is just amazing and what I what I what I like
most about my job is able to learn from 740,000 people.
I don't talk to each of them individually but the work that
we do for clients, the innovative ideas they come up
with is just super inspiring. You know the ways that they we
that the projects we do in terms of improving communities and
society through some of the work we do so.
So it's it's really a privilege to to to do it and I'm just
honored to have the role and to and to represent represent the
the amazing group of people that we have the amazing leadership
that we have And you know it it sounds it is a big company it's
a lot of people but it's it's a lot of small communities that
come together with a common culture is the way to think
about is the way to think about it and we we have the the system
we know how to hire you know people in volume if we need to
we we know we know how to. We build community and build
culture in our organization in a lot of different ways.
So some things, you know as you scale up and get bigger, some
things aren't that much harder to do at bigger scale and ended
up scaling, you know very well as you grow and that's what I've
know. What I've found is we've grown
the organization. So it's a it's a it's a lot of
fun. And it again, it's just a
privilege to, you know, be be in an organization like this and
have the role that I have. What's the hardest part?
I don't know all the 740,000 names, but I'm working my way
through as best I can. Hey Paul, question for you
regarding just being a techie. What's your favorite device?
Probably apps that I use. So what?
What are the device I'm really getting a kick out of is my aura
ring. Not really marketing for a
specific product, but it's simple device, It's the ring,
it's connected the app on the phone, and I'm finding it's
really helping me understand some patterns on how I can be a
little healthier and happier and get better sleep and such.
And compared to my sleep activity, compared to my sleep
cycle. You can track and I can track
and correlate my heart rate, my oxygenation, my breathing
patterns, all sorts of. Compared to my activity cycle
and it's we're data-driven you know and if you get better data
you can improve patterns and such.
That's one of my one of the things I'm playing around with
the right now that I'm I'm getting getting a lot of value
out of. So one of the things that's
interesting about the Aura ring is it represents like the whole
quantified self movement. So you now have your own
personal database of data that you can do whatever you want
with. Are you going to build anything
using your health data or is this just a personal experience?
I don't know but I I'm on that exact journey you mentioned, I'm
starting with now the by the personal bio understanding that
your bio more using the you know the self you know diagnostics
you can which has this another big impact on on.
Kind of health and Wellness. So yeah, I've been trying to get
more and more kind of data-driven and understanding,
you know, kind of what what, what makes me work and what
makes me healthy or not. So yeah, that's that is
something I'm going to continue doing.
It's funny because that's the big data that comes off of your
body and then you take that what works for you and implement that
at the enterprise at scale. I see what you're doing exactly
OK and with that we are out of time.
A huge thank you to Paul Doherty.
He's the Chief executive for Accenture Technology.
Paul, thank you for coming back again to CXO Talk.
We really, really do appreciate it.
It. Was a pleasure, Michael, and
it's great to do this with that with Q as well.
So thanks thanks to you both. And to the audience, those are
amazing questions. I wish I could be there and ask
the audience a lot of questions as well, but it's a pretty great
experience. Thank you and Q Harrison, it's
great to see you and and thank you for being such a great
cohost. That was a lot of fun, wasn't
it, Q? Indeed, man.
Thank you for having me everybody.
Thank you for watching. And as Paul said, you guys are
an amazing audience. Before you go, be sure to
subscribe to our newsletter, subscribe to our YouTube
channel, check out cxotalk.com and we will see you again next
time. We have amazing, really great
shows coming up. Have a great day everybody.
Bye, bye.