Hello, and welcome to this edition
of The Adobe Think Tank
on artificial intelligence
and the future of the enterprise.
I am Daya Nadamuni,
with Adobe's corporate strategy team,
and with me here today
is Doctor Anand Rao,
partner and global artificial intelligence
innovation lead at PWC.
Welcome, to the show.
Thank you, good to be here.
- Great to have you here.
So, Anand, yesterday you
had a great definition
of artificial intelligence
and I would love it
if you could repeat that for our viewers.
- Yeah, artificial intelligence
has been around for a long time,
so I try to use the
more classic definition,
it's a sixty year definition.
I know people try and
reinterpret AI in different ways,
for us it's very simple.
Any computer system that
can sense their environment
and sense their world, can
think about their environment
and act in this environment,
working with people,
working with other machines,
other computer systems,
but achieving a certain purpose or goal.
So, that's what we say is
artificial intelligence.
Sense, think and act,
and then it's very much an umbrella term
for a number of things.
So, when you talk about sensing,
now we can have the AI
essentially hear us,
it can understand English
language, documents,
or speech, can look at images,
all of that goes into sensing.
So, natural language processing,
computeration, speech
processing, all of that.
And then, we have machine learning,
deep learning, all part of thinking.
And then, similarly, robotics
and process automation,
all go into the acting piece.
So, that's the way we think about it
as a very umbrella term for
a number of technologies within that.
- Okay, and of course
there's lots of innovation
in each of those technology areas.
- That's right, things
have moved very fast,
especially on the sensing
part, thinking side,
everywhere, I think, there's
quite a lot of advances in AI,
which has essentially caught
the excitement of people,
and enterprises as well.
- So, in your role at PWC,
you interact with a lot of
large brands and enterprises
that are all trying to figure out
how to leverage artificial intelligence
as part of their digital strategy,
consumer experience strategy.
What advice would you give–
- Yeah, that's a good question,
so everywhere we go, they
say, "Where do we start?"
So, "We want to do something in AI,
but where do we start?"
And our advice to them is
don't worry too much about AI,
start with where you have
the most pressing problems.
So, look at your existing organization,
and almost every organization has
some kind of a transformation
already going on,
so we come to AI through a
number of different channels.
So, one could be, as you said,
a digital transformation,
or a customer-centric transformation.
Through that, you can come into AI,
for example, as you start digitizing,
obviously you'll have lots of data.
Lots of data means two things to us,
so, with lots of data
comes standardization,
simplification and automation, right?
So there are a number of
organizations which are
looking at internal processes
and how to automate them,
and as you automate them
you come in to natural language processing
and text documentation and all of those,
which leads you to better cost savings.
So, that's one channel.
The other channel with more data,
as we have seen in a number of
consumer-oriented companies too,
is you can start personalizing
the experience for customers.
Right? So once you personalize,
you're differentiating
your value proposition.
Whether it's for the consumer
or even to the businesses,
you can do a mass scale personalization,
which means better
revenue, better retention,
better experience, so
that's more a revenue angle.
So, you can approach it
in either of those ways,
but we always say, "Start
with some existing problem,
"existing transformation
that you are doing,"
and to us, it's essentially a continuum,
whether it's an automation continuum,
or an analytics continuum,
so at some point things
become more advanced
and people tend to call it AI,
because it's using different types of data
or large volumes of data, it is learning.
When those things start happening,
somehow the term goes into AI.
- So, it sounds like it
should be data first,
and then incrementally build on that--
- That's right.
- You add AI where it makes the most sense
into your current processes.
- Yep.
- Until at this point in time,
really have technology across
the spectrum of your work.
And–go ahead.
- And one of the other main things
is the difference between
traditional technology and the AI,
is AI machine learning, especially
the more experimentation.
So, we call it exploration
and exploitation.
So, in exploitation,
it's very similar to traditional software,
but there is a big exploration component,
where you get a lot of
data, train the system,
try if it works or not, so
in that sense it is different
to traditional software development,
but it can still be embedded
within a broader organizational set up.
- That's a very interesting point.
So, do you see AI disrupting
the way software is developed today?
Are we likely to change the way,
the workflows and the processes?
- Yes. So there were
some interesting comments
made by a number of people that
the software is aiding the world
and AI is aiding the software world.
And one would possibly
argue that machine learning
is aiding the AI world.
So, in that sense, people are looking at
the traditional software development
is very much you needed to know everything
and write a line by line piece of code,
whereas with machine learning,
you are feeding it a lot of data.
So, both the input as
well as the output data
and the system is then
finding the patterns.
Its not literally writing a program
as a human being would,
but it is looking for
patterns within the data
and then any new data that comes in,
it's able to use it's model, as they say,
and then say what that data should be,
or classify it, or do it.
- Okay, so it's disrupting,
or it's sort of changing
the way we're doing different workflows.
Customer experience, the
way we develop software,
and probably also the way we
use employee productivity,
how the business workflows,
there's going to be
disruptions everywhere.
- Yes. So AI is what they call
as a general purpose technology,
so it affects every enterprise
and every function
within every enterprise.
Just as computer systems
affected almost every industry,
starting from the 1970s and 80s,
internet start affecting
almost every walk of life
and every business and
every functional area.
So, now we have AI,
because it is essentially
making some of the decisions
and some of the tasks
that we do, automate them,
or, in some cases,
augment what we are doing.
So, in that sense, it's sort
of very general purpose,
so it will have a far reaching impact
into what enterprises do, not just now,
at least for the foreseeable
ten year, twenty year future,
we are going to be having AI embedded
in almost every enterprise application.
So, we may not be talking
about AI explicitly
five years from now, but
it will be all embedded.
- So, which brings up another good point.
As AI starts to get, you know,
more deeply embedded into
our corporate cultures
and productivity workflows,
there is concern that this
may lead to loss of jobs.
What– do you have any suggestions
of how enterprises can mitigate this?
- Yes, I know a lot of people
are worried about jobs.
So, there's sort of a
two part answer to this.
So, at one level, when you look at it,
from a global employment perspective,
or a US employment perspective,
what we believe, and a number
of people believe this,
is yes, there will be job losses,
but the number of jobs
that will be created
will be far more than the
number of jobs that are lost.
And we did a recent study where
we were able to quantify this as
the increase in productivity
is only 40% of it,
so that will result in some job losses,
but 60% of it would be
increased consumption,
and increased demand, coming
in from people consuming more,
whether it's consumers or businesses.
So, in that sense, it is a positive story,
but for someone who is doing
very manual repetitive work,
their jobs will be lost,
so we, as a society,
need to worry about,
how do we retrain them,
re-skill them, or if
you're unable to do that,
what kind of a support
system we can provide
for those kinds of people.
So, the new jobs will
be in a different area
to the jobs that are being lost.
So, that's where, I
think, at a micro level,
it is still an issue that a lot
of governments are tackling,
with this AI wave coming and
at the macro level, I think,
economies should be growing
with more consumption.
- That's a great perspective.
So, I understand you're
going to be giving a talk
on AI and how it's applied to gaming.
Can you tell us a little more about that?
- Yeah, so this is something
that we have been doing
for almost ten years now,
and this goes back to,
we have all seen great advances in AI
in the gaming world.
So we beat the chess,
the Jeopardy champion,
and then now recently Go,
and the Go systems don't even
need human provided data,
they just take the rules of the game
and are able to master everything.
So, we did all of that, and said,
"That's all great, but
what use does it have
"in the enterprise world."
- True.
- So what we have been doing
for quite some years now,
is taking any strategic problem
and converting that into a game.
So, businesses always are
competing in this environment,
where they want to increase
their market share,
or increase their valuation.
So, it's essentially a game against others
in the industry in any particular sector.
So, if we can take that
and convert it into a game
where there are a number of players,
you are essentially playing
for one particular company,
but you can model other companies as well,
you can model the greater economy,
you can model the environment.
Then the executives, we
call it a CEO cockpit,
instead of you flying an airplane,
you are essentially
sitting in the simulator
and flying your business.
Going into new markets,
pricing it differently,
targeting different customers,
and seeing how much of revenue you make
and how you will react
when the competition
comes after you in terms
of pricing or promotion.
So, all of that could
be converted into a game
so we can start using all of the advances
that we have in AI, especially the games,
in terms of reinforcement learning,
deep learning, all of that,
very much in the enterprise world.
So, that's the talk about,
and we have around eight or ten examples
where we have actually done this
across different industries,
and I'll be sharing just a
couple of those examples.
- Oh that's fascinating.
So, actually applying machine
learning to game theory?
- That's right, yeah. Exactly, yeah.
And that's something people
don't think of it as AI.
AI has been applied more
to operational problems,
more at the smaller scale,
whereas here we are talking about
AI being applied to big bets,
as the businesses like to call them.
- Wow, and you actually
have, like you said,
developed models for different industries?
- Yes.
- That's fascinating.
Well, so I guess we're at time.
It's been great talking
to you, Doctor Rao.
And thank you again for
tuning into this edition of
Adobe's Think Tank, please tune in again
for more sessions and episodes.
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