FIRAT TEKINER: Welcome to "AI--
The Future of Us," a podcast-style video series that
explores the ways AI is shaping our future
and how we can prepare for the challenges that lie ahead.
I'm your host, Firat Tekiner.
And today, we are joined by Bruno Aziza,
who is head of Data Analytics at Google Cloud.
Thank you for being here today, Bruno.
BRUNO AZIZA: Thanks for having me, Firat.
FIRAT TEKINER: I know that you speak to a lot of customers.
And you have been leading the data analytics at Google Cloud.
But before we get there, could you
share some of the key AI developments
that you've witnessed during your career?
BRUNO AZIZA: Absolutely, you're right, Firat.
I spent a lot of time with customers.
And I remember working in this field very, very long
time ago, in 2001 [INAUDIBLE] the business objects,
we were writing language recommendations
as an output of a dashboard.
So definitely, there's been tremendous amount of progress
since 2001.
And then you can see technology like Narrative Science
today, our great partners in ThoughtSpot,
and then of course, all the development
you've seen in GenAI.
And I think the main developments
are around the same goals, the four key goals to think about.
The first one is increase efficiency and productivity.
You want to use GenAI to automate tasks.
You want to reduce costs and save money
on certain costs that are taking time away from your most
valuable employees.
You want to improve customer service.
We see a lot of these use cases.
And I know we'll talk about detailed use cases here.
And then finally, you want to increase innovation.
These applications have the ability
to generate new ideas that is great companionship
with information workers.
Now what's amazing over the last six months
or so, you might have heard about examples,
like companies like Telus, or Accenture, Wendy,
or Cartier that are all innovating in this space.
That is really exciting.
I know we're going to talk about use cases.
But just give you a specific example,
Accenture, a company I interviewed
about six or eight months ago about their ALICE program.
ALICE Stands for Accenture Legal Intelligent Contract,
where they take over a million legal contracts
and run it through their artificial intelligence
programs to translate the obligations that Accenture
might have.
So lawyers now can get to the bottom line a lot faster.
They've helped over 2,800 professional globally
to deliver faster value to their customers with the use of AI.
And so definitely, we've gone a long way from 2001
when I was trying to get dashboard
outputs in human language to my end users.
FIRAT TEKINER: Thank you, Bruno.
It is interesting to see where we
have gone through within the changes in the ecosystem
within the past two decades.
And like Accenture and others, how
they're really leveraging the technology
that is being today as well.
Now I would like to focus a little bit on some
of the challenges or bridging the gap between the data
analytics and machine learning, in a sense.
And in your opinion, what are the main overlaps
that you see for two traditional, separate
disciplines?
Data analytics and machine learning coming together.
BRUNO AZIZA: So I'd actually argue that these disciplines
are not separate.
They're highly correlated.
When I was at Alpine Data Labs-- this
is an AI Workbench company-- we used
to talk about GIGO, garbage in, garbage out.
And the output of your models are
highly predicated by the quality of the input, the input
data, the quality of the data, but also the data that you
train your models on.
So I know it's not a super sexy topic
to talk about the quality of the data.
But that is the foundation that most of these models stand on.
It's great to have a great model, of course.
But if the data is not reliable, there
isn't much that's coming out of it that's a lot of value.
Now, this is not a new topic either.
If you Google the dimensions of data quality,
you'll find, I think, a paper that goes back to 1991
from MIT that talked about the 20 dimensions of data quality.
Now I'm not going to take you through all of them.
But I will tell you if you're listening to us,
there's probably three big questions
that you should ask yourself.
And they kind of spell this nice acronym, R-A-T, RAT.
One is, is your data reliable?
Is it complete, fresh, rich, and secure?
Is it actionable, meaning is it something that's
relevant to the end users?
Is it trustworthy?
And is it timely?
Can you understand it?
Is it actionable?
Does it get to you on time?
And so the connection of high quality data
that's relevant, actionable, timely with strong models
that are trained on data you can trust
is really how you're getting these
to what looks like separate topics
or separate disciplines that gets them to come together.
FIRAT TEKINER: I agree with you.
And your machine learning is really as good as your data
as well.
So you really need good quality data
to be able to get a good quality model out
at the end of the day.
But on the other side, what are the most pressing
considerations surrounding AI development
and use that you see as well?
BRUNO AZIZA: Yeah, there's quite a few.
The first thing is, AI's been around for many years.
We do have a ton of discussions around AI today,
but we've used it for a very long time around us.
And then the second, the place where AI is most helpful
is when it's applied.
So I would try to think about if you're in the enterprise
today, if you're a data leader, think
about what your principles around how using GenAI
should look like.
Now we have a great paper from [INAUDIBLE],, who
is the VP of Data Platforms at Walmart
and sharing how he thinks about it.
And if I were to summarize it, it probably again
would come to a simple acronym to remember.
And one of my friends, [? Andy ?] [? Guzman, ?]
actually helped me out with this acronym.
It's spelled TARMAC.
So how do you think about GenAI?
T is for Trustable.
And there are many dimensions of trust, right?
So you got to think about privacy.
You've got to think about reliability,
fidelity of the data, et cetera, fairness of the data.
So T is a really important aspect.
Then you've got to think about the A, the Applied aspects.
It needs to be applied to the workflows.
It's not very useful if you have a GenAI application that is not
integrated within the workflow.
So it just takes you away from the action.
R is for Recency.
So the ability to use data that's recent,
not data that's happened five years ago or two years ago.
You want data from yesterday.
It's really important in helping you make the right decisions.
M is Multimodal.
The best models will input, of course, text, data, images,
code, and will respond exactly the same way.
So this idea of having multimodal application's
really important.
And then C is Contextual.
What we're doing here, Firat.
We're having a conversation.
You remember what I said before, and we continue.
It's an expectation of an EGI application
is that you need to make sure that it's staying
with the context so you don't have to repeat yourself,
which could be a lot of frustration.
So I know this might be a long acronym,
so I'll make it simple for you.
When I think about AI, AI stands for artificial intelligence,
but it also stands for Applied--
applied to the workflow-- and in a way, invisible.
Because it is contextual, because it
feels like it's following you, in a way,
you almost stop realizing that is a GenAI application.
FIRAT TEKINER: I like your acronyms, Bruno.
I totally agree as well in respect
to it should be really unbiased as well.
So it is as if how you train your child.
You want to give the most unbiased information,
and they'll really become that person going forward.
But also we know that there are also challenges applying
and using GenAI as well.
We see a lot of data analytics professionals
also coming into and becoming part of this.
And what are the main challenges that you
see for the DA professionals, in particular, in terms of AI?
And also, do you think that we'll
have data scientists, data engineers, data analysts,
machine learning engineers?
And you can just take the number of persons that we have.
What roles do you think that they'll
be playing in the future?
BRUNO AZIZA: Yeah.
So I think it's a great question because it
could be very confusing today.
You're probably getting questions
from your CIOs or chief data officers,
if you're a data analytics professional today,
what are all the things I can do?
And I was just reading a paper by McKinsey
that was showing the 22 use cases for GenAI applications.
And I think that can get a little confusing
because, theoretically, there is an infinite amount of tasks
you can think about.
So what I would do is if I were you,
I'd think about this idea of jobs to be done.
What are the verbs that you're trying
to enable inside your organization
where your GenAI application can become a co-pilot of your work?
And so there's probably eight verbs
that I could come up with.
I'm sure there's more, but I'll give you
a sense of how they feel.
First one is create.
Your applications can help you create new content,
and they can co-author new content with you.
Second is summarize-- large body of text.
It can fairly effectively summarize--
you have to double check, of course,
don't just go with the summary just raw the way
it's giving it to you.
It can help you find better.
It can help you review better.
It can help you analyze better.
It can help you explain better.
It certainly, in the case of business intelligence,
the use of natural language can help democratize
the explanation of particular dashboards, reports
that otherwise would have been fairly
hard for regular information workers to understand.
They help you recommend, and they help you support.
So those are kind of the aid verbs.
I'm sure there's more.
I'd love to see in comments what you all think.
But think about that.
Don't just think about the specific task.
Think about the grouping of these tasks
because then it helps you explain better
to your management, the rest of the team, what is it
that you're working towards.
FIRAT TEKINER: Well, love your eight verbs, actually.
So it looks like the ecosystem will slightly change.
It is going to be more evolving in a sense, right?
And with that-- so what [INAUDIBLE] does [? at ?] GenAI
brings for organizations-- with your experience--
and I know that, as I mentioned at the beginning as well,
you do work with lots and lots of customers and organizations.
Where do you see the most value?
BRUNO AZIZA: Yeah, there's a few areas where--
as we said at the beginning of this conversation,
the key values are around increasing
efficiency, productivity, reducing cost,
improving customer service, increasing innovation.
I would say there's probably three key themes you've
got to think about as you think about the value
vector for GenAI inside your company.
The first one that I would really think about
is make sure you explain to the rest of the company it's not
magic.
These models are pattern recognition at massive scale.
And so it's possible because now we've
access to all the plumbing that's
required to make this happen.
We have, of course, the algorithm libraries,
but we've got compute.
We've got storage.
At-scale, that really enables you to do it,
but it's not magic.
So be sure to remind people of that.
The second one is it's not about machines versus humans.
It's about humans with a machine that can do more than humans
without a machine.
And so it's really about co-piloting versus autopilot.
And the reason for why I'm saying
that is one of the things we're discovering
is that with really good GenAI applications,
you can run into the risk of falling asleep at the wheel.
Because you're starting to rely on it so much,
and it becomes so believable that you might--
how would I say--
delegate almost too much to it.
So you've got to keep an eye on that where you're really
using the technology to co-pilot and not autopilot.
The one thing we've discovered watching people working
with these applications is there's something interesting
happening-- this human-type companionship
that if you're using these applications,
it's actually a great way to get feedback both ways.
So if you're building content, for instance, you
can talk to the chat as a way to get
maybe additional information, refine content, and so forth.
So interestingly enough, when you
would think that it's really an automated kind of relationship
or transactional one, if you start interacting
with it by giving it feedback and thinking about more
as a sounding board to the ultimate content
that you're creating, it could give you
this companionship that sometimes
is hard for content creators or starting with a blank page
and feel alone a little bit.
These GenAI applications actually
can be very helpful to you.
FIRAT TEKINER: That's great.
I think what we can see is that the AI is really
becoming the assistance to different personas
and different organizations and really
helping us to become more productive to get to the point
that we really want to get to.
But with that, I want to look ahead
and see what AI in business analytics really means.
And what do you believe is the most exciting potential
for an AI application that we have yet
to see with the data analytics in mind?
BRUNO AZIZA: There's a lot to be excited about.
Just that we talked about at the beginning of the conversation,
it's around efficiency, productivity, reducing costs,
improving customer service, increasing
innovation potentials.
Now, to be clear about this, we're
still experiencing a lot of innovation experimentation
with it.
Today, I think there was recent research that
showed that only 4% of our innovations
are in production today.
So we're certainly bound to find a lot of new use cases
out there.
But if I just think top of mind, I
think the first idea around poring through a lot of content
more automatically a lot faster so you can pick up
the patterns, I think, is where these applications are
very helpful.
So if you're a business analyst, for instance,
you're launching a GenAI application
towards a large body of content that otherwise
would have taken you a lot of time
to go analyze is a really key use case.
You could see this across multiple industries.
You could see in health care, for instance,
where you can streamline the discovery of insights.
You can improve medical imaging discovery and understanding.
You could see this in the legal context as well.
And so there are many, many areas where
you can take advantage of this.
There's also on the analysis side--
a topic we don't talk a lot about
is the ability to create synthetic data as well.
As you're trying to test theories and so forth,
and you want to not do this on personal data or data
you don't have access to, these GenAI applications
can help you create completely fake data
that you can run your experiments on.
So that's another way to think about it, both in poring
through existing data that would be a lot of time
and a lot of work for you, but also creating data
that you can work with as a way to experiment your methodology
and that would not expose private data or data that's
highly protected.
Those two examples are really interesting one.
There's hundreds of them across multiple industries.
But certainly, across banking, education, life science,
manufacturing, health care, there are hundreds of use cases
that I'm sure we're bound to see a lot about and certainly
deliver a lot of value to people listening to us today.
FIRAT TEKINER: I agree.
And we are really just seeing the tip of the iceberg.
There's tons of use cases to be identified.
I really like your generating data
really for different use cases because GenAI,
as the name suggests, allows you to create lots of data sets
to experiment with as well.
And my last question around this--
and definitely not the least--
so what advice would you give to individuals and businesses
looking to prepare for the future
whereby AI really plays a significant role?
I'm hearing this a lot.
I'm seeing lots of articles.
I'm talking a lot of customers, lots of professionals.
Everyone have this in mind--
how I can prepare myself, how I can prepare my organization
that I'm still valid in the coming years and months
as well.
BRUNO AZIZA: Well, the first thing I would say
is that be careful about getting enamored
by the marketing of many organizations
are going to tell you, solve all your problems
with these new applications.
I think you really got to study a little bit more
about the applications before you start applying them
in production and in real life.
And that's probably the first thing.
Like I said earlier, it's not magic.
And the second is, it is a co-pilot.
It's not an autopilot.
It is technology that can certainly be very helpful.
But in the end, you're making the decision,
and you're needed to make that decision because that's
critical for how you're driving value into your business.
And then finally, what I would say
is get trained; experiment with as many tools, as many models
as you can so you can see how it feels.
And remember that the quality of the data,
the quality input, the type of data you're training it on
is as important, if not more important, as the model itself.
And so you really want to spend a lot of time
on the fundamentals.
Those are [? now ?] changing.
You need to have high-quality data.
You need to connect your use cases to business value.
And of course, [? of ?] technology
in the way between the data acquisition and the data
activation, if there's capabilities
that can automate and facilitate your job,
you should definitely consider them.
But remember, you're in charge of the decision in the end.
FIRAT TEKINER: Yes.
And it is really an optimization problem at the end of the day.
So whether you invest a lot of effort in building your models,
or whether you are using existing ones
and high-quality ones, how much accuracy is good for you,
and whether you are really getting
the return on investment with the skills
that you are providing and the input that you're
putting into as well.
Thank you for joining us today, Bruno.
It has been incredibly insightful to hear
your thoughts from your experiences as well.
And we look forward to seeing how
AI is going to continue the data ecosystem around us.
And hope our viewers will be inspired
to embrace its potential and opportunities.
Thank you very much.
FIRAT TEKINER: Thank you, Firat.
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