Today on episode #793 of CXO Talk, we're speaking about Data
and a I. Our guests are Inderpal Bandari,
the Global Chief Data Officer of IBM, and Anthony Scriffiniano,
the former chief of Data scientists at Dunn and
Bradstreet Inderpal, welcome to CXO Talk.
It's great to see you. And please.
Tell us about your work at IBM. I'm actually a four time Chief
Data Officer. When I first became Chief Data
Officer in 2006, there were just four of us globally.
I was the first in healthcare and then the profession and the
related professions like Chief Analytics officer,
transformation officers, the century that took off and I
happened to be fortunate enough to ride with it and I've done
this job four times, IBM being the 4th and perhaps the most
complicated. At IBM, my strategy, data,
strategy has been to make IBM itself into an AI enterprise and
then use that as a showcase for our clients and customers,
because our clients look very much like us.
So that's what I've been doing for the last seven years, 7 1/2
years or so. And Anthony Scriffiniano,
welcome back to CXO Talk. It's your good friend.
It's great to see you and tell us about your work these days.
Thank you very much, Michael. It's, it's great to see both of
you. So as you mentioned, I was with
Dunn and Bradstreet for quite a long time, over 20 years.
Right now I'm doing a number of things and probably front foot
is as a distinguished fellow with the Stimson Center, which
is a think tank, think tank. I'll put in quotes because
there's a lot of what I would call applied research or action
research where they actually get involved in doing things, not
just writing about them I've been involved with.
Things that are called a I. The term's been around probably
since the 50s but I've been involved with it in as it's
become computational from its from its birth and and I know
Interpol has as well lots of things going on in the world
right now in terms of regulatory focus on a I as well as new
types of a I becoming sort of the greatest new shiny object
and everyone pays attention to them.
And I I stand for the, the, the size behind it.
What do you have to believe? What has to be true in order for
you to do that thing that you think is so cool and and why is
it better than what you're doing today and and what is the cost
of it? So I try to ask those emperors
new close kind of questions and that's the role I'm playing
right now. So we're talking about data and
AI. And I think where we need to
start is when we talk about an AI data strategy, what actually
is that Interpol, you wanna maybe take a crack at that to
start? AI is only as good as the data
that is used to train that AI, because AI has a training
sequence and then an inference sequence.
The training sequence has to do with seeing.
All kinds of related data, so that it can actually then train
itself to figure out what the right output is when it's shown
an input that it may not have seen before.
So if the data to begin with is flawed or low quality, the AI
will not work effectively. It's the garbage in, garbage
out, that kind of phenomenon that you would have.
So they go hand in hand. And very often you think of
people talking about AI, and if they haven't really looked at
the data, but they embark on a data on an AI strategy that is
going to be very high risk, it'll most likely fail because
they'll have to go back and straighten out the data strategy
first, just so that it's fit for purpose.
Now when you say fit for purpose, what that means is.
If you know what the business objective is that you're trying
to serve, so it could be something quite narrow like a
specific objective. It could be something like I
want to understand what segments of my business.
Should I try to try to expand to increase my top line then, in
which case, if it's segments of business, you know, data about
your clients, about your products, etc, those things
become very important. You'd want to make sure that
they are, that that data is of very high quality.
And on the other hand, if it's something at a strategy level,
which is what kind of what happened when I joined IBM, I
mean, you know, IBM wanted to be a cloud and AI company.
And to be a cloud and AI company, eventually we landed at
the point that, well let's transform ourselves internally
before we actually show this off to our clients and customers.
That became a strategy that was enterprise wide and we realized
that now while for instance not only do we have to make sure
that our structured data is in order, but also our unstructured
data because we are going to go after this.
And transform ourselves into an AI company.
So there are two, two aspects there that are relevant.
One is at a strategy level when you're aligning, aligning to the
business strategy or it could be more narrow to a specific
business objective. Anthony, the challenge of
aligning the data strategy to the business objectives is
something that many organizations struggle with.
What thoughts or advice do you have on making that work?
You've seen so many different scenarios.
You would really have to unpack what Interpol just said quite a
bit to really get at the essence of it, and I did when I was
listening to him, but he was using some terminology very
carefully there. A lot of times organizations
don't have one strategy, I mean make more money, you know grow,
grow, you know fill in the blank, right.
The the things that we learn in Business School, you can serve
your shareholders, you can serve your customers, you can serve
your employees. Kind of hard to do all of those
things at the same time because very often optimizing for one is
is less optimal for one of the others.
So the strategy of which we speak.
And we start to talk about AI has some very serious
implications, these methods that we're talking about.
And I should say that these days it's rare that only one method
gets applied very often. There are many methods being
applied simultaneously. There are some commonalities.
So one of the commonalities is that the quality of the data has
many dimensions, so truth. If if your a I is going to
ingest data, it's going to probably presume it's all true.
Well, all data is not necessarily simultaneously true.
It may have been true at the time that it was created, but
maybe not so much anymore at the time that it's curated.
So how old is the data? How is it still true?
How would you know that it's still true it before you consume
it into an algorithm or an approach that presumes that?
I love to say that. When we go to court, we swear to
tell the truth, the whole truth, and nothing but the truth.
That's because those are three different things, and those are
three different ways to manipulate veracity or
understanding. So when you get back to this
concept of strategy, well, whose strategy?
What part of the organization? Specifically, what objective?
How would we know when we were successful?
And asking those questions is very often a source of intention
because the people in the room that all think they want the
same thing. Realize they don't.
And when you unpack it a little further, they realize that to
get what the guy on the left wants, you have to get less of
what the person on the right wants.
And it's not pretty. So it's not really a technical
problem as much as it is an alignment problem.
And, you know, sort of getting everybody to agree on what they
want so that we would know that this strategy of which we speak
is actually what this a I of which we speak, is delivering
really difficult. These roles like the Chief Data
Officer, the Chief Transformation Officer, the
reason these are CXO roles is because of what Anthony just
said. You have to be part of that
discussion. It's not so much like there is
the concrete business objective. Sometimes you get into those
situations where it's very clear cut, but more often than not
it's a strategic discussion in terms of a understanding,
clarifying, perhaps even adding to the business strategy.
And then relating it back to what you're trying to do with
data and AI, and unless you're in a position to have that kind
of conversation and you also have the wherewithal to pull
that off, you know that you won't be able, you won't really
be successful. So that's why these are CXO
roles, because it's really part of the negotiation that goes on
to align the business strategy to the data or the AI strategy.
Maybe I can just add a little bit to that, that the whole
concept of being in the room. It's so important back in the
day that the the goals and the objectives would come down from
on high and that the folks with the the keyboards will just make
it so you know, it doesn't work that way anymore and it can't
really work that way anymore. And so it's so critical that the
folks in the roles that Inderpal and I have had have a seat at
the table understand what went into the ask and not just the
ask very often what. Folks want and what they need
are two very different things. And so without being arrogant
about it, you you asked a lot of questions and you get at what
they really needed in the 1st place, which is probably not
what they started out asking for.
You're talking about organizational alignment with
business strategy, and at a high enough level, this is true for
every business decision that needs to be made.
And yet, when you hear people talking about a I.
And data strategy, the conversation turns and very
quickly to what kind of data do we need?
Where do we get that data? What's the technology that we're
going to use to aggregate and to manipulate that data?
What kind of models are we using?
And so now I'm confused because you're talking about one thing
and I hear the entire world talking about something
different. The world tends to focus on the
hammers and the nails, and it tends to focus on.
The the tools that are going to be used for the the, the
purpose. If I come to your house and I
say and if then you want to put an addition on your house and
I've met with the architect and I understand your objectives,
let's talk and someone else comes to your house and says I'm
going to build you a beautiful addition and I'm going to use
the hammer, right. You don't really care about the
hammer, so of course the hammer is important.
It's very important that we have the right data, the right tools,
the right technology, the right people.
So it's people, process, tools and mindset.
All of those have to be aligned in order to get this right, but
it starts with making sure you're focused on the right
mission. Yes, that number changes that
piece of, you know, aligning back to the business it's.
So the way I would put it is, yes, no matter how promising the
technology, no matter how dramatic the advance, etcetera,
it doesn't let the organization off the hook for coming up with
a sound business strategy and then of aligning these elements
to that business strategy. That's still going to be very
much needed, in fact maybe even more so than before as you try
to go after these new approaches and methods.
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We have lots of them. So would you say that the
business side is more difficult or harder to achieve than the
data and technology foundations in your experience?
I would say that if you take something new, so you probably
want to draw the distinction between a mature technology and
a technology that's more recent or Macent or emerging.
And if you take something new like that like the latter then
it's you know there. There is a tremendous amount of
complexity on the technology side as well.
So early on when getting into this game, you know when you we
were working on on. AI for instance, at IBM, it
became very clear as we went forward that there were four
elements that had to move in lockstep, data, technology,
workflow and culture. And those four kind of had to
move at the same time. Otherwise the adoption was not
going to be not going to be effective and.
The technology piece for an emerging technology, so at that
time, you know the cloud was emerging, there was a lot of AI
techniques that were emerging, the deep learning stuff with,
you know, GPUs and things like that.
You have to make all that stuff work together.
So there is a significant complexity in the technology
piece, but there is also a significant complexity in the
data piece and the workflow workflow piece and then
eventually in the culture piece of the organization.
The the stuff that we were talking about in terms of the
negotiation, working with the C-Suite, you know, there's a lot
of the cultural aspect that goes into, there are many
organizations one could go into and you would essentially not,
Anthony said. They would still want to give
you a set of objectives and say here, go off implement this.
We really don't want to hear from you about anything else.
These are your marching orders. Go off and implement this.
Well, that's the wrong approach when you're trying to bring in
an emerging technology and use it to impact the business.
Anthony, we have a question exactly on this topic from
Twitter from Arsalan Khan and maybe you can share your
thoughts on this. He says we when we talk about
alignment, there's business strategy, enterprise business
architecture, change management culture and now data strategy.
All right, Anthony, so what's your prescription than to make
all these layers work together and align sounds almost
impossible? Almost impossible is a synonym
for possible. So if you said it was
impossible, you know, now we have to, we have to talk, right?
I think that first of all, thank you for the question.
From someone who knows that I ask a good question, I would say
it's really important. That you start with the question
with the objective. Everybody wants to jump to the
technology. They want to jump to the the
date of the deal. The the thing that we're going
to the the, the, you know that there's there's two factions in
the room. The one faction is focused on
the the revenue, the growth, you know what's going to happen to
the organization and the other faction is focused on, all
right, let's get going, Let's start, you know, doing stuff,
let's start cooking in the kitchen.
I'm usually the 1:00 somewhere in the middle of those two
saying let's make sure we're answering the right question
here. And I, you know, I'm not slowing
you down. I'm actually making sure we get
done in a way that we don't fall over the finish line.
So it is very difficult to get all those factions in the same
place. Probably the most important
thing you have to do is be able to listen to each other and not
start immediately talking about hammers and nails or immediately
talking start talking about what color we're going to paint the
finished product, right. But you know, somewhere in the
middle is, you know, why are we doing this?
What are we not doing while we're doing this?
Do we know there's a big difference between can we do it
and should we do it. So what are we giving up while
we do it? What about compliance, What
about regulatory, what about making?
How do we know that the data that we have is the right data
to make the decision you want Just because you believe it and
you have your confirmation bias and you found one or two pieces
of data that support your hypothesis doesn't make you
right. So we have to ask these
difficult questions and there's a very fine line between being
right and being dead. So you have to be able to ask
them in a way that doesn't annoy, it can annoy them a
little bit, but you have to annoy them just to the point
where they don't kick you out of the room and and keep asking
those, you know, help me understand kind of questions
until we get to a shared understanding of what it is
we're trying to achieve and the opportunity cost of all the
other things that we're not doing.
Interpol. But you're a technologist?
So if this is strictly then a business issue of organizational
alignment, why do technologists play such an important role in
this discussion of such a foundational fundamental?
Role. I think the best way to think of
my role of people in similar situations is that of a
changing. The catalyst for the change is
the technology. The change has to be affected in
the organization and in the business.
So you have to be able to bridge those two to be able to do this
successfully. So you know it's a
transformation and the transformation typically has
those elements that I talked about for what we do, what I do
data, technology, workflow and culture and I'll give you one
other thing, I mean it's. There is there is a lot to be
done in terms of changing the culture of an organization when
you try to bring bring about this change.
What we what we saw at IBM when we pushed forward with our data
and AI strategy was that the adoption of the platform was
triggered far more by the bottom up measures that we put in
place. So we actually had a team that
was empowered to engage with other teams.
That were working in the business, you know doing
workflows, go to cash procurement, you know things
like that supply chain. And so we have an empowered team
on the technology side which was didn't really need to come back
for direction or instruction but if they found a like minded team
they could go ahead and and move forward with the transformation.
We found that 85% of the adoption actually came from that
part. As opposed to the top down path
and so forth. So it really is all about how
you effect the change. But obviously if the catalyst is
the technology then you've got to be able to walk that walk as
well. So but you you can't discount
the other side of it, you have to really be the bridge.
Like I smiled when you called into call a technologist and he
very diplomatically didn't didn't respond.
I think you can tell by that answer that you have to be much
more than. Just an expert in the technology
to get what he just said right in large organizations there,
what's happening right now is a massive federation of data and a
I capability. It's not like you go to the room
where the people that know how to do that live and ask them to
do it for you. Almost anybody can get these
capabilities on their desktop. It doesn't mean that's the right
place to do it, but they can start doing it there and
everyone feels like they're an expert.
Just like when we all first got, you know, I'm trying not to name
a product, but I think I can say Harvard graphics or you know,
like in the days even before PowerPoint where all of a sudden
we could all, you know, lay things out on the screen, we all
thought we were experts in design and layout and font
selection and and all of that. And of course we weren't.
And and there's an old joke where the punchline is death by
PowerPoint. We we all know versions of that
joke. And I'm not picking up
PowerPoint. Federating A capability like
that across an organization or across the world comes with some
risk that those who really honor practitioners who know.
What? The differences between what you
can do and what you should do, who understand the implications
of going down a certain path and the difficulty of changing
course once you get too far down the path.
They have to be able to hear what's going on so for.
When Interpol is describing to work, well 85% of the time does
require an organization that actually talks to each other or
or at least talks up to people who talk to each other up and
down. But you know that's not always
the case. So you can't just throw
everything out in the middle of the floor and say here you go,
everybody play with this and do whatever you want.
That will not work. That will end in tears.
So you have governance, you have focus on these foundational
pieces. What about the interface between
the technology and what you're describing the whole world and
and organizations by and large tend to focus on that technology
piece. And so can you now maybe talk a
little about technology management as it relates to what
you're just describing and also selecting the right kinds of
technologies and especially? Selecting the right kinds of
data to match with the problems that you're trying to ultimately
address. And I would add time that it's
still relevant. I think I have a good example
for you. When when the pandemic broke
out, I don't think anybody was really expecting that.
All of a sudden organizations shifted to almost exclusively
working from home. There's laws about what data you
can access from home and what data you can access at your
desk. You have a different firewall
when you're working in the office than you do when you're
working at home. You've got developers that used
to be Co located that are not Co located anymore.
Organizations had to absorb all of that change while still
trying to serve their customers, and in some cases failure to do
so could have been life and death.
So you know there's an urgency about this as well.
You can't take forever to do it and you have to have good
discipline in place. So that when the unexpected
happens in the middle of the other unexpected that was
already happening, you have the resiliency survive that and come
out of that stronger. I'm not going to suggest,
although I could that I BM is one of those organizations, but
you know mature organizations that get it and do that.
We saw a lot of organizations that weren't so mature not
getting it in the middle of all that disruption.
So it's it's just a very big question you're asking.
The example of the pandemic actually was particularly
instructed, I think it goes to your data AI questions of the
earlier in the in the segment as well.
So when the pandemic hit in terms of being able to run your
business, for instance, make financial forecasts, make
forecasts about your supply chain, about your procurement
abilities etcetera, all the models that were in play were
essentially useless. Because we have now embarked on
a situation that was completely new.
And so no matter what technology we had in there from an AI
standpoint or a model standpoint, it had been trained
in a completely different world. And that's Anthony, was
Anthony's point, right? I mean, it may not be true now.
In fact, it wasn't true. What was true though was if we
were able to get the data, accurate data, pristine data.
Into the hands of the people who were running those different
departments along with an overlay of what was actually
happening in the pandemic, you know, where COVID-19 was
breaking, what were the incident reports in different areas.
So if you could like geographically, then overlay
that on what these guys were working on, whether it be
financial forecasts or sales with, you know, they expected to
close or procurement sites that were endangered, things like
that. They could make something out of
it and move forward with it. And then so that that's I think
also an instructive example of the relationship between data
and AI and how that plays out as it as things really unfold that
you know that are truly unexpected.
Let me draw first blood on saying something super nerdy.
There's a concept. I call it decision elasticity.
I kind of stole it from economics.
But how wrong can you be and still make the same decision
effectively? So you don't have to be perfect
to make a decision. And Interpol is talking about
training. There's an implication there
that you have longitudinal data data from the past that you can
project into a near term future that looks reasonably similar
and you can measure the elasticity of your decisions.
How wrong are they? And then if they start getting
wronger and wronger to coin a term, then you can stop and and
reexamine those methods. The problem is when you have
something completely disruptive, there is no data and the most
dangerous situation you can find yourself in.
Is when the world is changing faster than the data that
describes it. That's exactly where we were at
that moment. You can't just throw your hands
up and say, well wait, when you have five years worth of data,
come back and I'll, I'll retrain everything and and we'll be good
to go. You have to have methods in
place that are effective in a situation where and that's what
this environment taught us, that you can't just rely on one type
of learning, one type of projection into the future.
At that time, I was very involved with watching bad guys
do bad things well. When there's disruption, the
best bad guys, especially if they think they're being
watched, they change what they're doing.
If you model based on what they were doing, you're modeling how
the best ones are no longer behaving out of a dangerous
thing to do, right? But we know this.
And so the the flip side of that coin is if you know that the
environment changed such that the bad guys are going to
probably try to take advantage of it.
That many of them are probably going to do that unarthfully.
And so you may be more easily able to see them as they run.
You know, you turn on the light and the the, you know, the
little creatures run away, you can see that.
And so there might be an opportunity there along with
that risk. So it's it's sometimes these
these situations are, I would say almost never are they all
bad or all good. There's always something in it
that can teach you. There's always something in it
that can make what you're doing better if you have enough time
to breathe and observe what's going on and use the energy in
the best possible way. It doesn't mean the bad thing
will stop happening, but it may mean that you emerge from it in
a better way because you you took that time to be more
thoughtful about it. So we have a question from
Twitter. Elizabeth Shaw says the issues
you're describing are true of any business or technology
transformation. Are there particular points
issues that are more problematic for AI enabled initiatives?
Can you kind of drill down into that?
If you look at the advent of AI, the progression of AI, it's
moved very, very quickly in the consumer space, but not so fast
on the business space. And that's because in the
business context, people don't trust AI.
And they don't trust AI for multiple reasons.
I mean, there's the, we talked about some of the issues about
the data. So the robustness of the data,
the quality of the data, the currency of the data.
Then you also get into issues that have to do with the
fairness of the algorithms. You know that the results they
produce are going to treat people fairly if they pertain
to, if the, you know, the data pertains to people, you have the
issue of privacy being invaded. In terms of the algorithms
discovering something new, you know there's this famous example
of or infamous example of retail retailer, large retailer
actually looking at the shopping shopping data, shopping patterns
and shopping data and then inferring that.
That this person is is pregnant and actually mailing their home
and it turns out to be a young lady and you know, it was, it
was it was really a complete invasion of her privacy.
So those aspects come in. Then there are the issues around
the job displacement and things of that nature.
You know, if you're applying AI in the enterprise, there are two
flavors of it. There's the automation flavor
which has to do with when things are kind of straightforward.
And you go from one step to the other and you know what those
steps are and you can automate all that.
So there's job displacement associated with that.
But even on the decision making side, where the AI is actually
helping make a decision, there's a decision maker in play and
they have to trust it. They have to say, well, this is.
Going to, you know, this won't displace me.
So and extending that further, the executives as you put AI, we
kind of know by now that AI has to be infused into the major
workflows of the business, things like procurement, supply
chain, etc. That's the kind of IP that
doesn't get published in papers or patented or anything.
Those are the trade secrets of a company.
So they have to be able to trust whoever the vendor is of this
software that this is not something that's going to
disintermediate. Furthermore, the decision maker
that's working with the system has to understand it.
You know, years ago we I did this computer program called
Advanced Scout that ended up being used by every coach in the
NBA. And I remember the first time it
had a counterintuitive finding. It basically asked the coach to
play 2 backup players in a playoff game that they were on
the verge of elimination. And he, you know, he was very
concerned about that because he felt if I make, if I do this and
I lose, I'm gonna lose my job and reputation as well in
addition to the series. And we kind of solved that
problem by letting him see the video clips of when those two
players were on court. But that's the explanation
piece, right? So if you tell a doctor,
amputate the left leg. They're going to have all kinds
of questions, OK, why amputate? What other options were
considered? Why is amputation the right one
for this patient? Etc.
So explanation is another big part of it, and the AI systems
today don't do a good job of all that.
So those are the special aspects of AI and trust that come into
play. I think that was a fantastic
list and I won't vain to add to it, but I will suggest another
dimension to it. So great question.
Like how how are the A, I issues?
What's special about the A I issues?
I would say another one is that you have the opportunity to fail
faster and at larger scale. There's a tendency once these
sorts of systems are implemented, someone says, well,
it's 99% accurate, it's 92% accurate, it's 87% accurate and
you assume that means that 87% of the time the prediction will
be right. Well, no, that's based on the
past. In the future, right?
Very rarely do we measure fast enough to stop every conceivable
bad thing from happening. Interpol hinted at something
which is an observer effect that people, when told what to do by
a quote UN quote machine, will sometimes.
Think they know better or not want to be told what to do by a
machine and do something different just because a machine
told them to do it? To prove that they can do
something and not necessarily thinking it out loud.
Like that Question I get asked a lot is, you know, what about
someday when will people be reporting to robots or robotic
bosses of some sort And you say, oh, of course not.
I would never do that. And then the GPS tells you to
turn left or right and you do. And Outlook tells you to go to a
meeting and you go. We're already taking a lot of
direction from automation. I won't call it a I necessarily,
but from automation and the the the human fact of what we do as
human beings to accept or reject that device is essential to get
at trustworthy a I to get at making sure that we don't
marginalize others that are already marginalized more
because they don't have access to these technologies.
This concept of good and and not good is is a very, it depends on
where you're sitting sometimes whether it's good or not good.
There are certainly lots of volumes, books, committees
focused on trustworthy, A, I and explain ability.
There's legislation as we speak being considered that will hold
the feet to the fire of anyone who is implementing anything
called a I. So you know to say that it's not
being adopted by business. The adoption is lower, I think
in some ways because of some of these human factors.
It's not a lack of technology, it's a reticence to just push
that button so quickly. And you know, technology will
always outpace regulation. So you have to be careful where
you could find yourself in a in a world of hurt where now
they're coming after you because you use that technology that
made a better decision. Good luck trying to prove that
sometimes. This is a question from who Wong
and he says we can sometimes measure the cost of implementing
data solutions. But how can we measure the
operational costs when a business decides not to
implement certain solutions, such as governance or data
quality? The opportunity cost, the cost
of not doing something. And thank you way for that
question that would. That's that's a big one and I
think it's an important one. If we're going to decide not to
do something, we should decide not to do it on purpose, not
just because we got tired of arguing about it or because we
didn't want to take the effort to get all the data that will be
necessary to make that decision. So one of those annoying
questions that I usually bring into the conversation.
Is if we're going to decide not to do this because there's some
other thing that we want to do and that other thing has been
deemed more important, great let then let's make that decision.
But let's understand the opportunity cost, the cost of
not doing in. Many cases it does become clear
cut because you might have regulations that then levy huge
fines, for instance in the European Union GDPR for
instance, if you don't have the right set up for governance and
privacy and so forth. You'll be hit by a major fine.
In other cases though, when you know they're making these
decisions, they might choose not to do the governance of the
data, but it'll end up reflecting in the actual output
that's being produced, and then somebody has to go back and fix
it. So keeping a keeping tabs of
that, you know, I'm assuming here that you've lost the
argument and they've gone ahead with it without actually, you
know, then keeping tabs on that and raising that every time it
happens. I think very quickly you'll be
able to make a difference in the in the way people are viewing it
because nobody wants, you know wants a disaster.
And if the if the if what's if they've skipped that step which
is which has major magnitude, you know, as sometimes that'll
happen and the collaboration is the name of the game.
So you just want to then keep an eye on it, warn people that this
is going to happen. And every time it happens or
even before it happens, you raise your hand and say look, I
told you about this, now let's do it.
This is from Jav Boshinov, who is a professor at the Harvard
Business School, and he's also been a guest on CXO Talk, and he
says this and Interpol. Maybe I'll ask you first, is
there anything different between generative A I and more
traditional A I? And how should organizations
approach? This I think the best way to
think about generative A I. The promise is that you can do
things conversation. So just as you and I can have a
conversation and we can discuss something and try to get get to
some resolution. That's the hope.
So now if you apply that in a large organization and say I've
got some intelligence that can now conversationally help me do
client support, employee support, my IT operations, etc.
That's you know, hugely, hugely promising.
On the on the other hand, I mean the way these systems work
today. You know, the best way to
understand generative AI that I've been able to get my mind
around it is in a sense, each word is predicted, and then the
word, essentially that word is fed back into the input and then
the next word is predicted. It's almost like when you and I
are talking, I'll sometimes do this.
I'll go out on a limb, I'll start saying something and the
thought hasn't fully formed. Usually I'll manage to come out
of it. And but many times, you know,
I'll end up with my foot and my mouth.
So the generative AI techniques are essentially going out on a
limb every time because it's, which is also why they're not
always consistent with the response.
You know, you might have the same problem to give you a
different response because it's actually working off a
probability distribution. So I think there's a tremendous
amount of promise, but also a tremendous amount of work that
needs to be done to address some of the issues that we've raised
earlier. Anthony, Differences between
Generative AI. And traditional AI and
implications for the enterprise and pretty quickly please
generative. AI is is making stuff that
didn't exist before based on stuff that it observed.
And that stuff can be taxed, it can be images, it can be
anything that we as humans consume.
So the the the challenge to it is that you look at all the
stuff in the past and you you kind of compute on it and do a
lot of math and then you generate something that looks
like a human said it and a human didn't say it.
And so when the world changes and the corpus of data that it's
looking at didn't change fast enough, that nuance gets lost
and we lose the ability to understand something nuanced.
So if the if the purpose is to provide customer support based
on frequently asked questions, or if the purpose is to
summarize a whole bunch of things that you should have read
and didn't have time, it's a fantastic idea.
If the purpose is to to write some new thought leadership on
something. Maybe it's a starting point, but
it would be very careful when we consider that to be an ending
point. Share final thoughts on advice
that you would give to business and technology leaders who want
to be more effective using data using AI.
Interpol. You want to jump in with that
one first. I've been doing this for the
last 2025 years, starting from the days when I did that program
for the NBA to now. And I've always felt that
whenever I was doing it, I thought, oh, it can't get better
than this, but it always seems to get better than that.
And I think we're now in one of those moments where there is the
potential and the opportunity to have a tremendous impact not
just on business but also on society.
And I think because of that implication that there are these
major societal considerations as well, we absolutely have to get
involved. And that would be my biggest.
You know, advice to people either on the business side or
on the technology side, you need to really get involved with
what's happening here. And there's just tremendous,
tremendous potential and it's there's never been a better time
to be involved in data in there. Anthony, it looks like you're
going to get the last word here. Number one, I would say ask why
a lot? Why are we doing this?
What do we have to believe? Why this data?
Make sure that you understand before you jump into.
That method with that data. Make sure that method and that
data are in some way justifiable, like not only
against what you intend to do, but against what you're not
doing by by doing that instead. And then the second thing is
make sure that you pay very close attention that how the
environment is changing so that you don't get caught by the
change that makes what made sense no longer sensible.
And then the last thing is something I always advise, which
is to be humble. It is extremely rare.
When you know everything you need to know and have all the
information you need without widening that circle and
bringing in others that have some sort of expertise or some
sort of perspective that you don't have.
So inviting that expertise and that perspective is not a sign
of weakness, It's a sign of great strength.
With that, unfortunately, we are out of time.
I just want to say a huge thank you to Anthony Scriffiniano and
into Paul Bandari. Anthony, thank you.
It's. Wonderful that you're you've
been here again, and I hope you'll come back another time.
Absolutely. Thank you so much, Michael.
And Interpol, so honored that you joined us.
And again, I hope you as well will come back and be a guest on
CXO Talk again and on another date.
Delighted to do that, Michael. Thank you for having me.
And for those with unanswered questions, please, you know
Lincoln and we can continue the conversation.
Everybody, thank you for watching, especially those folks
who just ask such great questions.
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