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- [Rob Perogaro] Good afternoon.
So I have to say, when I heard about your study,
[and] I was offered this panel,
I thought, it's a study about applying AI and automation
to management and large organizations
and my first thought is,
"We're going to replace middle managers with robots, right?"
And apparently, it was misinformed.
That's not actually what your study [found].
Walk us through the broad findings,
how you came to them,
and what your lessons learned are from that.
- [François Candelon] So basically, what we can see,
and as you know, AI very often is seen
as a negative perspective compared to humans,
but what we see in this survey
that we did together with David
is really about the fact that it's AI plus humans,
and the fact that humans feel augmented,
and will have something, a very [virtuous] circle
where you deploy AI. Therefore,
the teams become more effective.
As they become more effective, they are more confident,
happier, and they trust AI more and therefore use
it in new use cases, and it starts once more.
- [Rob] And in our prep call,
we talked about two specific examples
of where organizations were able to leverage AI
to get insights that had not sort of bubbled up yet.
And, they were about use cases that I think would resonate
with the Web Summit demographic.
One was airplanes, and the other one was booze,
so let's go into those in succession.
- [David Kiron] All right, Rob, [did] you ever get on a
plane, you're ready to take off, everybody seems
like they're ready to take off,
and the plane doesn't take off?
- [Rob] That was how I spent five hours of the night
at Dullas Airport several years ago.
Good times.
- [David] Yeah, exactly.
So one of the reasons that airlines have these delays
is because they, like a passenger who's supposed
to be on the plane, doesn't actually make it on the plane
and their luggage is on the plane,
and then it needs to get taken off, and we talked to KLM,
and KLM used AI to solve this problem --
or address this problem.
They were able to predict, using AI,
which passengers were most likely
to not make it onto the plane, and so,
their baggage was tagged in a very specific way
so that it was easier for the baggage people
to come and get the baggage off the plane.
Now, if you've ever been in the cargo hold of a plane,
which I have not --
- [Rob] I actually have, not stuffed into it,
just a tour of an airplane.
- [David] Fantastic -- and that's good to hear --
but yeah, it's actually hard for the baggage handlers
to go in, crawl through the baggage,
and find this person's baggage.
So they can ... what KLM did was,
in addition to sort of projecting which bag
was going to be...
- [Rob] Offloaded.
- [David] Needs to get taken off the plane, they then,
use a very human additional solution to figure out what to
do; they tagged it with a VIP baggage sticker
so that it had the very same.
It was loaded last, it had this baggage sticker,
it was easy to find, and so what happened
as a result of that was the baggage handlers
were able to get -- retrieve -- the bag
much more quickly, the pilots were able
to take off more quickly, and the airline attendants
didn't need to pacify super frustrated passengers.
- [François] And I think that what is important here,
the fact that it helps different teams collaborate
and AI is a great tool
because AI doesn't care about the departments,
the services, whatever.
What AI does is it goes through it,
through the silos and silos the companies
and the corporations, and I think that it makes ground staff
and crews, in that case, work better together,
and this is a great opportunity for companies to improve.
- [David] Yeah, and I'd just add that, you know,
we talk to a lot of different companies
and their various uses of AI, but we also did a survey
of more than 2,000 executives
from companies around the world, and what we saw was that
if you had effective AI --
it means it was improving your efficiency
and your decision quality -- you were also much more likely
to be collaborative than any other company
that didn't have effective AI.
It's like, more than 75% of organizations
that had effective AI, were also were found
to be more collaborative as a result of using AI.
- [François] Yes, and as you said, because we define the
value is it's more collaborative,
there is more collective learning,
there is -- - [David] More morale.
- [François] more clarity in the roles and people learn
about what you do, and their role.
And as we said, people have a higher morale,
so I think that the combination of this
creates a real change in the organization.
- [Rob] Now, the other organization I looked at in the study
was Pernod Ricard, where I guess this was used
to improve the process of visiting stores.
Walk me through that.
- [David] Yeah, so this is, like, the second-biggest
alcohol company in the whole world, and, if you're a
salesperson for Pernod Ricard, you have a territory,
and you need to figure out where to go,
which stores to go to, and what you're going to sell.
AI, it turns out, turned out to be very effective
in helping these salespeople figure out
which stores to go to and what to sell
at each location, because they had a lot of information
tied to zip codes and other things that
helped the salespeople tailor what the mix of things
that they were recommending to a given store to sell.
- [François] Yeah, and what is interesting,
that Pernod Ricard, at the beginning,
believed that they will have a backlash,
but it was the other way around
because the salespeople embraced the technology
as they had the feeling that it was augmenting,
and not replacing, their knowledge.
- Augmenting, not automating. - So I think that this notion
of augmentation, which rates much more confidence, is a great opportunity to have a higher morale,
and you could say that the morale
is based on these higher confidence, on one hand,
and, as you were talking about automation,
to the fact that you remove low-value tasks
from the plates of the salespeople,
and this combination creates a real change
in terms of morale.
Let's say 80%, around 80% of people felt that, thanks to AI,
the morale in their company was much higher.
- [David] Yeah, and I'd just add to that,
when we're talking, we did a study
and the results of the study showed that
there are a lot of cultural benefits to AI
that tend to go unrecognized, but, as we were developing
the report, we [uncovered]
this happy story about AI.
Is it too happy a story?
Is it-
- [Rob] This is the one pro-AI panel you're gonna find
at Web Summit this year. (David laughs)
All the others are bad news-
- [François] We need to convince people, as well.
People who are against it.
- [David] Okay, but what we found was that there was,
there is a lot of mistrust in AI, in the organization,
and that is something, culturally, you need to overcome
in order to use AI at all in the enterprise.
But we found that, once you do have --
as we call it -- effective AI happening,
if you have AI that works, it actually has,
like, a bunch of salutatory effects
on the organizational culture at the team level,
and at the organizational level.
- [François] And your point is very important
because, when we try to understand
why people don't trust AI,
this is basically because they don't know AI.
We had in this survey, rolls about,
"Okay, why don't you believe in it?
"Is it because of you don't understand it?
"You don't have, let's say, you were not trained about it,
because of the data, because of the procedure, whatever?"
No, it was really because people, "We don't know it,
so we don't understand it."
And even in the companies where you have
companies that use AI, employees love it much more,
and I think this is important,
and in countries like Finland, where governments
have been able to really try to educate people on AI,
this is one of the main levers
for the competitiveness of a nation.
- [Rob] What have you learned about getting an organization
to buy into this change before you can point
to the positive results,
that you'd save this much money,
this much time, have this many more sales?
- [David] So very often what we see, and there is a question
about the maturity of a company, and very often,
what you need to do is to start with a couple of use cases
that will be seen as pilots, end to end,
but you need to move that end to end
through the company to make sure that it has impact.
And then, because people are looking at it,
they will start it on other use cases, as I mentioned.
But, I think that many companies today,
I've done, already a few use cases,
some stayed at pilots because they were not willing
or able to go through the silos.
Some others were doing it and were already good enough
to become effective, especially in the way
they were using AI plus humans in different roles.
This is an important topic, as well.
But then, the issue we face is that many companies
need now to bring AI at the core
of their operating model.
The question is not anymore,
"What should I do to improve my processes with AI?"
It's more, "How should I redesign my processes
to bring AI at the core of it?"
And it's difficult to understand the full potential of AI,
so, especially for, I would say for executives,
who are not trained with it.
- [David] So we talked to a lot of executives
who mentioned developing a culture of AI
in the organization, and I had the...
I don't know if pleasure is the right word ...
but, you know, I dug into the literature
on organizational culture --
and it is a deep, thorny, contradicting kind of literature,
but most people have a point of view
that organizational culture, just generally,
is about the behaviors in the organization,
the values that govern and drive those behaviors,
and the assumptions that underlie those values.
So it's a three-level perspective,
and that point of view was initially developed
by Ed Schein from MIT a long time ago.
If you think about a culture of AI,
using that kind of rubric of three levels, there's ...
This is just my, I mean, this isn't in the report,
but there's...
- [Rob] Bonus content! (chuckles)
- [David] It's about, at the assumption level,
it's about thinking about problem solving in a new way,
thinking about opportunities, cultivating them in a new way,
and it's like, "How can AI..."
And there's this belief, there's this assumption
that AI can help you learn, think about problems
in a new way, and you'll be more competitive
and that baseline assumptions about
that exists in a culture of AI.
And then, at the values level, well,
you need to have AI skills.
You need to have, you know, these AI deployments
need to be ethical, that you need to be interested
in augmenting people with AI.
And then, at the behavioral level,
it's, "How do you use AI to be effective
in lots of different ways?
"How do you use AI appropriately in the right use cases,
and how do you use AI properly with humans?"
- [Rob] So let's dig into a couple of those.
The ethics of AI, one thing we've learned too much about,
the last 10 years or so,
done right, it can, sort of, see through
and route around a lot of human biases, but done right,
it just hard codes them and perpetuates the problem
that it could have solved.
Did your study give any insights on how organizations
can choose door number one, and not door number two?
- [François] So it can happen very often.
But, coming back to this notion of AI, ethics in AI,
AI is just a technology, and this is where I agree with you,
David, is the fact that it's about the people
who develop and use it.
AI is neither responsible nor irresponsible.
It's a technology, and this is why we need,
absolutely, to make sure...
But, it is a great opportunity to have AI.
It is a great opportunity to come back to,
"Okay, what are the fundamentals?
"What are the values that we have?
"What is our ethics?
"What do we defend?
"What do we stand for?"
And I think that, of course it can happen,
what you're, replying to answering your question,
it can happen, from time to time, that,
because it's not that easy.
It's neither black nor white.
So there is a gray area, and you never know whether you,
when you take a stance, whether it will be
on one side or the other.
It's very difficult and very subtle.
- [David] Yeah, if you think about AI ethics
and problem solving and problem creation,
there's a lot of talk about, "AI is going to be creating
new problems that we're going to have to address."
The other is, you know, "AI is making worse certain problems
that already exist," but there's another side to it,
which is AI is actually helping us address,
like, a wide range of problems we already have.
Like, just take diversity in the workforce.
There are all kinds of AI solutions now that exist
for AI departments in recruiting a more diverse workforce.
There's more AI that helps you figure out
whether or not you have a diverse workforce
and what you can do about it.
There's AI that helps you identify
where you might go to develop a more diverse work...
That's where, that's just like one sliver
of a sliver of a problem that AI is helping with,
but that's an old problem that we're just,
that's becoming even more important,
and AI is helping solve it.
- [Rob] It forces me to wonder if an AI would have picked me
to moderate this panel, given the diversity issue
here right now. - [François] Yes, absolutely.
It needs to be diverse. - [Rob] Yes.
And, as a whole- - Europeans and Americans.
No, but I think that educating the C-suite
is absolutely critical.
They don't understand what it's about.
They don't understand the full potential of AI,
so, very often, what I say, they should have a ...
an AI driving license.
So, you have a driving license,
which means you don't understand what happens in the engine
but you know how to drive your car,
and it's important for them
so they need to understand that, with AI,
they need to revisit, because AI is basically what?
Three main things.
It's the fact that it's the ability to process big data
in real time, to go to, to achieve hyper regularity,
and to get to be at scale, at zero marginal cost,
and, I think that, if the C-suite
is able to understand that,
and to redesign its processes and its way
in its company, then this is a source of competitiveness
and we all know that grading and adopting AI,
is at the core of the competitiveness of a nation.
- [Rob] The other thing --
we talked a little bit about this backstage --
was where do humans belong in the loop?
And you were saying, there're some things
that you don't necessarily need the human
to give a yes or no vote.
Sometimes, you want that human judgment at the start,
sometimes at the finish.
- [François] So, we identified, last year, in our report
a series of roles between humans and AI,
from the fact that AI can support, let's say,
can help you identify good, new ideas.
For instance, Ben and Jerry's identified, through AI,
that they were able to, that many people
were eating breakfast, in songs, that many people
were eating ice cream at breakfast,
and it gave them an idea to launch a new ice cream.
So, down to, let's say, the automation,
where you have some mining, for instance,
look at the Pilbara Mine from Rio Tinto in Australia,
fully autonomized.
I will not say automatize.
I would say, autonomize.
In the meantime, you have different roles
where one could recommend the other, decide in some,
and I think that it doesn't mean that any role
is better than the other.
What makes a difference is to apply the right role,
respective roles, between human and AI,
depending on the use case.
- [Rob] Okay, I think that is a good point on which to end.
If there's one takeaway I think we can all agree on,
it's okay to have ice cream for breakfast.
All right.
Thank you both.
Thank you all. - Thank you.
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