As we talk about artificial intelligence,
machine learning, and terms like cognitive
computing, do we know what they really mean
and do we understand the impact on the enterprise?
That's our topic today on CXOTalk.
Fred Laluyaux is the CEO and the founder of
Aera Technology.
Aera Technology is a cognitive platform that
enables what we call the self-driving enterprise.
It's a platform for what we call cognitive
automation, technology that understands how
your business works, answers a lot of the
questions that you have.
It makes real-time recommendations on how
to improve the operations, it predicts business
outcomes, and it can take action autonomously.
We built this platform over the last few years
and we're rolling it out right now.
I think, Fred, we need to try to unpack these
terms, terms like cognitive computing, terms
like cognitive automation.
How do we begin understanding what this is?
It sits really on a vision, which is, we're
shifting from the era of people doing the
work, the work being planning, optimizing,
and running operations, business operations,
in finance, in supply chain, in sales, in
all the different functions of a company.
They're running this operation.
They're doing their job supported by data,
by tools, by collaboration platforms.
We're getting to the point where now we're
shifting from people doing the work supported
by software to software, computers doing the
work controlled by people.
That's what we mean by cognitive automation.
It's the automation and the augmentation of
how decisions are being made and executed
in an enterprise.
We're using intelligence to actually automate
and augment the decision-making process.
Fred, is it a matter of, first, you aggregate
data?
Let me play devil's advocate or be a little
bit facetious.
How is this different from analytics and reporting?
There are multiple levels of differences.
The first thing is, those systems are real-time
and always on.
You think about analytics and reporting.
You'll pull a report and you, as an analyst
or as a manager, you'll analyze the data and
start thinking about what decisions, what
actions you need to take based on what you're
analyzing.
That's the whole point about getting access
to those reports.
Here, the processing of collecting the data,
aggregating the data, making sense of the
data and executing a set of logical steps,
projections, predictions, optimizations are
actually done dynamically by the computer.
Really, think about it as a giant brain that
sits on top of your transactional systems
and does the work that analysts would actually
do, that managers would actually do.
It goes all the way to making the decision
and taking the action back into the transactional
system.
One is a static report that you look at to
make the decision.
The other one is a dynamic decision-making
system that will analyze and take the action.
When you talk about being self-driving, what
is that?
What does that mean?
Let me explain to you how it works.
The system, end-to-end, the platform starts
by crawling the transactional systems.
We take the Google analogy, right?
When you're crawling the Internet to create
a hot replica of every single away-page into
a single instance of the cloud so that you
can then index, rank, and make that data accessible
by a search algorithm.
That's the way Google works.
That's their breakthrough.
A very smart idea.
What we did is we applied the same kind of
concept to enterprise data, internal and external.
We deploy our crawlers and we create a replica
of the transactional data into a single instance
of the cloud.
Now, we do this across multiple types of ERPs,
planning, and other types of transactional
tools.
Once the data sits in our cloud, we harmonize
it.
We augment it.
We derive from all these billions of rows
of transactions, the business metrics that
you need to actually understand your business.
Think about a giant data layer.
We call it a cognitive data layer that sits
on top of all your transactional systems and
brings you the ability to find, in a single
instance of the cloud, all the information
that you need to make a decision, to think
through a decision-making process.
Now, once the data is in that foundation,
that cognitive data layer, then you can apply,
as you said, data modeling, artificial intelligence,
statistical forecasting, optimization, a series
of tools that allow you to actually run, dynamically,
a process.
That process says, "Hey, I found open orders
without matching inventory.
What do I do?"
I need to go and look for excess inventory
somewhere else.
Oh, I can't find it.
Now I need to look for production capacity
somewhere.
If I do, do I have the material?
Do you see how you can unfold that decision-making
process?
The reason why it works in real-time is because
100% of the information that you need to make
that decision sits in a normalized instance
of the cloud.
The crawlers work autonomously.
They're constantly updating the cognitive
data layer and the intelligence on top runs
24/7.
That's why we talk about self-driving is that
the system will, autonomously, based on some
criteria, take an action back into the ERPs.
Let's say I'm going to upgrade my forecast
for this product by 0.2% for this outlet for
this period or, if the decision requires human
supervision, it will generate a message, what
we call a recommendation that will sit in
your inbox.
That in-box will say, "Hey, Michael.
I recommend that you increase your forecast
by 3% because I've done all this analysis,
I've done all this work, and I'd like to get
your supervision."
At which point you can say, "Hey, Aera.
Yeah, let's actually take that action.
Thank you."
Or you say, "No, I don't want to follow Aera's
recommendation because you missed something,"
in which case Aera will ask you, "What did
I miss?"
As a result, the system learns from your experience
and your expertise and gets better over time.
The concept of self-driving is really to have
a system that autonomously does the work from
collecting, aggregating, augmenting the data
all the way to processing the multiple steps
that are required to make a decision.
Fully self-driving is the ability of the system
to take action right back into the transactional
systems.
Could we say that this is similar in a sense
to Amazon giving you product recommendations
only this is happening proactively and it's
giving you recommendations about the next
decision that you need to take or the next
action that needs to be performed in a process?
Somehow, you're absolutely right.
It actually will give you.
Where is cognitive automation in action today?
What are the use cases that we can apply and
deploy it for?
Forecasting, demand forecasting, collecting
all the data and helping to predict what is
the forecast level for a product, that's one.
You have inventory optimization.
You'd do promotion planning.
All those different, complex use cases are
there.
One of them, you talk about Amazon, is around
order management; helping a large, complex
organization to predict what is the available
to promise date for our complex order.
I think you had one of our clients a few weeks
ago on the show.
We deployed a technology pretty much for that
purpose initially.
What we do is we're able to give their customers,
their clients a very accurate delivery date
for their very complex order.
Now, the challenge here is, the data that
is required to actually process this compute
sits in 47 different ERPs.
First, you have to collect the data, harmonize
it, index it, augment it.
Then you deploy the algorithms.
Now we're able to tell the clients, in real-time,
"Your order will be delivered that day."
It sounds like an easy thing to do because
you take the Amazon example where we use that
every day.
When you put that at a scale of a very large
enterprise with a lot of data complexity and
multiple algorithms that have to be deployed,
it's a very hard problem to crack.
It sounds like the key then is, after you're
aggregating, collecting all of that data--I
don't want to minimize the difficulty and
the challenge of doing that--from the businessperson's
point of view, we've got the data and now
the system is making recommendations.
Those recommendations have to be A) accurate
and B)
things that I would not have thought of myself
because, if I can just do it in an easy way,
I don't need that system.
There are really two angles.
There are two things that we're automating.
The first thing is the expertise.
The expertise is, you know how to think through
a given problem.
That can be modeled using our modeling environment,
using data science.
The other thing that we need to capture is
your experience.
Algorithms are not going to get everything
in the first place.
The question that we have to crack is, how
do I build that interface between the Aera
brain, so to speak, and the users?
The way we've done this is by saying, "Hey,
you know what?
We're going to try false positives sometimes.
We're going to send you a recommendation to
deploy a 52-week calendar, a promotion plan,
and you'll say, "You know what?
For the third week of July, this promotion
makes no sense because people are on vacation."
Oh, so it makes no sense to promote this product
in that region.
Well, maybe the algorithm missed that in the
first place, but we're giving you the opportunity
to make some correction and bring the information
back to the algorithm that will then run again
and, over time, get better and smarter.
We have to digitize, basically, the expertise
that you have to think through a problem but,
also, the experience.
That results in two things, Michael.
It results in a level of automation.
I can actually do, 24/7, a lot of the work
that you do not have the time to do but, also,
augmentation because, over time, the recommendations
that are delivered by the system are more
accurate than what humans are able to do to
deliver.
There is really a concept of automation and
a concept of augmentation.
It's interesting to see how the system evolves
over time.
You get immediately the effect of having clean
data, single source, having deployed the most
advanced algorithms and data modeling capabilities
but you see how the accuracy of the recommendation
increases over time because we don't have
an issue with people leaving their job and
taking their knowledge with us.
Here, we build a permanent memory of all the
decisions that are made in an enterprise on
a given topic by all the different actors
that are participating through that process.
You would start aggregating those data points,
you can make sense of it, and you can improve
the quality.
I'll take a quick analogy, if you allow me,
on the self-driving car.
Self-driving cars have been programmed to
drive a certain way on the road but, as you
know, I think way more than 20 million miles,
maybe around that of driving a fleet on the
cars on the road to get those experience points.
We're doing the same thing with not a self-driving
car but with a self-driving enterprise.
We'll, over time, deliver more accurate forecasts,
more accurate recommendations on supply and
demand balancing, more accurate recommendations
for promotion planning, ATP, and so on and
so forth.
This is a live system that keeps learning
from the way the users are telling us.
You've got the data that you've collected
and how do you interpret that data to come
out with a model, so to speak, of the experience
of the people?
You've got data points, but you've got to
create almost a three-dimensional abstraction
of the people and their minds.
It's actually a little more simple than that,
Michael.
We keep going back to, you get the data.
Let me just pause one second here because
that's really the biggest problem that we
had to fix.
If you want to digitize any kind of decision-making
process, you need to have 100%, not 99%, 100%
of the information/data that you need available,
harmonized, with an indexed, understandable
by the machine's data model.
The first problem is, how do I go from having
30, 40, 50 different ERPs?
Sometimes, just one instance of an ERP doesn't
change.
We have to bring all these billions of transactions
into the cloud and process it into that cognitive
data layer.
Once the data is understood in that model,
then I need to deploy the logical steps.
Aera keeps track of where it is in the logical
steps.
If I go to you and I say, "I recommend that
you shift inventory from this place to that
place," so that you change the way your shipments
are organized from this distribution center
to another to see, I know exactly the context
in which I'm asking you as Aera, that decision.
I understand your business to risk your service
levels.
I understand the impact of the recommendation
that I'm making, and I understand, when you
grab the decision, if you decide yes, if you
decide no, if you override some of the number,
all of that is being captured.
Effectively, I'm creating the second level
of data, which is this decision data.
I understand what Michael decided to do at
this point in time.
Then, of course, it's another data set that
I can superpose to my financial and operational
data set and, therefore, start deriving some
very interesting insight and learning how
to either retrain you as a user and say, "You
know what?
You don't make an optimal decision here,"
or retrain the algorithm saying, "You know
what?
Michael is always right when he says no to
that decision.
Maybe I need to change the way the decision
is made digitally in the system."
As with other applications of machine learning,
it sounds like the gathering of the data is
the crucial piece and the algorithms are easier
in comparison.
I could not agree with you more.
This is really the fundamental problem is
collecting the data.
Back to my point of being able to pull the
data from an ERP, data lake, or whatever source
it is.
A lot of companies have built a data lake,
data grids, data oceans.
I'm hearing all sorts of words these days,
but we can go straight into the ERP, whatever
they are.
The first thing that we have to crack, really,
is how do I pull that data without materially
impacting the performance of the ERP because,
of course, your clients would not be very
happy if it takes the ERP down for half an
hour to pull the data.
We had to crack this.
There is a lot of work that's happening there.
Then there is a second stage of logic, which
is, how do I take this gibberish, I would
say, transactional data and transform it into
a clean data model?
That's been years of work, of brute force,
of mapping everything.
There is limited intelligence in that process.
It's a lot of human work that enables us to
build this at scale.
Once it's done, it's done forever.
That's the beauty of the model.
With one of our clients, we're running 2,800
crawls a day.
We're bringing in 1.1 billion rows of data
into the system every day and it runs like
a breeze.
It just delivers those KPIs, those augmented
KPIs that now feed the algorithm.
Now, the algorithm part is not trivial.
Algorithms are only as good as the way the
data has been prepared.
The strength of the system is because the
data is so clean and, with time series and
real-time of date, we are actually able to
prepare it very well so that the algorithms
deliver a good prediction in that case or
good optimization.
The second part is, how do you operationalize
the result of a prediction or a forecast?
How do I, in real-time, take that number that
the algorithm has delivered and make something
with it?
You don't want it to go into a spreadsheet,
then to a PowerPoint in front of users in
a committee.
We have found a way to say, "Hey, data comes
in.
The algorithm runs.
A decision is made.
It goes straight in front of you, Michael,
or automatically gets executed."
That's the self-driving concept.
It goes really fast, so the data preparation
is one thing but the execution, the operationalization
of the output is really critical as well.
We have a question from Twitter and Zachary
Jeans asks, "Are you able to use Aera, the
technology of Aera, in the running of your
business?
If so, do you have any examples of that?"
We call that project Aera on Aera.
It's going to make one of our guys super happy
that the question was asked.
We're literally doing it right now, to be
candid.
We've been working really hard for the last
three years to support some of the largest
companies in the world.
The technology that we've built really has
been designed for massive scale to be deployed.
I think you had J&J, Merck, and Reckitt Benckiser
on your show.
They're all using our technologies.
We have a question from Twitter from Sal Rasa
who says, "What about the cultural shifts
that are necessary for an organization to
leverage this type of cognitive automation
technology?
We're at the beginning of the journey.
We're only a year in with some customers being
truly live with this system, with the system
having somehow either taken over an entire
process or really being interacting with the
users.
My perspective is not long enough to draw
some firm conclusions.
That's the excitement about what we're doing
today is we're experimenting.
We're working with our clients in that experimentation.
What I can tell you and what surprised me
is that the aspect of automation came stronger
than I expected.
In other words, what we realized, if you think
of the company that has thousands of planners
that are basically operating their business,
the supply chain, manufacturing, finance,
coordinating everything, they're completely
overwhelmed.
The idea of giving them more data, more tools,
more computation capabilities, more collaboration,
faster everything, it's reached a point of
no return and people are stressed and tired
of being asked to do more when they see the
competition with digital natives who are actually
running a lot faster than them.
They really welcome the technology that we
provide because it helps them get through
a lot of the work that they can't otherwise
get through during a given day.
When the system is running 24/7 and doing
80% or 90% of the work, and you get to the
office in the morning, you can see how this
digital assistant, basically, has performed
work for you, real work, and taking actions.
It's a real relief, so now you can focus on
what you're really good at and what you're
uniquely positioned to do, designing the network,
designing some plans as opposed to writing
their execution.
There's been a real strong interest in that
automation part as well as the augmentation
because running some of these complex decisions
is very repetitive and very difficult.
The question that you ask, if I can talk about
one more thing, is super interesting in one
way.
We have a client who is running their entire
forecast process in what they call touchless
forecasting.
In other words, the entire processing is done
with Aera.
There is no human intervention.
The numbers that are called, the forecast
numbers that are called, are 100% called by
era.
Now, from the cultural perspective, who owns
the number?
Who owns Aera?
Who owns the number?
The business now is going to execute against
a plan that's been designed by a computer.
The initial reaction was like, "Well, that's
not my number."
What they discovered is that the accuracy
of the forecast, after you run it for a few
months, you realize that the accuracy is exceptional.
Then, suddenly, everybody aligns toward it.
Yeah, all these changes are happening.
Globally, right now, I think the system is
pretty much welcome by the users because it
really helps them.
From the point of view of the end-user, what
does the system look like?
What's the user interface?
There are four things that you have to crack
if you really want to build a cognitive automation
platform.
• The first thing is the data, and we've
talked about it.
• The second thing is the science.
How do I digitize the decision-making process?
• The third is process.
How do I embed this digital brain inside my
organization and work with the users?
• The fourth pillar that we work so hard
on is change.
How do I build a user interface and an ability
for the system to interact with the users
in real-time in such a hopefully pleasing
and easy way?
We built a series of tools.
I'm going to use the word "cognitive" a lot,
so I'm sorry about that in advance.
It's called a cognitive workbench.
What it does, basically, think about it as
your email, your Gmail.
We have different skills.
Aera has different skill sets that are working
for you 24/7.
It delivers those very clean recommendations
just like a message in your inbox saying,
"Michael, I recommend you change this number.
Michael, I recommend you change that promotion."
You right-click on it and say, "Tell me why,"
or you say, "Do it," or you say, "Don't do
it."
We have to really build that new tool that
allows you to interact with the system.
That interaction, actually, is real-time because
the recommendation that Aera will make at
any point in time might change.
I come to you at 2 o'clock on Tuesday saying,
"I recommend you change your shipment structure
for this product for this customer," but maybe
at 4 o'clock the business context has changed.
Aera has captured some external signals and
internal signals, and that recommendation
either changed or is now made obsolete before
you even touched it.
Meaning, we have to be able to get to you
24/7 if you want to, so we built this very
cool app where Aera can speak to you.
Literally, it's like Alexa or Siri.
You get in your car in the morning and you
say, "Aera, what are the open actions for
me or for my team?"
Aera will tell you, "You've got 24 open actions
that are open recommendations for an impact
of $47,000.
This is what they are.
Do you want to do it?"
You can literally use the voice to interact
with the system just like you would do with
Siri or Alexa to turn off the lights in your
house or put on some music.
The same kind of interaction has built.
It's a hit.
People really enjoy that.
I suppose it's a hit because, if it's giving
accurate results and, from the user perspective,
the way to access those results, it's pretty
simple.
There's no way to lie about this.
Aera will make a recommendation to do something
and it will calculate and will tell you exactly
this is the financial, operational, service
level risk.
This is the impact of that recommendation
and this is the timeframe.
You will check over time, was it right or
was it wrong?
The system, you build that trust because there
is no subjective aspect about it.
It's fully objective.
Was the recommendation right?
Did the recommendation work?
Talk about a change in adoption.
The best email I get is, every Friday, this
customer says, "This week, we had 147 recommendations
for this specific," whatever, "department,
unit from Aera for a total value of $547,000
out of which 97 were accepted.
The rest were objected.
This is why."
You can literally measure the impact of that
system in real-time, every week, every day.
Again, that's a very different way of thinking
about how you're running a business but it's
absolutely objective.
This concept of trust, please elaborate on
that.
That seems like a crucial point to me.
If I come to you and I say I recommend you
do something that impacts the way you're performing
at work, you'll challenge me and say, "Hey,
hold on, Fred.
Can you show me where the data is coming from?
Can you explain to me what is the logic that
you applied?
Did you check with so-and-so that they were
okay with that?
Did you measure the impact of that decision
on the rest of the impacted ecosystem?"
You're going to challenge me.
Then, after a while, when I come to you, if
I'm always giving you the right answers, then
you'll say, "Fred, yeah, I got it.
I trust you.
You can go ahead and run this for me."
The way the system runs initially is, you
can define the threshold.
At first, there are a lot of recommendations
that the users have to get through and say,
"Hey, I agree with Aera on this.
I don't agree with Aera on that.
You might run the system in parallel until
you realize that, you know what?
The accuracy of the recommendations delivered
by Aera is better than what we've been able
to achieve manually, which makes perfect sense
because the system works with a lot more data
and can manage a lot more complexity.
It's your way of thinking that's been digitized,
so you shouldn't be surprised there, and it
works 24/7.
When you see the numbers, you start trusting
the numbers.
Every time, every new client, we're going
through that process of, "Whoa.
What is that system telling me?"
Then you build that trust.
Without the trust, the automation is very
limited.
You need trust to actually deliver automation.
With automation, you actually build what I
call augmentation.
You interact more with the system.
The system gets smarter over time and it's
a virtuous loop, if you want.
We, as a company, can now monitor the performance
of every skill that we deploy with our clients
in real-time.
We actually have this concept of AAR, Aera
Accepted Recommendation and Aera Automated
Recommendation.
We monitor this in real-time.
It's not like a software that you just say,
"Hey, Michael.
I've installed it and call this number when
you have a problem."
We're actually in this continuous engagement
with our clients saying, "Hey, the accuracy
went down this week for this specific region
and for this specific forecasting process.
Let's look into it together."
Of course, clients look into it and we're
here to support.
You look at this as real-time engagement and
the trust builds over time.
Actually, if I say one more thing on this,
I was surprised because I was expecting trust
to take more time to be picking up.
I think I underestimated the amount of pain
that is going on right now in large enterprises
when they're trying to simply keep up.
We got to a point where we're expecting folks
in planners and others to actually become
as good as computers and that's not the right
thing to do.
We've increased the cadence, so to speak,
to a point that's not sustainable.
We need that digital relief.
Let's pick up on this topic of augmentation.
We have a question or comment from Zachary
Jeans again.
It's an interesting one.
He says, "With the Terminator movie fresh
in our minds, do Aera customers or prospective
clients have concerns about automation and
the whole concept of the robots taking over
our jobs?"
Of course, and we're now touching on something
a little broader, but it's unavoidable.
We have a client who has a very colorful way.
This is a show in the morning.
I'm not going to use that word.
Let's say he calls it bad jobs.
He says, "Look.
There is a lot of bad stuff that people have
to do that can be automated.
All that is to reposition people's attention,
work, and effort into the value-added stuff
that computers don't do very well."
Asking someone to repeat the same kind of
processing over and over, year after year,
is not interesting.
There is a level of automation, but the automation
is in our life everywhere.
What we're seeing right now happening is what
I call the center of the pyramid.
You've got your factories and the shop floor,
and then you have your CEO up there.
In the middle, there are a lot of repetitive
operations.
If you think of what happened on the shop
floor, we went from people doing the work
supported by big machines to machines doing
the work controlled by people.
We're just bringing that concept up.
I think it's creating a lot more opportunities
for people to work on interesting stuff.
Yes, of course, there is a level of automation.
As I said before, the reaction that we see
from those operators is like, "Thank God you're
helping me here because I don't have to spend
six hours a day pushing data from one tool
to an Excel spreadsheet to this to that or
running after people to get an approval and
coming home feeling that I only covered 20%
of what I was supposed to cover."
This is a helping tool.
This is a tool that takes away a lot of that
repetitive work but, also, delivers augmentation.
It's not just about doing what you were doing,
but it's doing it in a way that's more efficient
where computers can actually "beat the human"
and we should welcome that.
It's removing a lot of repetitive labor, essentially.
Yeah, absolutely and, also, enabling things
that would not otherwise be possible.
You were asking the question of augmentation.
Let me give you a very simple example.
If you think about promotion planning, you
create promotion plans so that, when you go
to a store, you get the buy one get one free
or you see the product in front of the shelf.
That has to be planned months in advance.
That budget that's allocated to a promotion
plan is the second-largest spend for consumer
packaged goods companies.
Now, think about the work that really has
to be done to adjust to the digital natives,
the marketplaces that are actually buying
products, doing promotions on the fly, and
shipping that to your home in real-time.
You cannot ask the account managers who are
responsible to build these promotions to constantly
monitor every single feed and adjust.
There is a lag time, actually, with your supply
chain.
Here, you have a system that literally, every
day, can read through billions of point of
sale data, merge it with Nielsen Elasticity
data, look I real-time at the levels of your
supply chain, predict what those levers are
going to be, and optimize, basically, that
supply and demand.
It makes no sense to make a promotion if you
can't supply and this is very complex, so
you want people to actually tell the system,
"This is the way I want you to think about
it, but then please run this 24/7."
Our brains are not meant to analyze data in
real-time across multiple time horizons and
we're not computers.
We need those computers to do the work for
us.
We have another question from Twitter, which
I think relates to this, which is, "Can you
kind of summarize the relevance of this kind
of technology to businesspeople?"
To put it another way, a business leader;
why should a business leader care about this?
Think about the disruption that Amazon has
brought to the world of retail, consumer packaged
goods, and so on and so forth.
Every leader that I meet that can be disrupted
by their technology and by their organization
is reaching out to us and saying, "The foundations,
the fundamental pillars of our organization
are being threatened by this world that's
moving very, very fast."
These digital disruptors think about everything
as a piece of technology, as a software, and
we're still structured in this big old pyramid
on top of 47 different ERPs.
We're trying to ask our people to run faster
and faster and make more accurate decisions.
We're trying to bring the decision-making
process closer to the point of impact and
closer to real-time.
They've reached a peak.
They know that the relative and the absolute
performance of a lot of their functions are
degrading rapidly as a result of rotations
in the workforce and a lot of different factors.
They know that, as I said, relative and absolute
performance is degrading and that, if they
don't start building that digital layer that
allows them to catch, really, and anticipate
and react to the digital disruption that's
pushed on them by the digital natives, they're
going to be in trouble.
There is a high level of relevancy.
If I candidly tell you what surprised me when
we launched Aera was how relevant the topic
was with C-level executives in the largest
companies in the world.
I was very proud.
It was two and a half years ago we launched
a concept of the self-driving enterprise,
the cognitive operating system.
I thought, "Wow, we're up there."
The execs that we talked to didn't say, "Wow.
Congratulations, guys."
They said, "Where have you been?
We've been waiting for this for a long time.
You, as an industry, keep telling us that
you're going to make our people better?
That's not the point, guys.
We don't want that.
we want to free our people from doing a lot
of the bad work that they shouldn't be doing
and focus on engaging with the clients.
Engaging with the community and doing all
this kind of intelligence stuff."
The reaction is there.
The appetite for that kind of technology is
clearly there.
The relevancy is higher than I've ever experienced
in my career.
Fred, as we finish up, what advice do you
have for business leaders who are listening
to this and saying, "Yeah, want to do this"?
How should companies prepare for adopting
these kinds of technologies and the changes
that it may bring?
My advice is always the same; "Jump in."
I'll loop back to something we talked about,
Michael, a little bit earlier, which is starting
to create that digital memory of how decisions
are made and executed in your enterprise is
the key to having the algorithms get smarter
over time.
You have to start building that data set.
The early adopters that have been doing this
for 12, 18 months already see an impact on
the quality, on the accuracy of the algorithm.
There's really a virtual circle when you get
going.
My advice is, take one process.
Take one function.
Logistics, supply chain, take whatever you
want, but get started.
Start learning.
Start operating with that support in mind.
Think about Aera or that cognitive automation
as a supporting platform.
If you wait, well, that data collection process
will actually be delayed and it will take
much longer.
Now, if you have that system running 24/7,
you get more accurate.
You run a lot faster.
You increase your agility.
You become a lot more competitive, I mean,
significantly more competitive.
You can adjust your pricing level, your supply
level, and so on and so forth.
Your competitors who are still analog are
not able to do that at the same speed.
My advice is, speak one topic, start deploying
the technology, and learn from it.
As far as preparation, there's not much because
we're built in a way that allows us to plug
into any landscape.
You can have 40, 50 different ERPs that are
not talking to each other.
We take care of that.
For us, the vision has always been to enable
non-digital native companies to actually operate
as fast and as efficiently as digital native
companies that were born in the last 20 years.
For that, without asking them to rethink what
I call their bedrock of ERPs, their fundamental
transactional landscape because, if we ask
them to touch that, they're not going to make
it.
It's just too big of a transformation.
We had to actually enable our technology to
plug on their diverse landscape as opposed
to asking them to come to our technology.
Long story short, I would say that my advice
is, start now.
I can see an increase interest.
We're doing pilots in many of the largest
companies in the world and it's really critical
to get that process started early so that
you can build that intelligence relative to
how decisions are being made and executed
in the company early.
Are there things that a company has to do
regarding the data collection?
Do they have to change their operations in
any way to start gathering the data?
No.
No, no.
That's exactly a critical point, which is,
our technology will plug on top of their ERP,
whatever they are, and understand the mapping
and do all this stuff.
You don't have to.
There is a bit of work and adjustments.
It's never as easy as it sounds but, in a
matter of weeks, you actually have that cognitive
data layer built on top of your ERPs or, as
I said before, your data ocean, links, swamp,
whatever you want to call it.
No, there is not a lot of preparation.
The preparation is in the validation of the
metrics that are calculated by Aera and it
really is on the cognitive automation and
the cognitive augmentation.
When you deploy a skill, you want to make
sure that it's adjusted to the way you operate.
There is work that needs to be done there.
Ultimately, then, it's giving feedback into
the system about the results that have been
achieved so that the system can correct itself
for the future.
Correct, yeah, and it's the system that corrects
itself, but it's also sometimes the user that
can correct themselves when you actually see
this is how you make these kinds of decisions
in that type of context for this business
value over time.
You may decide to change the way you actually
think as well.
It's a system adjusting and sometimes it's
the user readjusting to this new light that
we shed on how decisions are made in a company.
Okay, well, unfortunately, we are out of time.
It's been a very fast 45 minutes.
Fred, thank you very much for taking your
time to be with us today.
It's been a real pleasure.
Thanks for having me, Michael.
Thank you.
We've been speaking with Fred Laluyaux, who
is the founder and CEO of Aera Technology.
Before you go, please subscribe on YouTube
and hit the subscribe button at the top of
our website and we'll send you great information.
Thank you so much, everybody, and I hope you
have a great day.
Come back next time and we'll see you again
soon.
Bye-bye.
