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>> Hello from Las Vegas.
It's theCUBE live at AWS re:Invent 2021.
Lisa Martin and Dave Nicholson here.
We're on our fourth day, Dave.
We have two live sets of theCUBE.
There's a dueling set right across from us,
kind of like dueling pianos only a little bit louder.
We have had about 100 guests on the program
at AWS re:Invent this year.
We're pleased to welcome back one of our alumni.
Richard Potter joins us, the CEO of Peak.
Richard, welcome back to theCUBE.
>> Thanks, Lisa, great to be here.
>> We haven't seen you in a couple of years.
Talk to us about what's going on at Peak.
I know there's some news coming on this week.
>> Yeah, yeah, loads of things going on at Peak.
I mean, we've been growing really quick,
so since the last time you saw us,
which was, yeah, in London a few years ago,
we've grown to be essentially
the global leader in decision intelligence systems.
Us as an AI company, we specialize in
putting artificial intelligence
right into the heart of how companies run their businesses
and make their day to day decisions
which is why we call it decision intelligence.
We think it's the biggest things in software
and probably the biggest new category
of software we will see this decade.
So, it's super exciting to be in that position
and great to be back chatting to you guys on theCUBE.
>> Yeah, when were you guys founded?
>> We were founded in 2016
and yeah and as you can probably tell by my accent,
English company, headquartered in Manchester,
but we're global new.
We have operations in India.
We have a couple of development centers in India,
we have a growing customer base in Asia
and a growing customer base in the US as well.
So yeah, we're kind of international
but born out of northern English roots.
>> I like it.
Talk to me about, back in 2016,
what were some of the gaps in the market
that you saw from a...
Because you know, as here we are in almost 2022,
every company's a data company, they have to be,
but being able to extract intelligence timely, hard.
What gaps did you see back in 2016?
>> Well actually back then,
read on the market was really simple,
which was that companies that are going to harness data
to run themselves well, will win,
but that most companies were struggling
to make that change to be data driven.
So our itch was, as founders,
there's three of us who started the business,
was trying to explore that problem,
like what stops companies running on data?
There's loads of reasons, right?
Tech ones, skills ones.
Even just like business people using data
in their day to day decision making
rather than say their gut feel
which I think is also a data driven decision,
they just don't understand that necessarily.
So, we really honed in on that problem
and we grew quite quickly
to be the leading business
in the sort of applied data space in the UK.
A market leader in helping companies
perform better with data
and over time, that's taken us on this journey
to be the sort of global leader in decision intelligence
which is really cool,
but the itch we were scratching was that,
hey, you know, there's something in this,
we think companies that do this
and do it well are going to win,
but no one's doing it.
So, why is that?
And then we've built software
that effectively responds to that opportunity.
>> You mentioned harnessing data.
How do you balance the harnessing of data successfully
with being harnessed by data?
Because if you're talking about the concept of DI,
who's making the decision?
If the machine is making the decision, I better trust it.
Why should I trust it?
So, how do you strike that balance
to get people to trust the work
you're doing for them behind the scenes?
>> Yeah, I think it's really important
that humans trust the machines
that they're working alongside of.
I think that's the big change we're seeing, right?
So, this is a new industrial revolution,
the intelligence era that we're in.
But all previous industrial revolutions
have all amplified human potential.
They've amplified like physical potential,
whether it was machinery, steam power and so on,
or computers have amplified our cognitive capability,
but humans have always
controlled those machines if you think about it.
Now, in the intelligence era,
our machines can think with us,
they can think alongside us.
So, we have to learn how to, as people,
how to coexist with those machines
and let those machines amplify us
and essentially make us super human in what we do.
That's part of the challenge we face at Peak,
is how do we humanize that?
How do we make it as such that everyone trusts the machine?
We always have that.
Human in the loop is the way we think about it.
Decision intelligence empowers us to be awesome at our jobs,
make the great decisions all the time.
If we trust the machines so much
that we just want it to make the decision for us,
we can let it, but we're always in control,
and we're in control of how it thinks and what it does
and it's our job as a software company
to build software that lets you
understand why that recommendation
or that decision is being suggested to you.
So, I think the coexistence
of our machines alongside people in a new way,
that human to machine interface
is going to completely change
with artificial intelligence and decision intelligence
and us as people, we're going to have to relearn
how we work with our technology.
>> You just mentioned a couple of really good words
in terms of the people part, people process of technologies,
amplify and empower.
Those are two things that stuck out at me
is that's what you're giving people in any,
whether they're in operations or finance or marketing.
It's the amplification to do their jobs,
empowering them to do their jobs
with data that will help make them
more skilled and better able to
make decisions that benefit themselves,
the company, consumers, etc.
>> Richard: Exactly.
Yeah, because if you redact doing business to it's basics,
it is actually just making decisions, right?
Companies that make great decisions, they win,
and those decisions could be anything.
They could be product decisions,
they could be pricing decisions,
operational supply chain decisions,
but it's a sequence of decisions
that creates value for my company.
So, that's why I believe this technology is so empowering
because as people,
we're actually great at making those decisions.
What we're not great at
is making those decisions 24 by seven,
really, really quickly, very consistently.
So, you know, humans are awesome at forecasting.
They're awesome at choosing pricing
that would appeal to other people,
but alongside this technology,
we can have machines that do a lot of that thinking for us,
speed us up and help us make more quick,
great consistently awesome decisions.
And then that just makes us great at our jobs.
If you're a marketeer or in finance or in supply chain,
you become awesome.
I think that that empowerment
is key to the sort of humanization of AI in business.
And actually, that's what it means in practice.
It isn't AI coming for people's jobs or replacing jobs,
it's AI helping us all be great
and our companies grow faster
with wider profit margins when we do that
which creates more jobs for people which is really cool.
>> So, we talk about people
trusting machines to do things for them.
It's not necessarily a new concept,
we just sort of take some of those things for granted.
I trust my refrigerator at home
to measure the internal temperature
and make adjustments as necessary,
turn the compressor on, turn the compressor off.
I'm sorry, you're from England.
Refrigerator's this thing, it's a box.
We use it to refrigerate our beer to make it cold.
>> Richard: We do warm beer in England.
>> Yeah, so it's kind of good.
Got to love those cliches.
But so, can you give us an example
of a situation where a customer is trusting something
that it's gotten from DI, from Peak,
where if you as the CEO, heard that anecdotal story,
you would be absolutely delighted.
>> Richard: Well, I think there's
loads of great examples of that.
So, the reason we call
decision intelligence decision intelligence
is because it's applying AI
into the active decision making, right?
Artificial intelligence or machine learning
is making a prediction or a categorization
over a huge data set, right?
But that on it's own is kind of useless.
You need to take that prediction,
that forward looking view,
and then effectively infuse it with business logic,
constraints and like knowledge of how your company works
to give you a recommendation, right?
So, let's just say I'm a marketeer
and I'm trying to work out
who I should send a particular offer to
on black Friday over email,
or not even over email, over any channel.
If I was the CEO and I heard one of my team say,
what I've done is I've used
the decision intelligence platform
to tell me who are my customers
that are in market for X type of products
at Y kind of price,
and what channel do they like to be communicated to over?
I would think that's awesome.
And then that marketeer would typically infuse that message
with the sort of language and content
that would appeal to that customer,
but they're using the artificial intelligence
to be super targeted
and really like deliver the message
to that person in the way they want to consume it,
which creates a really enjoyable experience as a customer,
you don't feel spammed,
you don't feel like effectively used,
you feel like you're having
a direct one to one personal communication
with the brand or retailer that's talking to you,
which in itself creates loyalty
and like increases the life time value of that relationship
which is great for the retailer,
but I think using AI for those kind of decisions
is essentially a great example
of amplifying the human potential
of a marketing team, for example.
>> Lisa: Absolutely.
Because what we expect as consumers
regardless of what the product or service is
is that we want brands to know who we are, what we want.
If I just bought a tent on Amazon,
don't show me more tents,
show me other things that go with it.
I want you to know that.
So, we have this expectation
that brands, whatever industry they're in, no,
Richard bought this, he needs this next.
>> Richard: Exactly, exactly.
I think it starts to really jar.
Now you've got some retailers
and brands doing this really well
and you get really enjoyable
communications at the frequency you want
with the offers and the promotions
that are relevant to you.
When you just start to get
effectively stalked around the internet
for something you've already bought,
it becomes really jarring and frustrating
and then that actually creates a negative
brand effect for that particular brand.
So, it's super important
that these retailers, CPG companies, everyone really,
moves to this way of thinking
and tries to have a direct...
And that's the beauty of AI
and decision intelligence I think for retail,
if we get into retail specifically,
it allows us to treat individual customer individually,
because we can use the machine to make decisions
on a per customer basis
and then our marketing can be amplified by that,
whereas in the past, we bucketed customers into groups
and just treated them all the same,
which does create a rather impersonal experience.
So, yeah.
>> Right, which can be a negative
for a brand as you mentioned,
but gives them the ability to treat people individually
but at scale and in real time.
>> Richard: Exactly, yeah.
>> Which one of the things we learned in the pandemic
is that real time data access
is not a nice to have, it's an essential.
>> Richard: Exactly.
>> One of the themes too
that Dave and I have been talking about the last few days
is that we're hearing at re:Invent
is every company has to be a data company.
>> Yep.
>> Talk to me about, with that in mind,
are you talking to more chief data officers,
chief digital officers?
Where are your customer conversations
as we are in this explosion of data?
>> It's a great question that.
So, if every company has to be a data company
and a company that's powered by AI,
that means you have to be talking to everyone, really.
So, chief information officers, chief data officers,
CEOs, CFOs and every sort of head of business,
head of line of business, it's really important.
So, what we do at Peak
is as a decision intelligence platform,
Peak itself unifies everything you need
in one cloud platform into a single software product.
That gives you all the infrastructure
for your technical teams to process data,
for your data scientists to create the intelligence,
but then it gives you a place to work
for your business teams.
So, unifies your whole business around a platform,
and then that means our conversations
as the provider of that technology
are with technical teams
are with technical teams,
they're with business teams,
they're with business leaders,
because it has to permeate everything.
So, I think that's the future.
Companies will have to effectively run alongside,
they'll create their own intelligence basically
on a dedicated platform like Peak
and that intelligence will then
be distributed across the whole business
in the way we do it.
So, I think it's really cool and exciting, yeah.
>> Let's say hypothetically now,
this is something that would never happen,
but just hypothetically,
say an American goes to England
to take over coaching of British soccer, soccer or football.
>> Richard: Okay test.
>> Sounds crazy.
But how would I use Peak and DI and BI
to help improve my winning percentage
if I cared about winning?
Because it's possible that
I'm really only interested in
the personal development of my team as individuals,
but in athletics, is that something that is like impossible?
>> Yeah, for sure.
I mean, you're seeing an explosion of data science
and analytics and AI techniques being used in sport, right?
Peak, we're very much focused on
the commercial application of AI with our platform,
so we work with commercial businesses and so on,
but in that space, yeah, absolutely.
I mean, if you think about it,
what do you need to create that intelligence?
You need data.
You can see it on the back of every player's shirt,
they've got the little devices,
they're gathering data in training, in matches,
constantly monitor those data points, feed algorithms,
those algorithms can show us
if a player is fatigued, where they...
Or, they can even show us deep learning techniques
can help us see patterns of play
and understand like how should we better set our teams up,
how should we get players to interact for on a soccer field?
And yeah, you see Premier League clubs
use those sort of techniques all the time.
We don't do that at Peak,
but yeah, I think those sort of things
are readily available now
for those kind of clubs to do that kind of stuff.
>> I think Dave's angling to be a consultant on Ted Lasso.
I think that's what I'm hearing.
>> I think so.
>> Last question for you.
You guys are, from an AWS relationship perspective, Richard,
you guys were announced just yesterday,
you were named by AWS as an APM Partner of The Year
for 2021 for UK and congratulations.
>> Richard: Thank you.
>> Talk just a little bit about that.
>> It's super exciting for us.
It's a great recognition.
Obviously they give one of those awards out every year.
As a global company, it's nice to have that
sort of stamp of approval,
that AWS sees us as their independent
software vendor partner of the year.
It's a great recognition for us
because we come from a heritage
of starting Peak as a consulting company, actually,
just to do whatever it took
to help our customers be successful.
In doing that, we had an idea for a software platform.
We got some venture funding to do that
and we became a software company
a couple of years after we founded
and to get to this point now a few years later
where AWS are recognizing us
as their software vendor partner of the year is a huge--
>> Dave: It's fantastic.
>> It's fantastic, yeah.
It's a huge testament to our engineering teams
and the technical teams at Peak
that we've built something so impactful, you know?
>> Absolutely, that validation is really, really critical.
Last question in our last 30 seconds or so,
what are some of the things on the road map
that you're excited for Peak for 2022?
>> 2022 is going to be a huge year for us
because I think it's the year
that our platform goes out there
into the wild, into the mainstream.
So, we made a couple of big announcements
in the last few weeks.
We've launched some new products on the Peak platform.
So, there's three big platform products set now.
One very much geared around creating your AI ready dataset.
That's called Dock.
One that's very much geared around
creating your intelligence, which is Factory,
and then an area where our business,
like the business teams of our customers go to work
which is called Work, actually.
So, those three big feature sets
are going to be available from January,
and the platform is being totally
opened up as a self serve platform
for anyone, anywhere to build upon.
So, I think it's a huge moment for decision intelligence.
Gartner is saying decision intelligence
is the big tech trend of next year
and we feel, as the market leader,
we've got a platform that can help
everyone get on that trend, really.
So, I think we're really looking forward
to 2022 and what it brings
and we think that our platform
and our company is in a great shape
to help more and more businesses
take that leap into being
powered by decision intelligence, yeah.
>> It sounds exciting, Richard.
So, we'll have to follow up with you next year
and see what's going on.
We appreciate you joining us on theCUBE
talking about Peak, what you're doing,
your relationship with AWS
and how impactful decision intelligence
can be for everybody.
We appreciate it.
>> Awesome.
Thanks, Lisa.
>> For Dave Nicholson, I'm Lisa Martin.
You're watching theCUBE,
the global leader in live tech coverage.
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