July 11, 2024 • 32 mins
The retail landscape is riddled with challenges – staffing shortages, supply chain disruptions, and inflation. But there's a solution with a powerful ROI: AI in retail.
Imagine self-checkout with flawless image recognition, streamlined checkout, and reduced labor costs. AI can also free employees up so they can provide more meaningful customer interactions and boost customer satisfaction.
This podcast dives deep into how AI transforms retail operations and enhances the in-store experience. Discover how AI in retail improves efficiency, cuts costs, and boosts customer loyalty.
Join us as we explore these ideas with:
Matt Redwood, Vice President of Retail Technology, Diebold Nixdorf
Christina Cardoza, Editorial Director, insight.tech
Podcast Topics
Matt answers our questions about:
- Different challenges retailers face today
- Real AI benefits to in-store experiences
- Where retailers can start implementing AI
- The human element in AI transformations
- Real-world customer use cases
- Technology partnerships making AI in retail possible
Related Content
To learn more about AI in retail, read New Retail POS Solutions Transform the Checkout Journey. For the latest innovations from Diebold Nixdorf, follow them on Twitter/X at @DieboldNixdorf and on LinkedIn.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
(upbeat music)
- Hello and welcome to"insight.tech Talk,"
formerly known as "IoT Chat,"
but with the same highquality conversations,
around IoT, technology trends
(00:21):
and the latest innovationsyou've come to know.
I'm your host, Christina Cardoza,
Editorial Director of insight.tech,
and today I'm joined by Matt Redwood,
Vice President of RetailTechnology at Diebold Nixdorf.
Hey Matt, thanks for joining us.
- Hi, Christina,
it is great to be herespeaking with you today.
- So for those of ourlisteners who are not familiar
with Diebold,
what can you tell us about the company
(00:42):
and what you do there?
- So Diebold Nixdorf is atechnology company of two halves.
We provide banking systemsto the world's largest banks
and we provide retail technology
to the world's largest retailers.
I'm responsible for retail technology,
so we provide hardware, software,
and services to most of the25 top retailers globally,
(01:06):
as well as quite a number oftier two, tier three retailers.
And we generally coverfront-end technology,
which we'll go into more detail on,
software, enterprise software.
And then we provide mostof the services break fix
and help desk services to retailers
to make sure all of theirtechnology is up and running
for maximum time possible.
(01:28):
- Great, and obviously,
we'll be focusing on the retail aspect
of Diebold Nixdorf today.
We'll have to get someoneelse on a later podcast
to talk about the financialaspects of the company.
But the last time we spoke with you, Matt,
it was for an article on "Insight.Tech"
and we spoke about POS'stransforming retail checkouts
to improve customer experiences in stores,
(01:51):
but customer experiences, Ithink that's just one pain point
that retailers are facing today.
One challenge.
So, that's where I wanted
to start the conversation off today.
What are the differentchallenges retailers face today
in addition to customer service in stores?
- So, it's a bit of atough time for retailers
and I think regardlessof what sub-vertical
(02:11):
of retailer you are in,
I think most retailers are struggling
with the same challenges.
So on one side, as you said,
customer experience absolutely key.
The desire or drive to make sure
that the in-store experienceis as high as possible
with this ever changing horizon
or landscape amongst consumers,
that their expectations continue to rise.
(02:33):
So, the horizon of expectation continues
and retailers are reallychasing after that.
And we are really starting post-Covid
to see retailers reallyinvesting again very heavily
in that in-store experience,which is great to see.
On the flip side, on the topline and and bottom line,
they're being squeezed.
So, I think you can allread in the press global
(02:55):
and economic trends that aredriving the cost of goods up,
the cost of freight up,
the cost of managing andrunning their stores up.
So, their top line is being squeezed,
their bottom line is being squeezed
and they have to find waysof driving efficiencies
in the store while also delivering
that great consumer experience.
So, it's a real balancebetween getting the economics
(03:16):
of retail rights as well as satisfying
the needs of your consumers.
And competition is as highas it's probably ever been
in retail, which isgood in certain aspects.
It helps with pricing andkeeping inflation under control.
But on the flip side, itmeans that consumers are
at the very flippant in terms of where
(03:36):
they get their experiencefrom and where they shop.
If they get a bad experience in a store,
it's easy for them toflip to another brand
and get a better experience ofpotentially better products,
better prices.
So, it's a very dynamicallychanging environment,
very difficult one for retailers today.
- I've seen a lot of retailersstart adding new technology,
more intelligent technology and sensors
(03:57):
to be able to do some of these things,
collect data at the edge in real time,
so they can make decisionsas they're happening.
A lot of this is being poweredby artificial intelligence
and I think we're in astage or a point today
in the industry where AI is everywhere
and everybody's trying to use it
and get the benefits from it.
(04:18):
So, from your perspective,
how is AI being able to addresssome of those challenges
that you talked about andwhat's the reality of it?
Is it really,
what are the realbenefits that are coming?
Because I feel likesometimes there's hype,
but where can we start using
and getting actionable insights?
- Sure.
So, I think 2023 for most people,
(04:38):
will be known as the year of AI.
It's where generative AIreally took off in retail
and we started to see moreand more AI applications
in the retail market.
And certainly somecompanies really jumped to
what I would consider the end goal of AI,
which is completely changingthe technology landscape,
completely changing the customer journeys,
(04:59):
the staff journeys,
how you operate and run your stores
with this kind of euphoric view
that actually AI couldremove all technology
that existed within stores.
That's what I call thekind of the hype curve.
We're coming through the trough
and we're going back up again in that,
a lot of people realizethat that technology,
although fantastically advanced,
was probably quite a way offbeing realistically deployable
(05:23):
on mass.
The cost of the technology was high,
there were limitations interms of the size of the store
and the amount of productsand the amount of consumers.
So, trying to take that technology
and apply it to retailerstoday wasn't applicable.
So, what what we are seeing
and what a lot of retailers have done
is kind of take stock of the situation,
readdress what's really important,
(05:43):
focus in on the pain pointsand then really go again with
what we call point solution AI technology.
So, specific AI deployed
for a specific use case tosolve a specific problem,
but is very much forthat particular use case.
And we're starting to see more
and more of these solutions being trialed,
across retail stores, not only in grocery.
(06:05):
And the possibilities are really,
they are bountiful andthey're kind of endless.
And some of the examples
that we're seeing areeverything from health
and safety in store.
So, using AI on top of CCTV networks
to make sure fire exits aren't blocked
or there's not foreign objectsor liquid spill on the floor
where someone might slip over.
We're using it for heat mapping
to understand what is the flowof consumers around stores,
(06:28):
how do I make that flow easier,
but also how do I potentiallycommercialize that flow?
We're seeing AI on topof existing technology,
so something very closeto my heart self-service.
We're starting to see moreand more AI being applied
on top of existing technologiesto make them more efficient,
to make them easier touse, to close loopholes,
to boost the consumer experience.
(06:49):
So, technologies like facial recognition
for age verification.
I think we've all been in the situation
where we're trying to buyparacetamol or a bottle of wine
and you have to wait for amember of staff to come over
and approve your ID.
That's been compounded theeffect of that situation
by the fact that retailersare struggling to find staff.
So, now I'm having towait a little bit longer
(07:10):
to have a member of staff be available
to come and approve my ID.
So, using AI in that environment,
drives greater efficiencyat the front end.
It reduces that requirementon members of staff
and it boosts that consumer experience.
We're also seeing technologiescentered around the product.
So item recognition,really, really taking off.
So, not just for non-barcoded items
(07:31):
where we've seen fruitsand vegetables selection,
but also all item recognitionand in some environments,
particularly smaller stores,
why should you have to scan the barcode
when actually you can identifythe item by its its image.
So, that's a really exciting technology.
And then finally something
that we've been working onover the last 18 months,
(07:52):
which is really anti-shrinktechnology using AI.
Obviously shrink
is something that's reallygone through the roof
in a lot of retail environments,
driven by the cost of living crisis.
And we are now workingwith a lot of retailers.
We've got 54 different retailers
we're working with onanti-shrink technology
in one form whether or another
(08:12):
to try and close those loopholes
and make it more difficult
for those that aremaliciously trying to steal,
making it difficult forthem to be able to steal,
but also those that mayhave just be unfamiliar
with a process or genuinelyhave made a mistake.
Also making sure thatwe are catching that,
without making that a bad experience
for that particular consumer.
(08:32):
- It's interesting, in thebeginning of your response,
you mentioned how retailers,
they were adding this technology
to really transform everything
and they were sort of jumping to the end.
And especially
when you're implementingartificial intelligence,
which has so many connotations with it,
so many misconceptions.
It's interesting,
(08:52):
because I feel like these things,
need to be graduallyintroduced to consumers
for them to be able to accept it,
to understand it, to use it.
I can't tell you how many timesI've been in self-checkout
where we're using AI or computer vision
and I can't even put an item on the scale,
after I'm done scanning itbecause it needs to be in a bag
or I can't bag it yet becausethe bag weight is not.
(09:13):
So, it's just so complicated.
So, I know probably everyretailer has different challenges
and different areas of entry,
but would you say there is a easier place
of adoption happening right now
to adding some of thisintelligent technology
and then not only easierto adoption for consumers
and for the store,
but like you were talkingabout the facial recognition,
(09:35):
I know consumers haveprivacy concerns around that.
So, how can stores easily implement this
that makes the most sense for consumers
and for themselves and their business?
- Sure.
So complex question, I'm goingto break it down into parts.
So, when we talked about retailers
and some technologistsrushing to that end game,
it really was about tryingto boil the ocean with AI
(09:58):
to try and completely changethe landscape of retail.
And I think sometimes what we forget is,
although the technology may exist,
forget whether it's commercially viable
or practical to deploy it,
you have to also have consumer adoption.
If you don't have consumer adoption,
no one will use the technology in it.
It's worthless.
So we very much, we track thethe consumer adoption curve
(10:20):
and we track the technologydevelopment curve
and it's important tofind something broadly
in the middle of those two
in terms of what's the right technology,
what's the rightinnovation and technology,
why am I deploying it,
making sure consumers adopt it,
but crucially, makingsure that it solves a need
and it solves a businessor a consumer desire,
(10:43):
the build it and they will come mentality,
does not work with innovation
and it doesn't work broadlywith retail technology.
Consumers are savvy and retailersare much, much more savvy
in terms of deploying thetechnology, it has to deliver.
So, we always recommendstarting with data.
A lot of people talk about data,
there's a lot of data that exists.
It's very easy to be swamped by data.
(11:04):
We call it paralysis by analysis.
There's too much data out there.
But if you can really segmentyour data to understand,
if I'm looking at my transactional process
or my customer journey,
making sure that I'mlooking only at the data
that relates to that andhighlighting the problems.
I'd say 98%, 99% of ourcustomers that we work with now,
(11:27):
we actually work wellon a consultative basis
to actually really deeplyunderstand their stores,
how they're being run
and how their consumersshop in their stores.
And the data provides alot of insights to that.
So, really understanding
and analyzing how is thestore operating today?
Where is the friction associatedwith the staff journey
(11:48):
or the consumer journey?
Understanding and quantifying the effect
that that friction has,
really then builds the picture to say,
"Okay, I've got a problemstatement I want to try and solve.
"It's having this impacton consumers and staff
"and this is the impact to my business."
And that's relatively easy to calculate.
The more problematic piece
is then really finding theright innovation to solve that.
(12:10):
And very much we try and put the consumer
and the staff journey atthe center of everything
that we do.
If it doesn't providevalue for the consumer,
if it doesn't provide valuefor the members of staff
and it doesn't providevalue for the retailer,
that triangle of value
is at the center of everything that we do.
And if it's not tickingall three of those boxes,
(12:31):
we don't put it into the range
and we don't put it intothe solutions or the stores.
So, starting with that data isa bit like the treasure map.
It highlights where your biggestareas of inefficiency are
and then provides the compass
to kind of point youin the right direction
of what's the right technologythat you should be deploying
to the store to actually try and solve
that particular issue.
(12:52):
And when you break it down like that
and we start thinkingabout this kind of AI,
boil the ocean vision,
we start thinking aboutindividual point solution,
it becomes much easier,
A, because it's much more manageable
to deploy from a technology perspective,
it's much easier to developa solution that works
for a particular use case or problem
that you're trying to solve.
(13:13):
But it's also then arguably very easy
to actually measure howsuccessful it's been,
once you put it into the store.
The difficulty then comesis what you don't want to do
is collect a wholegroup of point solutions
that don't talk to each other
and it becomes very,very difficult to scale.
So, finding the right AI platform
that allows you to scaleall of these point solutions
(13:35):
on a singular platform isreally, really important.
- Yeah, I love one thing that you said,
which was basically, ifit's not solving a problem
or if it's not benefitingthe customer or the business,
then don't do that.
I feel like that's a major problem
that we have with implementing technology
and seeing shiny new things.
Let's just add it to add it,but why are we adding it?
(13:56):
It's not going to get youa return on investment
and it's not going to help your business
if it's not really doing anything for you.
So, I think that that was a great point.
I want to come back to also that,
facial recognition example again,
how obviously I think we've all dealt
with self-service checkouts
or checkouts where you'rescanning something,
it doesn't recognize it,
(14:17):
you need a human cashierto come and help you
and that just bottlenecksthe entire process.
But it seems to be a lot moreself-checkouts in the store.
How do the role of theemployees come into this?
I know talking about theconsumer misconceptions
(14:37):
that they have, there'sa lot of misconceptions
that this is going toreplace employee jobs
and especially when you see that it,
there's not a lot of cashierson the floor anymore.
So, where does the humanelement come into play
with some of these?
- So, the human element isreally, really important
to self-service and it's an element
that's quite often overlooked.
If you look at theevolution of self-service,
(14:58):
self-service was originallydesigned as a POS,
an attendant till replacement
to ultimately remove the costof the staffing from stores.
But self-service has beenaround for 20, 25 years now
and the drivers for deploying self-service
are very different todaycompared to 20, 25 years ago.
I'd say 100% of theretailers that we deal with
(15:21):
that are either putting in self-service
and they might be on theirsecond or third iteration
of self-service because they've been
in that business for a while
or they're putting self-servicein for the first time.
A lot of retailers outside of grocery
are just trying self-servicefor the first time.
The approach is very, very different
and it's very much lessabout removing staff
(15:42):
from the equation.
More about staff redistribution,
the inability to attract
and retain staff in retail
is a real big problem for retailers.
So, they have to use their staff wisely.
And where the consumers value
the staff interaction themost is where they need it.
And where they need itis where they generally
they need help eithernavigating the store,
(16:02):
finding an item, asking aquestion about a particular item
or just general assistance.
What self service isreally playing a major role
in retail today is itunlocks that member of staff.
So, I would say to anyonethat looks at self services
and said, "Oh, that's goingto replace people's jobs."
It's not, it's very much aboutlabor redistribution now.
(16:23):
It frees up a cashier thatcould be sat behind a till
for 12 hour shift to be up on their feet,
engaging with consumer shoulderto shoulder in the aisle
where it really makes sense
to deliver that consumer experience.
So, particularly through Covid,
we saw retailers thatthat had self-service
had much greater flexibility of operations
within their store.
Post-Covid,
(16:43):
we're now seeing that actually allows them
to boost the level of consumer experience
where it really counts.
Obviously there's alwaysbeen friction associated
with self-service and theadage of unexpected item
in the bagging area,
all of those commonfriction points perceived
with self-service,
they're starting to really drain away.
(17:03):
A lot of focus has beenput on actually fine tuning
and making sure that thebase technology works
to a much, much more acceptable level.
And we're now seeing self-servicethat's very efficient
that generally most of the time
you can sell through atransaction with no intervention,
no requirement for a memberof staff to come over.
We are now in the kindof the fine tuning era
(17:26):
of self-service and why I say fine tuning
is we're really looking for that last 5%
or 10% of efficiency gains.
So, Diebold Nixdorf,
we've really focused onthree core solutions,
initially out the bag
and those three coretechnologies have been developed,
because we identified via the data
where the biggest friction points were.
(17:47):
So, age verification,
22% of interventionsbroadly are age related.
So, that's a big number.
If we can use facialrecognition to identify the age
of the consumer
and remove that validationprocess that's happening,
A, much better experiencefor the consumer.
B, it means faster transactions,
faster transaction means lessstaff requirement at the till,
(18:11):
but it also means that consumers
are moving through the front end quicker.
So, that means less queues.
Less queues,
queuing is the biggestbug bearer of consumers
when they get to checkout.
So, we've removed two ofthe biggest friction points,
associated with checkoutwith one piece of technology.
Item recognition, particularly in grocery
for fresh fruit and vegetables
was another area of frustrationfrom a consumer perspective,
(18:34):
but also inefficiency froma retailer perspective,
spending 20, 30, 40, 50 seconds,
trying to find the type of apples
that I'm looking to buy is frustrating,
but it's also time consuming.
So, using item recognitionto identify those apples,
so the consumer doesn't haveto run that process again.
Good consumer experience,great productivity gains.
(18:56):
And then finally shrink,
we touched on it a little bit earlier,
but obviously retail shrink hasreally gone through the roof
and I think a lot ofretailers are battling
to really understand whereis there shrink happening?
So, of course the natural progression
in that argument would be to say,
well, self-service is anatural place for shrink,
because it's unmanned ina lot of environments.
But what we're actually finding is A,
(19:18):
there's two differenttypes of people that steal.
There's people thatmaliciously try and steal
and those that have just made a mistake
and it's genuinely unmalicious.
And how you treat those two individuals
has to be dealt withvery, very differently,
because you don't want toalienate or embarrass the consumer
that's genuinely made a mistake.
For those that aremaliciously trying to steal,
(19:40):
unfortunately, if weclose all of the loopholes
and make it impossibleto steal at self-service,
they will find somewhere elsein the store to go and steal.
So, we're in this kind ofWhack-A-Mole type environment
where we're trying toclose all the loopholes
as quickly as possible.
We've really focused our efforts on AI
with behavioral tracking.
And the reason why weuse behavioral tracking
(20:00):
is once you can startto identify behavior,
it doesn't matter whereyou deploy the technology
within the store,
you can identify malicious behavior
and that shrink environment.
So, we very much focusat the front end first,
we're deploying shrinkonto self-service checkouts
and onto POS lanes.
But the idea is that the nextnatural evolution is that
(20:21):
then run that same solutiononto the CCTV network
and then we can identifyshrink anywhere in the store.
The human element of this
is really, really importantbecause it's relatively easy
to identify if someone has stolen.
What you then do in that scenariois a difficult situation.
(20:43):
What you don't want todo is alienate a consumer
that might have non maliciously stolen.
If they're maliciously stealing,
you also need to deal with thatin a particular type of way,
but you also don't want to put your staff,
your cashiers in your store A, in danger
or B, in an environment
that they don't feel comfortable with.
So, we are very much puttinghuman element back into this
(21:04):
that actually, depending onthe use case of the theft,
we will then deal withthat situation differently.
But what we will always do
is put the information inthe members of staff's hands,
so that they can deal with that situation
in the way that they see appropriate.
So, with all of our shrink solutions,
whether it's on self-servicecheckout or POS,
once the shrink instancehas been identified,
(21:25):
an alert is sent to a memberof staff wearable technology,
whether it's smartwatch or tablet or phone
or even their POS lane,
they're notified thatthere's a shrink instance
that's happened, theyknow where it's happened
and they can even review the video clip.
So, now they're empowered
that they know what'shappened in that situation,
they know what to look for
(21:46):
and then staff trainingreally comes into play here
and we have a number of great partners
that we work with on staff training
who actually work through these scenarios
to give the staff members the toolkit,
so that when they approachthat member of the public
and they're approachingthem knowing exactly
what's happened,
they're trained to be ableto deal with that situation
in the most agreeable way possible
(22:07):
to disperse any aggression
or any risk that might be associated,
but also to make sureit's a good experience
for that end consumer.
So, the technology is only onethird of the actual solution.
The human element is a massive part of it
that shouldn't be overlooked.
- And I think the change in roles
and responsibilities forcashiers to being able
(22:29):
to have more meaningfulinteractions with customers,
that's not only benefitingthe customer experience,
that's benefiting theemployee experience as well.
Maybe keeping employee retention.
I was a cashier in college
and I can tell you that is atedious and redundant process.
I would have dreams of just scanning food
and shouting out numbers
(22:50):
and it's not only retail shrink and loss,
I think it's not only withmalicious actors or by accident,
but sometimes as a cashier Iwould hit the wrong number,
just because I was onautopilot going repeat.
It was an error-prone process.
So, I can see that helping it as well.
You mentioned that to reallybe able to be successful,
you need an AI solution thatconnects all of these together
(23:10):
so that this is not happening in silos
and the data is actually actionable.
Obviously we're talking to Diebold,
because you guys are aleader in this space.
So, I'm curious to hear howyou are helping customers,
if you have any real world examples
or case studies thatyou can share with us.
- Yeah, and I'll be completely honest,
we fell into the most obvious trap,
looking back at our journey on AI.
(23:32):
We've been working on thisnow for two and a half years,
nearly three years.
And we originally,
we went out to market to tryand find the best solutions
to solve these three use cases,
but what we quickly found were
there was lots of differentcompeting technologies.
There were a lot ofpotential third parties
that we could have worked with,
but the underlyingtechnology was the same.
(23:53):
And we quickly realized thatactually as a solution provider
who retailers work with
to actually build out their technology,
not just across their checkout,
but all the way across the store.
It was unrealistic to think that
we could have 20 or 30different solutions,
all in the AI space, allproviding different use cases,
but none of them talking together.
(24:13):
So, we actually kind of paused our program
and redesigned our go-to market strategy,
which was very much focusedon providing an AI platform
and we work with a thirdparty in this space
who have a very, very mature AI platform.
Were entering the retail market
and didn't necessarilyhave the applications
to run on top of it.
So, we've worked with them
(24:34):
to actually develop outthese three applications
as a starting point in the AI space.
But the nice thingabout the AI platform is
it effectively becomes the AI backbone
for anything the retailerwants to do within their store
from an AI perspective.
So, this means that we canreally satisfy our openness.
It's an ethos that we drivein our product strategy,
(24:56):
which is openness of software.
And what we mean by that
is we provide the buildingblocks for retailers.
We are the trusted partner,we're the integration partner,
but if there's a particularthird party out there
who has got the market leading solution
in a particular area,
it doesn't make sense for usto go and reinvent the wheel.
So, when we talk about openness,
(25:16):
the ethos that we taketo our retail customers,
but also that permeates through our R&D
and product management ethosis to very much work with
the best of breed within the market.
And our strategy is to basicallyprovide this AI platform
for retailers.
We will provide applicationsthat can sit on top of it,
(25:37):
like age verification, shrink reduction,
item recognition, processor people tracking.
But if there is aparticular partner out there
that is market leadingin health and safety,
we can plug them on top of the platform.
And what that means isthe retailer can build
this kind of ecosystem of AI partners,
all providing best of breed solutions,
(25:58):
but critically they're allplugged into a single platform.
So, they utilize the same business logic,
they utilize commondatabases like item database
or loyalty schemes and things like that.
That makes the solutionsvery, very scalable.
It makes them much easier to manage,
but it also means that they'reall talking to each other.
And the beauty of AI
is its self learning to a certain extent.
(26:20):
So, the more applicationsthat we plug into this,
the more physical touch pointsthat we have in the store,
the more information isflowing through the platform
and then the quicker it can develop
and the quicker it can learn.
So, it's very much a selfprop perpetuating solution
that we're very much at thebeginning of this journey.
As I say, we've got about54 different customers,
(26:40):
using AI in one form or of another.
But we very much see this asa much, much longer journey
where we're starting to buildan ecosystem of solutions
that will ultimately move us towards
what we call intelligence store.
And intelligence store for us
isn't necessarily removingthe physical touchpoint
or removing the technology.
It's about providingintelligence to retailers.
(27:02):
And what I mean by that is every device
that sits in the store iseffectively a data capture device.
And that's a two-way street.
You can push data down tothem, you can pull data back.
So, whether it's a shelf edge camera
or whether it's a staff deviceor a self-service checkout
or a scanner or a screen,these are all data inputs.
There might be AI pointsolutions associated with them,
(27:24):
but the AI platform allows you
to connect all of these together
and create an intelligent store
where intelligence really permeates,
every single area of the store.
It does mean there's a hugeamount of data available,
but I think the retailers
that are really going to advance quickly
are the ones that work outwhat to do with this data,
because it can and it shouldinform every single decision
(27:45):
or direction that you take as a retailer,
whether it's how I price my products,
where my products arepositioned within the stores,
how I afford loyaltysystems to the consumers,
how I staff my stores,
how I operationalize them,
but also what technologyexists within the stores.
So data, it's a cliché,
but data will form the basisof every single decision
(28:07):
that we make from eithera technology perspective,
solution provider perspective,
but also from a retail operations
and a store design perspective as well.
So, it's a really, reallyexciting journey that we're on.
- Absolutely, and I thinkit's really important
to find a solution provider
that is willing to workwith others in the industry
and leverage their expertise.
(28:28):
I think that helps prevent vendor lock-in,
it allows you to take advantageof the latest technologies
and enables you to innovate faster,
working with some of the bestin the breed of the market.
So, speaking of best inthe breed, insight.tech
and the "insight.tech Talk,"
we're obviously sponsored by Intel,
so I'm curious if there'sanything you can tell us,
about that partnership andthe technology that you use
(28:50):
to make some of your AIretail solutions happen.
- Absolutely.
So, we work very, very closely with Intel,
not just on the AI topic, butfrom our core platform itself.
Intel very much underpins alarge part of our portfolio,
so we have a very, veryclose working relationship
with them, not just on the solutions
that we deploy into stores today,
(29:10):
but also our roadmap on our development.
We work very closely withIntel on their developments,
where they're going with their solutions
and how we can better integrate them
into our solutions to giveretailers better solutions,
but also much, much greaterflexibility for the future.
And I think a good example of that
(29:31):
is probably the speed ofdevelopment of technology.
If you think abouttraditional point of sale
or self-service checkout,
if you go back five or 10 years,
a retailer would make a choice
for that particular type of technology
and that would sit in that store
for five, seven, 10 years sometimes
as long as the technology is running,
(29:52):
the speed of development of technology
is in increased immeasurably.
The expectations of consumers
has also increased immeasurably.
And so balancing thosetwo is really, really key.
Where we work very,very closely with Intel
is on some of their scalable platforms.
So, knowing that retailershave a requirement today,
(30:13):
but particularly with these AI topics,
the amount of computing power
that will be required inthree or five or seven years,
will be very, very differentto the requirements today.
So, providing retailers theability to scale this technology
so that whatever they deploy today
is not throw away in two years time,
they can evolve it andscale that technology
(30:33):
to meet their technology requirements
at that particular time isan absolute game changer.
And that's something
we're working with Intelvery, very closely on.
- Yeah, absolutely agree.
Things are changing every day,
not even five, six, seven years from now,
but five weeks from now thingscan be completely different.
So being able to scale and to adapt
is especially importantinto today's landscape.
(30:56):
Well, it's been great hearingabout all of these solutions,
especially how Diebold ishelping retailers from end to end
with the item recognition,facial recognition
and retail shrink.
We are running out of time,
but before we go, Matt,
I'm curious if there's any finalthoughts or final takeaways
that you want to leaveour listeners with today.
- I think there's a lot of misconception,
(31:18):
particularly around AI.
What I would say is start with the data.
Identify the business requirements
or the problem that youare looking to solve
and then find the right provider
that's going to enable you
to deliver against thoserequirements today.
But also gives you thatlongevity of scalability,
because AI is a journey,
it's very much a solution thatlearns over a period of time.
(31:40):
So, choosing your solutionprovider is extremely important
because it is a marriageand it is a long marriage
and you have to make sure thatyou've made the right choice.
So, use the data to helpinform those decisions
and yeah, it'll be very,very exciting to see where AI
and retail technology goes,
over the next two, three, five years.
(32:00):
- Yeah, absolutely.
And I would say also choosea partner that you can trust
and transparent about howthey are using the data.
Like with the ageverification for instance,
you want to make sure thatthat data isn't being saved
or that anything going into that,
that system is goingto protect your privacy
and your information.
- Absolutely.
(32:20):
Data privacy is absolutely key
and is a very, very careful consideration
when you are designingor choosing the solution
that you want to deploy to stores.
- Excellent.
Well thank you again for joining us.
I invite all of our listeners to visit
the Diebold Nixdorf website,
see how else they can helpyou in the retail space,
as well as insight.tech,
where we'll continue tocover partners like Diebold
and the latest trends in this space.
(32:42):
Until next time, this hasbeen "insight.tech Talk."
(upbeat music)
(upbeat music)
- Hello and welcome to"insight.tech Talk,"
formerly known as "IoT Chat,"
but with the same highquality conversations,
around IoT, technology trends
(00:21):
and the latest innovationsyou've come to know.
I'm your host, Christina Cardoza,
Editorial Director of insight.tech,
and today I'm joined by Matt Redwood,
Vice President of RetailTechnology at Diebold Nixdorf.
Hey Matt, thanks for joining us.
- Hi, Christina,
it is great to be herespeaking with you today.
- So for those of ourlisteners who are not familiar
with Diebold,
what can you tell us about the company
(00:42):
and what you do there?
- So Diebold Nixdorf is atechnology company of two halves.
We provide banking systemsto the world's largest banks
and we provide retail technology
to the world's largest retailers.
I'm responsible for retail technology,
so we provide hardware, software,
and services to most of the25 top retailers globally,
(01:06):
as well as quite a number oftier two, tier three retailers.
And we generally coverfront-end technology,
which we'll go into more detail on,
software, enterprise software.
And then we provide mostof the services break fix
and help desk services to retailers
to make sure all of theirtechnology is up and running
for maximum time possible.
(01:28):
- Great, and obviously,
we'll be focusing on the retail aspect
of Diebold Nixdorf today.
We'll have to get someoneelse on a later podcast
to talk about the financialaspects of the company.
But the last time we spoke with you, Matt,
it was for an article on "Insight.Tech"
and we spoke about POS'stransforming retail checkouts
to improve customer experiences in stores,
(01:51):
but customer experiences, Ithink that's just one pain point
that retailers are facing today.
One challenge.
So, that's where I wanted
to start the conversation off today.
What are the differentchallenges retailers face today
in addition to customer service in stores?
- So, it's a bit of atough time for retailers
and I think regardlessof what sub-vertical
(02:11):
of retailer you are in,
I think most retailers are struggling
with the same challenges.
So on one side, as you said,
customer experience absolutely key.
The desire or drive to make sure
that the in-store experienceis as high as possible
with this ever changing horizon
or landscape amongst consumers,
that their expectations continue to rise.
(02:33):
So, the horizon of expectation continues
and retailers are reallychasing after that.
And we are really starting post-Covid
to see retailers reallyinvesting again very heavily
in that in-store experience,which is great to see.
On the flip side, on the topline and and bottom line,
they're being squeezed.
So, I think you can allread in the press global
(02:55):
and economic trends that aredriving the cost of goods up,
the cost of freight up,
the cost of managing andrunning their stores up.
So, their top line is being squeezed,
their bottom line is being squeezed
and they have to find waysof driving efficiencies
in the store while also delivering
that great consumer experience.
So, it's a real balancebetween getting the economics
(03:16):
of retail rights as well as satisfying
the needs of your consumers.
And competition is as highas it's probably ever been
in retail, which isgood in certain aspects.
It helps with pricing andkeeping inflation under control.
But on the flip side, itmeans that consumers are
at the very flippant in terms of where
(03:36):
they get their experiencefrom and where they shop.
If they get a bad experience in a store,
it's easy for them toflip to another brand
and get a better experience ofpotentially better products,
better prices.
So, it's a very dynamicallychanging environment,
very difficult one for retailers today.
- I've seen a lot of retailersstart adding new technology,
more intelligent technology and sensors
(03:57):
to be able to do some of these things,
collect data at the edge in real time,
so they can make decisionsas they're happening.
A lot of this is being poweredby artificial intelligence
and I think we're in astage or a point today
in the industry where AI is everywhere
and everybody's trying to use it
and get the benefits from it.
(04:18):
So, from your perspective,
how is AI being able to addresssome of those challenges
that you talked about andwhat's the reality of it?
Is it really,
what are the realbenefits that are coming?
Because I feel likesometimes there's hype,
but where can we start using
and getting actionable insights?
- Sure.
So, I think 2023 for most people,
(04:38):
will be known as the year of AI.
It's where generative AIreally took off in retail
and we started to see moreand more AI applications
in the retail market.
And certainly somecompanies really jumped to
what I would consider the end goal of AI,
which is completely changingthe technology landscape,
completely changing the customer journeys,
(04:59):
the staff journeys,
how you operate and run your stores
with this kind of euphoric view
that actually AI couldremove all technology
that existed within stores.
That's what I call thekind of the hype curve.
We're coming through the trough
and we're going back up again in that,
a lot of people realizethat that technology,
although fantastically advanced,
was probably quite a way offbeing realistically deployable
(05:23):
on mass.
The cost of the technology was high,
there were limitations interms of the size of the store
and the amount of productsand the amount of consumers.
So, trying to take that technology
and apply it to retailerstoday wasn't applicable.
So, what what we are seeing
and what a lot of retailers have done
is kind of take stock of the situation,
readdress what's really important,
(05:43):
focus in on the pain pointsand then really go again with
what we call point solution AI technology.
So, specific AI deployed
for a specific use case tosolve a specific problem,
but is very much forthat particular use case.
And we're starting to see more
and more of these solutions being trialed,
across retail stores, not only in grocery.
(06:05):
And the possibilities are really,
they are bountiful andthey're kind of endless.
And some of the examples
that we're seeing areeverything from health
and safety in store.
So, using AI on top of CCTV networks
to make sure fire exits aren't blocked
or there's not foreign objectsor liquid spill on the floor
where someone might slip over.
We're using it for heat mapping
to understand what is the flowof consumers around stores,
(06:28):
how do I make that flow easier,
but also how do I potentiallycommercialize that flow?
We're seeing AI on topof existing technology,
so something very closeto my heart self-service.
We're starting to see moreand more AI being applied
on top of existing technologiesto make them more efficient,
to make them easier touse, to close loopholes,
to boost the consumer experience.
(06:49):
So, technologies like facial recognition
for age verification.
I think we've all been in the situation
where we're trying to buyparacetamol or a bottle of wine
and you have to wait for amember of staff to come over
and approve your ID.
That's been compounded theeffect of that situation
by the fact that retailersare struggling to find staff.
So, now I'm having towait a little bit longer
(07:10):
to have a member of staff be available
to come and approve my ID.
So, using AI in that environment,
drives greater efficiencyat the front end.
It reduces that requirementon members of staff
and it boosts that consumer experience.
We're also seeing technologiescentered around the product.
So item recognition,really, really taking off.
So, not just for non-barcoded items
(07:31):
where we've seen fruitsand vegetables selection,
but also all item recognitionand in some environments,
particularly smaller stores,
why should you have to scan the barcode
when actually you can identifythe item by its its image.
So, that's a really exciting technology.
And then finally something
that we've been working onover the last 18 months,
(07:52):
which is really anti-shrinktechnology using AI.
Obviously shrink
is something that's reallygone through the roof
in a lot of retail environments,
driven by the cost of living crisis.
And we are now workingwith a lot of retailers.
We've got 54 different retailers
we're working with onanti-shrink technology
in one form whether or another
(08:12):
to try and close those loopholes
and make it more difficult
for those that aremaliciously trying to steal,
making it difficult forthem to be able to steal,
but also those that mayhave just be unfamiliar
with a process or genuinelyhave made a mistake.
Also making sure thatwe are catching that,
without making that a bad experience
for that particular consumer.
(08:32):
- It's interesting, in thebeginning of your response,
you mentioned how retailers,
they were adding this technology
to really transform everything
and they were sort of jumping to the end.
And especially
when you're implementingartificial intelligence,
which has so many connotations with it,
so many misconceptions.
It's interesting,
(08:52):
because I feel like these things,
need to be graduallyintroduced to consumers
for them to be able to accept it,
to understand it, to use it.
I can't tell you how many timesI've been in self-checkout
where we're using AI or computer vision
and I can't even put an item on the scale,
after I'm done scanning itbecause it needs to be in a bag
or I can't bag it yet becausethe bag weight is not.
(09:13):
So, it's just so complicated.
So, I know probably everyretailer has different challenges
and different areas of entry,
but would you say there is a easier place
of adoption happening right now
to adding some of thisintelligent technology
and then not only easierto adoption for consumers
and for the store,
but like you were talkingabout the facial recognition,
(09:35):
I know consumers haveprivacy concerns around that.
So, how can stores easily implement this
that makes the most sense for consumers
and for themselves and their business?
- Sure.
So complex question, I'm goingto break it down into parts.
So, when we talked about retailers
and some technologistsrushing to that end game,
it really was about tryingto boil the ocean with AI
(09:58):
to try and completely changethe landscape of retail.
And I think sometimes what we forget is,
although the technology may exist,
forget whether it's commercially viable
or practical to deploy it,
you have to also have consumer adoption.
If you don't have consumer adoption,
no one will use the technology in it.
It's worthless.
So we very much, we track thethe consumer adoption curve
(10:20):
and we track the technologydevelopment curve
and it's important tofind something broadly
in the middle of those two
in terms of what's the right technology,
what's the rightinnovation and technology,
why am I deploying it,
making sure consumers adopt it,
but crucially, makingsure that it solves a need
and it solves a businessor a consumer desire,
(10:43):
the build it and they will come mentality,
does not work with innovation
and it doesn't work broadlywith retail technology.
Consumers are savvy and retailersare much, much more savvy
in terms of deploying thetechnology, it has to deliver.
So, we always recommendstarting with data.
A lot of people talk about data,
there's a lot of data that exists.
It's very easy to be swamped by data.
(11:04):
We call it paralysis by analysis.
There's too much data out there.
But if you can really segmentyour data to understand,
if I'm looking at my transactional process
or my customer journey,
making sure that I'mlooking only at the data
that relates to that andhighlighting the problems.
I'd say 98%, 99% of ourcustomers that we work with now,
(11:27):
we actually work wellon a consultative basis
to actually really deeplyunderstand their stores,
how they're being run
and how their consumersshop in their stores.
And the data provides alot of insights to that.
So, really understanding
and analyzing how is thestore operating today?
Where is the friction associatedwith the staff journey
(11:48):
or the consumer journey?
Understanding and quantifying the effect
that that friction has,
really then builds the picture to say,
"Okay, I've got a problemstatement I want to try and solve.
"It's having this impacton consumers and staff
"and this is the impact to my business."
And that's relatively easy to calculate.
The more problematic piece
is then really finding theright innovation to solve that.
(12:10):
And very much we try and put the consumer
and the staff journey atthe center of everything
that we do.
If it doesn't providevalue for the consumer,
if it doesn't provide valuefor the members of staff
and it doesn't providevalue for the retailer,
that triangle of value
is at the center of everything that we do.
And if it's not tickingall three of those boxes,
(12:31):
we don't put it into the range
and we don't put it intothe solutions or the stores.
So, starting with that data isa bit like the treasure map.
It highlights where your biggestareas of inefficiency are
and then provides the compass
to kind of point youin the right direction
of what's the right technologythat you should be deploying
to the store to actually try and solve
that particular issue.
(12:52):
And when you break it down like that
and we start thinkingabout this kind of AI,
boil the ocean vision,
we start thinking aboutindividual point solution,
it becomes much easier,
A, because it's much more manageable
to deploy from a technology perspective,
it's much easier to developa solution that works
for a particular use case or problem
that you're trying to solve.
(13:13):
But it's also then arguably very easy
to actually measure howsuccessful it's been,
once you put it into the store.
The difficulty then comesis what you don't want to do
is collect a wholegroup of point solutions
that don't talk to each other
and it becomes very,very difficult to scale.
So, finding the right AI platform
that allows you to scaleall of these point solutions
(13:35):
on a singular platform isreally, really important.
- Yeah, I love one thing that you said,
which was basically, ifit's not solving a problem
or if it's not benefitingthe customer or the business,
then don't do that.
I feel like that's a major problem
that we have with implementing technology
and seeing shiny new things.
Let's just add it to add it,but why are we adding it?
(13:56):
It's not going to get youa return on investment
and it's not going to help your business
if it's not really doing anything for you.
So, I think that that was a great point.
I want to come back to also that,
facial recognition example again,
how obviously I think we've all dealt
with self-service checkouts
or checkouts where you'rescanning something,
it doesn't recognize it,
(14:17):
you need a human cashierto come and help you
and that just bottlenecksthe entire process.
But it seems to be a lot moreself-checkouts in the store.
How do the role of theemployees come into this?
I know talking about theconsumer misconceptions
(14:37):
that they have, there'sa lot of misconceptions
that this is going toreplace employee jobs
and especially when you see that it,
there's not a lot of cashierson the floor anymore.
So, where does the humanelement come into play
with some of these?
- So, the human element isreally, really important
to self-service and it's an element
that's quite often overlooked.
If you look at theevolution of self-service,
(14:58):
self-service was originallydesigned as a POS,
an attendant till replacement
to ultimately remove the costof the staffing from stores.
But self-service has beenaround for 20, 25 years now
and the drivers for deploying self-service
are very different todaycompared to 20, 25 years ago.
I'd say 100% of theretailers that we deal with
(15:21):
that are either putting in self-service
and they might be on theirsecond or third iteration
of self-service because they've been
in that business for a while
or they're putting self-servicein for the first time.
A lot of retailers outside of grocery
are just trying self-servicefor the first time.
The approach is very, very different
and it's very much lessabout removing staff
(15:42):
from the equation.
More about staff redistribution,
the inability to attract
and retain staff in retail
is a real big problem for retailers.
So, they have to use their staff wisely.
And where the consumers value
the staff interaction themost is where they need it.
And where they need itis where they generally
they need help eithernavigating the store,
(16:02):
finding an item, asking aquestion about a particular item
or just general assistance.
What self service isreally playing a major role
in retail today is itunlocks that member of staff.
So, I would say to anyonethat looks at self services
and said, "Oh, that's goingto replace people's jobs."
It's not, it's very much aboutlabor redistribution now.
(16:23):
It frees up a cashier thatcould be sat behind a till
for 12 hour shift to be up on their feet,
engaging with consumer shoulderto shoulder in the aisle
where it really makes sense
to deliver that consumer experience.
So, particularly through Covid,
we saw retailers thatthat had self-service
had much greater flexibility of operations
within their store.
Post-Covid,
(16:43):
we're now seeing that actually allows them
to boost the level of consumer experience
where it really counts.
Obviously there's alwaysbeen friction associated
with self-service and theadage of unexpected item
in the bagging area,
all of those commonfriction points perceived
with self-service,
they're starting to really drain away.
(17:03):
A lot of focus has beenput on actually fine tuning
and making sure that thebase technology works
to a much, much more acceptable level.
And we're now seeing self-servicethat's very efficient
that generally most of the time
you can sell through atransaction with no intervention,
no requirement for a memberof staff to come over.
We are now in the kindof the fine tuning era
(17:26):
of self-service and why I say fine tuning
is we're really looking for that last 5%
or 10% of efficiency gains.
So, Diebold Nixdorf,
we've really focused onthree core solutions,
initially out the bag
and those three coretechnologies have been developed,
because we identified via the data
where the biggest friction points were.
(17:47):
So, age verification,
22% of interventionsbroadly are age related.
So, that's a big number.
If we can use facialrecognition to identify the age
of the consumer
and remove that validationprocess that's happening,
A, much better experiencefor the consumer.
B, it means faster transactions,
faster transaction means lessstaff requirement at the till,
(18:11):
but it also means that consumers
are moving through the front end quicker.
So, that means less queues.
Less queues,
queuing is the biggestbug bearer of consumers
when they get to checkout.
So, we've removed two ofthe biggest friction points,
associated with checkoutwith one piece of technology.
Item recognition, particularly in grocery
for fresh fruit and vegetables
was another area of frustrationfrom a consumer perspective,
(18:34):
but also inefficiency froma retailer perspective,
spending 20, 30, 40, 50 seconds,
trying to find the type of apples
that I'm looking to buy is frustrating,
but it's also time consuming.
So, using item recognitionto identify those apples,
so the consumer doesn't haveto run that process again.
Good consumer experience,great productivity gains.
(18:56):
And then finally shrink,
we touched on it a little bit earlier,
but obviously retail shrink hasreally gone through the roof
and I think a lot ofretailers are battling
to really understand whereis there shrink happening?
So, of course the natural progression
in that argument would be to say,
well, self-service is anatural place for shrink,
because it's unmanned ina lot of environments.
But what we're actually finding is A,
(19:18):
there's two differenttypes of people that steal.
There's people thatmaliciously try and steal
and those that have just made a mistake
and it's genuinely unmalicious.
And how you treat those two individuals
has to be dealt withvery, very differently,
because you don't want toalienate or embarrass the consumer
that's genuinely made a mistake.
For those that aremaliciously trying to steal,
(19:40):
unfortunately, if weclose all of the loopholes
and make it impossibleto steal at self-service,
they will find somewhere elsein the store to go and steal.
So, we're in this kind ofWhack-A-Mole type environment
where we're trying toclose all the loopholes
as quickly as possible.
We've really focused our efforts on AI
with behavioral tracking.
And the reason why weuse behavioral tracking
(20:00):
is once you can startto identify behavior,
it doesn't matter whereyou deploy the technology
within the store,
you can identify malicious behavior
and that shrink environment.
So, we very much focusat the front end first,
we're deploying shrinkonto self-service checkouts
and onto POS lanes.
But the idea is that the nextnatural evolution is that
(20:21):
then run that same solutiononto the CCTV network
and then we can identifyshrink anywhere in the store.
The human element of this
is really, really importantbecause it's relatively easy
to identify if someone has stolen.
What you then do in that scenariois a difficult situation.
(20:43):
What you don't want todo is alienate a consumer
that might have non maliciously stolen.
If they're maliciously stealing,
you also need to deal with thatin a particular type of way,
but you also don't want to put your staff,
your cashiers in your store A, in danger
or B, in an environment
that they don't feel comfortable with.
So, we are very much puttinghuman element back into this
(21:04):
that actually, depending onthe use case of the theft,
we will then deal withthat situation differently.
But what we will always do
is put the information inthe members of staff's hands,
so that they can deal with that situation
in the way that they see appropriate.
So, with all of our shrink solutions,
whether it's on self-servicecheckout or POS,
once the shrink instancehas been identified,
(21:25):
an alert is sent to a memberof staff wearable technology,
whether it's smartwatch or tablet or phone
or even their POS lane,
they're notified thatthere's a shrink instance
that's happened, theyknow where it's happened
and they can even review the video clip.
So, now they're empowered
that they know what'shappened in that situation,
they know what to look for
(21:46):
and then staff trainingreally comes into play here
and we have a number of great partners
that we work with on staff training
who actually work through these scenarios
to give the staff members the toolkit,
so that when they approachthat member of the public
and they're approachingthem knowing exactly
what's happened,
they're trained to be ableto deal with that situation
in the most agreeable way possible
(22:07):
to disperse any aggression
or any risk that might be associated,
but also to make sureit's a good experience
for that end consumer.
So, the technology is only onethird of the actual solution.
The human element is a massive part of it
that shouldn't be overlooked.
- And I think the change in roles
and responsibilities forcashiers to being able
(22:29):
to have more meaningfulinteractions with customers,
that's not only benefitingthe customer experience,
that's benefiting theemployee experience as well.
Maybe keeping employee retention.
I was a cashier in college
and I can tell you that is atedious and redundant process.
I would have dreams of just scanning food
and shouting out numbers
(22:50):
and it's not only retail shrink and loss,
I think it's not only withmalicious actors or by accident,
but sometimes as a cashier Iwould hit the wrong number,
just because I was onautopilot going repeat.
It was an error-prone process.
So, I can see that helping it as well.
You mentioned that to reallybe able to be successful,
you need an AI solution thatconnects all of these together
(23:10):
so that this is not happening in silos
and the data is actually actionable.
Obviously we're talking to Diebold,
because you guys are aleader in this space.
So, I'm curious to hear howyou are helping customers,
if you have any real world examples
or case studies thatyou can share with us.
- Yeah, and I'll be completely honest,
we fell into the most obvious trap,
looking back at our journey on AI.
(23:32):
We've been working on thisnow for two and a half years,
nearly three years.
And we originally,
we went out to market to tryand find the best solutions
to solve these three use cases,
but what we quickly found were
there was lots of differentcompeting technologies.
There were a lot ofpotential third parties
that we could have worked with,
but the underlyingtechnology was the same.
(23:53):
And we quickly realized thatactually as a solution provider
who retailers work with
to actually build out their technology,
not just across their checkout,
but all the way across the store.
It was unrealistic to think that
we could have 20 or 30different solutions,
all in the AI space, allproviding different use cases,
but none of them talking together.
(24:13):
So, we actually kind of paused our program
and redesigned our go-to market strategy,
which was very much focusedon providing an AI platform
and we work with a thirdparty in this space
who have a very, very mature AI platform.
Were entering the retail market
and didn't necessarilyhave the applications
to run on top of it.
So, we've worked with them
(24:34):
to actually develop outthese three applications
as a starting point in the AI space.
But the nice thingabout the AI platform is
it effectively becomes the AI backbone
for anything the retailerwants to do within their store
from an AI perspective.
So, this means that we canreally satisfy our openness.
It's an ethos that we drivein our product strategy,
(24:56):
which is openness of software.
And what we mean by that
is we provide the buildingblocks for retailers.
We are the trusted partner,we're the integration partner,
but if there's a particularthird party out there
who has got the market leading solution
in a particular area,
it doesn't make sense for usto go and reinvent the wheel.
So, when we talk about openness,
(25:16):
the ethos that we taketo our retail customers,
but also that permeates through our R&D
and product management ethosis to very much work with
the best of breed within the market.
And our strategy is to basicallyprovide this AI platform
for retailers.
We will provide applicationsthat can sit on top of it,
(25:37):
like age verification, shrink reduction,
item recognition, processor people tracking.
But if there is aparticular partner out there
that is market leadingin health and safety,
we can plug them on top of the platform.
And what that means isthe retailer can build
this kind of ecosystem of AI partners,
all providing best of breed solutions,
(25:58):
but critically they're allplugged into a single platform.
So, they utilize the same business logic,
they utilize commondatabases like item database
or loyalty schemes and things like that.
That makes the solutionsvery, very scalable.
It makes them much easier to manage,
but it also means that they'reall talking to each other.
And the beauty of AI
is its self learning to a certain extent.
(26:20):
So, the more applicationsthat we plug into this,
the more physical touch pointsthat we have in the store,
the more information isflowing through the platform
and then the quicker it can develop
and the quicker it can learn.
So, it's very much a selfprop perpetuating solution
that we're very much at thebeginning of this journey.
As I say, we've got about54 different customers,
(26:40):
using AI in one form or of another.
But we very much see this asa much, much longer journey
where we're starting to buildan ecosystem of solutions
that will ultimately move us towards
what we call intelligence store.
And intelligence store for us
isn't necessarily removingthe physical touchpoint
or removing the technology.
It's about providingintelligence to retailers.
(27:02):
And what I mean by that is every device
that sits in the store iseffectively a data capture device.
And that's a two-way street.
You can push data down tothem, you can pull data back.
So, whether it's a shelf edge camera
or whether it's a staff deviceor a self-service checkout
or a scanner or a screen,these are all data inputs.
There might be AI pointsolutions associated with them,
(27:24):
but the AI platform allows you
to connect all of these together
and create an intelligent store
where intelligence really permeates,
every single area of the store.
It does mean there's a hugeamount of data available,
but I think the retailers
that are really going to advance quickly
are the ones that work outwhat to do with this data,
because it can and it shouldinform every single decision
(27:45):
or direction that you take as a retailer,
whether it's how I price my products,
where my products arepositioned within the stores,
how I afford loyaltysystems to the consumers,
how I staff my stores,
how I operationalize them,
but also what technologyexists within the stores.
So data, it's a cliché,
but data will form the basisof every single decision
(28:07):
that we make from eithera technology perspective,
solution provider perspective,
but also from a retail operations
and a store design perspective as well.
So, it's a really, reallyexciting journey that we're on.
- Absolutely, and I thinkit's really important
to find a solution provider
that is willing to workwith others in the industry
and leverage their expertise.
(28:28):
I think that helps prevent vendor lock-in,
it allows you to take advantageof the latest technologies
and enables you to innovate faster,
working with some of the bestin the breed of the market.
So, speaking of best inthe breed, insight.tech
and the "insight.tech Talk,"
we're obviously sponsored by Intel,
so I'm curious if there'sanything you can tell us,
about that partnership andthe technology that you use
(28:50):
to make some of your AIretail solutions happen.
- Absolutely.
So, we work very, very closely with Intel,
not just on the AI topic, butfrom our core platform itself.
Intel very much underpins alarge part of our portfolio,
so we have a very, veryclose working relationship
with them, not just on the solutions
that we deploy into stores today,
(29:10):
but also our roadmap on our development.
We work very closely withIntel on their developments,
where they're going with their solutions
and how we can better integrate them
into our solutions to giveretailers better solutions,
but also much, much greaterflexibility for the future.
And I think a good example of that
(29:31):
is probably the speed ofdevelopment of technology.
If you think abouttraditional point of sale
or self-service checkout,
if you go back five or 10 years,
a retailer would make a choice
for that particular type of technology
and that would sit in that store
for five, seven, 10 years sometimes
as long as the technology is running,
(29:52):
the speed of development of technology
is in increased immeasurably.
The expectations of consumers
has also increased immeasurably.
And so balancing thosetwo is really, really key.
Where we work very,very closely with Intel
is on some of their scalable platforms.
So, knowing that retailershave a requirement today,
(30:13):
but particularly with these AI topics,
the amount of computing power
that will be required inthree or five or seven years,
will be very, very differentto the requirements today.
So, providing retailers theability to scale this technology
so that whatever they deploy today
is not throw away in two years time,
they can evolve it andscale that technology
(30:33):
to meet their technology requirements
at that particular time isan absolute game changer.
And that's something
we're working with Intelvery, very closely on.
- Yeah, absolutely agree.
Things are changing every day,
not even five, six, seven years from now,
but five weeks from now thingscan be completely different.
So being able to scale and to adapt
is especially importantinto today's landscape.
(30:56):
Well, it's been great hearingabout all of these solutions,
especially how Diebold ishelping retailers from end to end
with the item recognition,facial recognition
and retail shrink.
We are running out of time,
but before we go, Matt,
I'm curious if there's any finalthoughts or final takeaways
that you want to leaveour listeners with today.
- I think there's a lot of misconception,
(31:18):
particularly around AI.
What I would say is start with the data.
Identify the business requirements
or the problem that youare looking to solve
and then find the right provider
that's going to enable you
to deliver against thoserequirements today.
But also gives you thatlongevity of scalability,
because AI is a journey,
it's very much a solution thatlearns over a period of time.
(31:40):
So, choosing your solutionprovider is extremely important
because it is a marriageand it is a long marriage
and you have to make sure thatyou've made the right choice.
So, use the data to helpinform those decisions
and yeah, it'll be very,very exciting to see where AI
and retail technology goes,
over the next two, three, five years.
(32:00):
- Yeah, absolutely.
And I would say also choosea partner that you can trust
and transparent about howthey are using the data.
Like with the ageverification for instance,
you want to make sure thatthat data isn't being saved
or that anything going into that,
that system is goingto protect your privacy
and your information.
- Absolutely.
(32:20):
Data privacy is absolutely key
and is a very, very careful consideration
when you are designingor choosing the solution
that you want to deploy to stores.
- Excellent.
Well thank you again for joining us.
I invite all of our listeners to visit
the Diebold Nixdorf website,
see how else they can helpyou in the retail space,
as well as insight.tech,
where we'll continue tocover partners like Diebold
and the latest trends in this space.
(32:42):
Until next time, this hasbeen "insight.tech Talk."
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