August 31, 2025 • 34 mins
Rob Bate, Co Founder at FrontlineXP, shares insights from his extensive retail experience and explains how FrontlineXP is working to improve the experience of customer-facing employees through innovative technology solutions
• Frontline workers and operations teams are chronically underserved by technology and expertise
• The genesis of FrontlineXP came from wanting to help retail operations teams improve profitability and performance
• Successful transformation requires balancing technology, business process changes, and frontline worker experiences
• AI differs from automation in its ability to learn, adapt, and make decisions based on multiple data inputs
• Real-world AI applications are emerging in closed-loop workflows like Starbucks' drink preparation optimization
• Clean, comprehensive data is essential for effective AI implementation - "eat your vegetables" before expecting results
• Using AI primarily to reduce headcount in customer-facing roles undermines the service experience
• Technology should enhance rather than replace human interactions in retail environments
• Properly implemented AI can reduce employee stress, improve decision-making, and create more flexible scheduling
• Bringing loyalty and omnichannel data to associates can enable personalized service that doesn't feel like forced upselling
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Welcome to the Productivity Podcast.
Today, I'm delighted to bejoined by Rob Bate, co-founder
at Frontline XP.
Hi, rob.
Speaker 2 (00:07):
Hi how you doing.
Speaker 1 (00:08):
Yeah, good, thanks you.
Yeah, good, good, good.
So we're going to, I think,have a really interesting
conversation today about thework you're starting to do at
Frontline XP and how that kindof works into the world of
retail, hospitality and any ofthose organisations that have
got lots of people engaging withcustomers.
Before that, let's find out abit about you.
(00:29):
So I know some of your careerbackground from Boots, but for
the listeners that maybe haven'theard you before, do you want
to give us a bit of a career bioon how you ended up starting
Frontline XP?
Speaker 2 (00:41):
Yeah, I'd love to.
Yeah, I'll spare everyone thegory details of how you and I
know each other from our bootsdays, but I'll pick up from
there, I guess.
So I probably spent the last 25years or so in the retail space
and workforce management as asort of discipline probably an
equal split actually in retailindustry and consulting, and
also an equal split of US and UK.
(01:03):
So I actually moved to the USfor the best part of 11 or 12
years.
So when I left Boots in 2012, Ijoined a small consulting
company called RPL Group andthey landed a gig with a large
US client, otherwise known asWalmart, and they said we need
you to go out to Bentonville andhelp Walmart do this workforce
management project.
Would you be up for that?
(01:23):
And it sounded like a greatidea.
So I went out there for what wassupposed to be six months to 12
months ended up turning intomany, many years and actually we
grew, grew a business aroundthat.
The company got to a size ofaround 600 globally in the US.
We had a practice of about 95to 100 people, which was, which
was quite sizable, and wedelivered lots of workforce
management implementation andstrategy projects.
(01:45):
Walmart was one of them andthere's some other customers
like Starbucks, lululemon, giantEagle and a few others and we
then got acquired by Accentureand I spent three years
integrating the RPL US practiceinto the Accenture business and
then I moved back to the UK in2023 to focus on a company
called Avanade is what I movedback to the UK in 2023, to focus
on a company called Avanade iswhat I moved back to the UK with
(02:05):
, and that company is basicallythe Microsoft practice of
Accenture.
So I spent the last couple ofyears really focusing on
frontline worker technologythrough the Microsoft stack and,
in addition to that, helpingroll out and help clients
realize value from the co-pilotpackage and, naturally, ai,
generative AI and everythingthat falls off the back of that
is what I've been involved withover the last couple of years.
(02:27):
And then, truth be told, I gota little bit kind of fed up with
big consulting and wanted toget back to retail ops, hands-on
solving business problems, andI thought the best way to do
that would be to start abusiness with some co-founders
that I trust and have workedwith in the past, and that's
where we find ourselves today.
Speaker 1 (02:45):
Excellent, so a varied career.
It's amazing how many peopleI've spoken to at the moment
that have kind of spent sometime working abroad.
It feels like a more commonthing than not.
Did that make a big differenceand kind of shape your views or,
I suppose, market insight andhow you approach things now that
you've come back to the UK?
Speaker 2 (03:06):
Yeah, honestly it did .
I mean the sheer size and scaleof the US, not just in terms of
a sort of landmass, I supposeyou could say, but the business
culture.
There's a certain level ofadaption that you have to go
through to make sure that you'reunderstood.
It's a little bittomato-to-tomato in some
respects, but the scale ofbusiness out there and how it's
(03:28):
done kind of really gave me asort of broad exposure, if you
like, to how decisions get made.
We worked with lots ofdifferent clients, but the scale
of business there is really thething to behold.
And just to give you some ideaof that, I think Asda in the UK
has got something like 630 oddstores, something like this.
And in the US, like I mentioned,giant Eagle earlier on and
people are probably thinking whothe hell are Giant Eagle?
(03:49):
But they are bigger.
They're a regional grocer, theyoperate in just two or three
states and they are bigger thanAsda, both in terms of turnover,
employee count and number ofstores.
And nobody's probably everheard of them.
But that's the nature of the US.
And when you look at companieslike Walmart that have got you
know, 13,000 stores, 2 millionemployees, 1.4 of those, or 1.5
(04:16):
of those, are in the US.
You know it's just anotherlevel of complexity scale.
You know, expectation, evenfrom clients and what they
expect and what they define asvalue, is very different to what
I experienced in the UK upuntil that point.
So it definitely helped me getmy head around.
Okay, this is definitelydifferent.
There's different layers,different structures and also
all those retailers.
They work at different speedsand have different levels of
(04:37):
maturity and complexity.
So it gives you this hugeexposure.
And, coming back to the UK, youknow, after sort of 10 or 11
years not being here, I canbring that experience to the UK.
You know, after sort of 10 or11 years not being here, I can
bring that experience to thetable and it's actually really
insightful for a lot ofcustomers that you know they
want to understand.
You know, are they?
Are they very different totheir American counterparts?
Is their similarity?
You know, are we ahead of them?
(04:57):
Are we behind?
You know, this is obviouslygood insight that you can get
from all of that sort of stuff.
But yeah, to answer yourquestion, it definitely shaped
my thought process and just thescale of business.
There is just next levelbasically.
Speaker 1 (05:12):
Yeah, yeah, absolutely so.
Frontline XP.
Then tell us a bit about thethought behind it.
What created the idea?
What kind of stuff are youdoing?
Speaker 2 (05:23):
Yeah, I'd love to so the kind of I'll come to the
sort of pitch in a second but Iguess the thing that I saw,
especially over the last sort oftwo to three years you could
call it post COVID, I guess isreally just kind of the
frontline worker being quiteunderserved, and not just the
frontline worker, meaning youknow, the store manager, the
frontline employee, thecolleague, but also the sort of
(05:45):
operation side of the businessas well.
I saw this in the US and see itin the UK now and back here is
the operation side of mostbusinesses tends to be quite
underserved in terms of theirability to access things like
consultants that have depth andexperience in the upside.
We've seen a lot of the sort ofbig IT consultants offer
project and program managementand PMO and these sorts of
(06:07):
things, which is all well andgood, but it still doesn't
deliver the sort of things thatan expertise that an ops team
really require aroundforecasting, labor allocation,
workforce management, taskmanagement, employee experience,
helping to drive productivityon the floor.
You see a lot of these kind offunctions still being left to
the ops team to kind of figureit out, and they didn't really
get a lot of help in my view.
(06:28):
I think there's only a fewcompanies I've come across in my
history, you know, rethinkbeing one of them, where you're
in that position to really helpthem and I thought actually I
could probably help there.
So that was kind of the.
But I see frontline work andthe frontline worker being a
real area of of of change andinnovation and excitement.
Actually, I think you knowthere's a lot of talk about, you
(06:50):
know, next gen workforce andbeing demanding and their
expectations are shifting andchanging.
I actually find that sort ofstuff really exciting and if we
can help clients move into thatspace and take advantage of, you
know, and catch the wave ofwhat's coming, we can actually
put them in a really good spot.
And I just didn't see anyonedoing that really.
So I thought this would be agood opportunity to come into
(07:14):
that space and help.
So that's the kind of genesisof it, I guess.
In terms of Frontline XP andwhat we're looking to help with,
I guess the best way todescribe what we get up to is we
help clients improve theprofitability and performance of
their frontline workforce.
So we're very focused on thefrontline workforce.
Of course, central processesare key to that, but the
frontline worker and what theyget up to is the key thing for
us and we specialize inspecifically workforce
(07:35):
management, task management andwhat I'll call experience
platforms or colleagueecosystems.
But we do have a really broadview of a lot of different
technologies that the frontlineworker touches.
That could be learning anddevelopment systems, analytics
devices, even you know all thesethings go into a frontline
workers experience and the waythat we like to think of you
(07:56):
know how we can bring thesethings together.
Technology isn't the onlyanswer and we think that
bringing the right combinationof technology, business process
transformation and frontlineworker experiences can achieve
the overall outcome.
And we still see a lot ofclients out there going after
technology as the single solveor modifying a process and
hoping that will create somesort of transformational outcome
(08:17):
, and it really isn't the case.
You have to bring those threethings together and get them to
work in concert and in harmonyfor it to really make a
difference.
Bring those three thingstogether and get them to work in
concert and in harmony for itto really make a difference and
to bring that to life.
Really.
I guess really what we're sayingis we take a holistic view of
the frontline work and theworkforce and how we can bring
those things together to solvethings.
Like you know and these thingsare typically a front and center
for retailers and servicebusinesses but things like
(08:38):
lowering their operating costsso looking at planned and
unplanned labor, helping them toreduce the cost and impact of
turnover and disengagement,which I know is a big thing at
the moment and looking at thingsto improve their overall
productivity, reduce performancevariances from one store to the
next, from one bar andrestaurant to the next, and
warehousing there's a constantsort of chasing of variances
(09:00):
that are across business units.
So we can help clients solvethose sorts of challenges.
And then, second to that, wehelp clients take, I guess you
could say, a bigger swing or amore confident swing at things
like reducing complexity acrosstheir operation.
So we see a lot of applications, devices, information sources
out there, not necessarilystrung together in the right way
.
That can actually make workquite difficult for the
(09:22):
frontline worker to to get done,also makes it difficult for the
central teams to capture datafrom that and learn anything
from it, and causes a bit oftail chasing.
So we think, you know, reducingcomplexity is something that we
help clients with, and then acouple of other things enabling
AI.
Of course, you know every clientis saying where can I put AI
into my frontline?
Right, but which tech do I use?
(09:57):
I don't want to make a mistake,I don't want to buy the wrong
thing and then have to back itout.
And also, if I do use AI,where's the ROI going to come?
Is it within fiscal?
Is it going to cause me a bigsort of transformation effort
Like where do I begin?
Where do I start?
You costs.
And that's where we can startto look at things like
introducing schedule flexibility, changing how the work gets
done, how can we help managersmake better decisions that make
(10:17):
it a less stressful environmentand therefore, you know,
decisions can be not just ofhigher quality but also can have
a better business outcome andare made more consistently
across the business.
So we can help them in thosesort of two buckets of front and
center sort of tacticalimmediate challenges, as well as
help them with their long-termstrategy on how to become a more
lower cost business but alsoone that's perhaps more employee
(10:38):
centric, with able to leverageAI.
Speaker 1 (10:42):
Basically, yeah, very topical, with all the cost
challenges that are around atthe moment.
From again, we talk about it alot.
You know, all the tax rises,all the people cost rises, all
the shipping rises, all theuncertainty in the world that
leads to cost.
So very, very topical, yeah, ai.
(11:02):
So again, I think, on prettymuch every podcast we do now
there's some talk of AI, butwe're going to focus a little
bit more in this conversation.
So, automation on the frontline what type of things are you
(11:25):
seeing where AI is coming intoplay, and is it here now?
Is it something that's sixmonths away, 12 months away?
Describe the, I suppose, stateof the nation.
Speaker 2 (11:30):
Yeah, yeah, good question.
So in my view, I mean, I'llstate the obvious, I suppose, in
that there's been a heavy focuson the information worker thus
far when it comes to AI.
So CodePilot is a good exampleof the sort of package or
solution out there that'shelping people to, you know,
interrogate data and make itvery sort of conversational and
accessible for the most part,but it's really only going to
work for a laptop user.
(11:51):
So I think it's safe to saythat, from a hype cycle point of
view, we're very much past that.
Now.
I think there's a there's awide belief that AI is here,
it's real, it works.
It's just about applying it tothe right situations to then
figure out what benefit you wantto use it for, and I think
that's relatively sort of trueof head office users.
But when it comes to the sort ofand the reason why I talk about
(12:13):
information work is I thinkthat what it does, then, and has
done as well for clients thatI'm speaking to, it's sparking
this conversation of, and what'sgiving me confidence that we're
past the hype cycle is they'reasking okay, it works for me,
but how can I give it to amanager to help them make a
(12:33):
better scheduling decision?
How can I use AI to serve up aninsight that will guide them to
do the thing I want them to do?
How can I use AI to driveforecast accuracy and reduce the
number of people that I needacross the overall process?
Where can I automate some ofthat so that we can get more
people thinking about andmanaging outliers and these
sorts of things?
We see AI come up a lot in thescenario around.
Next best action is how can Ipush the right information to
(12:54):
the right person at the righttime on the right device to do
the thing, to take the action,to speak to the employee, to
edit the schedule in the rightway?
So these are kind of commonconversations that I'm having
all the time.
So that's why I know that we'repast the.
Is this going to be real or not?
It's not going to be like themetaverse.
You know that probably makeseveryone roll their eyes.
I think everyone's confidentwe've moved past that.
(13:14):
But in my view, this is where Ithink you know AI and
automation kind of come togetherand I do see them as very
separate actually.
So automation has been aroundfor the longest and the way that
(13:41):
I think of automation, or thebest way that I can think of
describing it, is that it's apredetermined outcome, it's
based on conditions, it'srepetitive in nature and
automation.
Automation is that it can learn, it can remember, it can adapt
and it can figure out how torespond based on all of this
input.
And those inputs could beeffectively infinite.
Right, a chatbot is probably acrudest version of AI, but it's
referencing what it should andshouldn't do.
(14:02):
It's referencing different datapoints so it can make a good
decision and provide multipleoptions, even that are probably
all good.
Right, and that's what makes itvery attractive.
For how can we present this?
Or use AI to presentinformation to a frontline
worker or a manager where,instead of kind of letting the
manager figure it out, perhapswe can use AI to say, well,
here's two or three optionsbased on what's going on.
(14:22):
You know, maybe there's sometraffic flow coming into the
store, maybe the weather ischanging.
Maybe there's some traffic flowcoming into the store, maybe
the weather is changing, maybethere's a late delivery coming
in.
You know all of these thingsthat a manager has to kind of go
through and go okay, well,what's my next best action?
What should I do now?
They typically do one of threethings Either do the thing they
did before, which probablywasn't particularly good, or
business focused.
They do nothing, take no action, or they do the easy thing,
(14:51):
which is to send someone on abreak and hopefully, by the time
they come off their break, thedelivery will be here and all
will be well with the world.
What they're not seeing is theimpact on sales, the impact on
missed opportunity to upsellproducts, things like this, and
it's that kind of somewhatinvisible data and also all
these other data signals that AIcan really kind of bring
together and serve up intosomething that is digestible and
useful and actionable for themanager.
So I think automation has itsplace, but you typically find it
(15:12):
in sort of backend processes, Iguess invoice processing, you
know this kind of if than else,like I said earlier, those sorts
of situations.
But a good AI strategyleverages, I think, both of
these.
So AI can really be kind offront and center just to the
customer, and we're seeing thatas well, you know, in places
like McDonald's and others,where it's, you know, suggesting
(15:32):
certain things based on yourbuying history and loyalty data.
But it can also be reallyuseful for the frontline worker
from the perspective of, youknow, helping them internally as
well.
You know, for things like HRpolicy, information, seeking
this sort of thing, but alsoselling product.
So you know, for things like HRpolicy, information, seeking
this sort of thing, but alsoselling product.
So again, you know, ai is, Ithink is going to be, a sort of
(15:53):
front and center technologythat's going to help an employee
be more effective in the moment.
So for me, the key questions,you know, to answer the question
directly, there's a fewcustomers or businesses that are
flirting with it at the moment.
I can give some examples ofwhere we're starting to see it
play out.
So AI is not magic.
(16:13):
It has to work within a definedworkflow.
It has to know what good andbad is, what's a good decision,
what's a bad decision.
To be frank, if you don't knowwhat your managers are doing
today, where a manager is beingmore successful with their
decision making than anothermanager, ai is not going to help
you there.
You've got to get under theskin of why is there a
difference and a variance?
What is the data points thatthe manager is looking at when
(16:33):
they're editing someone'sschedule, for example?
If you don't know that, ai isnot going to figure that out for
you.
It may give you some insightsand say, well, here are the
differences between those twomanagers, but what's the
motivation behind the schedule?
Edit, it won't be able to helpyou and therefore, you know,
training AI and modeling theoutput that AI can give you
won't be much use.
But the technology works, know?
(16:59):
I think that's the importantthing.
And just to give some example, Iguess, of what we're seeing out
there in the real world whereit's really playing a part, I
mean, there's a, an article fromstarbucks a few weeks ago.
Actually, they've got somethingcalled green dot assist just
for for those in the know or notin the know.
Rather, the green dot is whatthey refer to as their I'll call
them regular stores with thegreen Medusa logo.
They differentiate those fromtheir reserve stores, which are
(17:20):
their sort of big multi-storyexperience centers which you may
or may not have been to.
They're actually pretty, prettycool.
But for the barista, the usecase coming back to that bit
about defined workflow I saidearlier the use case is that
barista is stressed out becausethey're dealing with counter
workflow people walking in,they're dealing with mobile
orders, internet orders,optimizing the order in which to
(17:44):
brew each of those coffeesbased on the type of drink.
It is how long it takes toprepare so that they can
optimize around delivering adrink within five minutes.
That's their goal.
They've actually put AI aroundthat workflow, so it's their
effectively dynamically taskingon behalf of the baristas.
They haven't got to think aboutit, it's just kind of presented
to them hey, here's the nextorder.
(18:05):
It doesn't matter the order inwhich it was made, online or in
the store itself.
The brew order is then what'soptimized around that.
So that's where, in the moment,real time, super, super quick,
ai can help make those decisions.
That just takes the pressureoff the barista, and they've
made those decisions thousandsand thousands of times.
They just don't realize it.
So AI has got a really goodplace to play in that sort of
(18:26):
environment.
But we're also seeing thingslike Popeyes, taco Bell, other
sort of drive-throughrestaurants as well, use AI to
take orders.
Again, it's a limited workflow.
It's a limited menu.
The input is coming from thecustomer saying please, can I
have?
You've then got the opportunityto upward sell Do you want to
go large?
Did you want to drink with that, et cetera.
Those things are relativelyrepeatable, but it does need to
(18:49):
respond to the informationthat's going on at the time.
That's why AI, rather thanautomation, is probably a better
fit.
So I think we're starting tosee it emerge.
But it really does work well inthose kind of closed loop
workflows.
But as a business, you've gotto know what is a good decision.
Otherwise, ai will probablymake more bad decisions than
your managers do, unless youknow how to train them and what
information they should use tomake those decisions.
(19:11):
So it's coming, it's out therenow, but closed loop workflows
definitely works Open-ended.
What do you think I should dotoday?
Those type of questions?
It's not there yet, but that'swhere we see AI agents, you know
, starting to come and playtheir part in those workflows
and having very specificmandates in terms of how they
(19:34):
can help a manager make a betterdecision.
So we'll start to see moreagents as well be very
specifically deployed to tothose sort of situations.
Hopefully that makes sense.
I probably rabbited on a bitthere, but it's it's such a
broad area picking on one or twoand saying is that what is then
going to be prevalent across?
You know, all of retail is veryhard to say, but we're
certainly seeing it, you know,play its part in in certain
(19:57):
businesses at the moment yeah,and the one thing I suppose, as
we've educated ourselves more inin rethink, that surprised me.
Speaker 1 (20:05):
It's a bit like everything, isn't it?
The, the marketing of ai willdo this, that and the other for
you.
Great, and you know, I thinkthe reality is people are still
trying to find some user casesin retail.
If you're playing around onGrok or Gemini or ChatGPT and
making cool videos of peoplecats, skating on Mars in crash
(20:29):
helmets, eating ice cream, youknow brilliant and they look
great and they're, you know,movie quality, aren't they?
So I feel sorry for themarketeers out there Retail
probably still looking for it.
But the point I was coming towas the amount of good data.
You need to train some of thesethings.
I don't really know if we'veworked with a company yet.
That's got it.
(20:50):
They've got data, but good,clean, robust data.
There's hangovers from lockdown.
There's anomalies from X Y.
Robust data.
There's hangovers from lockdown.
There's anomalies from X, y andZ.
So that, for me, is the bitthat I'm not sure many people
have grasped yet.
Like anything, ai forecastingalgorithms, reports, you're
still good data in good resultsout.
Speaker 2 (21:11):
Yeah, 100%, and I think that's the bit.
I said this on a post actuallya few weeks ago and it got a few
likes.
It was put in my head bysomebody that I was working with
who said you've got to eat yourvegetables, which to until now,
but you've now got thishistoric issue with gappy data
and it being next to useless.
So you've got clients quiterightly saying, right, I want to
(21:43):
go, I want to put AI instraight away, and then you're
finding that you can't feed itwith anything useful and that
data cannot be correctednecessarily either.
We can perhaps restructure it,refactor it, but it really does
drive a direct impact on theresults that you get from AI.
And if I'd be really honest,you know, some of those early
customers that we were rollingCopilot to were saying, oh, this
doesn't work, it's rubbish.
(22:03):
You know it's giving, it'shallucinating and it's giving me
bad data, bad information, andit's because it was drawing on
internal documents, things likeHR policy documents that you
know you could only access.
Ones were for sort of 10 yearsago or whatever.
It was using the information asbest as it could to say, well,
here is what I think the answeris and people are like but this
is hr policy, it's got to beright.
(22:24):
It's like yes, that's whyyou've got to do some homework,
got to do some groundwork, gotto eat your vegetables, because
without that you really don'thave anything, and you know it.
Also true, on the other end,you know, without sort of
process change or behaviorchange, you won't get anything
from ai either, if you're notgoing to follow through with
what it suggests, but getting itto the point where it can
suggest something good anduseful, and that is, you know,
(22:44):
business outcome driven.
It really goes back to basics.
Obviously, my data in goodshape, and that's just not
necessarily what a client wantsto hear when they've got all
this excitement of all thesescenarios even going back to
what I said earlier, they've got50 scenarios.
I want to see where ai canspeed this up make a better
decision, push the rightinformation to the manager so
they act at the right time, likegreat.
(23:05):
But I need to slow you down.
The data is not there, so howcan we do it?
And that's where it's a bit ofa reality check.
But once that is in place andthose structures are there,
you're then off to the races.
So you know, for clients thatwant to dabble with AI.
You know it's great to buildthose videos.
Like you said, it's a bit of alaugh, but it's hard yards
(23:25):
getting AI to really work foryou.
But it is a big investment notto be understated.
Speaker 1 (23:30):
Yeah, and I'm sure every boardroom probably in the
world is conscious they shouldbe doing something with it.
I don't know if many peopleknow, as I say now, what they
should be doing, but it will beon the agenda for a conversation
and if it's not, that'sprobably a bigger worry.
So that's all kind of well andgood.
We, we help colleagues, weservice information to the right
(23:52):
people at the right time.
We get rid of some of the taskswe can automate and you know my
my bot speaks to the britishgas bot and works out my new
bill and comes back and tells mehow much I've saved and if I'm
happy with it and all that stuff.
But what's the impact on the endcustomer?
And then kind of one step backfrom that.
What's the impact on theemployee experience?
(24:13):
Because you know the news isfull of doom and gloom, of
interest rates are going to goup again, potentially
employments on the rise, whichtypically then means in retail
we end up with less people onthe floor.
That's counterintuitive withthe shrink situation.
So there must be an end benefitfor customer experience, but
(24:34):
hopefully there's also a benefitfor employee experience yeah,
for sure there is.
Speaker 2 (24:39):
Yeah, and my view on.
You know where can ai enhancethe employee experience?
I mean, making a betterdecision is one thing.
If we take the manager as agood example, if we can remove a
lot of the let's call itcognitive overhead, the what is
the right decision I need tomake at this moment in time, and
turn that more into insights,alternatives, options, choices.
That's going to help themanager make a better decision.
(25:02):
If they certainly if they havea choice of one or two, three
things that are all good for thebusiness are also good for, and
hopefully this is embedded intohow they sort of train the AI.
But making schedule edits as anexample, where it doesn't spend
over time but also is congruentwith HR policy and scheduling
policy, where we don't changesomeone's schedule on the day
(25:22):
that they're supposed to beworking, or the day before or
within the given week forchanging it weeks in advance,
you know giving the manager anudge and saying, okay, if you
change the schedule, you knowthe risk of this person leaving
goes up by 20%.
Do you really need to change it?
Is it really necessary?
You've got enough coverage.
You should be okay.
You still want to.
You still want me to change it,you know, and just having those
(25:44):
sort of checkpoints there forthe manager to go, you know what
, it's actually very useful.
Actually, you know what?
No, it's not worth it.
And just having that kind ofpause break, that's something
that ai is really good for aswell.
If you can serve theinformation up in the right way.
It means the manager's morefocused on their people, they're
less stressed about the 50 000things they have to do when they
come in and maybe there's justa little organized list of 10
things because ai is doing therest of it for them.
(26:06):
I think that's not just going tohelp with the stressful side of
things, but also, I think, youknow, with an external workforce
.
You know that maybe at thelet's call them a frontline
worker and hourly worker theylook at their manager at the
moment and go.
They have such a hard life.
I'm not sure I want to besticking around for that.
Maybe retail is long-term, isnot for me and they.
You know we see the turnover atthe sort of frontline worker
(26:26):
level where people don't want tolead a store or don't want to
be a team lead, and I think byhaving this kind of reforming
that that work.
It's going to help people seeretail, as you know, a
longer-term career and wherethey can lead other people,
especially if they don't believethey're going to have a ton of
admin to have to take on as well.
I think for the employeethemselves as well.
(26:47):
You know, using AI to do thingslike serving up shifts and
offering them up to people andhaving all of that managed.
I think it makes it lessdraconian than you know than the
manager scheduling everyone andhaving to manage every single
change, day off swaps somesolicited, some not.
It can remove a lot of thatpain and make it a bit more
democratized, so that employeesfeel like they have more of a
(27:09):
say in how they work when theywork, which is obviously a
really important thing Even now.
It'd be even more importantgoing forward as the Gen Z
population starts to make up abigger proportion of the
workforce, of course.
So I think AI is going to drivea lot of impact on employees.
I really do think and I have astrong opinion on this that
(27:29):
using it to remove roles on thesales floor is a bad idea.
I think it's very hard to growyour business and deliver
fantastic service if you havefewer people on the floor to
your point about shrink.
Those people are your brand.
People come to your storebecause they want an interaction
.
If you went to an Apple storeand it was all automated, I
(27:49):
think you'd be a bit miffedabout having to hand over 1,500
quid for a phone or a macbookand you serve yourself.
It's fine at mcdonald's becauseit's an order taking business.
Now, right, you know whatyou're getting.
We've probably all hadmcdonald's many times and it's
quicker, convenient and you buyinto the value that provides.
But for a service-basedbusiness, which is what
effectively retail is, using aito remove roles as a shortcut to
(28:13):
editing the labor model, forexample, or driving efficiency
purely for business benefit.
I think we see a glimpse ofthat in self-checkout.
Um, that I don't particularlyenjoy anymore.
Selfishly, it's gone so far.
The other way now I go into mylocal asda and there's one man's
checkout and 20 self-checkoutsyeah, there's limited choice to
(28:34):
do anything it's become a youknow it's effectively become an
obligation.
Now the choice has been removed,so it's.
I think when you get to thatpoint, it just makes it a bit
more, you know, uncomfortable.
But I think there'll be a lotof benefit for employees if, if
retailers and similar businessesemploy it for to enhance the
service, and they'll find thatactually they can leverage the
(28:55):
people they have in their storein a slightly different way to
be more customer focused, and Ithink they will actually enjoy
that.
If you're in retail, you're apeople person.
There'll always be some peoplethat want to be in retail to
then be in the warehouse.
Okay, the time for that role isprobably disappearing, but it
means we can spend more time onservice and actually when people
are working with people that wecan spend more time on service.
And actually when people areworking with people, that's when
(29:17):
they're most interested andexcited.
And if they get access toservices that make them able to
balance their work and theirpersonal life schedule
flexibility is a good example ofthat and they know what work
they're doing and they know whata good job looks like and they
get feedback and they get guidedthrough their day.
And for a new employee that'sjoining the business.
They get a curated onboardingexperience that is managed
(29:38):
through an ai process.
That's digital first.
I think people want thosethings and they'll find actually
, this is a good place to work.
Quite enjoy it.
I know what I'm doing, I knowwhat good job looks like and I
get feedback and I think thosesorts of things are really
important and attrition willdrop.
You know where we see employersdo some of these things.
Their attrition is ridiculouslylow.
We see a couple of those inthings.
Their attrition is ridiculouslylow.
We see a couple of those in theUS where they are
(29:59):
employee-centric, you know, andthey get the benefit.
It's you know.
I think businesses just have tohave the belief that these
things will turn into benefitFrom a customer point of view.
As long as that is passed on, Ithink they will get the benefit
.
I do also think as well we'veseen this with a few retailers
(30:20):
particularly the sort ofhigh-end, sort of luxury fashion
end of things which I guessprovides a glimpse of what's
possible elsewhere is bringingloyalty data to the surface so
that clienteling is more natural, you know, knowing that a
customer is in front of you,let's say they go to Lidl, lemon
or something, and if they're infront of you and they're
picking a pair of yoga pants orwhatever, if you had some form
(30:43):
of AI that could present to youas an employee.
Hey, by the way, did you know?
I can see that you're buyingthese pants here.
Did you know the top is 20% off?
I've only got one left.
Do you want me to go get it foryou?
You know, a customer's probablysitting there saying yeah, I
didn't.
I didn't realize that.
This is great, right, customergets a fantastic experience.
(31:04):
You, as the employee, areconfident because you've got
information in front of you thatyou can trust.
You know exactly where to goget that stock customer fit.
You know, and you put all thattogether and you go.
This is just a fantasticexperience that's very different
from the competitors.
Down the road, you know and youput all that together and you
go.
This is just a fantasticexperience.
That's very different from thecompetitors down the road, you
know, and when you startbringing in the sort of loyalty
data, digital data, omni-channel, if you can bring that
(31:24):
information and surface it tothe person that's in front of
you, they can deliver amazingservice and they can do it
confidently.
It doesn't feel like a horribleupsell.
You know where people thenstart to feel that kind of ick.
It can all be very, verynatural, you know, if those are
the sort of experiences that youwant to design.
So I think the customer can getan amazing output.
They can actually probably get,you know, offers and things
(31:52):
that are very unique to themserved up by a human.
I mean that's where I think AIcan really drive a proper impact
.
And you know you're then goingto see in average transaction
value, basket size and so on.
For me, the the outcomes arevery obvious, but it has to be
deliberately designed that wayto, I think, balance the
employee experience and thecustomer experience.
It's.
It can't all just be aboutinvesting the customer, give
them everything they want, andthen the employee comes like
(32:14):
second, third on the list.
You know, if the businessefficiency comes second,
turnover will still remain,people will still be disengaged,
and just because you presentsomething at the till, the
register or the tablet whereyou're serving that customer in
that yoga pant example I gavewell, what if the employee just
doesn't care and they don't wantto offer that top for 20% off
(32:37):
and it's the last one, then youhaven't made the sale, you know.
That's why all these thingshave to be threaded together and
designed deliberately inbalance, because if you don't,
what you'll gain in one area ofcustomer experience, you will
then lose an employee experience, and of course it's a cycle.
So probably convoluted answer,but that's kind of how I think
of it.
Speaker 1 (32:56):
No, that makes complete sense, and I think that
the key theme there was thatyou've got to pass the benefit
through.
So if you're using it as ablunt cost-cutting tool, then
clearly employees will sufferand customers will suffer.
If you're using it to free uptime and maybe there's a bit of
cost in there as well, becausepeople people always be
conscious, but not the primarydriver then you're probably
(33:18):
heading in the right direction.
So I think we could talk aboutthis for another couple of hours
, but we'll pause there.
I really appreciate your time.
If people want to continue theconversation, find out a little
bit more about how you can helpthem.
Where's the best place for themto reach out?
Speaker 2 (33:34):
yeah, good shout.
So I'm very active on onlinkedin.
I love to love to post everyday.
So, uh, you can hit me up on onlinkedin, drop me a direct
message.
We can connect there.
Uh, you can also go through mywebsite, book a book call with
me.
I'm an open book.
We want to help clients, youknow, understand the stuff and
have a perspective.
And actually something we Ishould have said earlier as well
(33:54):
is actually we're independent.
Much like yourself, simon, wewant the best outcomes for our
clients.
We won't back a singletechnology.
We're tech agnostic when itcomes to that sort of thing.
So we'd really love to haveconversations around how we can
help a client achieve theirbusiness outcomes without any
sort of bias.
So, just hit me up, book a call, pretty available.
(34:17):
So so, yeah, happy to discussthe business challenges, how we
might be able to help eitherbook a call or drop me an email.
Actually, it's robrob atfrontlinexpcom perfect.
Thanks, rob, appreciate yourtime yeah, thanks a lot, simon
cheers.
Welcome to the Productivity Podcast.
Today, I'm delighted to bejoined by Rob Bate, co-founder
at Frontline XP.
Hi, rob.
Speaker 2 (00:07):
Hi how you doing.
Speaker 1 (00:08):
Yeah, good, thanks you.
Yeah, good, good, good.
So we're going to, I think,have a really interesting
conversation today about thework you're starting to do at
Frontline XP and how that kindof works into the world of
retail, hospitality and any ofthose organisations that have
got lots of people engaging withcustomers.
Before that, let's find out abit about you.
(00:29):
So I know some of your careerbackground from Boots, but for
the listeners that maybe haven'theard you before, do you want
to give us a bit of a career bioon how you ended up starting
Frontline XP?
Speaker 2 (00:41):
Yeah, I'd love to.
Yeah, I'll spare everyone thegory details of how you and I
know each other from our bootsdays, but I'll pick up from
there, I guess.
So I probably spent the last 25years or so in the retail space
and workforce management as asort of discipline probably an
equal split actually in retailindustry and consulting, and
also an equal split of US and UK.
(01:03):
So I actually moved to the USfor the best part of 11 or 12
years.
So when I left Boots in 2012, Ijoined a small consulting
company called RPL Group andthey landed a gig with a large
US client, otherwise known asWalmart, and they said we need
you to go out to Bentonville andhelp Walmart do this workforce
management project.
Would you be up for that?
(01:23):
And it sounded like a greatidea.
So I went out there for what wassupposed to be six months to 12
months ended up turning intomany, many years and actually we
grew, grew a business aroundthat.
The company got to a size ofaround 600 globally in the US.
We had a practice of about 95to 100 people, which was, which
was quite sizable, and wedelivered lots of workforce
management implementation andstrategy projects.
(01:45):
Walmart was one of them andthere's some other customers
like Starbucks, lululemon, giantEagle and a few others and we
then got acquired by Accentureand I spent three years
integrating the RPL US practiceinto the Accenture business and
then I moved back to the UK in2023 to focus on a company
called Avanade is what I movedback to the UK in 2023, to focus
on a company called Avanade iswhat I moved back to the UK with
(02:05):
, and that company is basicallythe Microsoft practice of
Accenture.
So I spent the last couple ofyears really focusing on
frontline worker technologythrough the Microsoft stack and,
in addition to that, helpingroll out and help clients
realize value from the co-pilotpackage and, naturally, ai,
generative AI and everythingthat falls off the back of that
is what I've been involved withover the last couple of years.
(02:27):
And then, truth be told, I gota little bit kind of fed up with
big consulting and wanted toget back to retail ops, hands-on
solving business problems, andI thought the best way to do
that would be to start abusiness with some co-founders
that I trust and have workedwith in the past, and that's
where we find ourselves today.
Speaker 1 (02:45):
Excellent, so a varied career.
It's amazing how many peopleI've spoken to at the moment
that have kind of spent sometime working abroad.
It feels like a more commonthing than not.
Did that make a big differenceand kind of shape your views or,
I suppose, market insight andhow you approach things now that
you've come back to the UK?
Speaker 2 (03:06):
Yeah, honestly it did .
I mean the sheer size and scaleof the US, not just in terms of
a sort of landmass, I supposeyou could say, but the business
culture.
There's a certain level ofadaption that you have to go
through to make sure that you'reunderstood.
It's a little bittomato-to-tomato in some
respects, but the scale ofbusiness out there and how it's
(03:28):
done kind of really gave me asort of broad exposure, if you
like, to how decisions get made.
We worked with lots ofdifferent clients, but the scale
of business there is really thething to behold.
And just to give you some ideaof that, I think Asda in the UK
has got something like 630 oddstores, something like this.
And in the US, like I mentioned,giant Eagle earlier on and
people are probably thinking whothe hell are Giant Eagle?
(03:49):
But they are bigger.
They're a regional grocer, theyoperate in just two or three
states and they are bigger thanAsda, both in terms of turnover,
employee count and number ofstores.
And nobody's probably everheard of them.
But that's the nature of the US.
And when you look at companieslike Walmart that have got you
know, 13,000 stores, 2 millionemployees, 1.4 of those, or 1.5
(04:16):
of those, are in the US.
You know it's just anotherlevel of complexity scale.
You know, expectation, evenfrom clients and what they
expect and what they define asvalue, is very different to what
I experienced in the UK upuntil that point.
So it definitely helped me getmy head around.
Okay, this is definitelydifferent.
There's different layers,different structures and also
all those retailers.
They work at different speedsand have different levels of
(04:37):
maturity and complexity.
So it gives you this hugeexposure.
And, coming back to the UK, youknow, after sort of 10 or 11
years not being here, I canbring that experience to the UK.
You know, after sort of 10 or11 years not being here, I can
bring that experience to thetable and it's actually really
insightful for a lot ofcustomers that you know they
want to understand.
You know, are they?
Are they very different totheir American counterparts?
Is their similarity?
You know, are we ahead of them?
(04:57):
Are we behind?
You know, this is obviouslygood insight that you can get
from all of that sort of stuff.
But yeah, to answer yourquestion, it definitely shaped
my thought process and just thescale of business.
There is just next levelbasically.
Speaker 1 (05:12):
Yeah, yeah, absolutely so.
Frontline XP.
Then tell us a bit about thethought behind it.
What created the idea?
What kind of stuff are youdoing?
Speaker 2 (05:23):
Yeah, I'd love to so the kind of I'll come to the
sort of pitch in a second but Iguess the thing that I saw,
especially over the last sort oftwo to three years you could
call it post COVID, I guess isreally just kind of the
frontline worker being quiteunderserved, and not just the
frontline worker, meaning youknow, the store manager, the
frontline employee, thecolleague, but also the sort of
(05:45):
operation side of the businessas well.
I saw this in the US and see itin the UK now and back here is
the operation side of mostbusinesses tends to be quite
underserved in terms of theirability to access things like
consultants that have depth andexperience in the upside.
We've seen a lot of the sort ofbig IT consultants offer
project and program managementand PMO and these sorts of
(06:07):
things, which is all well andgood, but it still doesn't
deliver the sort of things thatan expertise that an ops team
really require aroundforecasting, labor allocation,
workforce management, taskmanagement, employee experience,
helping to drive productivityon the floor.
You see a lot of these kind offunctions still being left to
the ops team to kind of figureit out, and they didn't really
get a lot of help in my view.
(06:28):
I think there's only a fewcompanies I've come across in my
history, you know, rethinkbeing one of them, where you're
in that position to really helpthem and I thought actually I
could probably help there.
So that was kind of the.
But I see frontline work andthe frontline worker being a
real area of of of change andinnovation and excitement.
Actually, I think you knowthere's a lot of talk about, you
(06:50):
know, next gen workforce andbeing demanding and their
expectations are shifting andchanging.
I actually find that sort ofstuff really exciting and if we
can help clients move into thatspace and take advantage of, you
know, and catch the wave ofwhat's coming, we can actually
put them in a really good spot.
And I just didn't see anyonedoing that really.
So I thought this would be agood opportunity to come into
(07:14):
that space and help.
So that's the kind of genesisof it, I guess.
In terms of Frontline XP andwhat we're looking to help with,
I guess the best way todescribe what we get up to is we
help clients improve theprofitability and performance of
their frontline workforce.
So we're very focused on thefrontline workforce.
Of course, central processesare key to that, but the
frontline worker and what theyget up to is the key thing for
us and we specialize inspecifically workforce
(07:35):
management, task management andwhat I'll call experience
platforms or colleagueecosystems.
But we do have a really broadview of a lot of different
technologies that the frontlineworker touches.
That could be learning anddevelopment systems, analytics
devices, even you know all thesethings go into a frontline
workers experience and the waythat we like to think of you
(07:56):
know how we can bring thesethings together.
Technology isn't the onlyanswer and we think that
bringing the right combinationof technology, business process
transformation and frontlineworker experiences can achieve
the overall outcome.
And we still see a lot ofclients out there going after
technology as the single solveor modifying a process and
hoping that will create somesort of transformational outcome
(08:17):
, and it really isn't the case.
You have to bring those threethings together and get them to
work in concert and in harmonyfor it to really make a
difference.
Bring those three thingstogether and get them to work in
concert and in harmony for itto really make a difference and
to bring that to life.
Really.
I guess really what we're sayingis we take a holistic view of
the frontline work and theworkforce and how we can bring
those things together to solvethings.
Like you know and these thingsare typically a front and center
for retailers and servicebusinesses but things like
(08:38):
lowering their operating costsso looking at planned and
unplanned labor, helping them toreduce the cost and impact of
turnover and disengagement,which I know is a big thing at
the moment and looking at thingsto improve their overall
productivity, reduce performancevariances from one store to the
next, from one bar andrestaurant to the next, and
warehousing there's a constantsort of chasing of variances
(09:00):
that are across business units.
So we can help clients solvethose sorts of challenges.
And then, second to that, wehelp clients take, I guess you
could say, a bigger swing or amore confident swing at things
like reducing complexity acrosstheir operation.
So we see a lot of applications, devices, information sources
out there, not necessarilystrung together in the right way
.
That can actually make workquite difficult for the
(09:22):
frontline worker to to get done,also makes it difficult for the
central teams to capture datafrom that and learn anything
from it, and causes a bit oftail chasing.
So we think, you know, reducingcomplexity is something that we
help clients with, and then acouple of other things enabling
AI.
Of course, you know every clientis saying where can I put AI
into my frontline?
Right, but which tech do I use?
(09:57):
I don't want to make a mistake,I don't want to buy the wrong
thing and then have to back itout.
And also, if I do use AI,where's the ROI going to come?
Is it within fiscal?
Is it going to cause me a bigsort of transformation effort
Like where do I begin?
Where do I start?
You costs.
And that's where we can startto look at things like
introducing schedule flexibility, changing how the work gets
done, how can we help managersmake better decisions that make
(10:17):
it a less stressful environmentand therefore, you know,
decisions can be not just ofhigher quality but also can have
a better business outcome andare made more consistently
across the business.
So we can help them in thosesort of two buckets of front and
center sort of tacticalimmediate challenges, as well as
help them with their long-termstrategy on how to become a more
lower cost business but alsoone that's perhaps more employee
(10:38):
centric, with able to leverageAI.
Speaker 1 (10:42):
Basically, yeah, very topical, with all the cost
challenges that are around atthe moment.
From again, we talk about it alot.
You know, all the tax rises,all the people cost rises, all
the shipping rises, all theuncertainty in the world that
leads to cost.
So very, very topical, yeah, ai.
(11:02):
So again, I think, on prettymuch every podcast we do now
there's some talk of AI, butwe're going to focus a little
bit more in this conversation.
So, automation on the frontline what type of things are you
(11:25):
seeing where AI is coming intoplay, and is it here now?
Is it something that's sixmonths away, 12 months away?
Describe the, I suppose, stateof the nation.
Speaker 2 (11:30):
Yeah, yeah, good question.
So in my view, I mean, I'llstate the obvious, I suppose, in
that there's been a heavy focuson the information worker thus
far when it comes to AI.
So CodePilot is a good exampleof the sort of package or
solution out there that'shelping people to, you know,
interrogate data and make itvery sort of conversational and
accessible for the most part,but it's really only going to
work for a laptop user.
(11:51):
So I think it's safe to saythat, from a hype cycle point of
view, we're very much past that.
Now.
I think there's a there's awide belief that AI is here,
it's real, it works.
It's just about applying it tothe right situations to then
figure out what benefit you wantto use it for, and I think
that's relatively sort of trueof head office users.
But when it comes to the sort ofand the reason why I talk about
(12:13):
information work is I thinkthat what it does, then, and has
done as well for clients thatI'm speaking to, it's sparking
this conversation of, and what'sgiving me confidence that we're
past the hype cycle is they'reasking okay, it works for me,
but how can I give it to amanager to help them make a
(12:33):
better scheduling decision?
How can I use AI to serve up aninsight that will guide them to
do the thing I want them to do?
How can I use AI to driveforecast accuracy and reduce the
number of people that I needacross the overall process?
Where can I automate some ofthat so that we can get more
people thinking about andmanaging outliers and these
sorts of things?
We see AI come up a lot in thescenario around.
Next best action is how can Ipush the right information to
(12:54):
the right person at the righttime on the right device to do
the thing, to take the action,to speak to the employee, to
edit the schedule in the rightway?
So these are kind of commonconversations that I'm having
all the time.
So that's why I know that we'repast the.
Is this going to be real or not?
It's not going to be like themetaverse.
You know that probably makeseveryone roll their eyes.
I think everyone's confidentwe've moved past that.
(13:14):
But in my view, this is where Ithink you know AI and
automation kind of come togetherand I do see them as very
separate actually.
So automation has been aroundfor the longest and the way that
(13:41):
I think of automation, or thebest way that I can think of
describing it, is that it's apredetermined outcome, it's
based on conditions, it'srepetitive in nature and
automation.
Automation is that it can learn, it can remember, it can adapt
and it can figure out how torespond based on all of this
input.
And those inputs could beeffectively infinite.
Right, a chatbot is probably acrudest version of AI, but it's
referencing what it should andshouldn't do.
(14:02):
It's referencing different datapoints so it can make a good
decision and provide multipleoptions, even that are probably
all good.
Right, and that's what makes itvery attractive.
For how can we present this?
Or use AI to presentinformation to a frontline
worker or a manager where,instead of kind of letting the
manager figure it out, perhapswe can use AI to say, well,
here's two or three optionsbased on what's going on.
(14:22):
You know, maybe there's sometraffic flow coming into the
store, maybe the weather ischanging.
Maybe there's some traffic flowcoming into the store, maybe
the weather is changing, maybethere's a late delivery coming
in.
You know all of these thingsthat a manager has to kind of go
through and go okay, well,what's my next best action?
What should I do now?
They typically do one of threethings Either do the thing they
did before, which probablywasn't particularly good, or
business focused.
They do nothing, take no action, or they do the easy thing,
(14:51):
which is to send someone on abreak and hopefully, by the time
they come off their break, thedelivery will be here and all
will be well with the world.
What they're not seeing is theimpact on sales, the impact on
missed opportunity to upsellproducts, things like this, and
it's that kind of somewhatinvisible data and also all
these other data signals that AIcan really kind of bring
together and serve up intosomething that is digestible and
useful and actionable for themanager.
So I think automation has itsplace, but you typically find it
(15:12):
in sort of backend processes, Iguess invoice processing, you
know this kind of if than else,like I said earlier, those sorts
of situations.
But a good AI strategyleverages, I think, both of
these.
So AI can really be kind offront and center just to the
customer, and we're seeing thatas well, you know, in places
like McDonald's and others,where it's, you know, suggesting
(15:32):
certain things based on yourbuying history and loyalty data.
But it can also be reallyuseful for the frontline worker
from the perspective of, youknow, helping them internally as
well.
You know, for things like HRpolicy, information, seeking
this sort of thing, but alsoselling product.
So you know, for things like HRpolicy, information, seeking
this sort of thing, but alsoselling product.
So again, you know, ai is, Ithink is going to be, a sort of
(15:53):
front and center technologythat's going to help an employee
be more effective in the moment.
So for me, the key questions,you know, to answer the question
directly, there's a fewcustomers or businesses that are
flirting with it at the moment.
I can give some examples ofwhere we're starting to see it
play out.
So AI is not magic.
(16:13):
It has to work within a definedworkflow.
It has to know what good andbad is, what's a good decision,
what's a bad decision.
To be frank, if you don't knowwhat your managers are doing
today, where a manager is beingmore successful with their
decision making than anothermanager, ai is not going to help
you there.
You've got to get under theskin of why is there a
difference and a variance?
What is the data points thatthe manager is looking at when
(16:33):
they're editing someone'sschedule, for example?
If you don't know that, ai isnot going to figure that out for
you.
It may give you some insightsand say, well, here are the
differences between those twomanagers, but what's the
motivation behind the schedule?
Edit, it won't be able to helpyou and therefore, you know,
training AI and modeling theoutput that AI can give you
won't be much use.
But the technology works, know?
(16:59):
I think that's the importantthing.
And just to give some example, Iguess, of what we're seeing out
there in the real world whereit's really playing a part, I
mean, there's a, an article fromstarbucks a few weeks ago.
Actually, they've got somethingcalled green dot assist just
for for those in the know or notin the know.
Rather, the green dot is whatthey refer to as their I'll call
them regular stores with thegreen Medusa logo.
They differentiate those fromtheir reserve stores, which are
(17:20):
their sort of big multi-storyexperience centers which you may
or may not have been to.
They're actually pretty, prettycool.
But for the barista, the usecase coming back to that bit
about defined workflow I saidearlier the use case is that
barista is stressed out becausethey're dealing with counter
workflow people walking in,they're dealing with mobile
orders, internet orders,optimizing the order in which to
(17:44):
brew each of those coffeesbased on the type of drink.
It is how long it takes toprepare so that they can
optimize around delivering adrink within five minutes.
That's their goal.
They've actually put AI aroundthat workflow, so it's their
effectively dynamically taskingon behalf of the baristas.
They haven't got to think aboutit, it's just kind of presented
to them hey, here's the nextorder.
(18:05):
It doesn't matter the order inwhich it was made, online or in
the store itself.
The brew order is then what'soptimized around that.
So that's where, in the moment,real time, super, super quick,
ai can help make those decisions.
That just takes the pressureoff the barista, and they've
made those decisions thousandsand thousands of times.
They just don't realize it.
So AI has got a really goodplace to play in that sort of
(18:26):
environment.
But we're also seeing thingslike Popeyes, taco Bell, other
sort of drive-throughrestaurants as well, use AI to
take orders.
Again, it's a limited workflow.
It's a limited menu.
The input is coming from thecustomer saying please, can I
have?
You've then got the opportunityto upward sell Do you want to
go large?
Did you want to drink with that, et cetera.
Those things are relativelyrepeatable, but it does need to
(18:49):
respond to the informationthat's going on at the time.
That's why AI, rather thanautomation, is probably a better
fit.
So I think we're starting tosee it emerge.
But it really does work well inthose kind of closed loop
workflows.
But as a business, you've gotto know what is a good decision.
Otherwise, ai will probablymake more bad decisions than
your managers do, unless youknow how to train them and what
information they should use tomake those decisions.
(19:11):
So it's coming, it's out therenow, but closed loop workflows
definitely works Open-ended.
What do you think I should dotoday?
Those type of questions?
It's not there yet, but that'swhere we see AI agents, you know
, starting to come and playtheir part in those workflows
and having very specificmandates in terms of how they
(19:34):
can help a manager make a betterdecision.
So we'll start to see moreagents as well be very
specifically deployed to tothose sort of situations.
Hopefully that makes sense.
I probably rabbited on a bitthere, but it's it's such a
broad area picking on one or twoand saying is that what is then
going to be prevalent across?
You know, all of retail is veryhard to say, but we're
certainly seeing it, you know,play its part in in certain
(19:57):
businesses at the moment yeah,and the one thing I suppose, as
we've educated ourselves more inin rethink, that surprised me.
Speaker 1 (20:05):
It's a bit like everything, isn't it?
The, the marketing of ai willdo this, that and the other for
you.
Great, and you know, I thinkthe reality is people are still
trying to find some user casesin retail.
If you're playing around onGrok or Gemini or ChatGPT and
making cool videos of peoplecats, skating on Mars in crash
(20:29):
helmets, eating ice cream, youknow brilliant and they look
great and they're, you know,movie quality, aren't they?
So I feel sorry for themarketeers out there Retail
probably still looking for it.
But the point I was coming towas the amount of good data.
You need to train some of thesethings.
I don't really know if we'veworked with a company yet.
That's got it.
(20:50):
They've got data, but good,clean, robust data.
There's hangovers from lockdown.
There's anomalies from X Y.
Robust data.
There's hangovers from lockdown.
There's anomalies from X, y andZ.
So that, for me, is the bitthat I'm not sure many people
have grasped yet.
Like anything, ai forecastingalgorithms, reports, you're
still good data in good resultsout.
Speaker 2 (21:11):
Yeah, 100%, and I think that's the bit.
I said this on a post actuallya few weeks ago and it got a few
likes.
It was put in my head bysomebody that I was working with
who said you've got to eat yourvegetables, which to until now,
but you've now got thishistoric issue with gappy data
and it being next to useless.
So you've got clients quiterightly saying, right, I want to
(21:43):
go, I want to put AI instraight away, and then you're
finding that you can't feed itwith anything useful and that
data cannot be correctednecessarily either.
We can perhaps restructure it,refactor it, but it really does
drive a direct impact on theresults that you get from AI.
And if I'd be really honest,you know, some of those early
customers that we were rollingCopilot to were saying, oh, this
doesn't work, it's rubbish.
(22:03):
You know it's giving, it'shallucinating and it's giving me
bad data, bad information, andit's because it was drawing on
internal documents, things likeHR policy documents that you
know you could only access.
Ones were for sort of 10 yearsago or whatever.
It was using the information asbest as it could to say, well,
here is what I think the answeris and people are like but this
is hr policy, it's got to beright.
(22:24):
It's like yes, that's whyyou've got to do some homework,
got to do some groundwork, gotto eat your vegetables, because
without that you really don'thave anything, and you know it.
Also true, on the other end,you know, without sort of
process change or behaviorchange, you won't get anything
from ai either, if you're notgoing to follow through with
what it suggests, but getting itto the point where it can
suggest something good anduseful, and that is, you know,
(22:44):
business outcome driven.
It really goes back to basics.
Obviously, my data in goodshape, and that's just not
necessarily what a client wantsto hear when they've got all
this excitement of all thesescenarios even going back to
what I said earlier, they've got50 scenarios.
I want to see where ai canspeed this up make a better
decision, push the rightinformation to the manager so
they act at the right time, likegreat.
(23:05):
But I need to slow you down.
The data is not there, so howcan we do it?
And that's where it's a bit ofa reality check.
But once that is in place andthose structures are there,
you're then off to the races.
So you know, for clients thatwant to dabble with AI.
You know it's great to buildthose videos.
Like you said, it's a bit of alaugh, but it's hard yards
(23:25):
getting AI to really work foryou.
But it is a big investment notto be understated.
Speaker 1 (23:30):
Yeah, and I'm sure every boardroom probably in the
world is conscious they shouldbe doing something with it.
I don't know if many peopleknow, as I say now, what they
should be doing, but it will beon the agenda for a conversation
and if it's not, that'sprobably a bigger worry.
So that's all kind of well andgood.
We, we help colleagues, weservice information to the right
(23:52):
people at the right time.
We get rid of some of the taskswe can automate and you know my
my bot speaks to the britishgas bot and works out my new
bill and comes back and tells mehow much I've saved and if I'm
happy with it and all that stuff.
But what's the impact on the endcustomer?
And then kind of one step backfrom that.
What's the impact on theemployee experience?
(24:13):
Because you know the news isfull of doom and gloom, of
interest rates are going to goup again, potentially
employments on the rise, whichtypically then means in retail
we end up with less people onthe floor.
That's counterintuitive withthe shrink situation.
So there must be an end benefitfor customer experience, but
(24:34):
hopefully there's also a benefitfor employee experience yeah,
for sure there is.
Speaker 2 (24:39):
Yeah, and my view on.
You know where can ai enhancethe employee experience?
I mean, making a betterdecision is one thing.
If we take the manager as agood example, if we can remove a
lot of the let's call itcognitive overhead, the what is
the right decision I need tomake at this moment in time, and
turn that more into insights,alternatives, options, choices.
That's going to help themanager make a better decision.
(25:02):
If they certainly if they havea choice of one or two, three
things that are all good for thebusiness are also good for, and
hopefully this is embedded intohow they sort of train the AI.
But making schedule edits as anexample, where it doesn't spend
over time but also is congruentwith HR policy and scheduling
policy, where we don't changesomeone's schedule on the day
(25:22):
that they're supposed to beworking, or the day before or
within the given week forchanging it weeks in advance,
you know giving the manager anudge and saying, okay, if you
change the schedule, you knowthe risk of this person leaving
goes up by 20%.
Do you really need to change it?
Is it really necessary?
You've got enough coverage.
You should be okay.
You still want to.
You still want me to change it,you know, and just having those
(25:44):
sort of checkpoints there forthe manager to go, you know what
, it's actually very useful.
Actually, you know what?
No, it's not worth it.
And just having that kind ofpause break, that's something
that ai is really good for aswell.
If you can serve theinformation up in the right way.
It means the manager's morefocused on their people, they're
less stressed about the 50 000things they have to do when they
come in and maybe there's justa little organized list of 10
things because ai is doing therest of it for them.
(26:06):
I think that's not just going tohelp with the stressful side of
things, but also, I think, youknow, with an external workforce
.
You know that maybe at thelet's call them a frontline
worker and hourly worker theylook at their manager at the
moment and go.
They have such a hard life.
I'm not sure I want to besticking around for that.
Maybe retail is long-term, isnot for me and they.
You know we see the turnover atthe sort of frontline worker
(26:26):
level where people don't want tolead a store or don't want to
be a team lead, and I think byhaving this kind of reforming
that that work.
It's going to help people seeretail, as you know, a
longer-term career and wherethey can lead other people,
especially if they don't believethey're going to have a ton of
admin to have to take on as well.
I think for the employeethemselves as well.
(26:47):
You know, using AI to do thingslike serving up shifts and
offering them up to people andhaving all of that managed.
I think it makes it lessdraconian than you know than the
manager scheduling everyone andhaving to manage every single
change, day off swaps somesolicited, some not.
It can remove a lot of thatpain and make it a bit more
democratized, so that employeesfeel like they have more of a
(27:09):
say in how they work when theywork, which is obviously a
really important thing Even now.
It'd be even more importantgoing forward as the Gen Z
population starts to make up abigger proportion of the
workforce, of course.
So I think AI is going to drivea lot of impact on employees.
I really do think and I have astrong opinion on this that
(27:29):
using it to remove roles on thesales floor is a bad idea.
I think it's very hard to growyour business and deliver
fantastic service if you havefewer people on the floor to
your point about shrink.
Those people are your brand.
People come to your storebecause they want an interaction
.
If you went to an Apple storeand it was all automated, I
(27:49):
think you'd be a bit miffedabout having to hand over 1,500
quid for a phone or a macbookand you serve yourself.
It's fine at mcdonald's becauseit's an order taking business.
Now, right, you know whatyou're getting.
We've probably all hadmcdonald's many times and it's
quicker, convenient and you buyinto the value that provides.
But for a service-basedbusiness, which is what
effectively retail is, using aito remove roles as a shortcut to
(28:13):
editing the labor model, forexample, or driving efficiency
purely for business benefit.
I think we see a glimpse ofthat in self-checkout.
Um, that I don't particularlyenjoy anymore.
Selfishly, it's gone so far.
The other way now I go into mylocal asda and there's one man's
checkout and 20 self-checkoutsyeah, there's limited choice to
(28:34):
do anything it's become a youknow it's effectively become an
obligation.
Now the choice has been removed,so it's.
I think when you get to thatpoint, it just makes it a bit
more, you know, uncomfortable.
But I think there'll be a lotof benefit for employees if, if
retailers and similar businessesemploy it for to enhance the
service, and they'll find thatactually they can leverage the
(28:55):
people they have in their storein a slightly different way to
be more customer focused, and Ithink they will actually enjoy
that.
If you're in retail, you're apeople person.
There'll always be some peoplethat want to be in retail to
then be in the warehouse.
Okay, the time for that role isprobably disappearing, but it
means we can spend more time onservice and actually when people
are working with people that wecan spend more time on service.
And actually when people areworking with people, that's when
(29:17):
they're most interested andexcited.
And if they get access toservices that make them able to
balance their work and theirpersonal life schedule
flexibility is a good example ofthat and they know what work
they're doing and they know whata good job looks like and they
get feedback and they get guidedthrough their day.
And for a new employee that'sjoining the business.
They get a curated onboardingexperience that is managed
(29:38):
through an ai process.
That's digital first.
I think people want thosethings and they'll find actually
, this is a good place to work.
Quite enjoy it.
I know what I'm doing, I knowwhat good job looks like and I
get feedback and I think thosesorts of things are really
important and attrition willdrop.
You know where we see employersdo some of these things.
Their attrition is ridiculouslylow.
We see a couple of those inthings.
Their attrition is ridiculouslylow.
We see a couple of those in theUS where they are
(29:59):
employee-centric, you know, andthey get the benefit.
It's you know.
I think businesses just have tohave the belief that these
things will turn into benefitFrom a customer point of view.
As long as that is passed on, Ithink they will get the benefit
.
I do also think as well we'veseen this with a few retailers
(30:20):
particularly the sort ofhigh-end, sort of luxury fashion
end of things which I guessprovides a glimpse of what's
possible elsewhere is bringingloyalty data to the surface so
that clienteling is more natural, you know, knowing that a
customer is in front of you,let's say they go to Lidl, lemon
or something, and if they're infront of you and they're
picking a pair of yoga pants orwhatever, if you had some form
(30:43):
of AI that could present to youas an employee.
Hey, by the way, did you know?
I can see that you're buyingthese pants here.
Did you know the top is 20% off?
I've only got one left.
Do you want me to go get it foryou?
You know, a customer's probablysitting there saying yeah, I
didn't.
I didn't realize that.
This is great, right, customergets a fantastic experience.
(31:04):
You, as the employee, areconfident because you've got
information in front of you thatyou can trust.
You know exactly where to goget that stock customer fit.
You know, and you put all thattogether and you go.
This is just a fantasticexperience that's very different
from the competitors.
Down the road, you know and youput all that together and you
go.
This is just a fantasticexperience.
That's very different from thecompetitors down the road, you
know, and when you startbringing in the sort of loyalty
data, digital data, omni-channel, if you can bring that
(31:24):
information and surface it tothe person that's in front of
you, they can deliver amazingservice and they can do it
confidently.
It doesn't feel like a horribleupsell.
You know where people thenstart to feel that kind of ick.
It can all be very, verynatural, you know, if those are
the sort of experiences that youwant to design.
So I think the customer can getan amazing output.
They can actually probably get,you know, offers and things
(31:52):
that are very unique to themserved up by a human.
I mean that's where I think AIcan really drive a proper impact
.
And you know you're then goingto see in average transaction
value, basket size and so on.
For me, the the outcomes arevery obvious, but it has to be
deliberately designed that wayto, I think, balance the
employee experience and thecustomer experience.
It's.
It can't all just be aboutinvesting the customer, give
them everything they want, andthen the employee comes like
(32:14):
second, third on the list.
You know, if the businessefficiency comes second,
turnover will still remain,people will still be disengaged,
and just because you presentsomething at the till, the
register or the tablet whereyou're serving that customer in
that yoga pant example I gavewell, what if the employee just
doesn't care and they don't wantto offer that top for 20% off
(32:37):
and it's the last one, then youhaven't made the sale, you know.
That's why all these thingshave to be threaded together and
designed deliberately inbalance, because if you don't,
what you'll gain in one area ofcustomer experience, you will
then lose an employee experience, and of course it's a cycle.
So probably convoluted answer,but that's kind of how I think
of it.
Speaker 1 (32:56):
No, that makes complete sense, and I think that
the key theme there was thatyou've got to pass the benefit
through.
So if you're using it as ablunt cost-cutting tool, then
clearly employees will sufferand customers will suffer.
If you're using it to free uptime and maybe there's a bit of
cost in there as well, becausepeople people always be
conscious, but not the primarydriver then you're probably
(33:18):
heading in the right direction.
So I think we could talk aboutthis for another couple of hours
, but we'll pause there.
I really appreciate your time.
If people want to continue theconversation, find out a little
bit more about how you can helpthem.
Where's the best place for themto reach out?
Speaker 2 (33:34):
yeah, good shout.
So I'm very active on onlinkedin.
I love to love to post everyday.
So, uh, you can hit me up on onlinkedin, drop me a direct
message.
We can connect there.
Uh, you can also go through mywebsite, book a book call with
me.
I'm an open book.
We want to help clients, youknow, understand the stuff and
have a perspective.
And actually something we Ishould have said earlier as well
(33:54):
is actually we're independent.
Much like yourself, simon, wewant the best outcomes for our
clients.
We won't back a singletechnology.
We're tech agnostic when itcomes to that sort of thing.
So we'd really love to haveconversations around how we can
help a client achieve theirbusiness outcomes without any
sort of bias.
So, just hit me up, book a call, pretty available.
(34:17):
So so, yeah, happy to discussthe business challenges, how we
might be able to help eitherbook a call or drop me an email.
Actually, it's robrob atfrontlinexpcom perfect.
Thanks, rob, appreciate yourtime yeah, thanks a lot, simon
cheers.
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