Chapter 11 - AI

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Sue and Simon discuss Chapter 11 - AI from their book "Every Second Counts: How to Achieve Business Excellence, Transform Operational Productivity, and Deliver Extraordinary Results."
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Episode Transcript

Speaker 1 (00:00):
Welcome to the Productivity Podcast.
We are on the final chapter ofEvery Second Counts.
You might have heard thesummary by Notebook LM that we
pushed out, but this is realpeople, sue and I.
Hi, sue, hello, on artificialintelligence.
I wouldn't say this chapter wasan afterthought, but I think
it's fair to say, during thewriting process, which I was

(00:22):
clearly heavily involved in thewriting process, which I was
clearly heavily involved in thatAI grew legs and arms and
multiple heads and just wentballistic, didn't?

Speaker 2 (00:30):
it?
It did, and I think it's fairto say that it's probably
evolved since we.
The book was published as well,because kind of it was over a
year ago now, so it's a year's along time in AI, isn't it?
Five minutes, seemingly, is along time in AI isn't it?

Speaker 1 (00:43):
Five minutes, seemingly, is a long time in AI,
with things being developed,new machines, new bots, all
sorts of stuff.
Anyway, what did we talk aboutin the AI chapter of the book?

Speaker 2 (00:57):
Hopefully we were on the right track in as much we
talked about AI being a toolthat we saw as working alongside
people.
So particularly in things likehospitality, contact centres,
retail, where part of theexperience is that human contact
, then how can AI help that?

(01:17):
And we talked about kind of anumber of ways it might help.
You know it's been used inthings like contact centres for
a long time to help route peopleand manage demand, but I think
we're starting to see some newnew uses for it come through as
well.
Anywhere there's lots of data,it's it's a good application
priority.

Speaker 1 (01:36):
Yeah, and I've seen I suppose recently, to bring it
more up to date optimization ofthings like opening hours
capacity.
So, where you've got dedicatedtypes of organizations with
specialist people that needspecialist equipment or space to
work in, how do you optimizethe booking of those?
Or back to opening hours, themost convenient times to

(01:59):
maximize your demand based onwhen your customers are going?
Because in a world where and wetalked about numerous times, ni
, national Living Wage all thosethings that we know and can't
impact are happening to us, itseems like opening hours maybe
isn't one of the things thatpeople think about, it's just a
given.
But if you want to lay themodel with a minimum per opening

(02:21):
hour, every opening hour thatyou can save or get in the right
place has a significant salesor cost impact.
So there's, there's other datasets, like you say, that maybe
we've not explored yet andpeople are on the cusp of doing
yeah, no, I think there's goingto be some interesting things
and some useful applications.

Speaker 2 (02:40):
I think in there's a lot.
I think there's still a lot ofjargon talked about with ai,
which is something we put in thebook, and AI can be initials
added to anything and kind ofwhat is?
Ai is an interesting definition.
It's perhaps a bit differentfor different people.
If you think about it as a toolthat can help, then there's

(03:00):
some interesting use casescoming along and as those start
to be known about andaccelerated, I think it's going
to have some really big impactsin terms of how people do things
, and I don't think it's the,it's not the robots that are
coming and replacing everybody'sjobs.
It's about people workingsmarter and the things we've

(03:20):
always tried to do withproductivity in terms of getting
more benefit and more valuedelivered for every hour of work
input that you put in.

Speaker 1 (03:30):
Yeah, we've seen some interesting examples, haven't
we in, say, restaurants.
So where you've got people whoaren't necessarily in the store
taking the order for thedrive-thru, or even AI taking
the order for the drive-thru.
Can things be cookedautomatically, watching how long
it takes to cook something tothe optimum somebody really need
to be stood there and watchingthe fryer contact centers.

(03:50):
They were kind of at the cuspearly with all the call handling
technology and solutions.
But again, how does that grow?
How do you auto route thingsthrough?
And this isn't necessarily.
Are you on the cusp of again wetalked about it before this
intelligent automation pieceoffices.
So again, in a future world,will my ai bot talk to the

(04:13):
british gas ai bot and they'llwork out billing queries between
them and you tell them comeback and tell me what the best
tariffs to be on for my ev,whatever it might be.
So there's a whole bunch ofstuff in there and we shouldn't
forget retail.
You know it's there on the selfcheckouts there's a camera.
What's that camera doing?
Well, it's probably usingfacial recognition to do

(04:35):
something or other.
At the time of recording today,one of the supermarkets is
using ai to start to help withshrink, so recognised shot
lifters, which will beinteresting, again as a user
case, how that does or doesn'twork.
So, wherever you are, there'san application For me.
Time will tell whether it'strue AI or back to your point,

(04:57):
sue somebody's just put AI onthe end of something that
existed, which I think is a bitof Emperor's New Clothes.
It's out there at the moment,so we'll see, and we can all
make nice funny pictures of catswearing pyjamas on the moon,
talking into a mobile phone andall those lovely things, but
that doesn't really seem to bethe best use case of a bit of

(05:18):
fun.
But I'm sure you've all seenthe photos and the deep face
that exist for every type ofperson and walk away.

Speaker 2 (05:27):
And I think some of the if we think about instances
we've seen where you couldimprove workflows and streamline
things.
A lot of the things that get inthe way of doing that at the
minute are kind of gettingdifferent pools of data
connected and talking to eachother, and I know the tools
exist to make those connectionsbut actually they often seem a

(05:49):
bit more tricky to do than youknow.
It seems easy in principle tojust connect that data to that
kind of data, but actually itcan take a while.
So again, it'll be interestingto see if almost the development
of AI helps address some ofthose issues, because without it
it feels to me like it'll slowdown AI, whereas if there's
things that they can do whetherit's to do it a different way or

(06:11):
to speed up, to allow them tospeed up AI, it could have that
aspect of it could have widerimplications at all absolutely
so that that's the book.

Speaker 1 (06:20):
We've done it.
Um, clearly, I had a massivepart in writing it, so that was
a book writing process for you,seeing as you did most of it,
and let's be fair because we'vedone white papers and case
studies and that sort of thingbefore.

Speaker 2 (06:37):
We'd already got some of the material, which was a
good place to start from.
But actually it was interestingto reflect back on what matters
, what's important, and to thinkabout some of the different
case studies.
So it was an enjoyableexperience to get it all
together and then see it done.

Speaker 1 (06:55):
Just don't ask me to do another one.
I was going to say when's thesecond edition coming out.
So no, you never know.

Speaker 2 (07:01):
It has been talked about, it has been talked about
and we're thinking about someideas, aren't we?
Of what?
Would add value for people,because there's no point just
doing it as a vanity project.

Speaker 1 (07:13):
It needs to be something that's actually useful
for people, so I hope you'veenjoyed listening to us talk
through all the chapters.
Clearly, they're all availableas separate podcast episodes.
Hope you've enjoyed reading thebook.
If you haven't read the book,drops a note and I'm sure we can
sort your copy out.
Thanks for your hard work andit's soon.
You're welcome and we'll speaksoon.