Discover how artificial intelligence is revolutionizing the construction industry in our latest episode featuring Mahir Dheendsa. Mahir shares his valuable insights into the democratization of AI, highlighting how innovations are making artificial intelligence accessible to all, not just tech experts. We challenge common misconceptions about AI's novelty and explore the anxieties it brings to sectors like construction, where traditional roles stand at the brink of transformation.
Our conversation ventures into the heart of digital transformation in the workplace, tackling the crucial need for cross-generational support and education in new technologies. Mahir provides a thoughtful analysis of how companies are rethinking job roles and scopes due to technological advancements, and we reflect on the variations in digital adoption between younger professionals and seasoned employees. This discussion sheds light on the broader implications of technology across all educational fields and the cultural pushback that persists in some organizations.
The future of automation is here, and we paint a vivid picture of its potential impact on construction and beyond. From autonomous equipment to humanoid robots, we envision a future where AI platforms and digital literacy redefine operational norms. We also delve into the innovative tools reshaping industry practices and the exciting possibility of virtual roles in workplace safety. Tune in to hear how these advancements could transform traditional industries and embrace a digitally-driven future, all while celebrating Mahir’s impactful podcast debut and thanking our listeners for their ongoing support and engagement.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Hello everybody, how you guys doing Nice.
Can everyone hear us?
Okay, all right, so my name isJames Faulkner.
I am the host of the SiteVisitpodcast.
If you get a chance to listento it, we've got 150-something
episodes now that we've done alltopics in construction, a lot
of technology stuff.
I'm a founder of a technologycompany called SiteMax and did
(00:21):
that 10 years ago and so, yeah,love, love technology, like
driving it and um, yeah, so herewe're at the the building show
they've invited us.
We've done, uh, two otherepisodes before this and uh, now
we're here with mehir deenza.
How are you doing?
I'm good, james, how are you?
Ah, this is so so many podcastsbefore this.
(00:45):
No, this is my first one.
Well, so far you sound great.
Yeah, so you're good, you'regood.
So just a little intro here.
Mahir is the program and thespecialist for data integration
and transforming technology atLafarge so Concrete Company.
We talked before the podcast acouple weeks ago about some of
(01:08):
the great things you guys aredoing there, and he has a bee in
his bonnet when it comes to thedigital transformation, how
slow it is.
We've got one generation that'slike, hey, why do we need all
this stuff?
And you have the younger,younger generation going.
Why aren't we using all thisstuff?
So we got to get to this uh,this middle here where we have
uh some buy-in from all thedifferent generations and we've
(01:31):
got a lot of cool tech uh comingalong the way in terms of
software ai we're going to talkabout as well, and um yeah, so
give a round of applause for mehere.
Welcome to the site.
Visit leadership andperspective from construction
with your host, james Faulkner,recorded live on stage from the
(01:55):
Buildings Show in Toronto.
Speaker 2 (02:04):
Thanks for having me.
It's a privilege to be here.
Speaker 1 (02:06):
Yeah, you're welcome, you're welcome, Okay.
So I got questions and this iswhat we're going to get into Yep
, a number of things.
So it's big.
On the AI front, let's justchat about this for a second.
So you know, I was very quicklythere was a revelation very
fast that you really know thedifference between a lot of the
(02:28):
terms people hear, likegenerative AI, machine learning,
and a lot of people just stuffit all together and say, oh, ai,
this, and so maybe let's justtalk about a couple of things
there.
Just forget what are themisconceptions of ai?
Um, in general, in construction, like what, what are people
(02:49):
scared of?
What's the deal?
What's going on here?
Speaker 2 (02:51):
you know the biggest misconception in ai right now is
that ai is very new and chatgpt had a lot to do with that.
When chat gpt came out at somepoint last year, everybody
hopped on this and everyone'slooking at this like this is
some new technology and we haveto go all in on AI.
And you see all these companyexecutives pushing their IT
(03:14):
teams, pushing their digitalteams, saying, hey, you know,
take my money, let's build AI,whatever that means.
They don't know what that means, but just do something with AI.
Ai has been around for decadesand companies who've invested in
their digital infrastructureand data integration have been
leveraging AI for years.
Right, but what we really sawwith ChatGPT was a very good
(03:38):
model.
It's a language model, llm,which stands for large language
model and what it really did isit democratized AI Because it
made it available to the masses.
Anyone can go in, just uselanguage, natural language, and
do stuff with it, whereas beforeyou kind of had to be a
developer, know a little bit ofPython or some programming
(03:59):
knowledge.
So it democratized AI and thenthat's why it sort of went viral
.
But the good thing that cameout about through all of this is
it got us talking, especiallyin the construction industry.
It got us talking about AI andthen pushing us towards at least
the right steps.
Speaker 1 (04:17):
Okay.
So the misconceptions, though,is the fact that one of them
that it's totally new and it'sactually been around for a while
.
But what is new is this newparadigm of anybody can just use
this and I can.
You know, like my daughter canhack her algebra test right.
You can just basically put itin there and she can get out of
there and you know, on the youknow, doing a marketing program
(04:39):
or doing, uh, an rfp orsomething like that, you can
fire, you can put, put yourcommands or prompts right.
You put your prompts intoChatGPT and you can.
It will write an RFP for you.
Speaker 2 (04:53):
I mean it's crazy.
Speaker 1 (04:53):
You put the right information in it, so I think
that that, as you said, thedemocratization of using AI and
the general public having accessto this thing without having
any coding background or anykind of experience in computers
in general other than Gmail, andyou know, and Word Suite or
Microsoft products or Googlethat is.
(05:17):
I would agree with you.
That's sort of thatmisconception there, but it's
definitely changed quite a bit.
But in constructionspecifically, you know where do
you sort of see this going?
In terms of the, is there amisconception of the fear of the
sort of desk job being a threat?
You know project management,you know schedules.
(05:39):
That can be automatically doneIf you're going to put a
schedule together cannot begenerated.
You could say just like lastproject and and give it all the
different changes and suddenly,well, the thing that took me
like four days to do and now Ican have it on an hour yeah yeah
, so where's that kind of going?
Speaker 2 (05:57):
there's definitely a fear, and you know I've been in
this.
I've been in the space foralmost six years now and so I'll
tell you a little story.
When I started, I actuallystarted with Lafarge as a co-op
student fresh out of school andI come into the organization and
I start off as like a dataanalyst and one of my
responsibilities was producingmonth-end reports, and the
(06:19):
entire process was done usingExcel and I was just really just
copy pasting data from oneExcel file into another Excel
file, downloading data from thissource, that source, and then
producing this report and thenputting everything into a
PowerPoint to make it accessiblefor people, and I absolutely
hated this process.
Yeah, and I took a step backand I said you know, what are we
(06:40):
really doing here?
Right, we got to automate this,and so I did some work and we
wrote some scripts and became awhole project and I was able to
condense 40 hours worth of workinto maybe one hour.
And we're not talking aboutsome fancy AI, we're just
talking about just bare bones,fundamental automation and data
integration procedures.
And when I told someone in thecompany about this, they said oh
(07:03):
wow, that's pretty cool.
I hope you didn't tell yourboss.
I'm like what are you talkingabout?
He said well, you know, if itwas me, I would automate my job,
but pretend I'm working 40hours and then take the rest of
the week off.
And so a lot of times when wewent into site and tried to do
this for other people, there wasindeed that fear Like look, I'm
a data entry analyst people.
(07:25):
There was indeed that fear Like, look, I'm a data entry analyst
and now we can use AI and scanPDFs and use some sort of
training model to automate allthat data entry, and they take a
step back like hold on, so whathappens to my job?
But one thing that we're reallytrying to emphasize with the
program, the digitaltransformation program that I'm
leading at Lafarge is we're nottrying to replace people.
We're here to free up your timeso that you can do other things
(07:46):
.
And I use my example within thecompany as well.
When I freed up my time fromthat month-end task, I didn't go
obsolete, right?
I found there's tons of thingsthat we could have worked on and
I started working on some morecreative projects and actually
use my time more effectively,right.
Speaker 1 (08:02):
Than just copy-pasting here.
Yeah, no, I would agree, Iwould agree there on that.
But I mean, I think one of thepushbacks, I mean everyone's
going to have their sort oflet's say the word lived
experience on what they think isgoing to be their narrative to
that, to this discussion.
And if you were to look atsomebody who worked at a video
store, watch out for gone,because netflix is around,
there's no video there's goneBecause Netflix is around,
(08:24):
there's no video, there's noVHSs being rented around.
It's gone, gone, gone, gone.
Yes, so there are going to besome things that those positions
they're not going to be there,absolutely.
Speaker 2 (08:34):
The question is is that?
Speaker 1 (08:36):
what are we replacing that with?
What is that person going totranscend into?
That's going to be a meaningfulcareer.
And, uh, transcend into, that'sgoing to be a meaningful career
.
And obviously it's going to be.
Uh, okay, let me ask I'll getanother question, so I had this
conversation today on anotherpodcast was, let's say that a
project is a symphony, okay, andyou have all the different
(09:00):
players, uh, in in the symphony,you got the violinist, you got
the people on the tambourine,you've got the trumpets you got
all, you got the entireorchestra and then you have the
conductor and the conductor isis uh, you know, here's the
sheet music.
They know exactly what they'redoing and they're gonna they're
gonna make sure everything playsin sequence.
(09:20):
The question is, which job ismost at risk the conductor or
the orchestra?
Speaker 2 (09:26):
because of a I would say it's the orchestra.
Speaker 1 (09:30):
You would to an extent.
Yeah, I had somebody tell methe other day.
They think it's the conductorit depends.
Speaker 2 (09:36):
I think the conductor is the one who sort of brings
it all together, but there'sdifferent ways of looking at it.
Speaker 1 (09:43):
Yeah, I would agree.
Yeah, because I mean and thereason like that symphony works
well as a metaphor because inconstruction you have the one
major change clearly on thefield is the environment that
changes.
It's not like prefab where it'syou know, you're not building
Teslas where it's just like thehood is going on and then the
(10:03):
wheels are going on and it's thesame rinse and repeat in
construction is different everytime, right, because the, the,
the actual terrain changes, thebuilding is is being built and
it's everything's changing yeah,to an extent, but there are
still a lot of manual andrepetitive procedures as well.
Right, those are the what dothey call them?
The dull dangerous and 3ds, Ithink dull dangerous and, uh, I
(10:27):
don't know, okay there's a thirdone you haven't heard.
Speaker 2 (10:30):
No, no, not yet.
Speaker 1 (10:32):
Maybe I'll ask chat gpt, it'll probably know yeah
yeah, we see communications jobsgone, okay, okay, let's just
talk to that a little bit aboutum, like I think that people now
who are able to use AI tools tobecome more efficient, to get
stuff done, they can be theconductor in their own job,
exactly.
Speaker 2 (10:52):
And what I was getting at with the conductor
example is a lot of theserepetitive procedures.
And look, everybody has to goand fill out reports where you
might be conducting siteinspections, and there's always
these menial, repetitive tasksthat are part of really every
job, and there's usually themost draining tasks, right?
So the point is like therepetitive stuff can sort of be
automated.
We're seeing examples andprototypes now with these
(11:14):
autonomous AI agents which sortof work like AI employees, and
so now it's turning into asituation where everybody sort
of has their own AI intern andall that manual stuff, the data
entry, the sending emails,reviewing documents, all the
boring stuff that no one reallywants to do.
You can get AI agents to sortof do that, but you're still the
(11:35):
master of your own ship, right?
You've got to navigate that,and that's what I mean the
conductor, that's what theconductor needs to do.
Right Is set the goal.
Speaker 1 (11:52):
AI is not going to do that for you.
It's not going to replace youin the driver's seat, right,
okay?
So let's just say that, um,we've got.
Um, let's say shelly is workingand that's what she does, okay.
So shelly then is now uh, hasthey've gone through this
transformation meeting andthey've met with you and they're
like here's all these tools youcan do to do all your reports,
etc.
She's trained now to do thisstuff.
And then the company isthinking, wow, okay, so now what
do we do with her time?
(12:12):
Because it's a huge timesavings and so maybe the job
scope now changes.
Now that she is now able to dosomething, that is actually
going to add more projectconsciousness, because not stuck
behind a computer doing stupidreports all the time, even
though I'm saying reports arenot stupid, they're required but
it seems like such a bad use ofa human's time.
(12:35):
100 right so, but there's goingto be buy-in on the hr side of
keeping shelly around a, becauseyou know, construction, in my
opinion, is mostly good to mostpeople.
It's like a and most people aregood in terms of their
intentions around people theydon't want to like just fire
people and let go, especially inthe, in the sort of management
(12:55):
roles.
True, so shelly is like okay.
Well, we love shelly, we wantto make sure that she's around
and let's say shelly's like.
You know what I actually am,I've got a job somewhere else
and she leaves.
Now the job opening.
Will that job change or will itbe?
You know?
On the job description will itsay looking for PM or assistant
(13:21):
PM.
That does this and that's partof the job description, because
we will get there For sure,unless you don't know how to use
these AI tools in order to dowhat you do.
So I think schools are going tohave to show this as well, so
it's going to have to go all theway down the line.
Speaker 2 (13:34):
Yeah, schools, and I think it is changing.
Now we're starting to see itwhere basic, fundamental
computer skills are now beingembedded into every university
program and they're teachingthis in primary schools,
elementary schools now as well.
We know that engineers.
Before it was the case where ifyou're not a computer engineer
or a software engineer, you'renot going to know any sort of
programming.
But now we're even seeing civilengineers, who mainly deal with
(13:56):
concrete and structures eventhey're taking courses now on
programming.
So that's what's leading tothat shift.
Speaker 1 (14:03):
So you're right More people are going to have to use
this.
It's going to be a requirementthat's going to change the
entire project management school, trade school kind of paradigm
in terms of what's in thiscourse.
Speaker 2 (14:14):
But at the same time, I think especially now what
we're seeing with ChatGPT andnatural language, where you
don't necessarily need to be aprogrammer to interact with
these tools either yeah, they'repretty set up and forget it
right.
So the barrier to entry isdecreasing.
Speaker 1 (14:29):
Yeah, that's true, so it's going to be easier for
more people to participate, andI think what we're also seeing I
know this is based on theGoogle suite, for instance, like
Google Docs.
I mean you start to realize howgood that is at its predictive
stuff.
Speaker 2 (14:49):
I mean, mean it starts to be?
Speaker 1 (14:50):
like oh, okay, that's , do you want it this way?
I've been doing stuff onspreadsheets.
I'm like crap, this thing islike it's, it's, it's like in
terms of the formulas it'sgiving me hey, do you want this?
No, next one, next one.
Speaker 2 (14:54):
I'm like uh yeah, okay, that's pretty cool, right?
Speaker 1 (14:56):
yeah, it's pretty cool because, like, it just
takes the time down.
Yeah, um.
So let's just chat a little bitabout um.
We've got some pressures interms of the different
generations.
We have the generations who arepretty much tapped out in the
like how much, how many monthlyfees for SAS software do we need
(15:17):
to be paying for these licensesper users?
I mean the amount of moneythat's being spent and actually
being wasted on software that'snot being used.
It's true, there's tons of that.
And now there's this otherthing.
It's like okay, well, we nowwant to start using ai and there
is probably a okay, well, couldthat save you money or is this
(15:39):
going to make us more efficient?
There's going to be a curiosityor there's a.
Yes, but technology, technologyin general, not just AI, but
just digital transformation.
Why is there this resistancefrom the older generation
compared to the youngergeneration?
Who has this expectation that,if you're going to give me an
(15:59):
app and you're expecting me todownload and it's a bring my own
device to the job site andyou're going to give me an app
that sucks, why does this appthat I use for work, when I'm
doing my site orientation or I'mgoing to fill in my time card
for the day.
How come that doesn't feel likeAirbnb and Uber?
(16:20):
The expectation of how goodthat has to be down here and
then you have the othergeneration.
That's like, uh, yeah, I mean,how much of this do we really
need?
Speaker 2 (16:30):
it's true you know, in construction.
One thing I've noticed is thereis definitely an aging
workforce.
A lot of people are like a lotof baby boomers or have been in
the business for a while.
My old uh manager started withthe company when he was 25 and
he retired at 65.
And that's a trend.
I noticed, especially atLafarge, that people stick with
Lafarge for the entirety oftheir career.
(16:51):
That's not really the case somuch in this day and age, with
the new generation.
People are hopping jobs leftand right every couple of years,
every couple of years, and Imean it makes sense too, because
people want to change.
It's just the environment we'rein, but that definitely
contributes to the change.
But at the same time I don'tthink I would blame only that.
I think a lot of it is cultureas well, where there's sometimes
(17:16):
just resistance to technology.
I know I heard there were thesecases where they were
installing cameras into deliverytrucks that would just monitor
drivers, into delivery trucksthat would just monitor drivers,
and these drivers, in protest,would rip these cameras out
because they said this is justBig Brother watching us.
So there's a bit of resistancethere.
But at the same time and I'lltell you a little story about
one of the projects that I ledand what I learned from that.
(17:37):
I had a situation where welooked at the quality control
process and the entire processhad not changed since the 80s.
And that's just because themanagement had been around since
the 80s.
And so it looked like you walkinto this quality control lab
giant calendar on the wall, theway it has all the scheduling on
it.
They track all the test resultson pen and paper, put it into
(18:00):
like index cards and put it in alittle cubby, and then somebody
at some point will enter thisinto a computer.
And the point is, I mean, bythe time that data gets into the
computer it's probably too late.
Speaker 1 (18:10):
Oh and inaccurate.
Speaker 2 (18:11):
And inaccurate, because there could be mistakes.
Sometimes you can't read theirhandwriting, right.
So there's that.
And then so we said we're goingto digitize this process, we're
going to build an app.
It's a pretty and I said thisis going to be a quick win for
us, right?
So we sit in a boardroom andthis is important, my team we
sit in a boardroom we thinkabout what this process should
look like.
We build the app in a fewmonths, we take it to market or,
(18:32):
in this case, into the field,and we say here you go, this is
the app, it's all digital.
Now Thank us later.
Goodbye.
A month later or a few weekslater, we come back and we say
how's the app working?
It must be great.
And they say, yeah, you knowwhat?
We're not really using it.
I say why not?
I thought it would be ano-brainer.
And they said it's just notthere.
The pen and paper process waseasier for us.
(18:53):
It made sense, it's what we'reused to.
Speaker 1 (18:57):
And so that's what they did.
So can you just break down onthe different generations who
were saying and these are youngpeople too.
Speaker 2 (19:04):
These are technicians as well.
Yeah, people around my agesaying this stuff, right?
So I was thinking back.
I thought maybe this is justthe old heads who would push
against it.
But no, the young generationwas we're used to pen and paper.
It's just much easier.
So I go back to our management.
I say this is what I heard, andone of the responses I got from
management was give me theirnames, I'll talk to their boss,
we'll force them to use it.
(19:24):
Okay, because sometimes youknow what people are probably
just resistant to change andwe'll just force them to use it.
So I said okay.
So I go back a few weeks laterand I say are you using the app?
They're like, yeah, becausewe're forced to, we have to use
it, but we're also using the penand paper're actually still so
now they're doing both.
It's made it less efficient.
So I said okay, I'm definitelymissing something here.
(19:48):
I'm going to put my boots on,I'm going to put my hard hat on,
I'm going to spend a week withyou and let's go through this
process.
And then I realized you know,it's a construction site.
The way we set up the form islike you've got to go top to
bottom.
There's no safe progress.
You're wearing gloves, youcan't type on a phone.
So then I started seeing allthe little issues and, truly,
you know, you grab a pen, grab apaper, you just jot something
(20:08):
down, scribble something, andthen just forget about it.
So I realized that there weregaps in the app, and I think
that's contributing to the issueof adoption is the gap between
the boardroom and the field.
And so when I spent time on thefield and we really, really
refined that user experiencethat you were talking about
right, really get that down andthat process probably took maybe
(20:29):
three times longer than theactual development of the app.
But once we cracked that codeand we got the UX down to where
it needed to be, the app scaleditself.
And we started in one city, westarted in Hamilton, then we
started in Toronto, and thenthis app blew up and now it's
all over North America, evenincluding Mexico, and it came to
a point where, instead of usdealing with resistance from
(20:51):
these employees, people startedcalling me and saying, hey, I
heard this really cool app thatyou built.
When can I use it?
And so that's what I mean.
I think it's again the gapbetween the boardroom and the
field.
But once you spend time in thefield and address that gap,
these things will scalethemselves.
Speaker 1 (21:06):
Wow, that's a good story.
Yeah yeah, it is interestinghow we have this very high
benchmark level of userexperience that we require these
days.
It's true Because we all usethese apps on a daily basis.
Some of you might have useduber to get here.
I mean, I just flew in fromvancouver.
(21:28):
As soon as I land here, it'sshowing me all the places I went
when I was in toronto last timeI was here.
It's like which one of theseyou want to go to?
I'm like all right, there it isthat's where I went last time.
I mean it's just awesome rightand you know, I think that it
was the user experience thatanybody could have said look,
we're going to go and try andchange the taxi industry, but
(21:50):
unless that app was killerawesome on the driver's side and
on the customer side, it justwouldn't have worked.
So I think what you're sayingthere is that gap between the
boardroom and and the, the fieldworker.
Let's say we're the field level.
It's also like that in theoffice too, like sometimes the
(22:10):
software, the project managementsoftware, can be clunky.
It's like we're still dealingwith the email.
I mean yeah.
What's the a lot of companiesdealing with Slack these days.
Sure, yeah, slack, yeah, othersyeah, just real hands in
construction, slack Anybody flagyeah, okay, yeah, um, it just
(22:31):
seems we're in this time, rightnow, where things need to be.
Maybe ai is the solution.
I mean, the other day I lookedfor an ai email box cleaner
because I've got a couple ofgmail.
Like some of you guys, I've gota gmail, a couple of gmail
accounts, some I pay moreattention to than others.
I've got like 25,000 unreademails in a Gmail account.
(22:56):
I'm like, how do I even dealwith this?
And so an AI cleaner to just goin there and says okay, here
are all of the ones that youwere sponsored for, here are all
of the different contacts, hereare the ones that you opened
Any of these.
Do you want to just keepputting it down like that and
then delete the rest?
Speaker 2 (23:14):
Yeah, and I think you're right, ai will help.
I think it's a tool, maybe notthe only tool, but it is a tool
that would help.
One thing it definitely helpsus with, especially from a
development point of view, ismove faster.
Developers use AI tools towrite their code and also
improve their UI, and one thingwe're seeing now.
So before it's like you want tomake a UI change to a software,
(23:35):
there's a whole pipeline you'vegot to follow.
There is, yeah, but now there'sthese tools where it's
literally just a prompt movethis from here, move that there,
and it'll do the code in thebackground and change the UI.
So it helps you move a lotfaster, and I think what we'll
even see in the very near futureis customized user interfaces
for the end user.
Maybe you like seeing thingsone way, I like seeing things
one way, and it can all be veryprompt-based.
(23:56):
You don't need any codingknowledge and you'll be able to
customize your own uniqueexperience Exactly, and that's
one thing that we're finding.
Speaker 1 (24:03):
I mean, at SiteMax, a company I founded, we've got a
couple of views.
You've got one.
When you go into the project,you see that view.
And then, on the on the uh, thecompany side, you look at all
your projects.
You got that view.
I'm like, well, I will say to my, to my team, like well, maybe I
don't want to see that view.
Yeah, you know, like theselittle uh alerts that I'm seeing
here is just not relevant to me.
Um, but you know, what canhappen is if the structure of
(24:27):
the actual page or the mobileapplication is not ready for
like what?
If it looks really stark.
You know what I mean.
Like, let's say, you get a fieldworker, all they do is time.
Well, it'd be great if the appactually just was about time.
You know what I mean, ratherthan the empty box with just the
clock symbol.
You know what I mean?
Because it just makes it.
(24:47):
It's almost a self-esteem thing.
This is all I get to use, youknow.
So there's lots of things thatI think can be done on the UI UX
side.
Let's just chat a little bitabout when we had our call, a
pre-call.
Before, when we met each otheron the Zoom call, we talked
about risks of AI, like what aresome of the things that the
(25:09):
catastrophes that we need to belooking out for that could
happen here?
Speaker 2 (25:13):
So there's two things here.
One thing is it's all about thedata that you're feeding AI.
Right, it's all powered by data.
So if you have garbage datagoing in, you're going to get
garbage data going out.
So that's one issue, and Ithink that's something that
companies need to pay attentionto.
The second issue ishallucinations, and that's led
to a lot of funny incidents Iwould say humorous incidents
that we've seen.
(25:33):
I know we talked a little bitabout that Air Canada example
where they had this AI chatboton their website so somebody
asked it about their bereavementpolicy and it gave them
unfactual information and AirCanada had to honor it in court
because they said whatever thechatbot said is basically what
you have to honor.
There was another example where, I think, somebody convinced a
(25:55):
Chevrolet chatbot to give them anew truck for a dollar.
So there's always these risksand I think it just comes back
to corporate executives sort ofdriven by FOMO, the fear of
missing out, because we'reseeing these AI headlines
Everybody wants to cash in,nobody wants to be left behind
and sort of just jumping onthese things without really
understanding what they'resupposed to do and it's just
(26:17):
moving too quickly and notunderstanding the fundamentals.
But if you go back and you juststick to the data fundamentals
and do the data integrationright, you'll be fine.
Speaker 1 (26:25):
Yeah.
So when I look at AIintegration in the field, what
worries me a little bit isproject consciousness of okay,
that's got that dealt with forme, especially on the safety
side, like it is a.
I even see this now.
(26:45):
I see people who are justpunching radio buttons yeah,
yeah, yeah, yeah, yeah, yeahDone sign at the bottom.
They didn't actually look atthat one thing that was on that
line.
I see people who are justpunching radio buttons yeah,
yeah, yeah, yeah, yeah, yeahDone Sign at the bottom.
They didn't actually look atthat one thing that was on that
line.
They didn't look up and go,okay, yeah, that's good, some do
, hopefully most do, but someare just like going through
there and you add AI to that andlet's say it asks you a
(27:06):
question have you looked at this?
No-transcript if you start torely on something else.
Speaker 2 (27:38):
I've seen examples of that, especially like the data
entry example I mentioned, whereit's this great, big win we're
able to automate data entry.
You don't need to sit in frontof a laptop and enter numbers
anymore Great yeah.
But every now and then it mightmiss something, it might make a
mistake, and so if we're seeinga case where people just ignore
it and say, oh, the AI ishandling that, I'm just going to
(27:59):
move on.
Speaker 1 (27:59):
Oh, yeah, exactly.
Speaker 2 (28:00):
That's a bad practice , but I think it just comes back
into culture and establishinggood procedures and policies
within your organization andbeing mindful of that.
That it can make mistakes, but,to be fair, so could a human.
So, just making sure thatthere's this cross-validation,
that you're making sure thatthings are being done correctly,
but I think, look, it's alwaysthe response of the human in the
(28:21):
loop right.
Even when things are beingautomated, you've got to have a
human in the loop right, and AIis not here to replace us.
It's here to complement what wedo.
Speaker 1 (28:31):
Yeah, I like the human in the loop.
Wow, there needs to be awebsite with a human in the loop
.
You should make that.
Yeah, yeah.
Speaker 2 (28:37):
I don't think I came up with that.
I probably read it somewhere,oh you don't.
Speaker 1 (28:39):
Oh, I see.
Oh, that's cool.
So, in terms of, do youenvision this?
Let's go robotics, and let's gorobotics.
And so the hardware mixed withAI, like how is this going to be
manifesting itself?
Let's say, 30 years from now?
What's this job site going tolook like?
(29:00):
I mean, are we going to see asignificant decrease in the
amount of human beings on a jobsite?
Are we going to seedrive-by-wire?
Is it going to be installationby robots?
Like what do we?
I don't know, did anybody seethis?
The painting robot?
Have you ever seen this?
Yeah, I mean, the thing is, noone cuts as well as that painter
.
I mean, it's insane, it's true,it's perfect, right, and and
(29:24):
then that's a skill, right,that's a skill.
As a painter I'm.
I tried to paint my own, my own, uh, living space.
Yeah, not, it's not easy to todo a nice cut and not get on the
on the ceiling, that's true,but that thing's perfect and the
people who can do it havelearned over time with that
steady hand to be able to dothat thing, or they have a
specific tool that does that,etc.
(29:44):
But are we going to see this?
Just total transformation ofthe job site within 30 years?
Speaker 2 (29:51):
that's the million dollar question.
Right, are robots here to takeour jobs?
But you know, sometimes I say,instead of asking are robots
going to take our jobs, maybe weshould ask are our jobs turning
us into robots?
And and we, we look at.
You know, I like to use theexample and I get what you're
saying.
Speaker 1 (30:10):
We're already cyborgs right now.
I mean, if you look at mostpeople know I like to use the
example and I get what you'resaying we're already cyborgs
right now.
I mean, if you look at mostpeople walking around, they've
got their phone in their hand.
It's true, it's so bad.
I've walked around a number oftimes and I've said to my wife
honey, where's my phone?
She's like it's in your hand.
Oh, like I forgot it's there.
(30:32):
It's become this appendage,it's like.
It's like.
It's like.
So we're cyborg.
It's just that we can justdecouple it like we can just
drop it on the table.
And now I'm not a cyborganymore, even though I'm
yearning for that.
Will you ever leave your phoneat home?
That's terrifying.
You can't live without it.
No, I mean because we were justso connected to that.
That's right, okay.
So the question is let's go tolike what's the Neuralink
(30:52):
version of construction?
Look like.
Speaker 2 (30:56):
The Neuralink version of construction.
That's an interesting one, itis.
Well, we've seen what Neuralinkcan do, right?
You saw the monkey experimentthey did, where they installed
Neuralink into a monkey where itcan play Pong online.
So you're basically able tocontrol computers without using
your fingertips, right?
So are we going to see that,where people are plugged into
Neuralink and able to controlthings?
(31:17):
I don't know, it's tough to say, but one thing we can at least
see in terms of the trend isthat robotics are taking over
many procedures, especiallythings that are repetitive.
You mentioned the paintingexample.
Speaker 1 (31:30):
Here's automatic cranes or the dangerous stuff
like going into crawl spaces andall that kind of stuff.
That too right.
Speaker 2 (31:35):
We're seeing what drone technology is capable of
right in terms of LiDAR scanningand 3D modeling and just bridge
inspections.
Before you had to installscaffoldings to do inspections.
Now a drone just flies over andgives you a 3D model right.
Speaker 1 (31:53):
So, like, in terms of um, like, I always look at the
lowest common denominator of thedigital transformation side of
things.
So you know, I recentlyrenovated, you know, our condo
in vancouver and you know Igotta go get the drawings from
the city.
Okay, well, they're not givingme bim models and until all of
that is transferred over, we'regonna be stuck in that past,
because some of those aren'teven vector drawings.
Sure, they're raster old scansof, like hard drawings.
(32:16):
So you know for, in order, yeah, for sure, if you're going to
be building an airport, you'regoing to build a hospital,
that's going to be a bim model.
All the hvac, everything'sgoing to be in there, all the
layers.
You know about the stuff and andit's kind of ubiquitous on that
higher level, but that's notthe majority of the revenue and
construction in north america.
It's like renovations, it'slike ti's, it's building
(32:42):
warehouses, I mean it's it's notall the hospitals and airports.
So in order for that tech toreally really take hold, I
should be able to go to the cityplanning office and get a set
of 3D drawings.
You should be.
How long is that going to take?
And the question is will therebe an AI service that will
transform them into the 3D ones.
Speaker 2 (33:03):
That's a good question.
How long is the city going totake?
They'll probably be the lastones to adopt it, because it's
the government.
Speaker 1 (33:07):
But until then we're going to be stuck with 2D
drawing.
Speaker 2 (33:10):
It's true, and look even now, in terms of
information sharing, the choiceof most people is to share
information with PDFs.
If you're on a job site, you'regetting tests done, whether it's
a soil test or a concrete test.
Whatever.
That data is being transferredusing email and PDF, which is
one of the worst ways oftransferring data, because
(33:31):
someone has to open that PDF andthen probably type it into one
of their systems and thesesystems don't interact and don't
talk to each other, and I thinkthat's a problem that us as an
industry is going to need tocome together to solve right,
coming together with some sortof protocols or something.
Usually, I think, when there'sa large player in software, like
we see with Google's andMicrosoft's and these big
(33:51):
platform companies, they're theones who are really good at
driving that change.
The problem is these bigconstruction software companies.
They're very archaic andthey're due for disruption, and
I think that's what's going todrive all of this is, we disrupt
the big players, the bigsoftware players in construction
, and as more people adopt that,that's going to drive more
(34:12):
people to change.
Speaker 1 (34:12):
Yeah, I would agree with you there.
The question is is thateverything's typically about ass
covering, right If it goes tolegal?
Show me the PDF.
Speaker 2 (34:24):
That's true, it's true, it's what we're used to.
Speaker 1 (34:26):
Yeah, and so if everything comes down to the
fear of litigation and the fearof proving your point, then
you're going to want that lowercommon denominator thing.
It's true, that has that likewhat we're, I mean, with Sitemax
.
Right now we have threedifferent types of signatures.
One is certified and has acertified signature like a
docu-sign, and other ones arejust like yeah, we did it.
(34:48):
So, there's three differenttypes.
You can choose what you want onyour process.
But those different things, um,that digital signature is a
thing, right, so maybe that andit's a thing, a legal thing,
right, it means you were thereat that point and it was a
digital signature.
So, um, do you see thatchanging to?
(35:08):
Maybe you, you know, like afacial recognition that was me,
or like there's lots of thingsthat can change.
Speaker 2 (35:15):
It should be, I mean, yeah, the thing is, there's a
psychology concept where theystudied people who stick through
toxic relationships and thereason for that, they say, is
because we're not alwaysattracted to what's best for us.
We tend to chase what isfamiliar.
Yeah, and that's the thing Imean.
We can apply this to technologyand technology adoption as well
(35:37):
.
People don't like what's best,people like what's familiar.
So I guess it's something tothink about, especially for
digital transformation teams.
When we integrate newtechnology or introduce new
things, we have to make it seemfamiliar.
So there's less resistance tochange and I think you're right
that there's tons of ways ofdoing things better.
But it's about how do we makethis more resistance-free and
(35:58):
familiar.
Speaker 1 (35:59):
Yeah, I would agree with you.
There does need to be atechnology change on the
software side that is going torevolutionize something.
I remember back when PlanGrid,which is now an Autodesk, when
someone would say here, I'lljust send you the drawings.
They're like what, what do youmean?
Speaker 2 (36:17):
send me the drawings.
Yeah, just give me your phonenumber.
Speaker 1 (36:20):
Boom, there are the drawings.
I mean, that was atransformational change that
changed a lot of things.
People didn't have to have thatroll up of drawings in the tube
in order to get someinformation, it's true, right,
so you could do, you know, uh,you could just mark up a
screenshot of drawings and, justyou know, send it off to
someone else, that they could,you know, make a change of
(36:40):
drawings in other ways.
So I mean, this does that.
I just wonder what that nextthing is that's going to be like
on the jobs, like holy crap,have you seen that thing?
Speaker 2 (36:50):
It's true.
And, look, it's about findingthe incentives for the user
right.
If it can make your job easier,it's a fun feeling, it's a good
, comfortable feeling.
The problem is that, again backto that, we talked about the
city giving you scans, right.
What's their incentive to giveyou a 3D model?
Right?
And that's something we've gotto think about.
Even testing companies sendingyou PDFs with test results.
It's a pain for me to have togo through that PDF, but for
(37:13):
that company it's just export aPDF instead, right?
So it's and that's something weneed to figure out and
especially software companiesneed to come together.
And how do we incentivize thatfor the user?
Speaker 1 (37:23):
on the other side.
Yeah, because all they'rereally doing is just
transferring the inconvenience.
True, exactly the transfer ofinconvenience.
That's how construction isoften right.
Oh, here you go, you got yourthing, now you deal with it.
Oh, you got your thing, now youdeal with it.
It just keeps getting punteddown the line.
Speaker 2 (37:39):
Yeah, and again, I haven't figured out how to
incentivize that procedure yet,but I think that's something for
us to think about.
Speaker 1 (37:46):
Yeah no for sure.
So let's just chat about whatyou've done with Lafarge and et
cetera.
So you've been utilizing sometools there, utilizing AI,
different types of technologies.
Take us through some of thestuff that you've been doing,
Maybe some examples that peoplecan draw from.
Speaker 2 (38:00):
Sure.
So there's tons of examples.
One of the things is we take adata-first approach.
So when I started, like I toldyou, that month-end process,
everything was very Excel-based.
That was the case for amajority of the organization,
right, A lot of procedures werestill pen and paper.
So that's the first step is wegot to apply good data
integration principles, and whatthat means is your organization
.
You have tons of softwares,tons of systems, data scattered,
(38:23):
siloed all across.
You need to enforce a data lakeand a data warehouse
infrastructure, which basicallymeans you integrate all these
sources and bring all of thedata into one place and then you
clean it up and you organize itin a good relational format.
So is that what you guys did?
Speaker 1 (38:39):
That was the first step right Forget about
everything else.
Speaker 2 (38:41):
You've got to focus on your data fundamentals and
once you get there now you canhave advanced analytics.
Now you can apply machinelearning, and so then we work on
some pretty cool projects.
So right now we're working on aproject to as you probably know
, uh, cement is one of theleading causes of co2 emissions
in the world.
That's because co2 is abyproduct of cement production.
(39:02):
So anything we could do toreduce cement or increase cement
efficiency in our concretemixes is a huge win for us and
for the planet.
So that's what we're doing iswe're building these machine
learning models that are trainedon our historical performance
data, all the ready mix orconcrete tests that we perform,
and then trying to find cementefficiencies to reduce the
cement.
So that's a project we'reworking on.
(39:23):
We're piloting a project rightnow in one of our aggregate
quarries where there's this longtwo kilometer conveyor belt,
and right now we had twotechnicians whose job was to
just drive along this belt andmake sure there's no kinks, make
sure there's no cracks or anyissues with the belt.
Now we've actually employed arobot that's going to do the
(39:44):
exact same thing.
It just has a camera on it, itdrives around the conveyor belt,
and so we freed up time of youknow two technicians so that
they can go and perform otherthings.
And that same robot can do morethan just that it goes around,
it checks for safety, health andsafety violations.
Take us through that.
How does it do that?
It's just a robot on wheels, itjust drives around, it has ai
(40:13):
built into it and so it's atrain to to perform these tests,
because we've taken images andfed it historical data so it
knows what a problem could looklike.
Okay, so what?
Speaker 1 (40:18):
just just to get really in the weeds on that for
a second.
So what?
What specific things has itfound so far?
Speaker 2 (40:27):
Well, every now and then there could be a kink or a
tear or some issues within theconveyor belt and it'll take a
photo and it'll immediatelyalert somebody to address that,
pretty much the same way a humanwould.
At the same time it's alsodoing, like I said, those health
and safety inspections.
That's the part that I'mtalking about though Sure the
health and safety.
Speaker 1 (40:49):
What are the specific endpoints that it is finding or
looking for or checking?
Speaker 2 (40:52):
or cross-referencing, so as a robot is driving around
the site and you would havehealth and safety personnel do
the same thing, or if you seesomebody for example, but they
might be doing a checklist.
They do a checklist.
They do a checklist, so therobot does a checklist it has a
checklist as well, Okay so howdoes?
And there is a daily.
Is it seeing the checklistCircle?
Speaker 1 (41:07):
check.
Okay, yeah, yeah, okay.
So does it go to certainwaypoints then?
Speaker 2 (41:12):
It has a 3D model of the site in its system.
Okay, so it knows where thesite is.
Okay, gotcha, and yeah, you'reright, it sort the ability to
maneuver.
If there's an obstacle in theway, we'll go around the
obstacle.
(41:32):
So it does have some no coffeebreaks.
No, no coffee breaks,unfortunately.
But so that's what it's doing.
It's just taking photos again,just the routine tasks that
technicians would do.
Speaker 1 (41:35):
It's it's able to perform those routine tasks okay
, so, um, so, do you see that aswe move forward?
Do.
Do you see a virtualsuperintendent?
Do you see a virtual health andsafety officer on a job?
Speaker 2 (41:51):
To an extent, yes.
And one thing I do want toemphasize, though, and it's a
very important principle whenworking in automation.
There's a famous quote thatsays automation applied to an
inefficient system will onlyenhance the inefficiency, and
that's important for us torealize.
And even with this example, too, sure, we could have this robot
that drives around, but we alsoneed to think about is this the
(42:12):
best way of automating thatprocedure At the same, so that
robot can only be at one spot atone time?
Right?
In certain use cases it makessense.
It's a very long belt.
You've got to drive around thatbelt.
But certain cases, if we're justlooking for only health and
safety violations on your site,it may be more efficient to just
install cctv cameras, yeah, andhave real-time view all of the
(42:35):
time, instead of only when thatrobot is present.
So so, to an extent, yes, but Ithink software may replace a
lot of the hardware as well.
So you don't necessarily need ahumanoid robot.
Who's now the health and safetyinspector?
It's more of a procedure or asystem that's embedded and
integrated within all yourintelligence systems within the
company.
Speaker 1 (42:54):
Yeah, it's funny.
I had this podcast I've donetwo of them now with a company
called Super Droids Okay, andthey build these little robots.
With a company called superdroids okay, and they built
these little robots and, um, Ihad this one, they were on video
and they said do you want tosee this latest project?
And I said, okay, sure, andthey, and they said, well, it's
(43:17):
a humanoid, I'm like a humanoid,like how much money do you guys
have?
First of all, I mean to make ahumanoid pretty difficult, and
so I said, well, why would youmake, why would you go through
the brain, brain damage oftrying to get this thing to walk
and balance and do all thethings that we just find so
obvious as humans?
And he says, well, it doesn'tneed to do that, you just need
to sit it in the form factorthat was made for a human.
(43:39):
Okay, because, like in anexcavator, for instance, like
everything's here, the pedalsare here, it didn't need to
stand up and what it does is itretrofits, in a way, all old
equipment.
There's a lot of it out therewith the humanoid thing in it.
Yeah, yeah, yeah.
So as long as the actuatorswork in the hands and the
(44:01):
cameras there, yeah, and I thinkthis is what Tesla is doing
with theirs.
This is where they're seeingthis going.
So that 30-year thing, do wehave these flesh people and then
plastic people everywhere?
Speaker 2 (44:20):
There's something that has to be said about that.
And look, I don't know, I saylook, if you want to build a, a,
if you want to design aself-driving car, right, first
you build a robot and then therobot sits in the car and then
it drives the vehicle.
But you look at it like thesame example I gave you about
inefficiency right, that's avery inefficient way to have a
self-driving car.
(44:40):
Yeah, you look at what tesla'sdoing.
They, they've gotten rid of thesteering wheel.
You, you don't need thesteering wheel, right, you just
build that into the car.
So, again, into excavators ofthe future, you're probably not
going to have the pedals and allthese controls, you're just
going to get rid of them andeverything's going to be built
into the software.
Right, because that's the moreefficient way of doing it.
But back to the concept offamiliarity right, the majority
(45:01):
of the world is built for humansand we've relied on human
operators for a long time.
And I think, to address thatresistance of change and to
really push this forward atfirst, maybe humanoid robots are
the answer, because you sort ofhave this generic robot that
can perform many differentthings.
Speaker 1 (45:20):
Sticking in the old system.
Speaker 2 (45:21):
Yeah, and you know what A good similarity we could
draw to that is when electricitywas first introduced, it only
did one thing it powered thelight bulb.
There were no electricappliances, and one of the first
electric appliances was thewashing machine, and it was very
simple.
It just it was a motor thatspun and, by the way, there were
no off switches back then thoseand, by the way, there were no
(45:41):
off switches back then.
Those came later.
So what you would do is youwould unscrew the light bulb and
you would screw in the washingmachine port Because there was
no AC plug.
It was just the light bulb,right, but that's what it
required to introduce this,because it was what was familiar
.
Once people got a hang of itand got used to that.
(46:03):
Then we introduced AC ports andthen electricity changed the
world right.
So I don't know, maybe humanoidrobots will be everywhere.
But at the same time, I think,when we get to more efficient
design, I think you're justgoing to have equipment that is
just more well-suited for thejob at hand.
Speaker 1 (46:18):
Yeah, I mean the autonomous equipment or the
drive-by-wire equipment.
This is the other part which Itotally see If you see the movie
Avatar, and they're in theircontrol spaces and they're not
in a bunch of stuff, they'reactually just there with
joysticks.
I think that's going to be alot of the new future operators
is, they won't even be there.
The equipment will be there butthey'll be in the warmth and
(46:41):
being able to chill and doingtheir thing.
Or there's going to be thelittle command centers.
So that's going to be a veryinteresting place where that
will hit.
Do you think that as the peoplechange, will the jobs go away,
or will those people just bedoing something else?
Speaker 2 (47:00):
Well, certain jobs are definitely going to go away
and the market adapts and newjobs will be created.
It's as simple as that.
We look back at history and welook at the assembly line right
Before all the entire assemblyline to build the first Ford
Model Ts.
That was all done by humans.
Right, the modern assembly linefor vehicles is largely,
largely automated, but it hasn'tgotten rid of people.
(47:21):
The assembly lines just becomemore advanced and we've came up
with more careers and moreadvanced jobs for people to
perform.
Right, there were no health andsafety regulators back in the
early days.
Right, that's a new job thatwas created.
Right.
And again, same thing withcomputers.
So every new technology that'sbeen introduced, it definitely
gets rid of jobs, but it onlycreates new jobs, and I think
(47:42):
that we're going to see the samething with AI.
The market's going to adapt,people are going to adapt and
that's going to be the new world.
Speaker 1 (47:50):
We chatted about that company that I had a guy on the
podcast, josh Levy.
He's got this company calledDocument Crunch.
It's amazing.
You basically just put yourdocuments in and it just goes
and finds and it basically givesyou a summation of anything
that's kind of off.
Yeah, it gives you your risksof that contract.
What kind of documents Likecontracts, contracts and stuff.
(48:10):
Yeah, yeah, yeah, yeah, it'scrazy, very cool.
So the question is, I mean canyou upload?
Like, what's the new AI for?
Is it Notebook, right?
Notebook For what?
Google's new Notebook Notebook,ml.
They got A whole bunch ofthings.
Yeah, yeah, but that one youcan upload stuff into and that
can be pretty crazy.
So I think we're probably goingto see a huge transformation
happen over the.
It's pretty exciting, though.
A hundred percent, yeah, I mean.
(48:30):
So how are you pushing thisthrough?
Like, what is your?
How much of your day is likeworking at Lafarge, doing the
thing that you do, and how muchof it is like coming to do
things like this and advocacyand doing the?
Speaker 2 (48:41):
It's definitely a lot of both and we are trying to
change the culture right, changethe mindset.
Part of our program is we'reoffering these digital literacy
courses.
So we do a little assessment,we send it out to everybody.
Everyone's going to be atdifferent levels and, depending
on the level that you're at,it's going to recommend some
courses for you to take toimprove your digital literacy.
And not everybody needs to beat the same level.
(49:02):
Some people just need tounderstand basic digital data,
computer fundamentals and forsome people, depending on their
role, they can really reallyinvest in some hard skills that
are really going to transformthe way they do their jobs, like
learning Python, learningprogramming languages, learning
advanced Excel and BI, all that.
So that is again, it's a lot ofadvocacy, because we really
want to change the mindset.
(49:23):
The technology is changing.
We're not building technology,we're just integrating it.
But to integrate technology wereally need to understand enough
about it.
Well, you seem prettypassionate about it, I
definitely am.
I even read technology.
Speaker 1 (49:35):
Yeah, that's pretty awesome.
So do you have any advice onthe leadership side of things
for people here and for peoplelistening when we publish this
things?
For you know, for people hereand for people listening when we
publish this, is there anythingthat you know?
The resistance or the how tocommunicate change to your you
know your, your company and yourworkforce, that you know we are
going to invest in this changedoesn't necessarily mean your
(49:55):
job is at risk, but it's amatter of we're going to be
getting rid of the you know the,the stuff that was dull or it
was dangerous, it was or it wasjust repetitive, the things that
you hated doing.
We're going to try and makesure that you're not doing those
things anymore and you're doingstuff that's going to move the
project forward faster.
Is there any sort of advice youhave on the communication side
(50:17):
that you've learned to helppeople?
Speaker 2 (50:18):
Definitely, I think.
Look, leaders need to emphasizeto their workforce that, first
of all, all these automation AItools that we're introducing,
we're not doing these to replacepeople.
We're only trying to make theprocess more efficient.
And make sure you make thatclear and give them examples
where jobs were automated butthe people were not let go.
They were only given betterjobs that improve the quality of
(50:39):
their life and help themadvance in their careers.
Right, that needs to be theemphasis to get rid of the fear
of change.
So we move away from that talkabout, you know, maybe automated
out of the job.
Right, people should want toautomate their jobs so that they
can go and focus on betterthings.
That's number one.
This is number two, I'd say,for leaders to not be driven by
FOMO.
Right, to be really justfocused on the data fundamentals
(51:02):
.
Don't try to just use AI forthe sake of using AI or be
digital for the sake of beingdigital.
You know, there's this oneexample where there was this one
ReadyMix site I think it was inEurope and they managed to
automate the entire process, theentire, from when the truck
rolls in the batching process,everything out, the entire thing
done without any humans.
But the problem was it ranthree times slower, it's 10
(51:23):
times more prone to failure andit costs something like 10 times
more.
So I mean you look back at itand you've got to protect your
bottom line.
It's not about being digitaland automated for the sake of it
, but it's about doing thingsbetter.
Speaker 1 (51:35):
Wow, that's a pretty good speech.
Yeah, all right, that's awesome.
Okay, so how can people findout to you?
Can you give them bestpractices?
You can help them with stuff.
Speaker 2 (51:46):
I love to talk about this, so if I'm available on
LinkedIn, feel free to connectwith me.
I'll be around after.
And yeah, I'm on LinkedIn.
That's really the only place Iam right now.
Speaker 1 (51:55):
Is it?
Yeah, there's no new thingyou're going to be hopping on
Not yet, but maybe I'll thinkabout it, do you?
Don't do that?
No, no, okay, it wasinteresting.
The last guest was like themillennial guy was like I'm on
Instagram and the other guys andthe Gen X guy was like I'm on
LinkedIn.
Speaker 2 (52:11):
Yeah, maybe I'll give .
Speaker 1 (52:12):
TikTok a shot, oh, tiktok.
Yeah, you can TikTok that stuff.
Okay, well, that's pretty cool.
Could you guys all give me around of applause for Mahir,
here?
Thank you, we're here here andthank you very much for coming.
Thanks again for having me.
Speaker 2 (52:24):
That was pretty awesome, your first podcast.
Speaker 1 (52:26):
That's right, first podcast.
You sound good in the cans here, so this is good, so this was a
great success, thank you verymuch.
Speaker 2 (52:31):
I appreciate that, thank you.
Speaker 1 (52:31):
And thank you everybody for sitting and
listening to us banter here andhave the great rest of your show
.
Thank you very much.
Well, that does it for anotherepisode of the Site Visit.
Thank you for listening.
Be sure to stay connected withus by following our social
accounts on Instagram andYouTube.
(52:53):
You can also sign up for ourmonthly newsletter at
sitemaxsystemscom.
Slash the site visit, whereyou'll get industry insights,
pro tips and everything you needto know about the SiteVisit
podcast and Sitemax, the jobsite and construction management
tool of choice for thousands ofcontractors in North America
and beyond.
Sitemax is also the engine thatpowers this podcast.
(53:16):
All right, let's get back tobuilding.
Hello everybody, how you guys doing Nice.
Can everyone hear us?
Okay, all right, so my name isJames Faulkner.
I am the host of the SiteVisitpodcast.
If you get a chance to listento it, we've got 150-something
episodes now that we've done alltopics in construction, a lot
of technology stuff.
I'm a founder of a technologycompany called SiteMax and did
(00:21):
that 10 years ago and so, yeah,love, love technology, like
driving it and um, yeah, so herewe're at the the building show
they've invited us.
We've done, uh, two otherepisodes before this and uh, now
we're here with mehir deenza.
How are you doing?
I'm good, james, how are you?
Ah, this is so so many podcastsbefore this.
(00:45):
No, this is my first one.
Well, so far you sound great.
Yeah, so you're good, you'regood.
So just a little intro here.
Mahir is the program and thespecialist for data integration
and transforming technology atLafarge so Concrete Company.
We talked before the podcast acouple weeks ago about some of
(01:08):
the great things you guys aredoing there, and he has a bee in
his bonnet when it comes to thedigital transformation, how
slow it is.
We've got one generation that'slike, hey, why do we need all
this stuff?
And you have the younger,younger generation going.
Why aren't we using all thisstuff?
So we got to get to this uh,this middle here where we have
uh some buy-in from all thedifferent generations and we've
(01:31):
got a lot of cool tech uh comingalong the way in terms of
software ai we're going to talkabout as well, and um yeah, so
give a round of applause for mehere.
Welcome to the site.
Visit leadership andperspective from construction
with your host, james Faulkner,recorded live on stage from the
(01:55):
Buildings Show in Toronto.
Speaker 2 (02:04):
Thanks for having me.
It's a privilege to be here.
Speaker 1 (02:06):
Yeah, you're welcome, you're welcome, Okay.
So I got questions and this iswhat we're going to get into Yep
, a number of things.
So it's big.
On the AI front, let's justchat about this for a second.
So you know, I was very quicklythere was a revelation very
fast that you really know thedifference between a lot of the
(02:28):
terms people hear, likegenerative AI, machine learning,
and a lot of people just stuffit all together and say, oh, ai,
this, and so maybe let's justtalk about a couple of things
there.
Just forget what are themisconceptions of ai?
Um, in general, in construction, like what, what are people
(02:49):
scared of?
What's the deal?
What's going on here?
Speaker 2 (02:51):
you know the biggest misconception in ai right now is
that ai is very new and chatgpt had a lot to do with that.
When chat gpt came out at somepoint last year, everybody
hopped on this and everyone'slooking at this like this is
some new technology and we haveto go all in on AI.
And you see all these companyexecutives pushing their IT
(03:14):
teams, pushing their digitalteams, saying, hey, you know,
take my money, let's build AI,whatever that means.
They don't know what that means, but just do something with AI.
Ai has been around for decadesand companies who've invested in
their digital infrastructureand data integration have been
leveraging AI for years.
Right, but what we really sawwith ChatGPT was a very good
(03:38):
model.
It's a language model, llm,which stands for large language
model and what it really did isit democratized AI Because it
made it available to the masses.
Anyone can go in, just uselanguage, natural language, and
do stuff with it, whereas beforeyou kind of had to be a
developer, know a little bit ofPython or some programming
(03:59):
knowledge.
So it democratized AI and thenthat's why it sort of went viral
.
But the good thing that cameout about through all of this is
it got us talking, especiallyin the construction industry.
It got us talking about AI andthen pushing us towards at least
the right steps.
Speaker 1 (04:17):
Okay.
So the misconceptions, though,is the fact that one of them
that it's totally new and it'sactually been around for a while
.
But what is new is this newparadigm of anybody can just use
this and I can.
You know, like my daughter canhack her algebra test right.
You can just basically put itin there and she can get out of
there and you know, on the youknow, doing a marketing program
(04:39):
or doing, uh, an rfp orsomething like that, you can
fire, you can put, put yourcommands or prompts right.
You put your prompts intoChatGPT and you can.
It will write an RFP for you.
Speaker 2 (04:53):
I mean it's crazy.
Speaker 1 (04:53):
You put the right information in it, so I think
that that, as you said, thedemocratization of using AI and
the general public having accessto this thing without having
any coding background or anykind of experience in computers
in general other than Gmail, andyou know, and Word Suite or
Microsoft products or Googlethat is.
(05:17):
I would agree with you.
That's sort of thatmisconception there, but it's
definitely changed quite a bit.
But in constructionspecifically, you know where do
you sort of see this going?
In terms of the, is there amisconception of the fear of the
sort of desk job being a threat?
You know project management,you know schedules.
(05:39):
That can be automatically doneIf you're going to put a
schedule together cannot begenerated.
You could say just like lastproject and and give it all the
different changes and suddenly,well, the thing that took me
like four days to do and now Ican have it on an hour yeah yeah
, so where's that kind of going?
Speaker 2 (05:57):
there's definitely a fear, and you know I've been in
this.
I've been in the space foralmost six years now and so I'll
tell you a little story.
When I started, I actuallystarted with Lafarge as a co-op
student fresh out of school andI come into the organization and
I start off as like a dataanalyst and one of my
responsibilities was producingmonth-end reports, and the
(06:19):
entire process was done usingExcel and I was just really just
copy pasting data from oneExcel file into another Excel
file, downloading data from thissource, that source, and then
producing this report and thenputting everything into a
PowerPoint to make it accessiblefor people, and I absolutely
hated this process.
Yeah, and I took a step backand I said you know, what are we
(06:40):
really doing here?
Right, we got to automate this,and so I did some work and we
wrote some scripts and became awhole project and I was able to
condense 40 hours worth of workinto maybe one hour.
And we're not talking aboutsome fancy AI, we're just
talking about just bare bones,fundamental automation and data
integration procedures.
And when I told someone in thecompany about this, they said oh
(07:03):
wow, that's pretty cool.
I hope you didn't tell yourboss.
I'm like what are you talkingabout?
He said well, you know, if itwas me, I would automate my job,
but pretend I'm working 40hours and then take the rest of
the week off.
And so a lot of times when wewent into site and tried to do
this for other people, there wasindeed that fear Like look, I'm
a data entry analyst people.
(07:25):
There was indeed that fear Like, look, I'm a data entry analyst
and now we can use AI and scanPDFs and use some sort of
training model to automate allthat data entry, and they take a
step back like hold on, so whathappens to my job?
But one thing that we're reallytrying to emphasize with the
program, the digitaltransformation program that I'm
leading at Lafarge is we're nottrying to replace people.
We're here to free up your timeso that you can do other things
(07:46):
.
And I use my example within thecompany as well.
When I freed up my time fromthat month-end task, I didn't go
obsolete, right?
I found there's tons of thingsthat we could have worked on and
I started working on some morecreative projects and actually
use my time more effectively,right.
Speaker 1 (08:02):
Than just copy-pasting here.
Yeah, no, I would agree, Iwould agree there on that.
But I mean, I think one of thepushbacks, I mean everyone's
going to have their sort oflet's say the word lived
experience on what they think isgoing to be their narrative to
that, to this discussion.
And if you were to look atsomebody who worked at a video
store, watch out for gone,because netflix is around,
there's no video there's goneBecause Netflix is around,
(08:24):
there's no video, there's noVHSs being rented around.
It's gone, gone, gone, gone.
Yes, so there are going to besome things that those positions
they're not going to be there,absolutely.
Speaker 2 (08:34):
The question is is that?
Speaker 1 (08:36):
what are we replacing that with?
What is that person going totranscend into?
That's going to be a meaningfulcareer.
And, uh, transcend into, that'sgoing to be a meaningful career
.
And obviously it's going to be.
Uh, okay, let me ask I'll getanother question, so I had this
conversation today on anotherpodcast was, let's say that a
project is a symphony, okay, andyou have all the different
(09:00):
players, uh, in in the symphony,you got the violinist, you got
the people on the tambourine,you've got the trumpets you got
all, you got the entireorchestra and then you have the
conductor and the conductor isis uh, you know, here's the
sheet music.
They know exactly what they'redoing and they're gonna they're
gonna make sure everything playsin sequence.
(09:20):
The question is, which job ismost at risk the conductor or
the orchestra?
Speaker 2 (09:26):
because of a I would say it's the orchestra.
Speaker 1 (09:30):
You would to an extent.
Yeah, I had somebody tell methe other day.
They think it's the conductorit depends.
Speaker 2 (09:36):
I think the conductor is the one who sort of brings
it all together, but there'sdifferent ways of looking at it.
Speaker 1 (09:43):
Yeah, I would agree.
Yeah, because I mean and thereason like that symphony works
well as a metaphor because inconstruction you have the one
major change clearly on thefield is the environment that
changes.
It's not like prefab where it'syou know, you're not building
Teslas where it's just like thehood is going on and then the
(10:03):
wheels are going on and it's thesame rinse and repeat in
construction is different everytime, right, because the, the,
the actual terrain changes, thebuilding is is being built and
it's everything's changing yeah,to an extent, but there are
still a lot of manual andrepetitive procedures as well.
Right, those are the what dothey call them?
The dull dangerous and 3ds, Ithink dull dangerous and, uh, I
(10:27):
don't know, okay there's a thirdone you haven't heard.
Speaker 2 (10:30):
No, no, not yet.
Speaker 1 (10:32):
Maybe I'll ask chat gpt, it'll probably know yeah
yeah, we see communications jobsgone, okay, okay, let's just
talk to that a little bit aboutum, like I think that people now
who are able to use AI tools tobecome more efficient, to get
stuff done, they can be theconductor in their own job,
exactly.
Speaker 2 (10:52):
And what I was getting at with the conductor
example is a lot of theserepetitive procedures.
And look, everybody has to goand fill out reports where you
might be conducting siteinspections, and there's always
these menial, repetitive tasksthat are part of really every
job, and there's usually themost draining tasks, right?
So the point is like therepetitive stuff can sort of be
automated.
We're seeing examples andprototypes now with these
(11:14):
autonomous AI agents which sortof work like AI employees, and
so now it's turning into asituation where everybody sort
of has their own AI intern andall that manual stuff, the data
entry, the sending emails,reviewing documents, all the
boring stuff that no one reallywants to do.
You can get AI agents to sortof do that, but you're still the
(11:35):
master of your own ship, right?
You've got to navigate that,and that's what I mean the
conductor, that's what theconductor needs to do.
Right Is set the goal.
Speaker 1 (11:52):
AI is not going to do that for you.
It's not going to replace youin the driver's seat, right,
okay?
So let's just say that, um,we've got.
Um, let's say shelly is workingand that's what she does, okay.
So shelly then is now uh, hasthey've gone through this
transformation meeting andthey've met with you and they're
like here's all these tools youcan do to do all your reports,
etc.
She's trained now to do thisstuff.
And then the company isthinking, wow, okay, so now what
do we do with her time?
(12:12):
Because it's a huge timesavings and so maybe the job
scope now changes.
Now that she is now able to dosomething, that is actually
going to add more projectconsciousness, because not stuck
behind a computer doing stupidreports all the time, even
though I'm saying reports arenot stupid, they're required but
it seems like such a bad use ofa human's time.
(12:35):
100 right so, but there's goingto be buy-in on the hr side of
keeping shelly around a, becauseyou know, construction, in my
opinion, is mostly good to mostpeople.
It's like a and most people aregood in terms of their
intentions around people theydon't want to like just fire
people and let go, especially inthe, in the sort of management
(12:55):
roles.
True, so shelly is like okay.
Well, we love shelly, we wantto make sure that she's around
and let's say shelly's like.
You know what I actually am,I've got a job somewhere else
and she leaves.
Now the job opening.
Will that job change or will itbe?
You know?
On the job description will itsay looking for PM or assistant
(13:21):
PM.
That does this and that's partof the job description, because
we will get there For sure,unless you don't know how to use
these AI tools in order to dowhat you do.
So I think schools are going tohave to show this as well, so
it's going to have to go all theway down the line.
Speaker 2 (13:34):
Yeah, schools, and I think it is changing.
Now we're starting to see itwhere basic, fundamental
computer skills are now beingembedded into every university
program and they're teachingthis in primary schools,
elementary schools now as well.
We know that engineers.
Before it was the case where ifyou're not a computer engineer
or a software engineer, you'renot going to know any sort of
programming.
But now we're even seeing civilengineers, who mainly deal with
(13:56):
concrete and structures eventhey're taking courses now on
programming.
So that's what's leading tothat shift.
Speaker 1 (14:03):
So you're right More people are going to have to use
this.
It's going to be a requirementthat's going to change the
entire project management school, trade school kind of paradigm
in terms of what's in thiscourse.
Speaker 2 (14:14):
But at the same time, I think especially now what
we're seeing with ChatGPT andnatural language, where you
don't necessarily need to be aprogrammer to interact with
these tools either yeah, they'repretty set up and forget it
right.
So the barrier to entry isdecreasing.
Speaker 1 (14:29):
Yeah, that's true, so it's going to be easier for
more people to participate, andI think what we're also seeing I
know this is based on theGoogle suite, for instance, like
Google Docs.
I mean you start to realize howgood that is at its predictive
stuff.
Speaker 2 (14:49):
I mean, mean it starts to be?
Speaker 1 (14:50):
like oh, okay, that's , do you want it this way?
I've been doing stuff onspreadsheets.
I'm like crap, this thing islike it's, it's, it's like in
terms of the formulas it'sgiving me hey, do you want this?
No, next one, next one.
Speaker 2 (14:54):
I'm like uh yeah, okay, that's pretty cool, right?
Speaker 1 (14:56):
yeah, it's pretty cool because, like, it just
takes the time down.
Yeah, um.
So let's just chat a little bitabout um.
We've got some pressures interms of the different
generations.
We have the generations who arepretty much tapped out in the
like how much, how many monthlyfees for SAS software do we need
(15:17):
to be paying for these licensesper users?
I mean the amount of moneythat's being spent and actually
being wasted on software that'snot being used.
It's true, there's tons of that.
And now there's this otherthing.
It's like okay, well, we nowwant to start using ai and there
is probably a okay, well, couldthat save you money or is this
(15:39):
going to make us more efficient?
There's going to be a curiosityor there's a.
Yes, but technology, technologyin general, not just AI, but
just digital transformation.
Why is there this resistancefrom the older generation
compared to the youngergeneration?
Who has this expectation that,if you're going to give me an
(15:59):
app and you're expecting me todownload and it's a bring my own
device to the job site andyou're going to give me an app
that sucks, why does this appthat I use for work, when I'm
doing my site orientation or I'mgoing to fill in my time card
for the day.
How come that doesn't feel likeAirbnb and Uber?
(16:20):
The expectation of how goodthat has to be down here and
then you have the othergeneration.
That's like, uh, yeah, I mean,how much of this do we really
need?
Speaker 2 (16:30):
it's true you know, in construction.
One thing I've noticed is thereis definitely an aging
workforce.
A lot of people are like a lotof baby boomers or have been in
the business for a while.
My old uh manager started withthe company when he was 25 and
he retired at 65.
And that's a trend.
I noticed, especially atLafarge, that people stick with
Lafarge for the entirety oftheir career.
(16:51):
That's not really the case somuch in this day and age, with
the new generation.
People are hopping jobs leftand right every couple of years,
every couple of years, and Imean it makes sense too, because
people want to change.
It's just the environment we'rein, but that definitely
contributes to the change.
But at the same time I don'tthink I would blame only that.
I think a lot of it is cultureas well, where there's sometimes
(17:16):
just resistance to technology.
I know I heard there were thesecases where they were
installing cameras into deliverytrucks that would just monitor
drivers, into delivery trucksthat would just monitor drivers,
and these drivers, in protest,would rip these cameras out
because they said this is justBig Brother watching us.
So there's a bit of resistancethere.
But at the same time and I'lltell you a little story about
one of the projects that I ledand what I learned from that.
(17:37):
I had a situation where welooked at the quality control
process and the entire processhad not changed since the 80s.
And that's just because themanagement had been around since
the 80s.
And so it looked like you walkinto this quality control lab
giant calendar on the wall, theway it has all the scheduling on
it.
They track all the test resultson pen and paper, put it into
(18:00):
like index cards and put it in alittle cubby, and then somebody
at some point will enter thisinto a computer.
And the point is, I mean, bythe time that data gets into the
computer it's probably too late.
Speaker 1 (18:10):
Oh and inaccurate.
Speaker 2 (18:11):
And inaccurate, because there could be mistakes.
Sometimes you can't read theirhandwriting, right.
So there's that.
And then so we said we're goingto digitize this process, we're
going to build an app.
It's a pretty and I said thisis going to be a quick win for
us, right?
So we sit in a boardroom andthis is important, my team we
sit in a boardroom we thinkabout what this process should
look like.
We build the app in a fewmonths, we take it to market or,
(18:32):
in this case, into the field,and we say here you go, this is
the app, it's all digital.
Now Thank us later.
Goodbye.
A month later or a few weekslater, we come back and we say
how's the app working?
It must be great.
And they say, yeah, you knowwhat?
We're not really using it.
I say why not?
I thought it would be ano-brainer.
And they said it's just notthere.
The pen and paper process waseasier for us.
(18:53):
It made sense, it's what we'reused to.
Speaker 1 (18:57):
And so that's what they did.
So can you just break down onthe different generations who
were saying and these are youngpeople too.
Speaker 2 (19:04):
These are technicians as well.
Yeah, people around my agesaying this stuff, right?
So I was thinking back.
I thought maybe this is justthe old heads who would push
against it.
But no, the young generationwas we're used to pen and paper.
It's just much easier.
So I go back to our management.
I say this is what I heard, andone of the responses I got from
management was give me theirnames, I'll talk to their boss,
we'll force them to use it.
(19:24):
Okay, because sometimes youknow what people are probably
just resistant to change andwe'll just force them to use it.
So I said okay.
So I go back a few weeks laterand I say are you using the app?
They're like, yeah, becausewe're forced to, we have to use
it, but we're also using the penand paper're actually still so
now they're doing both.
It's made it less efficient.
So I said okay, I'm definitelymissing something here.
(19:48):
I'm going to put my boots on,I'm going to put my hard hat on,
I'm going to spend a week withyou and let's go through this
process.
And then I realized you know,it's a construction site.
The way we set up the form islike you've got to go top to
bottom.
There's no safe progress.
You're wearing gloves, youcan't type on a phone.
So then I started seeing allthe little issues and, truly,
you know, you grab a pen, grab apaper, you just jot something
(20:08):
down, scribble something, andthen just forget about it.
So I realized that there weregaps in the app, and I think
that's contributing to the issueof adoption is the gap between
the boardroom and the field.
And so when I spent time on thefield and we really, really
refined that user experiencethat you were talking about
right, really get that down andthat process probably took maybe
(20:29):
three times longer than theactual development of the app.
But once we cracked that codeand we got the UX down to where
it needed to be, the app scaleditself.
And we started in one city, westarted in Hamilton, then we
started in Toronto, and thenthis app blew up and now it's
all over North America, evenincluding Mexico, and it came to
a point where, instead of usdealing with resistance from
(20:51):
these employees, people startedcalling me and saying, hey, I
heard this really cool app thatyou built.
When can I use it?
And so that's what I mean.
I think it's again the gapbetween the boardroom and the
field.
But once you spend time in thefield and address that gap,
these things will scalethemselves.
Speaker 1 (21:06):
Wow, that's a good story.
Yeah yeah, it is interestinghow we have this very high
benchmark level of userexperience that we require these
days.
It's true Because we all usethese apps on a daily basis.
Some of you might have useduber to get here.
I mean, I just flew in fromvancouver.
(21:28):
As soon as I land here, it'sshowing me all the places I went
when I was in toronto last timeI was here.
It's like which one of theseyou want to go to?
I'm like all right, there it isthat's where I went last time.
I mean it's just awesome rightand you know, I think that it
was the user experience thatanybody could have said look,
we're going to go and try andchange the taxi industry, but
(21:50):
unless that app was killerawesome on the driver's side and
on the customer side, it justwouldn't have worked.
So I think what you're sayingthere is that gap between the
boardroom and and the, the fieldworker.
Let's say we're the field level.
It's also like that in theoffice too, like sometimes the
(22:10):
software, the project managementsoftware, can be clunky.
It's like we're still dealingwith the email.
I mean yeah.
What's the a lot of companiesdealing with Slack these days.
Sure, yeah, slack, yeah, othersyeah, just real hands in
construction, slack Anybody flagyeah, okay, yeah, um, it just
(22:31):
seems we're in this time, rightnow, where things need to be.
Maybe ai is the solution.
I mean, the other day I lookedfor an ai email box cleaner
because I've got a couple ofgmail.
Like some of you guys, I've gota gmail, a couple of gmail
accounts, some I pay moreattention to than others.
I've got like 25,000 unreademails in a Gmail account.
(22:56):
I'm like, how do I even dealwith this?
And so an AI cleaner to just goin there and says okay, here
are all of the ones that youwere sponsored for, here are all
of the different contacts, hereare the ones that you opened
Any of these.
Do you want to just keepputting it down like that and
then delete the rest?
Speaker 2 (23:14):
Yeah, and I think you're right, ai will help.
I think it's a tool, maybe notthe only tool, but it is a tool
that would help.
One thing it definitely helpsus with, especially from a
development point of view, ismove faster.
Developers use AI tools towrite their code and also
improve their UI, and one thingwe're seeing now.
So before it's like you want tomake a UI change to a software,
(23:35):
there's a whole pipeline you'vegot to follow.
There is, yeah, but now there'sthese tools where it's
literally just a prompt movethis from here, move that there,
and it'll do the code in thebackground and change the UI.
So it helps you move a lotfaster, and I think what we'll
even see in the very near futureis customized user interfaces
for the end user.
Maybe you like seeing thingsone way, I like seeing things
one way, and it can all be veryprompt-based.
(23:56):
You don't need any codingknowledge and you'll be able to
customize your own uniqueexperience Exactly, and that's
one thing that we're finding.
Speaker 1 (24:03):
I mean, at SiteMax, a company I founded, we've got a
couple of views.
You've got one.
When you go into the project,you see that view.
And then, on the on the uh, thecompany side, you look at all
your projects.
You got that view.
I'm like, well, I will say to my, to my team, like well, maybe I
don't want to see that view.
Yeah, you know, like theselittle uh alerts that I'm seeing
here is just not relevant to me.
Um, but you know, what canhappen is if the structure of
(24:27):
the actual page or the mobileapplication is not ready for
like what?
If it looks really stark.
You know what I mean.
Like, let's say, you get a fieldworker, all they do is time.
Well, it'd be great if the appactually just was about time.
You know what I mean, ratherthan the empty box with just the
clock symbol.
You know what I mean?
Because it just makes it.
(24:47):
It's almost a self-esteem thing.
This is all I get to use, youknow.
So there's lots of things thatI think can be done on the UI UX
side.
Let's just chat a little bitabout when we had our call, a
pre-call.
Before, when we met each otheron the Zoom call, we talked
about risks of AI, like what aresome of the things that the
(25:09):
catastrophes that we need to belooking out for that could
happen here?
Speaker 2 (25:13):
So there's two things here.
One thing is it's all about thedata that you're feeding AI.
Right, it's all powered by data.
So if you have garbage datagoing in, you're going to get
garbage data going out.
So that's one issue, and Ithink that's something that
companies need to pay attentionto.
The second issue ishallucinations, and that's led
to a lot of funny incidents Iwould say humorous incidents
that we've seen.
(25:33):
I know we talked a little bitabout that Air Canada example
where they had this AI chatboton their website so somebody
asked it about their bereavementpolicy and it gave them
unfactual information and AirCanada had to honor it in court
because they said whatever thechatbot said is basically what
you have to honor.
There was another example where, I think, somebody convinced a
(25:55):
Chevrolet chatbot to give them anew truck for a dollar.
So there's always these risksand I think it just comes back
to corporate executives sort ofdriven by FOMO, the fear of
missing out, because we'reseeing these AI headlines
Everybody wants to cash in,nobody wants to be left behind
and sort of just jumping onthese things without really
understanding what they'resupposed to do and it's just
(26:17):
moving too quickly and notunderstanding the fundamentals.
But if you go back and you juststick to the data fundamentals
and do the data integrationright, you'll be fine.
Speaker 1 (26:25):
Yeah.
So when I look at AIintegration in the field, what
worries me a little bit isproject consciousness of okay,
that's got that dealt with forme, especially on the safety
side, like it is a.
I even see this now.
(26:45):
I see people who are justpunching radio buttons yeah,
yeah, yeah, yeah, yeah, yeahDone sign at the bottom.
They didn't actually look atthat one thing that was on that
line.
I see people who are justpunching radio buttons yeah,
yeah, yeah, yeah, yeah, yeahDone Sign at the bottom.
They didn't actually look atthat one thing that was on that
line.
They didn't look up and go,okay, yeah, that's good, some do
, hopefully most do, but someare just like going through
there and you add AI to that andlet's say it asks you a
(27:06):
question have you looked at this?
No-transcript if you start torely on something else.
Speaker 2 (27:38):
I've seen examples of that, especially like the data
entry example I mentioned, whereit's this great, big win we're
able to automate data entry.
You don't need to sit in frontof a laptop and enter numbers
anymore Great yeah.
But every now and then it mightmiss something, it might make a
mistake, and so if we're seeinga case where people just ignore
it and say, oh, the AI ishandling that, I'm just going to
(27:59):
move on.
Speaker 1 (27:59):
Oh, yeah, exactly.
Speaker 2 (28:00):
That's a bad practice , but I think it just comes back
into culture and establishinggood procedures and policies
within your organization andbeing mindful of that.
That it can make mistakes, but,to be fair, so could a human.
So, just making sure thatthere's this cross-validation,
that you're making sure thatthings are being done correctly,
but I think, look, it's alwaysthe response of the human in the
(28:21):
loop right.
Even when things are beingautomated, you've got to have a
human in the loop right, and AIis not here to replace us.
It's here to complement what wedo.
Speaker 1 (28:31):
Yeah, I like the human in the loop.
Wow, there needs to be awebsite with a human in the loop
.
You should make that.
Yeah, yeah.
Speaker 2 (28:37):
I don't think I came up with that.
I probably read it somewhere,oh you don't.
Speaker 1 (28:39):
Oh, I see.
Oh, that's cool.
So, in terms of, do youenvision this?
Let's go robotics, and let's gorobotics.
And so the hardware mixed withAI, like how is this going to be
manifesting itself?
Let's say, 30 years from now?
What's this job site going tolook like?
(29:00):
I mean, are we going to see asignificant decrease in the
amount of human beings on a jobsite?
Are we going to seedrive-by-wire?
Is it going to be installationby robots?
Like what do we?
I don't know, did anybody seethis?
The painting robot?
Have you ever seen this?
Yeah, I mean, the thing is, noone cuts as well as that painter
.
I mean, it's insane, it's true,it's perfect, right, and and
(29:24):
then that's a skill, right,that's a skill.
As a painter I'm.
I tried to paint my own, my own, uh, living space.
Yeah, not, it's not easy to todo a nice cut and not get on the
on the ceiling, that's true,but that thing's perfect and the
people who can do it havelearned over time with that
steady hand to be able to dothat thing, or they have a
specific tool that does that,etc.
(29:44):
But are we going to see this?
Just total transformation ofthe job site within 30 years?
Speaker 2 (29:51):
that's the million dollar question.
Right, are robots here to takeour jobs?
But you know, sometimes I say,instead of asking are robots
going to take our jobs, maybe weshould ask are our jobs turning
us into robots?
And and we, we look at.
You know, I like to use theexample and I get what you're
saying.
Speaker 1 (30:10):
We're already cyborgs right now.
I mean, if you look at mostpeople know I like to use the
example and I get what you'resaying we're already cyborgs
right now.
I mean, if you look at mostpeople walking around, they've
got their phone in their hand.
It's true, it's so bad.
I've walked around a number oftimes and I've said to my wife
honey, where's my phone?
She's like it's in your hand.
Oh, like I forgot it's there.
(30:32):
It's become this appendage,it's like.
It's like.
It's like.
So we're cyborg.
It's just that we can justdecouple it like we can just
drop it on the table.
And now I'm not a cyborganymore, even though I'm
yearning for that.
Will you ever leave your phoneat home?
That's terrifying.
You can't live without it.
No, I mean because we were justso connected to that.
That's right, okay.
So the question is let's go tolike what's the Neuralink
(30:52):
version of construction?
Look like.
Speaker 2 (30:56):
The Neuralink version of construction.
That's an interesting one, itis.
Well, we've seen what Neuralinkcan do, right?
You saw the monkey experimentthey did, where they installed
Neuralink into a monkey where itcan play Pong online.
So you're basically able tocontrol computers without using
your fingertips, right?
So are we going to see that,where people are plugged into
Neuralink and able to controlthings?
(31:17):
I don't know, it's tough to say, but one thing we can at least
see in terms of the trend isthat robotics are taking over
many procedures, especiallythings that are repetitive.
You mentioned the paintingexample.
Speaker 1 (31:30):
Here's automatic cranes or the dangerous stuff
like going into crawl spaces andall that kind of stuff.
That too right.
Speaker 2 (31:35):
We're seeing what drone technology is capable of
right in terms of LiDAR scanningand 3D modeling and just bridge
inspections.
Before you had to installscaffoldings to do inspections.
Now a drone just flies over andgives you a 3D model right.
Speaker 1 (31:53):
So, like, in terms of um, like, I always look at the
lowest common denominator of thedigital transformation side of
things.
So you know, I recentlyrenovated, you know, our condo
in vancouver and you know Igotta go get the drawings from
the city.
Okay, well, they're not givingme bim models and until all of
that is transferred over, we'regonna be stuck in that past,
because some of those aren'teven vector drawings.
Sure, they're raster old scansof, like hard drawings.
(32:16):
So you know for, in order, yeah, for sure, if you're going to
be building an airport, you'regoing to build a hospital,
that's going to be a bim model.
All the hvac, everything'sgoing to be in there, all the
layers.
You know about the stuff and andit's kind of ubiquitous on that
higher level, but that's notthe majority of the revenue and
construction in north america.
It's like renovations, it'slike ti's, it's building
(32:42):
warehouses, I mean it's it's notall the hospitals and airports.
So in order for that tech toreally really take hold, I
should be able to go to the cityplanning office and get a set
of 3D drawings.
You should be.
How long is that going to take?
And the question is will therebe an AI service that will
transform them into the 3D ones.
Speaker 2 (33:03):
That's a good question.
How long is the city going totake?
They'll probably be the lastones to adopt it, because it's
the government.
Speaker 1 (33:07):
But until then we're going to be stuck with 2D
drawing.
Speaker 2 (33:10):
It's true, and look even now, in terms of
information sharing, the choiceof most people is to share
information with PDFs.
If you're on a job site, you'regetting tests done, whether it's
a soil test or a concrete test.
Whatever.
That data is being transferredusing email and PDF, which is
one of the worst ways oftransferring data, because
(33:31):
someone has to open that PDF andthen probably type it into one
of their systems and thesesystems don't interact and don't
talk to each other, and I thinkthat's a problem that us as an
industry is going to need tocome together to solve right,
coming together with some sortof protocols or something.
Usually, I think, when there'sa large player in software, like
we see with Google's andMicrosoft's and these big
(33:51):
platform companies, they're theones who are really good at
driving that change.
The problem is these bigconstruction software companies.
They're very archaic andthey're due for disruption, and
I think that's what's going todrive all of this is, we disrupt
the big players, the bigsoftware players in construction
, and as more people adopt that,that's going to drive more
(34:12):
people to change.
Speaker 1 (34:12):
Yeah, I would agree with you there.
The question is is thateverything's typically about ass
covering, right If it goes tolegal?
Show me the PDF.
Speaker 2 (34:24):
That's true, it's true, it's what we're used to.
Speaker 1 (34:26):
Yeah, and so if everything comes down to the
fear of litigation and the fearof proving your point, then
you're going to want that lowercommon denominator thing.
It's true, that has that likewhat we're, I mean, with Sitemax
.
Right now we have threedifferent types of signatures.
One is certified and has acertified signature like a
docu-sign, and other ones arejust like yeah, we did it.
(34:48):
So, there's three differenttypes.
You can choose what you want onyour process.
But those different things, um,that digital signature is a
thing, right, so maybe that andit's a thing, a legal thing,
right, it means you were thereat that point and it was a
digital signature.
So, um, do you see thatchanging to?
(35:08):
Maybe you, you know, like afacial recognition that was me,
or like there's lots of thingsthat can change.
Speaker 2 (35:15):
It should be, I mean, yeah, the thing is, there's a
psychology concept where theystudied people who stick through
toxic relationships and thereason for that, they say, is
because we're not alwaysattracted to what's best for us.
We tend to chase what isfamiliar.
Yeah, and that's the thing Imean.
We can apply this to technologyand technology adoption as well
(35:37):
.
People don't like what's best,people like what's familiar.
So I guess it's something tothink about, especially for
digital transformation teams.
When we integrate newtechnology or introduce new
things, we have to make it seemfamiliar.
So there's less resistance tochange and I think you're right
that there's tons of ways ofdoing things better.
But it's about how do we makethis more resistance-free and
(35:58):
familiar.
Speaker 1 (35:59):
Yeah, I would agree with you.
There does need to be atechnology change on the
software side that is going torevolutionize something.
I remember back when PlanGrid,which is now an Autodesk, when
someone would say here, I'lljust send you the drawings.
They're like what, what do youmean?
Speaker 2 (36:17):
send me the drawings.
Yeah, just give me your phonenumber.
Speaker 1 (36:20):
Boom, there are the drawings.
I mean, that was atransformational change that
changed a lot of things.
People didn't have to have thatroll up of drawings in the tube
in order to get someinformation, it's true, right,
so you could do, you know, uh,you could just mark up a
screenshot of drawings and, justyou know, send it off to
someone else, that they could,you know, make a change of
(36:40):
drawings in other ways.
So I mean, this does that.
I just wonder what that nextthing is that's going to be like
on the jobs, like holy crap,have you seen that thing?
Speaker 2 (36:50):
It's true.
And, look, it's about findingthe incentives for the user
right.
If it can make your job easier,it's a fun feeling, it's a good
, comfortable feeling.
The problem is that, again backto that, we talked about the
city giving you scans, right.
What's their incentive to giveyou a 3D model?
Right?
And that's something we've gotto think about.
Even testing companies sendingyou PDFs with test results.
It's a pain for me to have togo through that PDF, but for
(37:13):
that company it's just export aPDF instead, right?
So it's and that's something weneed to figure out and
especially software companiesneed to come together.
And how do we incentivize thatfor the user?
Speaker 1 (37:23):
on the other side.
Yeah, because all they'rereally doing is just
transferring the inconvenience.
True, exactly the transfer ofinconvenience.
That's how construction isoften right.
Oh, here you go, you got yourthing, now you deal with it.
Oh, you got your thing, now youdeal with it.
It just keeps getting punteddown the line.
Speaker 2 (37:39):
Yeah, and again, I haven't figured out how to
incentivize that procedure yet,but I think that's something for
us to think about.
Speaker 1 (37:46):
Yeah no for sure.
So let's just chat about whatyou've done with Lafarge and et
cetera.
So you've been utilizing sometools there, utilizing AI,
different types of technologies.
Take us through some of thestuff that you've been doing,
Maybe some examples that peoplecan draw from.
Speaker 2 (38:00):
Sure.
So there's tons of examples.
One of the things is we take adata-first approach.
So when I started, like I toldyou, that month-end process,
everything was very Excel-based.
That was the case for amajority of the organization,
right, A lot of procedures werestill pen and paper.
So that's the first step is wegot to apply good data
integration principles, and whatthat means is your organization
.
You have tons of softwares,tons of systems, data scattered,
(38:23):
siloed all across.
You need to enforce a data lakeand a data warehouse
infrastructure, which basicallymeans you integrate all these
sources and bring all of thedata into one place and then you
clean it up and you organize itin a good relational format.
So is that what you guys did?
Speaker 1 (38:39):
That was the first step right Forget about
everything else.
Speaker 2 (38:41):
You've got to focus on your data fundamentals and
once you get there now you canhave advanced analytics.
Now you can apply machinelearning, and so then we work on
some pretty cool projects.
So right now we're working on aproject to as you probably know
, uh, cement is one of theleading causes of co2 emissions
in the world.
That's because co2 is abyproduct of cement production.
(39:02):
So anything we could do toreduce cement or increase cement
efficiency in our concretemixes is a huge win for us and
for the planet.
So that's what we're doing iswe're building these machine
learning models that are trainedon our historical performance
data, all the ready mix orconcrete tests that we perform,
and then trying to find cementefficiencies to reduce the
cement.
So that's a project we'reworking on.
(39:23):
We're piloting a project rightnow in one of our aggregate
quarries where there's this longtwo kilometer conveyor belt,
and right now we had twotechnicians whose job was to
just drive along this belt andmake sure there's no kinks, make
sure there's no cracks or anyissues with the belt.
Now we've actually employed arobot that's going to do the
(39:44):
exact same thing.
It just has a camera on it, itdrives around the conveyor belt,
and so we freed up time of youknow two technicians so that
they can go and perform otherthings.
And that same robot can do morethan just that it goes around,
it checks for safety, health andsafety violations.
Take us through that.
How does it do that?
It's just a robot on wheels, itjust drives around, it has ai
(40:13):
built into it and so it's atrain to to perform these tests,
because we've taken images andfed it historical data so it
knows what a problem could looklike.
Okay, so what?
Speaker 1 (40:18):
just just to get really in the weeds on that for
a second.
So what?
What specific things has itfound so far?
Speaker 2 (40:27):
Well, every now and then there could be a kink or a
tear or some issues within theconveyor belt and it'll take a
photo and it'll immediatelyalert somebody to address that,
pretty much the same way a humanwould.
At the same time it's alsodoing, like I said, those health
and safety inspections.
That's the part that I'mtalking about though Sure the
health and safety.
Speaker 1 (40:49):
What are the specific endpoints that it is finding or
looking for or checking?
Speaker 2 (40:52):
or cross-referencing, so as a robot is driving around
the site and you would havehealth and safety personnel do
the same thing, or if you seesomebody for example, but they
might be doing a checklist.
They do a checklist.
They do a checklist, so therobot does a checklist it has a
checklist as well, Okay so howdoes?
And there is a daily.
Is it seeing the checklistCircle?
Speaker 1 (41:07):
check.
Okay, yeah, yeah, okay.
So does it go to certainwaypoints then?
Speaker 2 (41:12):
It has a 3D model of the site in its system.
Okay, so it knows where thesite is.
Okay, gotcha, and yeah, you'reright, it sort the ability to
maneuver.
If there's an obstacle in theway, we'll go around the
obstacle.
(41:32):
So it does have some no coffeebreaks.
No, no coffee breaks,unfortunately.
But so that's what it's doing.
It's just taking photos again,just the routine tasks that
technicians would do.
Speaker 1 (41:35):
It's it's able to perform those routine tasks okay
, so, um, so, do you see that aswe move forward?
Do.
Do you see a virtualsuperintendent?
Do you see a virtual health andsafety officer on a job?
Speaker 2 (41:51):
To an extent, yes.
And one thing I do want toemphasize, though, and it's a
very important principle whenworking in automation.
There's a famous quote thatsays automation applied to an
inefficient system will onlyenhance the inefficiency, and
that's important for us torealize.
And even with this example, too, sure, we could have this robot
that drives around, but we alsoneed to think about is this the
(42:12):
best way of automating thatprocedure At the same, so that
robot can only be at one spot atone time?
Right?
In certain use cases it makessense.
It's a very long belt.
You've got to drive around thatbelt.
But certain cases, if we're justlooking for only health and
safety violations on your site,it may be more efficient to just
install cctv cameras, yeah, andhave real-time view all of the
(42:35):
time, instead of only when thatrobot is present.
So so, to an extent, yes, but Ithink software may replace a
lot of the hardware as well.
So you don't necessarily need ahumanoid robot.
Who's now the health and safetyinspector?
It's more of a procedure or asystem that's embedded and
integrated within all yourintelligence systems within the
company.
Speaker 1 (42:54):
Yeah, it's funny.
I had this podcast I've donetwo of them now with a company
called Super Droids Okay, andthey build these little robots.
With a company called superdroids okay, and they built
these little robots and, um, Ihad this one, they were on video
and they said do you want tosee this latest project?
And I said, okay, sure, andthey, and they said, well, it's
(43:17):
a humanoid, I'm like a humanoid,like how much money do you guys
have?
First of all, I mean to make ahumanoid pretty difficult, and
so I said, well, why would youmake, why would you go through
the brain, brain damage oftrying to get this thing to walk
and balance and do all thethings that we just find so
obvious as humans?
And he says, well, it doesn'tneed to do that, you just need
to sit it in the form factorthat was made for a human.
(43:39):
Okay, because, like in anexcavator, for instance, like
everything's here, the pedalsare here, it didn't need to
stand up and what it does is itretrofits, in a way, all old
equipment.
There's a lot of it out therewith the humanoid thing in it.
Yeah, yeah, yeah.
So as long as the actuatorswork in the hands and the
(44:01):
cameras there, yeah, and I thinkthis is what Tesla is doing
with theirs.
This is where they're seeingthis going.
So that 30-year thing, do wehave these flesh people and then
plastic people everywhere?
Speaker 2 (44:20):
There's something that has to be said about that.
And look, I don't know, I saylook, if you want to build a, a,
if you want to design aself-driving car, right, first
you build a robot and then therobot sits in the car and then
it drives the vehicle.
But you look at it like thesame example I gave you about
inefficiency right, that's avery inefficient way to have a
self-driving car.
(44:40):
Yeah, you look at what tesla'sdoing.
They, they've gotten rid of thesteering wheel.
You, you don't need thesteering wheel, right, you just
build that into the car.
So, again, into excavators ofthe future, you're probably not
going to have the pedals and allthese controls, you're just
going to get rid of them andeverything's going to be built
into the software.
Right, because that's the moreefficient way of doing it.
But back to the concept offamiliarity right, the majority
(45:01):
of the world is built for humansand we've relied on human
operators for a long time.
And I think, to address thatresistance of change and to
really push this forward atfirst, maybe humanoid robots are
the answer, because you sort ofhave this generic robot that
can perform many differentthings.
Speaker 1 (45:20):
Sticking in the old system.
Speaker 2 (45:21):
Yeah, and you know what A good similarity we could
draw to that is when electricitywas first introduced, it only
did one thing it powered thelight bulb.
There were no electricappliances, and one of the first
electric appliances was thewashing machine, and it was very
simple.
It just it was a motor thatspun and, by the way, there were
no off switches back then thoseand, by the way, there were no
(45:41):
off switches back then.
Those came later.
So what you would do is youwould unscrew the light bulb and
you would screw in the washingmachine port Because there was
no AC plug.
It was just the light bulb,right, but that's what it
required to introduce this,because it was what was familiar
.
Once people got a hang of itand got used to that.
(46:03):
Then we introduced AC ports andthen electricity changed the
world right.
So I don't know, maybe humanoidrobots will be everywhere.
But at the same time, I think,when we get to more efficient
design, I think you're justgoing to have equipment that is
just more well-suited for thejob at hand.
Speaker 1 (46:18):
Yeah, I mean the autonomous equipment or the
drive-by-wire equipment.
This is the other part which Itotally see If you see the movie
Avatar, and they're in theircontrol spaces and they're not
in a bunch of stuff, they'reactually just there with
joysticks.
I think that's going to be alot of the new future operators
is, they won't even be there.
The equipment will be there butthey'll be in the warmth and
(46:41):
being able to chill and doingtheir thing.
Or there's going to be thelittle command centers.
So that's going to be a veryinteresting place where that
will hit.
Do you think that as the peoplechange, will the jobs go away,
or will those people just bedoing something else?
Speaker 2 (47:00):
Well, certain jobs are definitely going to go away
and the market adapts and newjobs will be created.
It's as simple as that.
We look back at history and welook at the assembly line right
Before all the entire assemblyline to build the first Ford
Model Ts.
That was all done by humans.
Right, the modern assembly linefor vehicles is largely,
largely automated, but it hasn'tgotten rid of people.
(47:21):
The assembly lines just becomemore advanced and we've came up
with more careers and moreadvanced jobs for people to
perform.
Right, there were no health andsafety regulators back in the
early days.
Right, that's a new job thatwas created.
Right.
And again, same thing withcomputers.
So every new technology that'sbeen introduced, it definitely
gets rid of jobs, but it onlycreates new jobs, and I think
(47:42):
that we're going to see the samething with AI.
The market's going to adapt,people are going to adapt and
that's going to be the new world.
Speaker 1 (47:50):
We chatted about that company that I had a guy on the
podcast, josh Levy.
He's got this company calledDocument Crunch.
It's amazing.
You basically just put yourdocuments in and it just goes
and finds and it basically givesyou a summation of anything
that's kind of off.
Yeah, it gives you your risksof that contract.
What kind of documents Likecontracts, contracts and stuff.
(48:10):
Yeah, yeah, yeah, yeah, it'scrazy, very cool.
So the question is, I mean canyou upload?
Like, what's the new AI for?
Is it Notebook, right?
Notebook For what?
Google's new Notebook Notebook,ml.
They got A whole bunch ofthings.
Yeah, yeah, but that one youcan upload stuff into and that
can be pretty crazy.
So I think we're probably goingto see a huge transformation
happen over the.
It's pretty exciting, though.
A hundred percent, yeah, I mean.
(48:30):
So how are you pushing thisthrough?
Like, what is your?
How much of your day is likeworking at Lafarge, doing the
thing that you do, and how muchof it is like coming to do
things like this and advocacyand doing the?
Speaker 2 (48:41):
It's definitely a lot of both and we are trying to
change the culture right, changethe mindset.
Part of our program is we'reoffering these digital literacy
courses.
So we do a little assessment,we send it out to everybody.
Everyone's going to be atdifferent levels and, depending
on the level that you're at,it's going to recommend some
courses for you to take toimprove your digital literacy.
And not everybody needs to beat the same level.
(49:02):
Some people just need tounderstand basic digital data,
computer fundamentals and forsome people, depending on their
role, they can really reallyinvest in some hard skills that
are really going to transformthe way they do their jobs, like
learning Python, learningprogramming languages, learning
advanced Excel and BI, all that.
So that is again, it's a lot ofadvocacy, because we really
want to change the mindset.
(49:23):
The technology is changing.
We're not building technology,we're just integrating it.
But to integrate technology wereally need to understand enough
about it.
Well, you seem prettypassionate about it, I
definitely am.
I even read technology.
Speaker 1 (49:35):
Yeah, that's pretty awesome.
So do you have any advice onthe leadership side of things
for people here and for peoplelistening when we publish this
things?
For you know, for people hereand for people listening when we
publish this, is there anythingthat you know?
The resistance or the how tocommunicate change to your you
know your, your company and yourworkforce, that you know we are
going to invest in this changedoesn't necessarily mean your
(49:55):
job is at risk, but it's amatter of we're going to be
getting rid of the you know the,the stuff that was dull or it
was dangerous, it was or it wasjust repetitive, the things that
you hated doing.
We're going to try and makesure that you're not doing those
things anymore and you're doingstuff that's going to move the
project forward faster.
Is there any sort of advice youhave on the communication side
(50:17):
that you've learned to helppeople?
Speaker 2 (50:18):
Definitely, I think.
Look, leaders need to emphasizeto their workforce that, first
of all, all these automation AItools that we're introducing,
we're not doing these to replacepeople.
We're only trying to make theprocess more efficient.
And make sure you make thatclear and give them examples
where jobs were automated butthe people were not let go.
They were only given betterjobs that improve the quality of
(50:39):
their life and help themadvance in their careers.
Right, that needs to be theemphasis to get rid of the fear
of change.
So we move away from that talkabout, you know, maybe automated
out of the job.
Right, people should want toautomate their jobs so that they
can go and focus on betterthings.
That's number one.
This is number two, I'd say,for leaders to not be driven by
FOMO.
Right, to be really justfocused on the data fundamentals
(51:02):
.
Don't try to just use AI forthe sake of using AI or be
digital for the sake of beingdigital.
You know, there's this oneexample where there was this one
ReadyMix site I think it was inEurope and they managed to
automate the entire process, theentire, from when the truck
rolls in the batching process,everything out, the entire thing
done without any humans.
But the problem was it ranthree times slower, it's 10
(51:23):
times more prone to failure andit costs something like 10 times
more.
So I mean you look back at itand you've got to protect your
bottom line.
It's not about being digitaland automated for the sake of it
, but it's about doing thingsbetter.
Speaker 1 (51:35):
Wow, that's a pretty good speech.
Yeah, all right, that's awesome.
Okay, so how can people findout to you?
Can you give them bestpractices?
You can help them with stuff.
Speaker 2 (51:46):
I love to talk about this, so if I'm available on
LinkedIn, feel free to connectwith me.
I'll be around after.
And yeah, I'm on LinkedIn.
That's really the only place Iam right now.
Speaker 1 (51:55):
Is it?
Yeah, there's no new thingyou're going to be hopping on
Not yet, but maybe I'll thinkabout it, do you?
Don't do that?
No, no, okay, it wasinteresting.
The last guest was like themillennial guy was like I'm on
Instagram and the other guys andthe Gen X guy was like I'm on
LinkedIn.
Speaker 2 (52:11):
Yeah, maybe I'll give .
Speaker 1 (52:12):
TikTok a shot, oh, tiktok.
Yeah, you can TikTok that stuff.
Okay, well, that's pretty cool.
Could you guys all give me around of applause for Mahir,
here?
Thank you, we're here here andthank you very much for coming.
Thanks again for having me.
Speaker 2 (52:24):
That was pretty awesome, your first podcast.
Speaker 1 (52:26):
That's right, first podcast.
You sound good in the cans here, so this is good, so this was a
great success, thank you verymuch.
Speaker 2 (52:31):
I appreciate that, thank you.
Speaker 1 (52:31):
And thank you everybody for sitting and
listening to us banter here andhave the great rest of your show
.
Thank you very much.
Well, that does it for anotherepisode of the Site Visit.
Thank you for listening.
Be sure to stay connected withus by following our social
accounts on Instagram andYouTube.
(52:53):
You can also sign up for ourmonthly newsletter at
sitemaxsystemscom.
Slash the site visit, whereyou'll get industry insights,
pro tips and everything you needto know about the SiteVisit
podcast and Sitemax, the jobsite and construction management
tool of choice for thousands ofcontractors in North America
and beyond.
Sitemax is also the engine thatpowers this podcast.
(53:16):
All right, let's get back tobuilding.
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