KCAA: Inside Analysis with Eric Kavanagh (Sun, 23 Jun, 2024)

KCAA: Inside Analysis with Eric Kavanagh on Sun, 23 Jun, 2024

Episode Transcript

(00:00):
Inside Analysis dot Inside Analysis dot Comand now here's your host, Eric Kavanaugh.
J All right, folks, welcometo the future. Indeed, you're
truly Eric Kavanaugh here on the onlycoast to coast radio show that's all about

(00:22):
the information economy. It's called InsideAnalysis, and folks, we have an
all star cast lineup for you today. I'm very excited. This is the
first of a three part series ofa pilot called The Foundry, and we've
got the founder of the Foundry onthe call right now, CEO Neil haunches
with us today. And also we'vegot Alex Freed from Schematic Ventures, and
we have Suketu Gandhi from at Kearney, a supply chain expert. So let's

(00:47):
just go around the room and introduceourselves real quick. Neil Haunch, welcome
to the show. Thanks for joiningthe Fray. Pretty hot topic these days.
What do you think, no question, Eric, Thanks for thanks again
for having me back on the showand excited for the conversation. You know,
just to provide the quick overview ofFoundry, right, we were a
business started ten years ago. Wewere acquired by Carney last summer and our

(01:10):
core business is helping the Fortune thousandnavigate external innovation and seeing you know,
increasingly over the years, looking tounderstand what is happening with all of these
emerging companies the world over. Doesn'tmatter what industry we're talking about or where
these these large Fortune one thousand playersare based, but to your point,
figure out what's happening. And oneof the through lines of all the corporates

(01:33):
we work with supply chain, andso you know, it is a universal
importance. It has been in particularlyinteresting over the last few years, which
we'll get into over the course ofthis show. But thank you again for
having me. Yeah. Absolutely,and Alex Freed dialing in, tell us
a bit about yourself and what yourrole is in the world of supply chain.

(01:53):
Absolutely and Eric also want to thankyou for having me on the show
and excited to be here with Neiland Sukett today. So I'm a partner
at Schematic Ventures. We're in earlystage venture capital fund based in San Francisco,
and we are sector focused, sowe invest in all things industrial technology
related, in particular companies building inthe areas of supply chain, logistics,

(02:15):
manufacturing, transportation, decarbonization. Soa lot of fun stuff going on in
that world, and you know,we invest in brand new companies, so
we see founders at the very beginningof their journey, sometimes at the ideation
stage, and get to kind ofsee the latest and greatest of all the

(02:36):
new technologies that folks are thinking aboutas applied to these categories. That's a
lot of fun. That's a coolplace to be. You know. Years
ago, I spent I guess aboutfive or six years on the advisory board
for the south By Southwest Innovator.They had an accelerator accelerator program basically,
which was all about new companies comingon and so it's kind of the same
thing, right, I get tojudge the different applicants, and it was

(02:59):
all about learning like what the cuttingedge is, what are people working out
right at the racers That it's acool place to be in. Last,
but not least, you cet toGandhi from at Kearney, you've written a
book about supply chain. What's thetitle of the book. Well, the
title of the book should have beenthings are changing fast, you need to
know how to deal with them.But the publishers said, hey, you

(03:21):
know what, that may not sell. So what we're doing is is really
talking about supply chains and what arethe fundamental principles in this dramatically changing world,
and how do you act today,how do you act tomorrow, and
more importantly, how do you planfor day after tomorrow where things could have
even more variability in the way,you know, the various crises, but

(03:46):
also opportunities. There's a lot oftalk on crises, but I think there
should be even more talk on theopportunities that are coming as a result of
these disruptions. Well, you know, it's funny you should say that.
I recall learning years ago that theChinese character or chaos is very, very
similar to the Chinese character for opportunity, because out of chaos, there's tremendous

(04:06):
opportunity, right, And you lookat the world now, and you know
it's funny. So we talked beforethe show. I've got some background in
supply chain, and supply chain isprobably the most complex space in business.
I mean absent. Maybe life scienceis or something if you really get into
the human genome and things of thatnature. But supply chain is so complicated
because there are so many interdependencies,there are so many branches, there are

(04:30):
so many bits of details about waypeople capture information. I'm a data guy,
so I think in terms of howyou persist data. And it's very
difficult to even show a supply chainto any meaningful depth because you know,
for an automobile, how many partsgoing to automobile? Thousands of parts and
all these suppliers. And what's interesting, I'll throw it back to use Ketty,

(04:50):
is that we had fair warning beforeCOVID because we had all those tariffs,
and I remember that was tremendously disruptive. I'm a cyclist, and you
could not get a bike part forlike a year all through whatever it was
twenty eighteen or something like that intotwenty nineteen. You could not get bicycle
parts because they were all in Chinaand we couldn't get them because the tariffs.
So it's like we had advanced warningand then COVID hit, and then

(05:12):
post COVID, So we're in avery strange place right now. But you
know, what's your take on howto navigate through that? How do you
advise your clients to just get ahandle and make sure you're moving in the
right direction. So to start with, you know, you were lucky you
actually had a bike, because bicycleswere really hot during COVID and then the
parts that go with that. Sothe way I wanted to kind of put

(05:34):
a simple definition of whether supply chainsright. Supply chains are a mechanism where
you deliver products and services that acustomer wants, and then you bring in
your internal resources, your factories,your distribution centers, your planning people,
and your suppliers together to deliver itwhere they wanted, when they wanted,

(05:58):
how they wanted, and for afair price. So supply chain is the
what I would call the heart ofthe business combined with the stomach, if
you may, to make it verysimple. So that's the really important part.
Now, what happens is anytimes,anytime there is a small better disruption.
For a long period of time,supply chains were broken down into silos
that said, you're a product guy, so you think about products. You're

(06:20):
a guy who plans what should bemade. You're a guy who distributes,
you're a galhu manufacturers, and youare here in somebody in procurement who deals
with the suppliers. And everything wasbroken down into silos because we were in
a game, or we ever fooledinto a world that said everything will work
as it's supposed to work. Weknow that doesn't happen. So one of

(06:42):
the big things we are talking aboutare one, think about this end to
end and second you said something reallyimportant, Eric, which is think about
the data, because the data itgives is the one that gives you the
light to understand what is happening inthe whole end to a end. And
the latest view is there are twofactories in the world, the AI factory

(07:06):
as Jensen Wong called them, andthen the second one is factories that produce
things and bring them together. Andyou have the supply chain of tomorrow.
In simple words, Yeah, that'sfascinating because with AI we can do lots
of different very powerful things like predictfor example, I mean, and just
to give some background to the audience, as a procurement person, obviously you've

(07:30):
got your bill of materials that youneed to supply to your warehouse, to
your manufacturing plant, et cetera.And being able to see who has which
part and also how reliable those peopleare, because that's one of the key
factors, right is understanding reliability overtime. It's like Okay, XYZ provider
has a better price, but arethey reliable well, let's take a look.

(07:53):
If you have the data and youcan build a baseline, then even
in difficult times, you can predictwell maybe they're maybe we should just pay
more and make sure we get thatright. It's always a balancing act.
But the thing is with the dataand with the trained algorithms, and they
have to be trained and they haveto be focused and very on point.
But if you have that, thenyou could do a much better job of

(08:13):
managing this end to end process.And that's where the magic is, right,
that is an absolute little where themagic is so the old days,
you know, is to follow whatwe would call the plan and pray model.
You plan for something and pray likeheck that the products would be ready.
But the world we are in thatmy friend Neil and I work a
lot on is we call it asense of pivot word, which is you're

(08:35):
constantly sensing what's happening on the demandside, You're constantly sensing what's happening within
your four walls, You're sensing what'shappening on the supply side, and depending
on the situation, you pivot,and you pivot. One of the complexities
in supply chain is because it dealsboth with carbon based life forms and silicon

(08:56):
based life forms. Silicon bileased lifeforms are depending on data and they can
move quickly, whereas carbon based taketime. So you can't just open a
factory, you can't move logistics networksovernight, and so now you have to
create those buffers in the middle thatallow you to deal with these variances along
the way, both on the supplysite as well as the demand side.

(09:18):
That's why we call it sense andpivot. Yeah, no, I like
that a lot, and I'll throwone more question over you and then maybe
bring you into the conversation. Oneof the real keys in supply chain and
I think we're making good ground thesedays, but you can let me know
your thoughts is having visibility not justinto what you need, but into what
all of your partners and suppliers have. And you know, consumers who who

(09:41):
shop online have seen the leading indicatorsof this. Meaning you'll be stopping for
a jack and I'll say fourteen leftin stock, for example. There are
systems of record being monitored to beable to deliver that kind of information.
Because you're tracking the warehouse, you'retracking what's available, and so the more
visibility you have into your suppliers andwhat their conditions are. And I mentioned,

(10:03):
of course the score reliability score,but just knowing what they have and
what you can get access to ina reasonable period of time, that's the
key. But that's a very difficultthing to do because you need all of
them to publish information in a formatthat you can then access and consume in
your enterprise. Is that about right? You are spot on. And what

(10:24):
is wonderful is both consumers and Bto B customers actually send indication on what
they're going to need in the nextfew weeks things. So for example,
we have trained algorithms that tell usabout twelve weeks out with ninety four percent
accuracy on what somebody might buy.Right, So that's the first thing.

(10:45):
The second thing is data is available, but we always look for definitive data.
It means is it one hundred percentaccurate and the realities it doesn't need
to be. I need to applystatistical tools to them to understand what is
the probability of something happening, andthat is a significant mindset shift. So

(11:05):
I can deal with partial data.Data may not be perfect, but at
least it's a wonderful indicator and thatis a significant mindset shift from the old
days where we say everything had tobe perfect, right. It doesn't have
to be right now that that's areally interesting point you just made them.
I'm reminded of Schrodinger's cat right,like is it in the boxes? It

(11:26):
out of the box? And Iexplain to people, like, for sure,
in a real world, it's eitherin the box or out of the
box. Okay, there's no wayit's in and out of the box.
But for purposes of developing probabilistic algorithmsand determinations, that's why you talk about
it being maybe in the box,maybe outside of the box. But you
make an excellent point. It doesn'thave to be perfect. You just want
a good a high score. Iwant it to be like ninety percent or

(11:50):
higher to be able to make myplans because things change. To your point,
I mean, I was joking withnot really joking, but talking with
the before the show about who the'shaving hyph brasonic missiles firing at ships in
the Red Sea? Like who sawthat coming? They don't want to have
that in a Bingo card for thisyear. Not me. So you're right
that you don't have to be perfectbut you do want to have the data.

(12:11):
You do want to score the dataand then adjust it over time.
Real quick final comment that I'll bringNeil and go ahead to get there.
No, I think that's a greatway to think about it, right,
Shortinger's cat, whether it's alive ordead, really depends on the data.
And now we have sensors for example, that allows you to connect everything from
where the product is being used,what do the trucks say, what do

(12:33):
the factories tell us, and whatis happening at your suppliers. So you
know, Shortinger's cat today would befully instrumented, and we would use that
information to drive our supply votes.That's a cool I love that. All
right, Neil, I'm going tobring you back in and I'm going to
laugh for a couple of minutes.Here Schroedeger's cat is now fully instrumented.
That's brilliant. But Neil, youconsult with companies all the time. Your

(12:54):
clients look to you for information abouthow to innovate, how to stay on
top of things. Thinks we kindof made an excellent point there. The
supply chain now is very well instrumented, and that's wonderful stuff because these old
devices. They tell the truth,right, they're seeing what's happening. You're
not asking some of their opinion.You're like, device, are you on?
Okay? You're on? Good?Go ahead? And Neil, yeah

(13:16):
no. And I'm trying to seeif I can keep building on the metaphor.
I think I think I can't,but you know, you're reminded me.
Actually last week's Suquto and I wehad a two day Future of Supply
Chain of that that we were hosting, you know, and so in the
audience are a wide collection of globalyou know, supply chain officers, chief
supply chain officers, and it remindedme of one of the comments and courses.

(13:37):
There is no silver bullet, andthere probably will never be a silver
bullet, but you know, abronze bullet would be incredible, right,
It meaning and how do you getthere? And it is it's all of
these devices are right, data collectionpoints, and then there's the next order,
which is you know, and thosewill will do nothing more than continue
to grow and grow and grow.But then there's the next order of Okay,

(13:58):
but what do you do with thedata right? Right? How do
you make how you make it actionable? And at some point, how do
you automate the actions that are takenfrom it? And and I think this
is certainly what we see as wekind of bridge over to startup planned,
right, which is either startups thatare empowering the collection of data so industrial
IoT you know, low worth orbitsatellite networks. They can track the ships

(14:20):
in the sea. So it's juston and on and on. You know.
It also does kind of remind meand harkomeback to like blockchain is going
to be a panacea, right,you know, big vendors would require a
big buyers would require all their vendorsto be on it and transparency in real
time. Still a lot of workto be done there. I was also
enjoying this is probably just the sideof our age. You know. Back
when r f I d tags andit was okay, we're going to RFID

(14:41):
tag every single you know, toothbrush, it turned out the tag was more
expensive than the brush itself. Butyou know, but again, just all
of this is is is the massincrease in the data points that then can
be parlayed into all of these theseelements of what a supply chain professional has
to worry about. You know,I think the other thing that's certainly the

(15:05):
next word after data is all thingsAI. And if we if we look
at the main categories that we're seeingand as we advise the corporations we work
with on okay, if the intersectionof the words supply chain AI, which
would we be thinking about? Whereare the ways it's going to impact positively
our day to day job? Youknow, I think a handful that I

(15:26):
would rattle off, you know,and there are in varying degrees of maturity,
varying degrees of venture investment going intothem, which I'm sure will be
able to touch upon. But youknow, it's things like back office automation.
There's a lot of blocking tackling logisticsautomations, autonomous trucks and what have
you. Warehouse automation think you know, robots, robots robots and computer vision
automated quality checks coming down the line. That's also often computer vision inventory management.

(15:50):
I think we've already touched a bithere about predictive analytics demand forecasting,
and then there's even the region specificforecasts, right, you know, whether
it's seasonality, weather right. Sonow you've got a lot more AI models
trying to bake those and there's anendless series of data points, right that

(16:10):
they can consume and once trained.So anyway, so I think this is
and this is kind of indicative.There is so much innovation happening out there,
much of it coming from the technologyplatform incumbents, but we might posit
even more coming from companies that didn'texist two years ago or five years ago.
And when we go back to thatsilver bullet comment and one of the

(16:30):
questions we asked that there is noright answer, but where do you think
that innovation is going to come from? The players that you've been buying from
for the last ten, twenty thirtyyears or players that are just forming right
now and you are our viewers,of course, is it's going to be
a mix of boat right? That'sright, And I mean you made a
bunch of excellent points there, andjust for the audience to understand. The
key is to be able to stringtogether the end to end process within your

(16:53):
organization such that you're planning software,your logistics that the software your people are
you using can consume, this informationcan consume these predictions, can understand and
you have your your options right,you want to have options if this plant
is on fire, somewhere in Ukraine, and we're not gonna be able to
get that part anymore. What areother options? In the bottom line is

(17:15):
that machine learning, algorithms and AIare much better at scanning across vast trobes
of data points and human beings willever be. So you want to be
able to use these technologies to youradvantage, but don't touch out. That'll
be right back. You're listening toInside Analysis. Welcome back to Inside Analysis.

(17:38):
Here's your host, Eric Tabanac.All right, folks, back here
on Inside Analysis talking to an allstar cast of supply chain experts. We
heard from Neil Haunch and Suketu Gandhiin the opening segment, and now up
next Alex Freed. Alex, you'reout there on the front lines trying to

(17:59):
figure out where are the hot tanks, who has the right technology? And
you know, the I guess theblessing and curse of the whole supply chain
world is that there is an embarrassmentof riches and opportunities, right. I
mean, you have everything from thedata collection, the instrumentation, and I
think probably the hard is not tocrack is the orchestration of all that data

(18:19):
being able to bake policies in andrules and preferences and other such things to
manage the data, because how doyou even visualize this stuff? And what's
interesting to me is no one seemsto have really dominated the IoT space.
I don't see any player who reallyowns everything. All the big guys have
some some skin of the game,but no one really owns it yet.

(18:41):
But Alex, what do you seeout there and where are you focused?
Well as it relates to IoT.You know, we definitely saw a large
wave of new companies building IoT relateddevices. But let's say predictive analytics looking
at predictive maintenance or perhaps to beable to track goods and provide visibility into

(19:03):
where they are during transportation. Soyou know, a sensor that goes on
a parcel or a palette or insideof a container, or whatever the case
may be. I think what wefound there is one a lot of value
that those are providing, in particularfor high value machines or high value goods.
So for example, those sensors foundthat let's say, if it was
high value electronics or perhaps things likejewelry or precious metals or even pharmaceuticals,

(19:33):
which in that case are also temperaturesensitive and sensitive to other things tons of
value, but as you get downto other types of goods that are perhaps
less valuable in in of themselves,it was hard to justify the ROI for
companies. So I think that's alwaysbeen a potential friction point of even though
the technology is amazing and we'd loveto have that, you know, how

(19:55):
much does it cost and what isthe ROI related to it? One just
an example on that, and isin the warehouse people wanted to put better
tags and trackers on every single item, but at the end of the day,
barcodes are still what everyone uses becausethey're super cheap. So that's kind
of on the IoT side of theworld. And then I think one of

(20:15):
your questions as well was what arewe seeing now in terms of new technology
and new companies being founded? AndI think, to no surprise to anyone
here, seeing a lot around LM'sand generative AI. And I think in
particular where we've seen that type ofAI used to great effect as it relates
to collaboration across multiple parties. Sowe know supply chains are highly fragmented multi

(20:42):
agent systems, and today the commondenominator for collaboration across parties is still email,
phone, text, WhatsApp, wechat, kind of all these more
traditional or horizontal methodologies. And we'veseen in the past companies, let's say,
create a supplier portal where the supplierhas to come in and interact with

(21:03):
that portal such that that communication thenflows to the to the brand or the
manufacturer of the OEM, and everythingtheoretically lives within that platform, but in
reality, that supplier might have youknow, ten clients are working with with
ten different platforms, or perhaps theengagement on that platform is infrequent, and
so they forget about and things goout of date. But you know,

(21:26):
one really valuable application I think we'vebeen seeing of LMS and generated AI is
the fact that it can interpret emails, put them into your system, and
then you know, communicate back tofolks. And it's also an area where
the cost of mistakes is relatively low. Like if you screw up a little
bit on an email or something alongthose lines, usually it's not going to

(21:48):
be the end of the world.It's not making a decision that is hyper
critical, especially without a human inthe loop for oversight. So when we
when we look at that part ofwhat we think about then, is we
know that this technology is really effectivewith these modes of communication to both ingest
them and then also draft something backout to communicate or automate that communication.

(22:12):
And so what is the workflow thatthis company is putting together around that core
technology such that it fits with theusers on both sides? And to what
extent did they understand the real lifeworkflow of these this customer base in order
to make sure that the chology isused to you know, the highest degree

(22:32):
of effectiveness. Yeah, that's that'svery very interesting stuff. I love that
you brought in lms to consume emailsand then parse, because one of the
really cool things these engines can dois to summarize and to just serve as
an agent with which you can communicate. So basically you can have every email
coming in absorbed by the LM andalso going to its target, so you

(22:56):
get the target. But you knowwhat I found, I'd be carey theory
this alex AI is very very goodat reminding people or making suggestions about things,
and I think that's where probably eightypercent of AI's impact will take place
is in suggestion. So you're inyour console, you're doing your job of
procurement and being a little message comesup that says, hey, we just

(23:17):
saw an email from Bangalore and they'resuggesting that this product is delayed. Do
you want to use the other supplier? For example? And the user will
go, well, gosh darn yeah, I will click yes, And then
of course every time you do that, you're helping the model train theoretically.
What do you think about that,Alex I agree, definitely a lot of

(23:38):
application as relates to making suggestions forthe user, for the end customer there.
You know, one creative example thatwe've seen a company mentioned or be
interested in, is you have anorganization, but let's say and twenty one
hundred operations folks who have their ownsuppliers or their own clients who they're speaking

(24:00):
to, and there could be asupply chain chock or an event perhaps it's
like a weather related event happening inChicago that's going to affect multiple customers or
multiple modes of transportation. But me, as an individual operator, I only
have three clients or you know,three partners that would be impacted. But
my company probably has one hundred,and if someone starts getting a few pings

(24:22):
or a few emails. Then,because this you know AI agent or system
has visibility across everyone's communication, everyone'semails, it can recommend to me to
say, hey, by the way, we noticed other people in your organization
are seeing this. You may wantto proactively reach out to your partners or
your customers and get ahead of this. So one interesting, I think example

(24:45):
of this type of you know AIsuggesting things that in a timely manner that
we've seen that could be quite effectiveand very interesting across the organization. Bridging
together people who have point and visibilityand combining it all together. Yeah,

(25:06):
that is just as an absolutely hugedeal. And maybe Suqutto will bring you
back into the conversation here to commenton this. One of the best things
I've heard about AI is to treatit like a team member essentially and realize
that it is an active force inthe organization. Of course, you have
to set it up properly, youhave to manage it. All these things
are kind of requirements, but nonetheless, as Alex just suggested here, if

(25:33):
you set it up right, ithas access to all these emails going around,
so it can sense patterns that areoccurring right now and give you feedback
and say, hey, maybe youshould jump on this. It looks like
this is going to impact you aswell. What do you think about that
as a strategy, as a goforward strategy to at least start investigating how
to use these kinds of technologies,because once you live in a world where
you're getting those recommendations, it's reallyhard to go back to the darkness.

(25:56):
What do you think, No,I think that it is you will put
what Alex said and the way yousurmised it, but what is I'll give
you a practical example. We areusing alms with the rags to understand what
is happening in the world of someof the complex logistics procurement. You know,

(26:18):
the LM might think that, hey, a load is a load.
It might think of our financial loador things of that sort. But the
moment you add a rag on top, it tells you, hey, in
this context, a load is thegoods you put on a truck or And
then you take that information and nowyou have the massive ability to take all
of that information and apply heuristics andlogistics against that because you have a data

(26:47):
set that is being collected over thelast twenty to thirty forty years of time
in terms of you know, reliability, costs, impact, things of that
sort, and now you can say, here are the recommended actions, if
you may. The second thing youtalked about was agents, and it's really
important. And had a conversation withchro for a very large company and they

(27:08):
have a couple hundred thousand people.So do you start thinking about your agents
as actual employees, because if youdo, then you will start thinking about
evaluating their actions, understanding their actions, and modifying them based on what they
do good and areas that need improvement. Because that's a different way about thinking

(27:32):
about this new technology that comes inand it brings together the concept we call
the whole brain, that is humanintelligence plus artificial intelligence should be the brain
of the enterprise, which is thinkingof them separately, and that's where the
power we think is going to beunlocked over the next three to five years
in a massive way. Yeah,I have to agree, and maybe I

(27:55):
shall throw it back over to you. Figuring out how and where to deplay
these kinds of technologies, that's oneof the biggest challenges. Figuring out which
vendors should be on your short listfor a particular space is a big consideration
as well. But I almost thinkthat at this point in time, changing
minds and opening minds is a bigpart of the conversation. And these llms,

(28:17):
from what I've seen, I've beenin this business a long time,
these lms have absolutely opened the eyesof business leaders. They now see,
oh wow, we really should befocused on this stuff. It's kind of
like Cloud was ten years ago.I thought Cloud was going to take off
like twenty years ago. It tookabout ten years longer than I thought,
and then all of a sudden itjust started really going in full steam.

(28:37):
And I think a big part ofthat is a guy named Satya Nadella from
Microsoft who somehow managed to pivot thisaircraft carrier in a different direction a pretty
short period of time. And Ithink we're in that big of a pivot.
Speaking back to this concept of pivotright since and pivot, I think
organizations have got to start pivoting toreally understanding that these AI technologies are the
game changers and if you don't investigatethem, you're going to be in big

(29:02):
trouble. What do you think,Alex No I one hundred percent agree.
And I think there's two elements ofthat. One is the leadership of an
organization needs to be forward thinking andthinking about the applic you know, how
to apply technology within their business.And obviously Neil and Suketu do a lot

(29:22):
of work with those corporations to helpthem structure. They're thinking around that aspect
of it. And then the otherpart that we like to consider as well
is then the implementation of that technology. And part that comes back to Eric.
I think what you mentioned, youhave different technology vendors and which one
is going to work better for aparticular organization, right, so, you

(29:45):
know, we're talking a lot aboutLMS. One example, in the physical
world, you would see sometimes let'ssay a small factory or a machine shop,
they would want to adopt robotics andbe kind of a top down initiative,
and they would get a robot andeveryone's very excited about it. And
then once the executives go away andeveryone goes back to producing the goods that

(30:07):
they were producing, the robot iskind of pushed into a corner and forgotten
about because it wasn't really implemented well, and it wasn't it wasn't running as
well as as expected. Perhaps therewas a lot of issues with the way
was integrated into the line, orperhaps the other folks, you know,
the people working there didn't trust itfor one reason or another, and then

(30:32):
it just gets wheeled out again whenthe executives come back in. So how
do you avoid that, not justwith a physical robot, but also with
some of these AI related tools.And I think a lot of it comes
down to ensuring that the way thatthey are implemented is truly valuable to the
people who are going to interact withthem, and also is done with an
understanding of their actual day to daywork. Right. It's not trying to

(30:55):
force them into a structure that doesn'tmake sense or doesn't fit the realities of
that or of that, you know, whatever function that that aigent is operating
in, but very critical to actuallyfit in with the way that these people
do their work and provide value tothem, to promote that engagement and make

(31:15):
sure it's not just sitting in thecorner and you know, wheel that when
they want to show it off tosomeone and then it goes right back back
with no, that's exactly right.And you know, we only have two
minutes left in this segment. Butas you were talking, and this is
what I love about doing these showswith smart people like we have on the
show today. I have these epiphaniesabout things, and there's something going to
tease this subject and sucqutta. Iwant you to just offer a quick answer

(31:37):
and then in the next segment let'sdive into this. I'll tell you something
that's going to be very compelling.I just I sensed this. You can
tell me if I'm wrong. Therehas been for years and years this ITOT
divide right information technology operational technology,and very few people know how to bridge
those two worlds. But I'll beta smart AI engine can figure out how
to bridge those two worlds because lllmscan write not just an in French and

(32:00):
Spanish, they can write in Cobol, they can write in Java. They
can write in C sharp and Cplus plus. Do you see got like
sixty seconds, do you see LM'splaying a significant role in infusing Finally,
these roles of OT and IT communications, Well, so potato potato is what
used to be with IT and OTandy both of them thought that they were

(32:22):
special to be transparent. LLMS willplay an important role, But there are
even simpler technologies right like you talkedabout Eric in the past of hey,
how do you get the underlying datain a simple, cohesive manner, So
that allows you to interpret it througha machine very easily. So that's where
the biggest help is gonna come itAnd r OT was real in realist,

(32:45):
the real trumps if false dichotomy createdbecause that's where the work was being done.
Right, I work on the machinefloor, so I am ot.
I work on the planning side,so I am it. But it's all
a supply chain, and the waywe break it down is with this end
to end silo and elellent scale helpthere sixty seconds. Okay, that was

(33:07):
excellent, So folks, don't touchthat doll. I'm like totally immersing this
conversation. We'll be right back.You are listening to Inside Analysis. Welcome
back to Inside Analysis. Here's yourhost, Eric Tabanac. All right,

(33:30):
folks, welcome back to inside Analysis. What a fantastic conversation we were having
here with Neil Haunch of Silicon Foundry, Alex Freed of some MARKI but did
I get that right? What isit? Adventures? Schematic ventures? Schematic
ventures. I'm sorry, that's mybad. Like there's a company SAMARKI it
just jumped into my head. Schematicadventures like it right next time and suketto

(33:51):
Gandhi from at Kearney all about thefalse dichotomy of it and oh tm Kenny.
We're talking about supply chain today andwe're talking about AI and where can
you This is what every business personis wondering now, like where can I
leverage this stuff? It looks great. Elms themselves not too good at lots
of enterprise tasks. A lot ofcompanies are kind of learning that now.

(34:13):
Yes, it's good at writing copy, that's for sure. It's a lot
of these engines know a lot ofstuff. I mean. But the key
to remember with these lms is thatthey are consensus engines because you vectorized this
information. So dog has been vectorizedin this particular fashioned cat. Schrodinger's cat
is just a version of a cat, but the vector is very similar to
just a cat. So they're they'regiving you an average of something and that's

(34:36):
good for a lot of different usecases. But business people want to know
where can I leverage AI for businessbenefit? Today I'll throw it over to
Neil Haunch. How do you counselyour clients when they're asking you these questions.
Yes, and so I think,you know, a big chunk of
the conversation on the show here todayhas been the intersection of AI the supply
chain. But I think you know, if we back up and to your
point, you know, the corporateexecutives are trying to figure out what's real,

(34:59):
what's not, you know, what'sactionable today. I think also in
the context of what popular press iscovering, so certainly things like ad tech
and martech, right, content generation, uh, you know, video and
what have you. But I thinkwhat we see is is the lowest hanging
through that have the biggest impact andare already proven use cases. So things
like, you know, if thecompany has software coders, right, you're

(35:21):
going to use AI solutions to helpon the coding side of things right off
the bat, Uh, just knowledgemanagement. I think a lot of the
use cases we've seen here's a massivecompany with stores of knowledge spread throughout the
classic you know, siloed disparate systems. It was a head of AI at
a large finishing services institution who gavethis example. It's not the most possible

(35:43):
and if he said, you know, how many times a day do folks
ask the question of you know,how do I fire somebody at the company,
right and it takes them four hoursto figure out? You know,
I'm boie. I think put thatin the chat GPT, your equivalent,
that's pulling from the the internal sources. So just things like that. When
you add it up at scale,you know, hundreds thousands knowledge worker hours

(36:05):
areas like customer service, customer support, which that does get a lot of
popular press. But you know,we're talking to organizations the other day and
you look at it as a complimentto the agent, and yes, we
recognize it also can be a replacement. Right how many text support calls?
Or I forgot my password? Andso I think these are you know,
and then a lot of you know, a lot of the areas within supply
chain this was seen obviously AI iswell then into all the robotics automation solutions

(36:30):
as roven, into all the computerrevision really solutions. So but I think,
you know, it's kind of thefor some some of these large companies
is the walk before you run.It's the work. And I absolutely get
value out of this, and ofcourse we always think about it, you
know, regardless if it's on theleading edge, the bleeding edge, or
what we've would define is right downthe middle right now is how can I

(36:50):
do the pilot and then do asuccessful roll out at scale? You know,
for these companies that have thousands,if not tens of thousands, or
in some cases hundreds of thousands ofworkers spread across the globe. Yeah,
you know, you just brought upsomething very interesting and I haven't heard anyone
say this yet, but I thinkit's it is truly inspired. This is
knowledge management. Like well, yearsand years ago, we're talking thirty plus

(37:12):
years ago, knowledge management was abig thing, but we just didn't really
have the tech. We didn't havethe capacity to absorb large amounts of unstructured
data and then deliver some harmonized viewof that information. Now we do.
That's what these AI agents or that'swhat these llms do extremely well, is
knowledge management. So to your point, if you have the confidence to point

(37:36):
in LM to vectorize basically all yourinformation. And it's my understanding that Google,
this isn't pilot. I think.I don't think you can use it
with your business account, but onyour personal account, if you're using Gemini,
you can type at drive and atGmail, and it is they have
already pre vectorized all of your content, which is like, that's pretty cool,

(37:58):
right, So are you seeing companiesyet do this or is that still
kind of forward looking? Neil,go ahead, Uh, seeing companies do
this today. And in terms ofthe arms providers, you know, a
good example and as you were kindof referencing a company called Glean startup and
it raised significant dollars. But we'vebrought them in to meet with a number
of the companies that we work with, and we're hoping to work with across

(38:21):
all different industries. And it's justthat, right, And the founding team
there believe they came from Google Searchin the early days, right, But
that's you call it enterprise knowledge management, enterprise search, and I think also
to your point, it can searchinternal and external and the same at the
same time, the same interface.And and whether it's the c suite of
an oil and gas company or acosmetics company or some people are company when

(38:45):
they see the demo, you know, you can see the wheels turning immediately
of oh gosh, what this coulddo for the efficiency of our people in
their day to day roles. Sure, and just understanding policy for example,
so you take the standard policy doc. And let's say it's thirty pages or
forty pages. Okay, how manypeople think anyone ever sat down and read
the whole forty page policy document.I'll bet you not many people did.

(39:07):
But your very few employees ever didthat. And search old fashioned search.
I mean I actually talked to avery very smart lady who was at the
Google search team years ago, andyou know, I remember thinking jatbots came
around largely because corporate search stinks,like on websites, Like if you ever

(39:27):
go to a website five years ago, if you went to the corporate website
and search for something you want tofind, you would get absolute nonsense,
press releases and stuff that just meansnothing. That's not helpful. But to
your point, I actually heard astory of just that, of using it
to find out how to fire someone. But the suketto, what do you
think that's a hard one to followhow to fire someone? Because I don't

(39:49):
know. But to there's an aspectof this that you were pointing to both
Neil and Etick is you got tounderstand how your business processes operate if you're
gonna apply these new technologies, andthat is a really critical part of it.
So knowledge management only works if youknow what you're gonna do with the
knowledge, not otherwise. So yougot to know where to apply these and

(40:15):
how to apply this, and howdo your companies operate. And what has
happened is in larger and larger companiesthey started with a very simple process.
Customer needs something, I'm gonna makeit and find a way to deliver.
And then we started adding layers ontop, whether it was management layer,
information layers, or process layers.And one of the big things that we

(40:35):
see happening is you're stripping these downright. It is a reverse Mandel Brought
I know, you said Schrodinger,So I had to go to Mandel Brought,
but reverse manual broad where you're startingto clean everything up and go,
this is how the process should run, and this is how a GENI will
apply. So in the world ofprocurement, we created this thing. It's
called seven Steps of procurement. Nowwe are working through and say can we

(41:00):
do it in two And the answeris yes, and we're seeing it in
action. Well that's the kind ofrethinking that is needed. Yeah, that's
awesome. I mean I have tosay, maybe Neil will throw it over
you to comment on it, andit's very encouraging because I go back to
this mindset issue. People get intoa certain mindset, they do things in
certain ways how we've always done them. Now is the time, if ever,

(41:23):
to challenge those presumptions because to Security'spoint, Okay, you've done this
seven step process for years, doyou really need all those seven steps?
Like can't you get a predictive scoreon steps two, three, and four
and then move right to step five? And that's kind of where we're seeing
things go. That is innovation.That is where you save tremendous amounts of

(41:43):
time and also keep your partners betterinformed, your employees better informed. And
this gets to my soapbox topic morale. When morale is high, good things
happen. When morale is low,you could have all the money and resource
in the world and bad things aregoing to happen. Are you seeing this
too, Neil, that companies arefiguring out how to leap fraud over traditional
sort of staid processes. Yes,I mean, and this is the classic

(42:08):
you know, opportunity conundrum, rightis the corporates are optimized around capital efficiency
and risk reduction, and you know, if public obviously quarter to quarter,
you know, metrics and numbers.You know, you're kind of reminding me.
But then hey, sometimes that leaproad disruption requires folks that are not
from industry. You know, I'mgenerally the champion. Someone left industry because

(42:30):
they saw an opportunity. It lefttheir existing role to go start a company
to solve you know, to addressan opportunity or solve the challenge. But
I remembering earlier, earlier in mycareer, we met with the founders of
Square before Square existed, right,and it was, well, wait a
minute, these founders have never workedin the financial services industries, you know,

(42:50):
mership processing. You know, thiscan't work, right, the things
that they're talking about doing. That'snot the way it works, the industry
works. But that's that is whatit took, right, as someone who
was not bounded in existing reality.Right. But obviously you know, and
you know, maybe my last pointwould be just weaving in a culture of

(43:12):
innovation, a culture of risk taking. Certainly we see it. I don't.
I don't think there's a corporate wework with where the people that we
serve specifically go gosh. You know, our massive organization, you know,
we are nimble, we are flexibleand you know instead it's you know,
at times, got to beat thehead against the wall, right in terms
of to get changed to happen.And I think it's top down, bottoms

(43:36):
up, but it's a universal challengeand it's it's also one born out of
these very large companies that have beenvery successful in some caids, not just
over years and decades, but centuries, march and centuries, and that'll always
be, always be a challenge.Yeah, no, that's right, and
I think you are seeing it atthe big companies too. Of folks,
podcast bonus segment is coming up next. You were listening to Inside Analysis,

(44:00):
All right, folks back here onInside Analysis. Time for the podcast bonus
segment. And I'm talking to anexcellent group of individuals here, Alex Freed,
Neil hanch Suketu Gandhi Alex, I'mgonna throw it over to you because
you're in the startup world. Youtalk to startups all the time from the
ideation stage and beyond, and there'sa lot of activity in the supply chain

(44:20):
world because guess what, necessity isthe mother of invention. When you have
supply chain disruptions, there's a lotof money on the line. There are
a lot of companies that are like, ooh, what are we gonna do?
And that's where startups come in.What do you see happening, What
are some of the more interesting venturesthat you've come across, and where do
you think things are going? Eric, I think I'll have to plug a
couple of our portfolio companies that Ithink are doing really exciting things, especially

(44:44):
as it relates to this topic.So one of them is Altana AI and
so effectively they have a vast networkof both public and private data. They
believe it's one of the largest organizedbodies of supply chain data in the world,
calling it the altana at list,and it's helping to customers visualize kind
of their global value chains. Andso I think we mentioned earlier on the

(45:07):
show kind of the difficulty of understanding, you know, you have your tier
one supplier, but what about thenext tier and the next tier and going
all the way to the end tier. So that's one of the things that
they're helping to solve through that atLISS and then working with both let's say,
the US Customs and Border Protection tocompanies like marit to Boston Scientific to
help them understand, you know,are they complying with trade laws, Are

(45:31):
there any of their suppliers that are, you know, using forced labor or
doing other things that bad actors do, or perhaps are there any potential risks
or bottlenecks in the fourth tier theirsupply chain where they think they have a
lot of supply redundancy or diversity lessof the tier one or tier two stage,

(45:51):
but they find out that there's actuallythis bottleneck at the fifth tier.
So Altana helps identify those types ofthings. So in terms of visibility and
an understanding of your overall global supplychain, I'd say that's one of the
most exciting companies we've seen. Awayfrom the software side, I think there's
a lot these days that you seewith let's say the robotics, the physical

(46:15):
robotics and automation pieces as well.The there's a maturation happening with some of
the existing technology, so articulated arms, autonomous mobile robots, asrs, autonomous
storage and retrieval systems, and nowyou're also seeing some of these humanoid robots
come in and there's a it's bothexciting but also an open question of is

(46:37):
the human robot form factor going tooutperform some of these you know existing arm
you know, mobile based style formatsas well. And then the last piece
that I would mention is just youknow, we've talked about it before,
but in terms of citing AI relatedtechnologies that are coming in the generative,

(47:00):
aipieces and even just outside of let'ssay the LMS, understanding emails and so
on, but helping design products andgoods faster, more efficiently, to kind
of increase the speed of innovation andthe speed at which companies can release products.
So yeah, three different areas,including one plug for one of our
companies. Now that's good, Andyou actually hit on a real hot button

(47:22):
issue for me, which is alternativedata, or as I sometimes refer to
it, real world data at scale, because when you can start capturing massive
amounts of data, whether let's justsay it's product data, understanding how many
oranges came in yesterday, understanding howmany computer parts arrived, if you can

(47:43):
get a baseline of what is outthere in TOTO and then map that against
your internal data, Holy Christmas,that's where some amazing things can happen to
ketto. I see you now atyour head there, what do you think
about that the marriage of what Icall real world data at scale, which
is all this alternative data, andyou can buy it from all kinds of
companies. You can buy it fromcredit card companies, you can buy it
from states, from counties, fromcountries, and as long as it's quality

(48:07):
data to be able to map thatagainst your own view of the world.
That's some pretty compelling stuff. Whatdo you think, Yeah, no,
it is. It is compelling andit is useful. But one of the
structural things that you know, especiallyNeil and I talked to CEOs and boards
all the time that they're asking isis this another transformation or what does this

(48:30):
look like for the next three tofive years? And you know, we
tend to use the word regenerative,that you are going to be constantly regenerating.
And CEO Client had this wonderful quote, I'm not trying to figure out
the endgame. I'm trying to figureout that the game never ends. And
in that kind of work, that'sgood. What happens is you are constantly

(48:51):
looking and going, Okay, Igot a new set of data, whether
it's artificial or internal, wishing fora little bit more information. We all
get it all here on KCAA Radiothe most diversified radio station on the dial,
KCAA. The information economy has aride. What happens is you are

(49:22):
constantly looking and going, Okay,I got a new set of data,
whether it's artificial or internal. Howdo I use it? How does it
change? So you go and runit, see the improvement, and then
onto the next one. And thisis a variation on you know, fail
fast. This is learned fast model, not a fail fast model, because

(49:43):
you have a lot of data comingin constantly and you can learn. And
you know, one of the wonderfulthings we have always seen is that bugs
are not failure. Bugs are anopportunity to improve your process. And that
is the heart of regeneration that goes. Always keep the customer in mind,
Always understand why do you exist,Always understand why your people are there,

(50:07):
and then bringing these agents to constantlylearn. So again, you know,
fail fast wonderful, anybody can dothat. But learn fast that's the company
for the long term. I likethat. I think that's brilliant. That's
a very very clever Moniker because likefail fast, like why do you want
to fail? They'll fail? LearnYou're not failing, You're learning. I'll
throw it over to Neil for finalthoughts here, and I have to say,

(50:30):
Neil, this regeneration of knowledge management, I think is absolutely spectacular.
I think that's where a lot ofthe magic is going to come. But
companies do need to be careful.You need to have some sort of governance
practice in place. Now we canscan massive amounts of unstructured data. We
can identify maybe data that's not sogood, or maybe data that's personally identifiable
information or sensitive. Do that first, but then absolutely leverage all that stuff.

(50:54):
Now it comes back to life.It's been sitting on a shelf,
you know, and share point fromthe last seven years. Maybe that's the
best idea you've never acted on yourcompany, and because of this new AI,
we're gonna be able to find that. What do you think final thoughts?
Yeah, No, I think it'sI'm enjoying the fail fast. But
if you're going to fail, learnfrom it and then iterate and integrate it

(51:15):
in and don't be afraid to andyou've got to take the risks at least
to a degree. I mean,I think it's also consistent with just innovation
and dare I say disruption is happeningfaster than ever right now. And so
if you're the corporate executives like you'vegot to make some of these bets,
you've got to release the resources totest and then integrate and then scale and
solutions, so you can say,it's more exciting time than ever before.

(51:37):
It's less about what are my directcompetitors doing, It's what's happening out there
that's relevant for my business, youknow, And and take innovation disruption as
a positive rather than negative, rightbecause it can make you it more efficient,
more opportunities for the top line.And it's an exciting and probably uniquely
scary time to be an executive withendless decision and endless sets of new data

(52:00):
they have to digest and then figureout what the action is to take against
it. But you know, Iwould not be doing my job. I
didn't say. That's why we're here. That's why Cartnis here, that's why
Silicon's founder here to help be apartner, a thought partner, a guide
through all of this. That's greatstuff. We'll look all these folks up
online, ladies and gentlemen. We'vetalked to Neil Haunch of Silicon Foundry and

(52:24):
Suquetu Gandhi of at Kearney and ofcourse Alex Freed of Schematic Adventures and send
me an email if I want tobe in the show. Info at inside
analysis dot com. That Go,Go's our show you've been listening to Inside
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but Grissom gets into big trouble whenhis Liberty Bell seven capsule sinks in the
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The station that leaves no listener.Behind KCAA NBC News Radio, I'm
Chris Karagio Tomorrow marks two years sincethe Supreme Court overturn Roby Wade, which
unraveled the constitutional right to an abortion. In an interview with ABC's This Week,

(58:05):
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(58:28):
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He spoke to a gathering of Christianconservatives in Washington, d C. Yesterday
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(58:50):
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Senator Lindsay Graham is slamming President Bidenover his latest action allowing certain undocumented immigrants
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(59:14):
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(59:36):
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