Inside Analysis dot Inside Analysis dot Com and now here's your host, Eric Kavanaugh.
J All right, folks, welcome to the future. Indeed, you're
truly Eric Kavanaugh here on the only coast to coast radio show that's all about
the information economy. It's called Inside Analysis, 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 of a pilot called The Foundry, and we've
got the founder of the Foundry on the call right now, CEO Neil haunches
with us today. And also we've got Alex Freed from Schematic Ventures, and
we have Suketu Gandhi from at Kearney, a supply chain expert. So let's
just go around the room and introduce ourselves real quick. Neil Haunch, welcome
to the show. Thanks for joining the Fray. Pretty hot topic these days.
What do you think, no question, Eric, Thanks for thanks again
for having me back on the show and excited for the conversation. You know,
just to provide the quick overview of Foundry, right, we were a
business started ten years ago. We were acquired by Carney last summer and our
core business is helping the Fortune thousand navigate external innovation and seeing you know,
increasingly over the years, looking to understand what is happening with all of these
emerging companies the world over. Doesn't matter what industry we're talking about or where
these these large Fortune one thousand players are based, but to your point,
figure out what's happening. And one of the through lines of all the corporates
we work with supply chain, and so you know, it is a universal
importance. It has been in particularly interesting over the last few years, which
we'll get into over the course of this show. But thank you again for
having me. Yeah. Absolutely, and Alex Freed dialing in, tell us
a bit about yourself and what your role is in the world of supply chain.
Absolutely and Eric also want to thank you for having me on the show
and excited to be here with Neil and Sukett today. So I'm a partner
at Schematic Ventures. We're in early stage venture capital fund based in San Francisco,
and we are sector focused, so we invest in all things industrial technology
related, in particular companies building in the areas of supply chain, logistics,
manufacturing, transportation, decarbonization. So a 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 beginning of their journey, sometimes at the ideation
stage, and get to kind of see the latest and greatest of all the
new technologies that folks are thinking about as applied to these categories. That's a
lot of fun. That's a cool place to be. You know. Years
ago, I spent I guess about five 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 coming on and so it's kind of the same
thing, right, I get to judge the different applicants, and it was
all about learning like what the cutting edge is, what are people working out
right at the racers That it's a cool place to be in. Last,
but not least, you cet to Gandhi from at Kearney, you've written a
book about supply chain. What's the title of the book. Well, the
title of the book should have been things are changing fast, you need to
know how to deal with them. But the publishers said, hey, you
know what, that may not sell. So what we're doing is is really
talking about supply chains and what are the fundamental principles in this dramatically changing world,
and how do you act today, how do you act tomorrow, and
more importantly, how do you plan for day after tomorrow where things could have
even more variability in the way, you know, the various crises, but
also opportunities. There's a lot of talk on crises, but I think there
should be even more talk on the opportunities 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 the Chinese character or chaos is very, very
similar to the Chinese character for opportunity, because out of chaos, there's tremendous
opportunity, right, And you look at the world now, and you know
it's funny. So we talked before the show. I've got some background in
supply chain, and supply chain is probably the most complex space in business.
I mean absent. Maybe life science is or something if you really get into
the human genome and things of that nature. But supply chain is so complicated
because there are so many interdependencies, there are so many branches, there are
so many bits of details about way people capture information. I'm a data guy,
so I think in terms of how you persist data. And it's very
difficult to even show a supply chain to any meaningful depth because you know,
for an automobile, how many parts going to automobile? Thousands of parts and
all these suppliers. And what's interesting, I'll throw it back to use Ketty,
is that we had fair warning before COVID 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 for like a year all through whatever it was
twenty eighteen or something like that into twenty nineteen. You could not get bicycle
parts because they were all in China and we couldn't get them because the tariffs.
So it's like we had advanced warning and then COVID hit, and then
post COVID, So we're in a very strange place right now. But you
know, what's your take on how to navigate through that? How do you
advise your clients to just get a handle 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 bicycles were really hot during COVID and then the
parts that go with that. So the way I wanted to kind of put
a simple definition of whether supply chains right. Supply chains are a mechanism where
you deliver products and services that a customer wants, and then you bring in
your internal resources, your factories, your distribution centers, your planning people,
and your suppliers together to deliver it where they wanted, when they wanted,
how they wanted, and for a fair price. So supply chain is the
what I would call the heart of the business combined with the stomach, if
you may, to make it very simple. 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
a guy who plans what should be made. You're a guy who distributes,
you're a galhu manufacturers, and you are here in somebody in procurement who deals
with the suppliers. And everything was broken down into silos because we were in
a game, or we ever fooled into a world that said everything will work
as it's supposed to work. We know that doesn't happen. So one of
the big things we are talking about are one, think about this end to
end and second you said something really important, Eric, which is think about
the data, because the data it gives is the one that gives you the
light to understand what is happening in the whole end to a end. And
the latest view is there are two factories in the world, the AI factory
as Jensen Wong called them, and then the second one is factories that produce
things and bring them together. And you have the supply chain of tomorrow.
In simple words, Yeah, that's fascinating because with AI we can do lots
of different very powerful things like predict for example, I mean, and just
to give some background to the audience, as a procurement person, obviously you've
got your bill of materials that you need 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 people are, because that's one of the key
factors, right is understanding reliability over time. It's like Okay, XYZ provider
has a better price, but are they reliable well, let's take a look.
If you have the data and you can build a baseline, then even
in difficult times, you can predict well maybe they're maybe we should just pay
more and make sure we get that right. It's always a balancing act.
But the thing is with the data and with the trained algorithms, and they
have to be trained and they have to be focused and very on point.
But if you have that, then you could do a much better job of
managing this end to end process. And that's where the magic is, right,
that is an absolute little where the magic is so the old days,
you know, is to follow what we would call the plan and pray model.
You plan for something and pray like heck that the products would be ready.
But the world we are in that my friend Neil and I work a
lot on is we call it a sense of pivot word, which is you're
constantly sensing what's happening on the demand side, You're constantly sensing what's happening within
your four walls, You're sensing what's happening on the supply side, and depending
on the situation, you pivot, and you pivot. One of the complexities
in supply chain is because it deals both with carbon based life forms and silicon
based life forms. Silicon bileased life forms are depending on data and they can
move quickly, whereas carbon based take time. So you can't just open a
factory, you can't move logistics networks overnight, and so now you have to
create those buffers in the middle that allow you to deal with these variances along
the way, both on the supply site as well as the demand side.
That's why we call it sense and pivot. Yeah, no, I like
that a lot, and I'll throw one more question over you and then maybe
bring you into the conversation. One of the real keys in supply chain and
I think we're making good ground these days, but you can let me know
your thoughts is having visibility not just into what you need, but into what
all of your partners and suppliers have. And you know, consumers who who
shop online have seen the leading indicators of this. Meaning you'll be stopping for
a jack and I'll say fourteen left in stock, for example. There are
systems of record being monitored to be able to deliver that kind of information.
Because you're tracking the warehouse, you're tracking what's available, and so the more
visibility you have into your suppliers and what their conditions are. And I mentioned,
of course the score reliability score, but just knowing what they have and
what you can get access to in a reasonable period of time, that's the
key. But that's a very difficult thing to do because you need all of
them to publish information in a format that you can then access and consume in
your enterprise. Is that about right? You are spot on. And what
is wonderful is both consumers and B to B customers actually send indication on what
they're going to need in the next few weeks things. So for example,
we have trained algorithms that tell us about twelve weeks out with ninety four percent
accuracy on what somebody might buy. Right, So that's the first thing.
The second thing is data is available, but we always look for definitive data.
It means is it one hundred percent accurate and the realities it doesn't need
to be. I need to apply statistical tools to them to understand what is
the probability of something happening, and that is a significant mindset shift. So
I can deal with partial data. Data may not be perfect, but at
least it's a wonderful indicator and that is a significant mindset shift from the old
days where we say everything had to be perfect, right. It doesn't have
to be right now that that's a really interesting point you just made them.
I'm reminded of Schrodinger's cat right, like is it in the boxes? It
out of the box? And I explain to people, like, for sure,
in a real world, it's either in the box or out of the
box. Okay, there's no way it's in and out of the box.
But for purposes of developing probabilistic algorithms and 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't have to be perfect. You just want
a good a high score. I want it to be like ninety percent or
higher to be able to make my plans because things change. To your point,
I mean, I was joking with not really joking, but talking with
the before the show about who the's having hyph brasonic missiles firing at ships in
the Red Sea? Like who saw that coming? They don't want to have
that in a Bingo card for this year. Not me. So you're right
that you don't have to be perfect but you do want to have the data.
You do want to score the data and then adjust it over time.
Real quick final comment that I'll bring Neil and go ahead to get there.
No, I think that's a great way to think about it, right,
Shortinger's cat, whether it's alive or dead, 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
the factories tell us, and what is happening at your suppliers. So you
know, Shortinger's cat today would be fully 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 to bring 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, you consult with companies all the time. Your
clients look to you for information about how to innovate, how to stay on
top of things. Thinks we kind of 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
no. And I'm trying to see if 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 we had 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 global you know, supply chain officers, chief
supply chain officers, and it reminded me of one of the comments and courses.
There is no silver bullet, and there probably will never be a silver
bullet, but you know, a bronze bullet would be incredible, right,
It meaning and how do you get there? And it is it's all of
these devices are right, data collection points, and then there's the next order,
which is you know, and those will will do nothing more than continue
to grow and grow and grow. But then there's the next order of Okay,
but what do you do with the data right? Right? How do
you make how you make it actionable? And at some point, how do
you automate the actions that are taken from it? And and I think this
is certainly what we see as we kind of bridge over to startup planned,
right, which is either startups that are empowering the collection of data so industrial
IoT you know, low worth orbit satellite networks. They can track the ships
in the sea. So it's just on and on and on. You know.
It also does kind of remind me and harkomeback to like blockchain is going
to be a panacea, right, you know, big vendors would require a
big buyers would require all their vendors to be on it and transparency in real
time. Still a lot of work to be done there. I was also
enjoying this is probably just the side of our age. You know. Back
when r f I d tags and it was okay, we're going to RFID
tag every single you know, toothbrush, it turned out the tag was more
expensive than the brush itself. But you know, but again, just all
of this is is is the mass increase in the data points that then can
be parlayed into all of these these elements of what a supply chain professional has
to worry about. You know, I think the other thing that's certainly the
next word after data is all things AI. And if we if we look
at the main categories that we're seeing and as we advise the corporations we work
with on okay, if the intersection of the words supply chain AI, which
would we be thinking about? Where are the ways it's going to impact positively
our day to day job? You know, I think a handful that I
would rattle off, you know, and there are in varying degrees of maturity,
varying degrees of venture investment going into them, which I'm sure will be
able to touch upon. But you know, it's things like back office automation.
There's a lot of blocking tackling logistics automations, 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.
I think we've already touched a bit here about predictive analytics demand forecasting,
and then there's even the region specific forecasts, right, you know, whether
it's seasonality, weather right. So now you've got a lot more AI models
trying to bake those and there's an endless series of data points, right that
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 technology platform incumbents, but we might posit
even more coming from companies that didn't exist two years ago or five years ago.
And when we go back to that silver bullet comment and one of the
questions we asked that there is no right 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 thirty years 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's right, And I mean you made a
bunch of excellent points there, and just for the audience to understand. The
key is to be able to string together the end to end process within your
organization such that you're planning software, your logistics that the software your people are
you using can consume, this information can 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 are other options? In the bottom line is
that machine learning, algorithms and AI are much better at scanning across vast trobes
of data points and human beings will ever be. So you want to be
able to use these technologies to your advantage, but don't touch out. That'll
be right back. You're listening to Inside Analysis. Welcome back to Inside Analysis.
Here's your host, Eric Tabanac. All right, folks, back here
on Inside Analysis talking to an all star cast of supply chain experts. We
heard from Neil Haunch and Suketu Gandhi in the opening segment, and now up
next Alex Freed. Alex, you're out there on the front lines trying to
figure out where are the hot tanks, who has the right technology? And
you know, the I guess the blessing and curse of the whole supply chain
world is that there is an embarrassment of riches and opportunities, right. I
mean, you have everything from the data collection, the instrumentation, and I
think probably the hard is not to crack is the orchestration of all that data
being able to bake policies in and rules and preferences and other such things to
manage the data, because how do you even visualize this stuff? And what's
interesting to me is no one seems to have really dominated the IoT space.
I don't see any player who really owns everything. All the big guys have
some some skin of the game, but no one really owns it yet.
But Alex, what do you see out 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 related devices. But let's say predictive analytics looking
at predictive maintenance or perhaps to be able to track goods and provide visibility into
where they are during transportation. So you know, a sensor that goes on
a parcel or a palette or inside of a container, or whatever the case
may be. I think what we found there is one a lot of value
that those are providing, in particular for high value machines or high value goods.
So for example, those sensors found that let's say, if it was
high value electronics or perhaps things like jewelry or precious metals or even pharmaceuticals,
which in that case are also temperature sensitive and sensitive to other things tons of
value, but as you get down to 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 always been a potential friction point of even though
the technology is amazing and we'd love to have that, you know, how
much does it cost and what is the ROI related to it? One just
an example on that, and is in 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 because they're super cheap. So that's kind
of on the IoT side of the world. And then I think one of
your questions as well was what are we seeing now in terms of new technology
and new companies being founded? And I think, to no surprise to anyone
here, seeing a lot around LM's and generative AI. And I think in
particular where we've seen that type of AI used to great effect as it relates
to collaboration across multiple parties. So we know supply chains are highly fragmented multi
agent systems, and today the common denominator for collaboration across parties is still email,
phone, text, WhatsApp, we chat, kind of all these more
traditional or horizontal methodologies. And we've seen in the past companies, let's say,
create a supplier portal where the supplier has to come in and interact with
that portal such that that communication then flows to the to the brand or the
manufacturer of the OEM, and everything theoretically lives within that platform, but in
reality, that supplier might have you know, ten clients are working with with
ten different platforms, or perhaps the engagement on that platform is infrequent, and
so they forget about and things go out of date. But you know,
one really valuable application I think we've been 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 to folks. 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 along those lines, usually it's not going to
be the end of the world. It's not making a decision that is hyper
critical, especially without a human in the loop for oversight. So when we
when we look at that part of what we think about then, is we
know that this technology is really effective with these modes of communication to both ingest
them and then also draft something back out to communicate or automate that communication.
And so what is the workflow that this company is putting together around that core
technology such that it fits with the users on both sides? And to what
extent did they understand the real life workflow of these this customer base in order
to make sure that the chology is used to you know, the highest degree
of effectiveness. Yeah, that's that's very very interesting stuff. I love that
you brought in lms to consume emails and then parse, because one of the
really cool things these engines can do is 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 and also going to its target, so you
get the target. But you know what I found, I'd be carey theory
this alex AI is very very good at reminding people or making suggestions about things,
and I think that's where probably eighty percent of AI's impact will take place
is in suggestion. So you're in your console, you're doing your job of
procurement and being a little message comes up that says, hey, we just
saw an email from Bangalore and they're suggesting 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
application as relates to making suggestions for the user, for the end customer there.
You know, one creative example that we've seen a company mentioned or be
interested in, is you have an organization, but let's say and twenty one
hundred operations folks who have their own suppliers or their own clients who they're speaking
to, and there could be a supply chain chock or an event perhaps it's
like a weather related event happening in Chicago 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
or a few emails. Then, because this you know AI agent or system
has visibility across everyone's communication, everyone's emails, it can recommend to me to
say, hey, by the way, we noticed other people in your organization
are seeing this. You may want to proactively reach out to your partners or
your customers and get ahead of this. So one interesting, I think example
of this type of you know AI suggesting things that in a timely manner that
we've seen that could be quite effective and very interesting across the organization. Bridging
together people who have point and visibility and combining it all together. Yeah,
that is just as an absolutely huge deal. And maybe Suqutto will bring you
back into the conversation here to comment on this. One of the best things
I've heard about AI is to treat it like a team member essentially and realize
that it is an active force in the organization. Of course, you have
to set it up properly, you have to manage it. All these things
are kind of requirements, but nonetheless, as Alex just suggested here, if
you set it up right, it has access to all these emails going around,
so it can sense patterns that are occurring right now and give you feedback
and say, hey, maybe you should jump on this. It looks like
this is going to impact you as well. What do you think about that
as a strategy, as a go forward 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 really hard to go back to the darkness.
What do you think, No, I think that it is you will put
what Alex said and the way you surmised it, but what is I'll give
you a practical example. We are using alms with the rags to understand what
is happening in the world of some of the complex logistics procurement. You know,
the LM might think that, hey, a load is a load.
It might think of our financial load or things of that sort. But the
moment you add a rag on top, it tells you, hey, in
this context, a load is the goods you put on a truck or And
then you take that information and now you have the massive ability to take all
of that information and apply heuristics and logistics against that because you have a data
set that is being collected over the last 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 you talked about was agents, and it's really
important. And had a conversation with chro for a very large company and they
have a couple hundred thousand people. So do you start thinking about your agents
as actual employees, because if you do, 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
about this new technology that comes in and it brings together the concept we call
the whole brain, that is human intelligence plus artificial intelligence should be the brain
of the enterprise, which is thinking of them separately, and that's where the
power we think is going to be unlocked over the next three to five years
in a massive way. Yeah, I have to agree, and maybe I
shall throw it back over to you. Figuring out how and where to deplay
these kinds of technologies, that's one of the biggest challenges. Figuring out which
vendors should be on your short list for a particular space is a big consideration
as well. But I almost think that at this point in time, changing
minds and opening minds is a big part of the conversation. And these llms,
from what I've seen, I've been in this business a long time,
these lms have absolutely opened the eyes of business leaders. They now see,
oh wow, we really should be focused 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 took about ten years longer than I thought,
and then all of a sudden it just started really going in full steam.
And I think a big part of that is a guy named Satya Nadella from
Microsoft who somehow managed to pivot this aircraft carrier in a different direction a pretty
short period of time. And I think we're in that big of a pivot.
Speaking back to this concept of pivot right since and pivot, I think
organizations have got to start pivoting to really understanding that these AI technologies are the
game changers and if you don't investigate them, you're going to be in big
trouble. What do you think, Alex No I one hundred percent agree.
And I think there's two elements of that. One is the leadership of an
organization needs to be forward thinking and thinking about the applic you know, how
to apply technology within their business. And obviously Neil and Suketu do a lot
of work with those corporations to help them structure. They're thinking around that aspect
of it. And then the other part 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, you have different technology vendors and which one
is going to work better for a particular organization, right, so, you
know, we're talking a lot about LMS. One example, in the physical
world, you would see sometimes let's say a small factory or a machine shop,
they would want to adopt robotics and be kind of a top down initiative,
and they would get a robot and everyone's very excited about it. And
then once the executives go away and everyone goes back to producing the goods that
they were producing, the robot is kind 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 there was a lot of issues with the way
was integrated into the line, or perhaps the other folks, you know,
the people working there didn't trust it for one reason or another, and then
it just gets wheeled out again when the executives come back in. So how
do you avoid that, not just with 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 that they are implemented is truly valuable to the
people who are going to interact with them, and also is done with an
understanding of their actual day to day work. Right. It's not trying to
force them into a structure that doesn't make 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 actually fit in with the way that these people
do their work and provide value to them, to promote that engagement and make
sure it's not just sitting in the corner and you know, wheel that when
they want to show it off to someone 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. But as you were talking, and this is
what I love about doing these shows with smart people like we have on the
show today. I have these epiphanies about things, and there's something going to
tease this subject and sucqutta. I want you to just offer a quick answer
and then in the next segment let's dive 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. There has 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 bet a smart AI engine can figure out how
to bridge those two worlds because lllms can write not just an in French and
Spanish, they can write in Cobol, they can write in Java. They
can write in C sharp and C plus plus. Do you see got like
sixty seconds, do you see LM's playing 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 OT andy both of them thought that they were
special to be transparent. LLMS will play an important role, But there are
even simpler technologies right like you talked about Eric in the past of hey,
how do you get the underlying data in a simple, cohesive manner, So
that allows you to interpret it through a machine very easily. So that's where
the biggest help is gonna come it And r OT was real in realist,
the real trumps if false dichotomy created because that's where the work was being done.
Right, I work on the machine floor, so I am ot.
I work on the planning side, so I am it. But it's all
a supply chain, and the way we break it down is with this end
to end silo and elellent scale help there sixty seconds. Okay, that was
excellent, So folks, don't touch that 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 your host, Eric Tabanac. All right,
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 is it? Adventures? Schematic ventures? Schematic
ventures. I'm sorry, that's my bad. Like there's a company SAMARKI it
just jumped into my head. Schematic adventures like it right next time and suketto
Gandhi from at Kearney all about the false dichotomy of it and oh tm Kenny.
We're talking about supply chain today and we're talking about AI and where can
you This is what every business person is wondering now, like where can I
leverage this stuff? It looks great. Elms themselves not too good at lots
of enterprise tasks. A lot of companies are kind of learning that now.
Yes, it's good at writing copy, that's for sure. It's a lot
of these engines know a lot of stuff. I mean. But the key
to remember with these lms is that they are consensus engines because you vectorized this
information. So dog has been vectorized in 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're giving you an average of something and that's
good for a lot of different use cases. But business people want to know
where can I leverage AI for business benefit? Today I'll throw it over to
Neil Haunch. How do you counsel your 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 today has been the intersection of AI the supply
chain. But I think you know, if we back up and to your
point, you know, the corporate executives are trying to figure out what's real,
what's not, you know, what's actionable today. I think also in
the context of what popular press is covering, so certainly things like ad tech
and martech, right, content generation, uh, you know, video and
what have you. But I think what we see is is the lowest hanging
through that have the biggest impact and are already proven use cases. So things
like, you know, if the company has software coders, right, you're
going to use AI solutions to help on the coding side of things right off
the bat, Uh, just knowledge management. I think a lot of the
use cases we've seen here's a massive company 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 gave this example. It's not the most possible
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 hours to figure out? You know,
I'm boie. I think put that in 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
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 compliment to the agent, and yes, we
recognize it also can be a replacement. Right how many text support calls?
Or I forgot my password? And so 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 is well then into all the robotics automation solutions
as roven, into all the computer revision really solutions. So but I think,
you know, it's kind of the for 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 of course we always think about it, you
know, regardless if it's on the leading edge, the bleeding edge, or
what we've would define is right down the middle right now is how can I
do the pilot and then do a successful roll out at scale? You know,
for these companies that have thousands, if not tens of thousands, or
in some cases hundreds of thousands of workers spread across the globe. Yeah,
you know, you just brought up something very interesting and I haven't heard anyone
say this yet, but I think it's it is truly inspired. This is
knowledge management. Like well, years and years ago, we're talking thirty plus
years ago, knowledge management was a big thing, but we just didn't really
have the tech. We didn't have the capacity to absorb large amounts of unstructured
data and then deliver some harmonized view of that information. Now we do.
That's what these AI agents or that's what these llms do extremely well, is
knowledge management. So to your point, if you have the confidence to point
in LM to vectorize basically all your information. 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 on your personal account, if you're using Gemini,
you can type at drive and at Gmail, and it is they have
already pre vectorized all of your content, which is like, that's pretty cool,
right, So are you seeing companies yet do this or is that still
kind of forward looking? Neil, go ahead, Uh, seeing companies do
this today. And in terms of the arms providers, you know, a
good example and as you were kind of referencing a company called Glean startup and
it raised significant dollars. But we've brought them in to meet with a number
of the companies that we work with, and we're hoping to work with across
all different industries. And it's just that, right, And the founding team
there believe they came from Google Search in the early days, right, But
that's you call it enterprise knowledge management, enterprise search, and I think also
to your point, it can search internal 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 a cosmetics company or some people are company when
they see the demo, you know, you can see the wheels turning immediately
of oh gosh, what this could do 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 many people think anyone ever sat down and read
the whole forty page policy document. I'll bet you not many people did.
But your very few employees ever did that. And search old fashioned search.
I mean I actually talked to a very very smart lady who was at the
Google search team years ago, and you know, I remember thinking jatbots came
around largely because corporate search stinks, like on websites, Like if you ever
go to a website five years ago, if you went to the corporate website
and search for something you want to find, you would get absolute nonsense,
press releases and stuff that just means nothing. That's not helpful. But to
your point, I actually heard a story 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 follow how to fire someone? Because I don't
know. But to there's an aspect of this that you were pointing to both
Neil and Etick is you got to understand how your business processes operate if you're
gonna apply these new technologies, and that is a really critical part of it.
So knowledge management only works if you know what you're gonna do with the
knowledge, not otherwise. So you got to know where to apply these and
how to apply this, and how do your companies operate. And what has
happened is in larger and larger companies they started with a very simple process.
Customer needs something, I'm gonna make it and find a way to deliver.
And then we started adding layers on top, whether it was management layer,
information layers, or process layers. And one of the big things that we
see happening is you're stripping these down right. 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 starting to clean everything up and go,
this is how the process should run, and this is how a GENI will
apply. So in the world of procurement, we created this thing. It's
called seven Steps of procurement. Now we are working through and say can we
do it in two And the answer is yes, and we're seeing it in
action. Well that's the kind of rethinking that is needed. Yeah, that's
awesome. I mean I have to say, maybe Neil will throw it over
you to comment on it, and it's very encouraging because I go back to
this mindset issue. People get into a certain mindset, they do things in
certain ways how we've always done them. Now is the time, if ever,
to challenge those presumptions because to Security's point, Okay, you've done this
seven step process for years, do you really need all those seven steps?
Like can't you get a predictive score on 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
time and also keep your partners better informed, 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 are going to happen. Are you seeing this
too, Neil, that companies are figuring out how to leap fraud over traditional
sort of staid processes. Yes, I mean, and this is the classic
you know, opportunity conundrum, right is 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 leap road disruption requires folks that are not
from industry. You know, I'm generally the champion. Someone left industry because
they saw an opportunity. It left their existing role to go start a company
to solve you know, to address an opportunity or solve the challenge. But
I remembering earlier, earlier in my career, we met with the founders of
Square before Square existed, right, and it was, well, wait a
minute, these founders have never worked in the financial services industries, you know,
mership processing. You know, this can't work, right, the things
that they're talking about doing. That's not the way it works, the industry
works. But that's that is what it took, right, as someone who
was not bounded in existing reality. Right. But obviously you know, and
you know, maybe my last point would be just weaving in a culture of
innovation, a culture of risk taking. Certainly we see it. I don't.
I don't think there's a corporate we work with where the people that we
serve specifically go gosh. You know, our massive organization, you know,
we are nimble, we are flexible and you know instead it's you know,
at times, got to beat the head against the wall, right in terms
of to get changed to happen. And I think it's top down, bottoms
up, but it's a universal challenge and it's it's also one born out of
these very large companies that have been very 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 at the big companies too. Of folks,
podcast bonus segment is coming up next. You were listening to Inside Analysis,
All right, folks back here on Inside Analysis. Time for the podcast bonus
segment. And I'm talking to an excellent group of individuals here, Alex Freed,
Neil hanch Suketu Gandhi Alex, I'm gonna throw it over to you because
you're in the startup world. You talk to startups all the time from the
ideation stage and beyond, and there's a lot of activity in the supply chain
world because guess what, necessity is the mother of invention. When you have
supply chain disruptions, there's a lot of 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 ventures that 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 I think are doing really exciting things, especially
as it relates to this topic. So one of them is Altana AI and
so effectively they have a vast network of both public and private data. They
believe it's one of the largest organized bodies 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. And so I think we mentioned earlier on the
show kind of the difficulty of understanding, you know, you have your tier
one supplier, but what about the next 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 at LISS and then working with both let's say,
the US Customs and Border Protection to companies like marit to Boston Scientific to
help them understand, you know, are they complying with trade laws, Are
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 their supply chain where they think they have a
lot of supply redundancy or diversity less of the tier one or tier two stage,
but they find out that there's actually this bottleneck at the fifth tier.
So Altana helps identify those types of things. So in terms of visibility and
an understanding of your overall global supply chain, I'd say that's one of the
most exciting companies we've seen. Away from the software side, I think there's
a lot these days that you see with let's say the robotics, the physical
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 now you're also seeing some of these humanoid robots
come in and there's a it's both exciting but also an open question of is
the human robot form factor going to outperform some of these you know existing arm
you know, mobile based style formats as well. And then the last piece
that I would mention is just you know, we've talked about it before,
but in terms of citing AI related technologies that are coming in the generative,
aipieces and even just outside of let's say the LMS, understanding emails and so
on, but helping design products and goods faster, more efficiently, to kind
of increase the speed of innovation and the speed at which companies can release products.
So yeah, three different areas, including one plug for one of our
companies. Now that's good, And you actually hit on a real hot button
issue for me, which is alternative data, 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 just say it's product data, understanding how many
oranges came in yesterday, understanding how many computer parts arrived, if you can
get a baseline of what is out there 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 at your head there, what do you think
about that the marriage of what I call real world data at scale, which
is all this alternative data, and you can buy it from all kinds of
companies. You can buy it from credit card companies, you can buy it
from states, from counties, from countries, and as long as it's quality
data to be able to map that against your own view of the world.
That's some pretty compelling stuff. What do you think, Yeah, no,
it is. It is compelling and it is useful. But one of the
structural things that you know, especially Neil and I talked to CEOs and boards
all the time that they're asking is is this another transformation or what does this
look like for the next three to five 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 figure out that the game never ends. And
in that kind of work, that's good. What happens is you are constantly
looking and going, Okay, I got a new set of data, whether
it's artificial or internal, wishing for a little bit more information. We all
get it all here on KCAA Radio the most diversified radio station on the dial,
KCAA. The information economy has a ride. What happens is you are
constantly looking and going, Okay, I got a new set of data,
whether it's artificial or internal. How do I use it? How does it
change? So you go and run it, see the improvement, and then
onto the next one. And this is a variation on you know, fail
fast. This is learned fast model, not a fail fast model, because
you have a lot of data coming in constantly and you can learn. And
you know, one of the wonderful things we have always seen is that bugs
are not failure. Bugs are an opportunity 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,
and then bringing these agents to constantly learn. So again, you know,
fail fast wonderful, anybody can do that. But learn fast that's the company
for the long term. I like that. I think that's brilliant. That's
a very very clever Moniker because like fail fast, like why do you want
to fail? They'll fail? Learn You're not failing, You're learning. I'll
throw it over to Neil for final thoughts here, and I have to say,
Neil, this regeneration of knowledge management, I think is absolutely spectacular.
I think that's where a lot of the 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 can scan massive amounts of unstructured data. We
can identify maybe data that's not so good, or maybe data that's personally identifiable
information or sensitive. Do that first, but then absolutely leverage all that stuff.
Now it comes back to life. It's been sitting on a shelf,
you know, and share point from the last seven years. Maybe that's the
best idea you've never acted on your company, 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's I'm enjoying the fail fast. But
if you're going to fail, learn from it and then iterate and integrate it
in and don't be afraid to and you'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 happening faster than ever right now. And so
if you're the corporate executives like you've got to make some of these bets,
you've got to release the resources to test and then integrate and then scale and
solutions, so you can say, it's more exciting time than ever before.
It's less about what are my direct competitors doing, It's what's happening out there
that's relevant for my business, you know, And and take innovation disruption as
a positive rather than negative, right because 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 with endless decision and endless sets of new data
they have to digest and then figure out what the action is to take against
it. But you know, I would 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 a partner, a thought partner, a guide
through all of this. That's great stuff. We'll look all these folks up
online, ladies and gentlemen. We've talked to Neil Haunch of Silicon Foundry and
Suquetu Gandhi of at Kearney and of course Alex Freed of Schematic Adventures and send
me an email if I want to be 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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Chris Karagio Tomorrow marks two years since the Supreme Court overturn Roby Wade, which
unraveled the constitutional right to an abortion. In an interview with ABC's This Week,
Massachusetts Senator Elizabeth Warren, warrened about the impact a second Trump presidency would
have on reproductive rights. If Donald Trump is elected to the presidency, he
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