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
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