For large corporations, supply chains define the word ‘complex’. Huge functional teams, immense spans of control, ever-increasing volumes of data, not to mention a massive reliance on partners, macroeconomic conditions, and ever-evolving consumer expectations. Despite this complexity, modern supply chains are incredibly adept at delivering results and providing a critical link between corporate strategy and the end consumer.
We continue the conversation with Kevin Syslo, and Joe Boggio from Capgemini’s Applied Innovation Exchange in San Francisco, and Frederic Laluyaux, CEO of Aera Technology on how this complexity can actually become a business advantage in part 2.
Hi and welcome to the Applied Innovation podcast. Today’s topic is part two of a conversation between Kevin Syslo and Joe Boggio of Capgemini’s Applied Innovation Exchange, and Frederic Laluyaux, the president and CEO of Aera Technology. They’re talking about how having a complex supply chain can actually be advantageous to your business. If you haven’t heard part one, please check the link in the show notes and have a listen to that one first. Then come back and check out the rest of what they have to say here. And with no further ado, I’ll turn it over to Kevin.
As the cycles there for contracts get shorter and shorter and shorter, it opens amazing options, but it also forces innovation back into the marketplace, which is, I think part of like you said, what we’re seeing and it makes it super interesting and valuable. But if I try and apply that back to some of the topics that we’re talking about specifically here with the use of data, that is not in any way a new concept. We’ve been doing this for ever and ever and ever.
Now increasingly the conversation turns more towards, yeah, but not just data, not just advanced analytics, but AI. I’m curious how these things intersect. So as the cycles get shorter and people are pushing more towards new types of innovation, what has really changed? Like aside from I think the way in which the model is delivered, so software-as-a-service, a quicker implementation because of more clever architectures. That’s all fantastic. Is there something implicitly different by applying AI, or are we really just doing advanced analytics and calling it something different? What’s the big difference here from a technology perspective that enables some of this reduction of complexity? I’m curious to get your reads because I’d … There’s a lot of overlap in my mental model between really good use of data and AI where these two things are … they’re not perfectly separated.
Yeah, I mean, this is a really, really good question. I think, I’m going to resist the temptation to approach it from an Aera Technology perspective, like what do we do about this. I’ll come to that. I think the first thing is the appetite of the market to get more complex problems resolved faster now. So it’s demand. Talk about supply chain. It’s a demand driven problem.
What is happening today is if we went to see a company 10, 15 years ago with, “Hey, we have the ability to do cognitive augmentation and shift some of the responsibility that sits within your planners or forecasters or whatever they are into a computerized system that can actually accelerate the decision making and take action autonomously,” I think the market was not ready for that. So the first thing is why now? Yeah, I mean the algorithms that we’re using, some of them are very old. And that’s not the problem. I think we would like to live in our technology bubble and think that everything is fresh and new. You’re right. I mean, I like to … I love to say that we’re resolving old problems, we are resolving very simple problems. We have now better tools to do it.
Now, when it comes to the technology aspect of it, yeah, the ability to compute at scale massive amount of data, going back to my Google and LinkedIn and Facebook analogy, that, and internet scale technology is there. And the rear that we’re about to crack, which to me is fundamental is real time. So real time is not real time because you’re still dependent on an ERP batch to be close, but the ability for us to consolidate. We talked to a company yesterday. They have 86 different transactional system. We just went live with a client, that was a few weeks back, was we have to crawl, just like Google crawls the internet, we crawl their systems. 47 different systems. We bring 1.2 billion rows of data, 1.2 billion rows of data every day. We do 2,800 crawls every day for that client. To derive what? Do you know how simple it is to basically be able to give their ATP data available to promise. And you’re like, how is it not possible to do that?
Well, it’s not possible because the answer to that very simple question, which is when is my order going to be delivered lies at the intersection of 47 different systems that don’t speak to each other. And yes, you build the data lake, which I like to call the data swamp because now you’re just a bunch of data, but it doesn’t speak business. So you have to now take that data and transform it. With what? Formulas, it’s nothing complicated, into an integrated data model that gives you a real time view of your business end to end.
I’m using some very old dirty words here, end to end real time visibility. The only difference is that we have the data, we have the scale to actually do it. And the real time changes everything because now decisions can be automated. So old problems that we’re resolving, technology is not fundamentally new conceptually, but the scale at which we can operate is such that we’re now enabling real time decision making. And at that point you have to now figure out a way for the AI, the models, it’s data modeling, it’s not that complex, to interact with the people so that we really move to, as I said earlier, people doing the work, making the decisions to, with the help of tools and data, to now enough power and cleansiness in the data and sense in there to have the machines doing the recommendations or doing the work controlled by the people.
This is what happened in the shop floor 50 years back. We’re just doing that now inside a big giant pyramid. Impact will be a completely, what I called a process of delayeralization. I believe that very large organizations will naturally delayer because a lot of the functions in that big pyramid that are pushing up and down, decisions up and down will be made unnecessary, giving the ability for companies to reorganize more horizontally in a network and increase their wingspan.
I think that change is coming, but what’s driving it, it’s not an AI revolution. It’s simply the availability of real time computing at scale. And we’re basically back to where we should have been. We’re not being stuck in that ERP [inaudible 00:06:48]. And that’s pretty much the problem. We’re catching up. We’re resolving a problem and catching up. Not doing sci-fi here. I mean when we have Aera software talking to you on your phone, answering business questions, and, yeah, it’s cool. The voice thing is sci-fi and it’s new, but the processing of the data, it’s good old enterprise modeling and processing. It’s not … Data processing it’s not that complicated.
Yeah. One of the words though that you used is interesting to me. So you said, in your case in particular Aera’s making recommendations to a person who then takes the ultimate decision. And those two words are fascinating for me because I think there’s a distinct difference between are you recommending or are you trusting enough for a decision to be made? And I’m very curious, your read on how receptive people are to losing that autonomy on decision making. Are they still biased towards, I want recommendations, not decisions? Or is it kind of bifurcated into different segments? I’m fascinated by those two words.
I got it so wrong. I got it so wrong. So the way, not specifically talk about Aera, just to understand how it works. We crawl all these enterprise systems. We process all that data. We apply all the skills, which is the intelligence on top of it, which combines data science and models and so on and so forth to deliver what? Recommendations. So if you have, if you’re a Aera customer, you have this inbox called a cognitive workbench because inbox was way too simple.
So we call that a cognitive something, right? It’s cognitive workbench. And that cognitive workbench is literally like a Gmail inbox and you select a skill and you see the recommendations. Very interesting because the recommendation are timestamped and there is a life cycle to it. I recommend you move inventory from this point to this point to convert open orders into revenue, but you have three hours to do this. Hence, the need to go mobile, to have voice, to make it super simple for people to take the recommendation.
Now, if I do that with you, you’ll say, “I accept the recommendation and Aera, please go ahead and execute,” literally run the execution. Or you’ll say, “No, I don’t want to do that because it’s stupid,” and we’ll ask you why and we’ll capture that. Or, we’ll say, “No, actually I’m not going to even do half of what you’re recommending.” But I’m capturing that data and I’m building a permanent memory for the first time of how decisions are being made. And I know the context in which decisions are being made across multiple variables, your attainment of forecast and so. Your forecast attainment all of this kind of stuff. And I know what the expected business outcome is across multiple dimensions, cash calls, service levels.
So now I’m capturing all that data because it’s still happening in that inbox, and I’m now offering you some amazing analitics of when Aera makes a recommendation for demand forecasting increase in that context, three weeks before the end of the quarter, users in that area tend to react that way not other that way for that impact. But be careful, because it’s all multidimensional. The impact might look good on cost savings, but it’s maybe horrible on service levels. And now we’re giving the visibility onto this how decisions are being made.
But back to your point around augmentation versus automation and acceptance. Where I get it all wrong is I initially thought that of course you have to present a recommendation in front of the users for the users to actually say, yes, no, and we can capture that knowledge. Some of our clients are basically saying, “Yeah, we don’t need that. We don’t need that Fred because you know what the reality is. Our team at this point is completely overwhelmed. The complexity that is such for them to make the right decisions on how to build a promotion or how to plan for X, Y, Z function, it’s just so complex that they just can’t do it. So we’re just going to automate and see how the algorithm goes.”
So the point where I was wrong is I thought people would say, “Oh no, no, we want our users to say something.” And in some cases they’re like, “No. Let’s automate right away, free the users to actually do the another part of their job, where we absolutely need them to work on.” And that was, that came to me as a surprise. We talked about augmentation. We’re still doing augmentation, but there’s a lot of automation as well that’s going on.
Yeah. Fascinating comments. I think just reflecting on, yeah, how do you start to unlock this stuff because so many of these things are not new concepts. So it kind of is the … You mentioned Aera couldn’t have existed five years ago. And I think part of the phenomenon that we’re seeing is, yeah, are related to demand and supply. So the demand signals we’re seeing from clients now are CEOs coming in. And if you look at an archetype of a CEO today for a large company, odds are they have relatively low digital literacy. Odds are they’ve never really had to lead through a fundamental sector level disruption and shift. And from a supply standpoint, we see entrepreneurs like yourself.
So there’s an inevitability of these are really complex problems that the demand side of our world is learning very quickly that this must be done, and they’re increasing their literacy and they’re starting to dive into these problem statements, and they typically come out in the form of we need to move faster, we need to reimagine, reinvent how we work, or our culture, reinvent our business model, and then the consequences, all kinds of change through the organization. But they’re going to free that up. The demand is there and these are strong, equipped, capable leaders under a lot of pressure to change the organization.
And then the supply side is, on the talent side you’ve got a new workforce surfacing up that is probably going to react to that question Kevin very differently than the incumbency workforce of I grew up augmenting everything I do digitally. It’s a natural thing for me. And if I don’t have it, I’m going to be really ill-equipped to know how to make decisions unless you’ve Google-ized my tasks at work. Plus, and I look at where’s top talent in the world going, where are dollars in the world going, and it’s going to attack these large scale problem sets and to start to give more voice to these ambiguous problem statements.
I look at it as, from a consultancy perspective, we’re right in the middle of all that thrashing, change in demand for us and change in supply for us. And being here in the Bay Area, you really see amazing new talent coming up on the supply side that when done right … So I would imagine you in front of a client who’s got this ambiguous problem, that they can viscerally feel very well. They just can’t put words to yet. So when they see your slides, they’ll go, “Can I have your slides?” So you’re giving voice to the ambiguous problem, combining it with all these tailwinds of new talent and in the inevitability of this.
I think the fascination I have with this is that’s what we kind of exist to help our clients with, is to give voice to it and then start to unlock the organization because they’ve got data everywhere. They’ve got these foundational challenges. But once that top leadership and the entrepreneurial supply start to marry, the inevitability is we’re going to go tackle these things that are tackleable. These aren’t fundamentally difficult things to do, but they’re tactically difficult things to do.
Yeah, no, you’re 100% right. And this is why I feel personally so fortunate to do what I do right now because it’s the intersection of technology. I mean, you have to have this deep knowledge of how large enterprise work and have worked for 30 years in order to be able to communicate the right level of change, what’s possible, because that CEO who’s getting more digital savvy even though he’s not. But he’s getting those voices inside the company saying, “Hey, we got this boss. We’ve got it. We’ve done this forever. We know how to do it.” And he’s torn between the, “We’ve got this. We’ve done this forever. Here is the last 10 million bucks we spent on that system three years back. It must be good.” Well no, not any more. Not necessarily.
The speed at which they have to change is dramatically increasing. For us it’s you have to understand the patient, really … If you want to instill that change, you have to be able to talk that good, old, ugly enterprise language, understand the complexity, how change is being driven, but also engage with solutions.
And I think there has been a lot of skepticism driven by so many, I don’t want to be negative, but abuse somehow in our industry that people that have been taken for a ride more than once. And here’s the new wave of transformation. And here’s AI, the new cloud and this cloud, blah, blah, blah, client server. I can go back for a long time there. That my recommendation is always like what is the problem right now? As I said before, can we go ahead and tackle it quick and see some results? Just really have that iteration and build that partnership between you guys, between us and the client, and say take one, three months, four months, five months max and then see what we can get and build that trust.
There is a ton of skepticism that CEO, as I said, he’s hearing voices internally saying, “We’re good.” Is he seeing pressure? And he’s seeing his guts or use the word. It’s exactly right. They know that if they don’t disrupt themselves, they’re going to be disrupted. And that threat is there. And we’re talking about some of the largest companies in the world that are feeling fundamentally threatened.
It’s an incredibly exciting time for us. I think the impact of what we do, and personally I feel like it’s the first time in my career that we’re having such a massive impact that goes way beyond, “Hey, here is the new widget that replaces the old widget. But I guarantee you it’s going to be the same results.” I mean, as an industry, we’ve been telling our clients, “Hey, put my new tool instead of the old tool. But it’s going to be just the same.” Well, now we’re saying, “No, just the same is actually killing you. You need different.” So you’re going to implement a new solution to really think and act differently.
And that is where I say build that trust by doing some quick validation. Can the technology enable that? And the point you made about this, people asking for a slide is very valid. It’s funny to see how the industry’s copying us right now. But the point of the slides is to really use some simple analogy that people can relate to. They understand what a self-driving car does. We talk about a self-driving enterprise. We build that analogy. We draw some parallels, and I go, “Okay. I get the point. I think it’s working.” And the emergence of new winners in the market is going to be dramatic. The next five years it’s going to be really, really brutal.
Being in the Bay Area where often kind of the mindset of the unicorn and the disruptor and flattening of industries, and if you’re an incumbent, you’re disadvantaged and you’re going to be challenged with this legacy issue. When you talk to large incumbent companies, what advantage do you think that they have that an upstart doesn’t? You mentioned earlier that the barriers to entry are getting lower and lower, so you’re going to continue to see more entry of new players in. But if I’m a global scale maker of water and soap, what advantages do I have that I may not be aware of and how do I take advantage of those?
I’ll only repeat what I’ve heard when I’ve had that exact conversation with them. What are you good at? I mean go back to … Again, it’s always simple. What are you good at? Oh, we’re really, really good at understanding consumers and building brands that people can trust. We’re really, really good at building a packaging that when put on the shelves attracts the eyes and makes people want to buy it with a bottle that are basically not round because we want …
But now what happens is you take that knowledge and you say, “Well, that doesn’t work,” when you’re buying a product from a website. The colors are different. The shape of the bottle doesn’t matter. But you’re good at this. You’re good at understanding this. That is what I’m hearing. I have no … I can’t presume that I know that, but what I’m hearing from all clients is focus on what you’re really, really good at. You’re good at making good products. Great, focus on that. And if you do that, you’ll continue to win. If you get lost along the way and you get scared by the digital transformation, then effectively you might end up losing. And if you lose, you lose very big.
Great perspective. Excellent. In both of the conversations that we’ve just had, the topic of what are you good at, what do you retain, what do you change, and if you need to change, how do you do that is kind of a consistent theme. And you mentioned, Fred specifically, we’re not talking about a new widget. We’re talking about something that’s foundationally different. And we’re not talking about an eight-year plan. We’re talking about a two-month experiment.
So both of these things, we’re talking about really big change that happens really fast. What we consistently keep saying is companies that come in to talk about transformation that aren’t just wanting to talk about what needs to happen or what needs to be retained, but like how does that affect my people? How does that affect the culture that I’m building in my company to manage that change that is going to happen really big and really fast? I increasingly am curious about the effects that has on talent on both sides. So the talent on partners like us that we’ve got to change with our clients, and the talent that is incumbent to those companies.
Joe, I’m curious from your side. I’ve seen an increase in that topic kind of top of mind for the executives that we host. But would you agree with that? Do you see an increased emphasis, or is that just the same level of emphasis that may be people are just trying to still figure out? To me it seems bigger.
I fully agree, and I think, yeah, just again, kind of reflecting on these five things that we challenge against, three of the five I think the technology and service ecosystem is pretty mature on. So navigating a new ecosystem. There’s accelerators, incubators. You get lots of competency there, obsessing about a customer and design firms and design language is pretty mature in that space. You think about building world-class software. There’s abundance of methods and approaches that you can get after.
Leadership and culture are the two that I think holistically, when our clients come in with these big ambiguous challenges, those are the two toughest ones. And that’s the inner work. That’s inside out to a degree. If that leader’s mindset isn’t oriented in the right direction, the consequence of leadership is culture.
And I think that’s where, to a good degree, the role of a service partner and a technology partner is a bit different. If you really, if you think about it on a personal level, if you really want to transform, you’ve got to have the mindset right. Because if you don’t have the will and you don’t have that drive, you don’t have a purpose to change, you’re probably going to struggle.
You think about change. You mentioned word patient. If the patient wants to change, patients, the world-class athlete has a lot of coaches and a lot of latest tools and mechanisms. But the end of the day that coach didn’t go in on the court with the athlete. The athlete’s got to internalize the new skill, the new capability. That muscle memory is tough, especially if you’re world-class at it today.
So you’ve been running let’s say a world-class manufacturing or supply chain for … in the middle of an amazing success story, in the middle of, I assume, a great culture. Nothing’s really broken. Like we’re on the top of the mountain winning. But in order to remain there, we’ve got to now relearn and challenge ourselves to operate in a very different way. And a lot of these things don’t feel intuitive, don’t feel natural at first because you’re so conditioned and you’re so good at doing something in a way that has gotten socially reinforced for a long period of time and now you got to shift.
So that one for me is absolutely, I think when we have clients that are in, that when we get to the point where we get to a real emotional connection on the problem statement and a real emotional connection on the purpose of that company, moving into this next era of business and the leader recognizing I have to change and I have to show up differently to lead in this era of business and I have to think differently around the culture that we’re creating, what we’re recognizing, what we’re incentivizing, how we work, that’s where I think we start to feel like, all right, we’re in the right path with this client.
I’m sitting here and looking at the clock that’s on the wall and realizing that we’re going to be short on time pretty quickly. So maybe here is a good time to just pause and say, any final reflections? I know we’ve talked about a ton in the hour that we’ve been chatting. But any final reflections from your side Joe, from your side Fred?
I think one macro observation or reflection is just kind of looking at you Fred as a serial entrepreneur, product developer, leader, and you’ve had market impact at large company and generating a new company now. To me a macro takeaway is a really key indicator that significant change is needed in the space. Your instincts are probably pretty strong. You’ve built products for you say 23 years. So extremely successful companies. So if that fact alone, not even knowing anything about your technology, if I were in a supply chain today, I would want to know that.
And it kind of makes me think of Katerra. Michael Marks leaves and goes and starts to create Katerra. If I’m in the construction space, I’m going to be incredibly curious about what is he doing and how was he doing it. So to me that’s a big takeaway that you’re seeing convergence of this technology tailwind applying to necessity level core processes with the unique vantage point of you’re not a 26-year-old entrepreneur, right?
I wish I was.
The market benefits from that you’ve been in big tech ERP world, you’ve seen and understand this space. So I think that, to me that’s a takeaway of you’re a leading indicator for where these things are going, and then clearly everything you’re doing is just fascinating.
I appreciate the generous and overly generous comments here. But it’s a lot simpler than that. I just listen. This is very simple. I listen to customers all the time. You want to build products? We don’t invent anything. We just listen to our clients, over the many years that we’ve done this. This company is the result of having heard clients say things like, “Hey, your tech is great, but it gets us to the wrong numbers faster.” Or, and you go like, “Ouch. Wow, that hurts. What should we do?” And they tell you, “Well, if we applied the science here and this and that, but the problem is this.” And you go, “Well maybe, maybe by looking at technology … ”
The way you build great products is simply by listening to our great, your great clients. And the way you launch a company like Aera is you say, “Okay, that’s the problem we need to resolve. We think there is a way to resolve it at last that will fundamentally disrupt the status quo.” I would say at last, because I think I’ve been looking for that moment for many, many years, up to 20. I mean it’s a lifetime effort, a lifelong effort.
And when you get those two things, then the technology, you understand the problem and you see the technology, and you put it together. Two things are needed. A) it’s a great team. And the reason why the 26-year-old might struggle a little bit is that those great teams you build over many, many, many years. I have some folks that are working with me at Aera that I’ve worked with for 20 years, 22 years. And not just one. A lot. So we work as a pack of wolf and we’ve tried this together to achieve, to climb that mountain multiple times. We know how to work together, but we respect each other’s talents. That’s one because otherwise without our own people you can’t do it.
And the second is you need to have customers that trust you. The minute we launched Aera, the phones started ringing kind of to the comment you made earlier, saying, “What is that thing now? What have you done? What are you doing this time?” And, “Oh, you’re resolving the problem that we’ve discussed five years ago, three years ago.”
So that moment is how you can get things to your supply and own demand so to speak kind of match. But the genesis of all of these ideas is simply sitting down, doing what I’m not doing now, which is a lot of talking, and just listening to what your clients say.
The concept of self-driving supply chain, it was the first time I heard it with a client. The first … You hear those concepts and then you basically like a sponge, suck it all in. Now, what you have to do is to have the right team to transform that into an actionable solution. And then the rest is a bit of luck and a lot of hard work.
Excellent. Well, thank you both. I have thoroughly enjoyed this conversation. For anyone listening that has also enjoyed the conversation, quick ways to contact myself, LinkedIn, Kevin Syslo, Joe Boggio probably the same way. And then Fred, if they’re interested in learning a little bit more about Aera, what’s the best way to learn more?
Aeratechnology.com, A-E-R-A technology.com or LinkedIn me as well.
Excellent. Well, thank you both.