Jonas Christensen 3:08
Min Chen, welcome to Leaders of Analytics. I am so excited to have you on the show. I've learned a bit about you the last few weeks and I think this is going to be a great episode. Welcome to the show.
Min Chen 3:22
Thank you very much. I'm really honoured to be here.
Jonas Christensen 3:24
Let's get straight into today's questions. So I've given a bit of an intro before people hear from you straight up. But in your own words, could you tell us a bit about yourself, your career background and what you do?
Min Chen 3:37
Sure, I have a background in technology and business with 20 years of experience. I got my bachelor's in computer engineering. Then I got a master's programme in Carnegie Mellon. And then I got into executive education programmes at the University of California, Berkeley, and Stanford. And I have been an entrepreneur for over 15 years. This is my second company. And I consider myself a multicultural, really curious person. Because I was born in China. I grew up in Latin America. I studied in the United States. I lived in several countries. I speak three languages. And now I'm back into the city of San Francisco. So it's a little hard for me to answer the question, ''Where are you from?'' when people asked me that.
Jonas Christensen 4:25
I think I can safely say you're the first person with a Chinese name, a Hispanic accent who lives in the US that I have ever met. So it is quite a unique combination. What do you think that actually has given you in your life? I know that's a big question. But what's that worth to you? All those things?
Min Chen 4:42
I find it easier for me to connect with people who are different because I have had interaction with three cultures. But I think that helps me understand the differences even with people that are from countries that I have not been into and also helped me understand global problems. Like what are the consequences and from a business perspective, this also helped me to be more conscious when we are expanding to other countries. The way that it works in the United States, it might not work in other countries, or it will definitely not work in other countries, because it's just that the values are different and people and even the language also determines how people think and behave. It's really cool to be able to connect at that level.
Jonas Christensen 5:32
Yeah, I think personally, when I moved to Australia from Denmark many years, even though the cultures are quite similar, I had some cultural faux pas nowadays, where I would say things that might be normal in Denmark, but not so normal here. Having that background that you have is, I think, just a unique strength that very few people would have. Okay, now, you in yourself is an interesting person. But you have also done something very interesting, which is you have founded an AI company, and I wouldn't mind hearing a bit more about that. So you're the co-founder and CEO of Wisy. Could you tell us a bit about that company and the problems you're trying to solve for your customers?
Min Chen 6:07
Yes, so Wisy is the second company I founded. We are solving a problem that affects everybody who shops at a store. All of us have the inconvenience of not finding products that we want to buy. And this is a $1.9 trillion problem for companies in the consumer packaged goods industry. Those companies that create the products that we want to buy. Most of these problems are due to out of stock, so the product wasn't there when we wanted to buy it. The flip side of that is that a lot of products go to waste, because they were not sold on time. And you can imagine that this problem happens for many reasons. From having a shortage to not knowing when to restock the products. And that's the most common problem. Companies do not have the information for the person who has to restock the product on time. And that's the problem that we help solving with artificial intelligence. So we see ourselves as giving superpowers to field employees. So imagine the sales representatives, merchandisers, even the store associates of this industry, instead of relying on their eyeballing capabilities, their fingers and their memory to check whether all the products that we want to buy are there in the right quantity and the right pricing, they just take a picture with their cell phone or their iPad, and we return that answer within seconds and help them figure out what the next step should be and where to focus their energy. So yeah, very interesting.
Jonas Christensen 7:37
That's fascinating. So they literally can use the device that's in their pocket to scan the shelf. So how does it work?
Min Chen 7:43
Yes, they can. We have both the picture taking capability, but also videos cam capability to make it more user-friendly for the field employee. So if it is a huge shelf, they might not want to take a lot of pictures, but they just take a video and scan the shelf, or If they want to get closer and they can take several pictures and they don't have to be overlapping, by the way. And that's an innovation we have to create to make AI suitable for humans.
Jonas Christensen 8:14
So to understand the technology, if you take a picture or video of the drinks aisle, and it'll count the number of every product that's on the shelves there. Is that how it works?
Min Chen 8:25
Yes, it counts divisible products to help answer questions such as, ''Are there enough products for each of the flavours?''. So, imagine there are thousands of flavours of each category or variations. Are there in the right position? Are there the right amount of facings for each one? Do they have the right pricing? Is there any product that shouldn't be there but it's somehow there? What happened? So we can answer many questions with the same picture in a matter of seconds, saving the field employee maybe at least 15 minutes that it takes them to count the products. And I mean, not only counting but also relying on their memory of what are the products and the variations of the products that have to be at that specific store. And it varies per store and it varies based on the season as well.
Jonas Christensen 9:21
You're reminding me of my late teens. When I was going through school, I worked at a supermarket and I remember doing all these manual tasks like going to the back freezer to get stuff for a customer because whatever was meant to be there in the counter was sold out. And sometimes it took me 15 minutes to figure out whether we actually had it or not because I had to look for the box with the name on it. Frozen peas or whatever it might have been. Sometimes we do stock take of the whole store and that will take every employee combined hours and hours to do that. And there was miscounting and we had to double count because we had to make sure that we were counting correctly, in fact. So, it was counting of counting. And other than that every night, as I worked in the perishable goods department, we would just throw out hundreds and hundreds of products that we're about to go off that were, for all intents and purposes just as good as new. But they didn't meet the quality standards of the supermarket. So as someone who's been on the ground there, I see the massive waste that go through that industry. But perhaps could you put some numbers on the benefits that your customer received from using your platform? What's the typical uplift?
Min Chen 10:37
So, as you already mentioned, the waste in this industry is huge. Like, we have had customers telling us that maybe 20% of their products are gone to waste because they were not sold on time. And the reason they were not sold on time is because they either purchased too many products for that specific store, that product was not rotating enough or they did not restock the shelves and the products stay in the back of the store. So that's one side. But the other side is also the outer stocks in the US are 8.5% and even higher outside the US. So it's like the same root cause is creating problems with outer stocks and also waste. So using this kind of technology that helps field staff to increase their efficiency by around 25% and increase the accuracy of these emerging, - You know, human counting is 60%, accurate, 80% accurate at most. And we're increasing this to consistently be above 90%. This can lead to a combined benefit of recovering revenue, 3%, 10%, depending on the situation of the company before they started using this kind of technology.
Jonas Christensen 11:47
Yeah, that's massive. So my quick maths is that 1 in 11, 1 in 12 products are just not on the shelf at any point in time, because they're out of stock. So, you can see what a massive benefit, the sort of solution can be to.
Min Chen 12:01
Yeah, and sometimes they're not on the shelf because it wasn't obvious for the person who had to restock the shelves that the product was missing. So, for example, sometimes there is a hole because one flavour sold out. And then somebody else at the retailer will just cover that hole with another product, because, you know, they don't want to look at holes. That gives a bad impression. And then the person that has to restock the shelves is looking for the holes, and that person doesn't see the hole. So that product doesn't get restocked. It doesn't get purchased again and it doesn't get sold. So, there are a lot of issues that would lead to the same out of stock problem. But most of the time, it's not because the product was not available in the back of the store.
Jonas Christensen 12:47
And there's a big customer experience element to that, too, isn't there? There's not getting the product but I remember a few situations where I have purchased the products that I thought was on discount. But in actual fact, someone had put a different product there and I hadn't read the label properly. And they put part of the product there because the thing that was on discount was sold out.
Min Chen 13:11
Exactly.
Jonas Christensen 13:13
Maybe I need to read signs better. That's another element. But I can definitely see the benefit of the solution here to retailers. How did you end up starting this company in the first place? And why did you choose AI as the solutions to these customer problems?
Min Chen 13:30
We are four co-founders. And before starting this company, we had already decades of industry experience. So we knew that for many industries, collecting data at physical locations and get that real time feedback to improve their operations was a challenge. So we created a platform to help companies collect data at physical locations and process that information in real time. And then over time, we focus exclusively on the consumer packaged goods industry, because that's the one with a major problem. A large market size is a global problem. And it also allowed us to scale faster than any other industry. And on top of that is also an industry that touches every household. Every household has a consumer packaged product, at least. And the average is like 2,200 products per household. So through this industry and helping innovate in this industry will also allow us to improve the quality of life for every family in the world.
Jonas Christensen 14:34
Yeah, nice. So how did you start with this? Did you start coding in a garage as the story often is? How did you get started? What was day one like?
Min Chen 14:46
Yes, so Wisy is my second company. My previous company was a innovation management firm where I was helping large corporations including CPG companies to do digital transformation. So, this started as a project. First, it was a hypothesis that evolved into a project within my first company. And then in a few months, it just took off. Like, we started building this platform. We started selling the platform. And we had customers from different industries paying to be the first customers or among the first customers to use this kind of technology. And that's how we bootstrap the first two years because - So, we started the company in Panama, because we were all there. Because there's no investments for startup, there's no ecosystem, we have to do innovation as a traditional company. So meaning, looking for customers really early, which is also a good practice for startups anyway. We knew that we had to expand to Silicon Valley because Panama was just a testbed for us.
Jonas Christensen 15:55
Yeah. So there's something to say about these companies that come out of environments that are not that conducive to actually growing companies. They must have some resilience. And it sounds like you really had a customer take up very early on. So that must have kind of confirmed that your idea was right, as well as giving you cash flow.
Min Chen 16:14
Yes, it was a good situation. But it also created a lot of challenges for down the road, by having early customers from different industries. So one of the things a startup should do is to focus on one thing and be specialised in that thing. To be the become the best. Especially when you're in Silicon Valley where, you don't have to build a Swiss knife. You only have to build like the German knife that will only cut that vein, for example, because you have all the resources to be able to specialise. So when we expanded to Silicon Valley, that was one of the feedback we were frequently getting: Focus on one industry. But when you have customers, paying to use your technology, from different industries, is difficult to answer, to choose. And many people will say, ''Well choose where the money's coming from''. Well, yeah, but it's coming from all these industries. So, where do I start? If we didn't have any customers, it was going to be an easy answer: Nobody wants this. We just close it or pivot completely and do something else. If we had one customer, it was going to be also an easy answer, because well, it seems that it only resonated within industry. But we had the benefit of having customers at the beginning. But the fact that they were from different industry, using the tool, the platform, to solve different data problems, also made it a challenge for us to choose. So it took us a while to decide to solely focus on the CPG industry.
Jonas Christensen 17:45
So what industries were in play in the beginning? And how did you end up making that, what would have been a very hard choice, I imagine.
Min Chen 17:56
We had from construction companies that were collecting data about construction site. To portholes because they wanted to sell a contract to the government. To gas and oil because they wanted to collect data about the gas stations, and to also CPG. So CPG was early on one of the earlier customers. And within these companies, we also have several departments wanting to use the platform. We talked to Marketing, Sales Operations, and even to Human Resources that also wanted to use this platform to collect and also engage with people. So it was a multi-layer process to figure out, okay, which industry, which area in that industry, and one of the many problems that our platform could potentially solve. We want to focus on it first. Now we know it's the consumer packaged goods industry, specifically in the sales execution area.
Jonas Christensen 18:54
So is it fair to say that you were looking for where you could see opportunity in the industry itself? The value creation that you could create? Sounds like each of those examples that you gave her a good ideas in their own right. But was there also an element of the pull from the industry that you felt as in the desire for the product or the solution?
Min Chen 19:18
Yeah, so the we felt the pool after the pandemic. Because before that we had - Like, every company we approached, there was some kind of interest and then some ended up being customers. So we had that interest but the pull really happened after the pandemic. That we went from getting leads in a few countries to now in 20 countries. And most of them were not even companies that we try to contact. These were companies that came to us, because they found about us on their web search, or they were - For example, we have a big partnership with SAP that accelerated our company. So they came through SAP and so forth. So the pull happened after the pandemic. And that gave us a confirmation and of what we were already looking into. That the CPG industry was the one to focus on first.
Jonas Christensen 20:07
So what changed during the pandemic that made retailers or CPG, particularly open up their eyes to the need for something like this?
Min Chen 20:17
Yeah. So, there were several things. First, that you have to be able to communicate with your teams remotely, because there's grocery stores and every physical location was limiting the amount of people that could be at a certain moment, including employees, partners and customers. So they had less people doing the same thing. And they had to collaborate with them remotely. So the being able to give them feedback or collect data became a problem, high rotation because people were getting sick, or even now where these companies have problems finding people, and when you find people, they don't know your products, and they have to memorise hundreds of products per store. Learning curve is long, so a lot of mistakes there. So having a technology that will help these people to reduce that learning curve and be consistent, regardless of the location. And then also the pressure of being able to react or prevent problems with data. Especially because customer behaviour is changing every minute with the pandemic. And companies cannot rely on data that they get a month later to make decisions anymore. Now you need data to take actions, to take corrective actions on the spot. So AI will allow you to do that. And on top of that, also more omnichannel sales that now people are buying online or they buying at the store or they are buying online, but then they go to the store to pick it up. And you see retail shops that were designed for consumers now being used by pickers. People who put together what we bought online. So that creates a lot of out of stock problems, out of planogram issues that were there before the pandemic, but now with this omnichannel model are even more challenging to solve. So all these trends combined has accelerated the need and the appetite for this very traditional industry to adopt something that is really new.
Jonas Christensen 22:34
Yeah, fascinating. The pandemic has really changed us all in so many ways, hasn't it?
Min Chen 22:39
Yes, hopefully for the best.
Jonas Christensen 22:42
Yeah, I think there's a lot of good to take away from it. And you touched on an interesting point there at the end, which is that companies have had to rapidly innovate and, and take up new ways of working to deal with the situation. If you go a bit broader than that, a lot of these CPG and retail firms are probably attracted to the idea of using data and AI in their operations. But there is a big difference between wanting to do that and implementing solutions that might create marginal improvements vs something that's truly transformational at scale. What do you think are the biggest challenges you've faced in introducing your solutions to the CPG and retail industries in this space?
Min Chen 23:23
Well, before the pandemic, one of the big challenges was that this industry was very traditional, very slow. And it would take several years for them to understand and be willing to take a risk or a bet. They were waiting for other companies to be the first and none of them or a few of them wanted to be the first. So that challenge has been mitigated with the pandemic. As I mentioned, a lot of these companies, like most of these companies are willing now to be among the first to use this technology because they need it. But then the next challenge is for them to understand what it takes. Because this is not just a new feature. This is not a incremental improvement on a platform that you already have. This is a new way of thinking and doing business. So also understanding what it takes from designing the packages. So for computer vision solutions to work, the packaging has to be very different, one from another. So, from preparing your products, your processes and your people to use this and be a data driven company, to actually also having the budget to do so. Having the budget I wouldn't say is the hardest problem because when the company has a problem and you have the solution, they will make the budget. Right. But the first thing is to acknowledge the problem and then be willing to go through that process. And I compare this to when the barcodes were introduced. Like, in the beginning nobody was using a barcode. Then there were a few companies, visionary companies that, you know, they could see the future and they were betting on this technology. And then eventually it became the standard. And you cannot grow, if you don't have this kind of technology. Like, how are you going to manage all the variations of products that you have? Well, the same thing with AI and computer vision. Like, how are you going to manage all the different store settings or product assortments that you have in hundreds of thouands of stores. And you cannot have a standardised planogram for thousandss of stores. Like, five standardised planograms for thousands of stores. People are changing every day and the same store at a different location will need a different assortment. And it will need a different product assortment depending on the season as well. So all these changes, this personalization, customization is pushing the industry to use this new technology. So that's it. And it's not just because of AI is because of where the world is moving towards and how consumers are changing and AI is the tool for today's problem.
Jonas Christensen 26:10
Yeah, nice way to put it. So, in that there's a lot of complexity that has to come together. There is a computer vision you're using. And there's an algorithm underneath that. But there's also how you serve that up in a package software and how it connects with the rest of the operational pipeline of the customer, which is just as complex as what you're producing, in a sense, or maybe even more complex. How did you ensure product market fit? And how did you develop a solution that was really helpful?
Min Chen 26:45
Yes. So we talk to customers, potential customers on a daily basis. And we hear - First, we want to understand: What are the challenges they're facing today? What have they tried to do? Some of them have already tried technology similar to ours, and we want to hear what were the challenges, what didn't work. Sometimes, depending on the region, it was because of the cost. But most of the time it was because the technology was not friendly enough for the human. And that's a change that has to happen on the technology side in that the way we find product market fit beyond listening to customers, is also improving the design of our product technology and processes to make it human first. So, I want to elaborate on that. Up to maybe a year, computer vision technologies for this industry were designed for robots. You had to either - A robot would take the pictures of the shells or you will have to instal specialised cameras to take the pictures. And then the few companies that tried to do it, to let humans take pictures, they require humans to take overlapping pictures that were perfect, perfectly aligned. And that is not a user- friendly approach. It's a very time consuming approach that will take away any potential benefit that you can get from this technology. So our vision is that we are not creating AI to monitor humans. We are creating AI for humans. To give them superpowers. And this is quite a challenge, because we have to make it easy for them to collect the data. We have to make it error-proof. We have to make it real time, not ''I will give you the information 10 minutes later''. 10 minutes is a lot of time for this industry. I have to give this within one minute and sometimes it's not fast enough. I have to give this in microseconds to you. So finding that product market fit was quite a process. And we're still discovering what we can do for this industry. This is just the tip of the iceberg. We're all learning that AI can increase your efficiency. But AI can also allow you to do things that you couldn't do before without AI. But you need to go through a path before we can get there.
Jonas Christensen 29:17
Yeah. And one of the decisions you made recently is to move your company from Panama to Silicon Valley. You've already touched on this. Why did you do that and what have you gotten out of that move so far?
Min Chen 29:34
Yeah, so when we founded the company, we knew we wanted to solve a global problem. We didn't want to solve a problem that was just specific to Panama. Especially because Panama is a very small country and very small market. We knew that at some point we had to expand. We still have operations in Panama and team members there. But we wanted to expand to Silicon Valley because my last job over 15 years ago was in Silicon Valley, so I already knew the place. I've been coming here for, you know, the last 17 years. My friends from Carnegie Mellon, most of them are also here. So we were already connected to the ecosystem here. So this was for us the most natural move. Moving to maybe other really good startup ecosystems in the world as well. So, this was the one that we were mostly connected to. So we decided to expand. And the other reason beyond the market size in Panama is also the lack of resources, the lack of ecosystem. For startups, Panama is unfortunately like a desert. Nothing grows there. We are a mutant seed that could grow to a certain point even in the desert, because of the resiliency of my co-founders and team members and also our industry. Like, we have been in this industry for decades. And we have all studied and worked abroad. And we also had a very good network there. But we needed a fertile soil and Silicon Valley is that fertile soil for us. And when we moved here, we knew that we would achieve more because of the resources and the culture here. But it has been beyond our imagination what we could have achieved in this almost three years that we have expanded here.
Jonas Christensen 31:22
So I think there's an important point here for listeners, because a lot of people might not appreciate what a business unfriendly environment looks like. So, Silicon Valley is the heartbeat of startups. And there's the right political and structural regulatory environment around it to do that. And a lot of listeners will come from countries that probably have a similar ease of setting up companies and getting employees and so on. Could you describe to listeners what it looks like in Panama, and why it's so hard to get something like this off the ground there?
Min Chen 31:57
Yes, there are several layers to that. So first, there is the legal tax layer. So in Panama, you had to pay taxes, since the moment you register a company. Like, you haven't even made a dime and you're already paying taxes. So that's very unfriendly for any kind of business. It was also very difficult to know what was the legal procedure to establish a company. You have to go to five different government entities and you couldn't find information unless you hire a lawyer, which also costs money you if you're bootstrapping. It's not that friendly. And then there is the market layer. Like, most companies there, there are small companies because the country is small, but most of them were also afraid to work with startups. I remember in my previous company, the first question they will ask us, ''How big is your company? Where is your office located, and how many big customers like me you already have?''. So I mean, for established company is easier. But if you're starting, you know, there is no appetite for innovation there. And then there is the talent pool. It is a small country. So even if you have some talented people there, it's going to be a few. And then finally, the lack of resources for startups. So lack of capital, lack of investment, but also lack of ecosystem of people doing similar things that you can also learn from and lean on. So there were many reasons why we had to expand here. Maybe other founders from other countries, and even founders in Panama, who were solving a different problem, they might not have the challenges that we have. The fact that we're solving a global problem for the largest CPG companies in the world, it made us have to expand to Silicon Valley.
Jonas Christensen 33:45
Yeah, and that's a benefit for Silicon Valley. But a real shame for countries like Panama that don't have the right environment for the seeds to grow. And you can just see how over time, it really just creates a different path, not just for the founders, or the potential founders, but for the country as a whole. So thank you for elaborating on that, and sort of making it really clear to us what that looks like in Panama.
Min Chen 34:10
Yeah. And well, that's also, of course, we have this responsibility with our investors and customers and my team members to make Wisy work but we are also the hope of the country that you can do great things, even if you're in Panama, and we are trying to influence decision makers in the country and in any country. We have been invited to talk and speak to decision makers, several countries on how they can improve their ecosystem to allow for more innovation, to promote more innovation. And this is the harsh feedback we give them. It's not just about talking and creating webinars and showcasing founders. It's about making real change in the legal system, the tax system, education and also the culture. Like, are companies willing to work with startups? Are people willing to work for startups, or they are looking towards working the largest multinationals in the world?
Jonas Christensen 35:12
Fascinating, I really hope you succeed, because this is just such a big thing for parts of the world that don't have the right regulatory environment. But it's also just a large part of the human race that is not set up to basically take advantage of the skills that they were born with, in the same way that people in Silicon Valley and other places like that can do. Now, Min, I want to move on to a bit broader than your company and talk about the future of AI and retail more generally. What in your mind are applications of AI in the Consumer Packaged goods and retail industries with the most potential?
Min Chen 35:54
Well, there are so many challenges that are yet to be solved. So for example, we are using AI, specifically right now computer vision, to solve the problems at the moment of truth. And we call it the moment of truth is because when you and I go to the store, and the product is not there, it doesn't matter how good the product was and how attractive the design was and how many millions of dollars these companies invested in doing marketing and promotions and how much research and development they did in the product. At the moment of truth, the product was not there. So we are focusing with computer vision in that. But then there are other opportunities. Like, how do you forecast your sales? How do you define the right assortment for that store? How do you think about the next product you should be developing? So, a lot of those things can be, I wouldn't say like, solve completely with AI, but AI would allow this industry to learn from data, but also anticipate what could happen. To move from a reactive mode of ''Okay.I am getting data at most once a month'' and the strategy level, you make decisions, but nothing happens at the operational level. To move from reacting to acting. Getting this data in real time to improve operations, and then eventually move forward to forecasting and preventing this and then innovating. Like, what if we do this? What would be the impact? So there are a lot of areas, I think. We are just exploring the tip of the iceberg. I don't think anybody can anticipate what AI can do for this industry and of course many other ones. In a responsible manner. So that's what we have to look for.
Jonas Christensen 35:55
So what are the things that we should consider in that category of AI responsibility? What are the things that you perhaps look for in your company?
Min Chen 37:58
Yes, we are very focused in the human experience, instead of the AI experience. Like, we really Jump hoops and have to innovate in making AI user-friendly as possible. Because we are moving away from an area where when you say AI, people would think of robots. We have to move into our area, when you say AI you think of superhumans. How can AI augment human capabilities? And that's the way we at Wisy see our role and that's why we're called Wisy. Wisy comes from wise and easy. AI for humans. That's the change that we are creating and I hope that other companies that are in AI are also working on innovation from this angle. Like, how do we make this easier for humans to be able to do their jobs better, or be happier, and companies to be more profitable, but also more sustainable and then together create a better version of our world?
Jonas Christensen 39:09
Nice. I love that name. Wise and easy.
Min Chen 39:14
Making things easy is not that easy, but you have to do it.
Jonas Christensen 39:19
I think ease is the cornerstone of most applications, whether they are B2C or B2B. The success of something is often up to how easy it is to use. And that might be stating the obvious, but sometimes people forget to actually consider that when they build things, whether it's really simple in house applications that they built for themselves or are marketed solutions like yours. Now, you talked a bit about a few applications of AI here. Wat are the biggest hurdles for the CPG and retail industries to overcome before these dreams become reality?
Min Chen 40:01
The first part is to be a data driven company I'll say. Even before AI, like, learning to observe their own operations, collecting the right amount of data and looking into the data, and not just collecting data for data's sake, and then having so much data they don't know what to do. So what are the business challenges that these companies are facing nowadays? They're facing a lot more challenges and new challenges that they didn't have before. And how can they leverage the tools, the new tools that are designed for today, to help solve this? So, doing that insight within the company, I think, is the first step for any kind of change, whether it is AI driven or not. So that's the first step and then AI should come in as a tool if AI is the right tool to solve this problem. We are not a good fit for certain kinds of companies, even in the CPG industry. And then if that is the case, then no AI will be able to solve the problem. So the first part is, again, for companies to really understand what are the challenges they're going through. And then from there, identify the tactics that can solve those and which of those they really need AI. And data is pervasive, right? Like, you don't need to use AI. But you can do data to solve some of those.
Jonas Christensen 41:31
I couldn't agree more. And what I often say to people is, we're kind of like where we were in early 90s, where we had an IT revolution. And one of the things we had to do was educate everyone on using computers in their day-to-day. So, everyone became computer literate. We learned how to use Word Processing and all the tools that we take for granted now in our business operations. And much like that time, even though it's 30 years ago, we're not seeing the same trend, but with data. So people need to become as data literate, as they are computer literate. So, you don't put down your resume today that you know how to use Word or Outlook, because that's expected, that you know how to do those things. But if you go back 20 years per se, that was the thing that you were really proud of, because not everyone knew how to do those things. And I think it's incumbent on industries, but also everyone working in industries that can be data driven to actually consider that and take on, take it upon themselves to educate themselves in how to become data driven. So thank you for calling that out. The other thing that you made me think of there was the size of your customers. And we haven't heard of this, but what I'm imagining when you talk, it's this big supermarket. The Walmarts, or the Tescos, where you have shelves and shelves and shelves of stuff. Are smaller retailers are able to use your solution and get the same benefit?
Min Chen 43:06
The companies that can get the best benefits out of AI, ike not just Wisy, but any AI, a computer vision solution, are companies that have a lot of products in one category. So let's say, you're a dairy company and you have a lot of yoghurt flavours or you're a shampoo company and you have a lot of shampoos and conditioners and all the hair products, and you're selling in many, many stores. Thousands of stores. Hundreds of users, like sales reps or and merchandisers. Because before you can use AI, you have to go through a process of machine learning. And if you don't have the volume as a CPG company or retailer, then it wouldn't make sense from a financial perspective to make that investment in time and also cost to do so. So it's like, let's say, if you have a few millions of annual revenue, then you know, it might not make sense, an investment in AI in our kind of solution. But if you are billing billions of dollars per year, and just improving 1% or 3% of all your auto-stocks that returns a lot more millions, then yes definitely. This kind of technology is what is going to help you recover that revenue but also make that jump to this new era of doing business.
Jonas Christensen 44:31
Okay, I think that gives us a good idea. Now, Min, before we get to our closing questions, I have probably the toughest question of today. And I'm gonna ask you to predict the future. Who do you think will be the winners and losers of this AI revolution in retail?
Min Chen 44:50
I think the winners will be the agile companies. Not the strongest or the biggest but the ones that can adapt. That's going back to Charles Darwin. And what do I mean by adapting? Especially after you ask the question, like for smaller retailers and CPG companies does it make sense to use AI? I think it makes sense to start learning about it, to prepare your company to be able to use it when you already ready? And how do you start preparing? You have to learn about data, quantify your business, be more numbers driven and they make decisions using that instead of just doing estimates or winging it or relying on the heroic efforts of your personnel. You can start learning in a more objective way and collecting data and learning about it. And keep improving on different areas. So maybe product design or making your products ready for AI when you have achieved that volume. So I think the winners are the ones that will learn, will adapt and are visionary to jump into this when it's the right moment for them. And then the losers are the ones that can change that they believe they know everything and that ''This has worked for us in the past hundreds of years. Why wouldn't work in the next 100?''. Well, times are changing really fast. Think after the pandemic. Like, we have all seen that it can change from one day to the other. So yeah.
Jonas Christensen 46:30
Very good summary. And one thing that I really related to there was the ability now to make decisions using data. So a lot of people have this anxiety about the amount of data that they'd have to understand and what data driven decisions are and so on. But it's actually a massive benefit that we've never had before that we can measure accurately what's going on in our end to end operations. And we don't have to just rely on anecdotes and gut feel when we make decisions. So this is a real opportunity to create competitive advantage with data that you're highlighting. So thank you for that, Min. Now, Min, before we get to closing remarks, is there anything else that you'd like to get across that we haven't covered in this space?
Min Chen 47:20
I think we have covered a lot of things, but a personal preference in this industry of data and AI, it sounds so cold, so objective. Right? So factual. But we have to remember that this objectiveness and data driven approach should always go back to improving human experiences. How can we be more conscious now that we seen the data? How can we be more sustainable? How can we be more inclusive? How can we remove biases or do we even have biases in our data? Right? Or is this data driven approach creating even more biases? Because that that's something technology can do. Technology accelerate things and we have a formula to make mistakes. With technology, we'll be making more mistakes. So, that's something I would like to see. Certainly something that we are pushing forward. And yes, we are at the beginning of this revolution. The fifth revolution. And it is really exciting to see companies that want to be more conscious when they are creating this new technology that instead of replacing human is going to give humans superpowers.
Jonas Christensen 48:35
Thank you for that great summary. Now, Min, I have two questions for you at the end here. The first one is: I always ask the guests on the show to pay it forward. And the way we do that is that I ask you the question of who you'd like to see as the next guest on Leaders of Analytics and why?
Min Chen 48:55
Yes. I know, several founders who are working in biotech and their approach is to use AI to be able to anticipate problems, cancers or viruses, that can allow health professionals to prevent and treat those patients quickly. So I don't have a specific name right now. But I can think of several ones. Like, I already have several ones in mind. It will be really interesting to hear from them how data and AI is solving such difficult problems that affect a lot of the population in the world. And they are these people are heroes.
Jonas Christensen 49:42
I haven't explored that topic yet on the podcast. So I'm really intrigued by the opportunity to get in contact with some of these people and it's definitely one of these areas that have so much promise when it comes to AI and improving the lives and livelihoods of millions and millions of humans. So I will follow you up on that, Min, ....
Min Chen 50:03
Of course.
Jonas Christensen 50:04
... after the show. Thank you for the recommendation. Now the last question: Where can people find out more about you and get a hold of your content?
Min Chen 50:12
To find out about Wisy, our website is wisy.ai. And we frequently publish new content there. Special announcement here: We are starting to develop an educational series to help the audience understand why we are also learning. So we are going to share what we are learning, the observations, what are the challenges that we see that also we see in the industry. Challenges that we, as creators of AI, are facing and just put it out there. So wisy.ai, our website. And then my LinkedIn and account, my Twitter and other social media accounts as well. It's mean Min Chen.
Jonas Christensen 50:57
I do recommend listeners check out Wisy. I learned a lot about the company from the website and could really see this product in action. So that made it very real and very visual for me. Min Chen, thank you so much for being on Leader of Analytics. It's been such a pleasure to learn more about you and your company today. And all the best with future endeavours and Wisy the company.
Min Chen 51:20
Thank you, Jonas. Thank you. So it's been a pleasure to have this conversation with you and your audience.
