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Transcript
Peter Crosby:
Welcome to Unpacking the Digital Shelf where we explore brand manufacturing in the digital age.
Hey everyone. Peter Crosby here from the Digital Shelf Institute. Okay, yeah. So you need to pay attention to this AI thing. Steve Engelbrecht, Founder and CEO of Sitation and his team have developed an AI tool that is capable of generating 15,000 product descriptions per hour for their enterprise customers. Connected with their PIMS and the workflows to keep humans in the loop. And that's just the tip of the iceberg. What does this all mean? Where's it going? What are the risks? Is this a job killer? Can it write my podcast intros? Steve provides expert perspective to Lauren Livak and me on all this and more in today's conversation.
Steve, thank you so much for joining Lauren and I on the podcast today. We're really grateful to have you on.
Steve Engelbrecht:
Thank you very much for having me. Really excited to be here.
Peter Crosby:
Well, I mean, I'm very excited to talk about how robots are taking over the world. I mean, that's what we're talking about, right?
Steve Engelbrecht:
That's right. We better record this quick before computers are doing it for us.
Peter Crosby:
Before we're not alive any longer. We've been sacrificed. All right, we are actually going to talk about really certainly one of the most buzzworthy topics these days, AI. It's top of mind for brands and retailers. We are in this moment where everyone wants to do more with less. I often describe the digital shelf as a hungry beast. It will always want more data, more content to get better and better search results, better conversions on product pages. All of that will take richer, deep content in addition to other capabilities. And we're all trying to figure out how to do that because we're not going to put a lot more money and a lot more people at it because we have to make it profitable. So that's the world we're in right now. And I think, well one, I want to ask if that resonates with you, what the possibilities are here for a business value perspective? I think it's just laying right there to start being experimented on. Do you agree?
Steve Engelbrecht:
Yeah, I absolutely agree. I think it's important to set the context of how big this is of what we're looking at and how disruptive this is. This is not an incremental improvement in computing capabilities or in the integration of computing into how we're doing business. This is a major, huge disruptive step. I truly believe that this will have the potential to change everything about the way that we're working in the e-commerce world for sure, really, in any information business, anything that requires a depth of knowledge or anything that's looking at large data sets, I mean, this is a revolution as big as the internet itself or the smartphone in my opinion. And this is going to change a lot about our economy and how we're working and interacting with each other and with our computing systems.
So with all that said, I mean this is an opportunity to be really innovative and think about all the different places that this technology has the potential to change and improve our work and our business in the e-commerce world is one that's really ripe for innovation because of just a huge amount of information that's changing hands every day and just how good the AI systems are at helping to automate and systematize some very important aspects of working with product data.
Peter Crosby:
And anyone who's listening also knows the dark side of that that we've been hearing about of it's only as good as the data it's accessing and I've forgotten, I work with some really smart people at Salsify and Adam, our Chief Technology Officer is one of those, and he says, "AI lies, but it lies really well."
Steve Engelbrecht:
The funniest descriptor I heard for it was, this is spicy auto correct. But I think that's way underplaying just how big this is. So certainly there's a very easy access to this, and that's one of the things that makes this so revolutionary. For the first time ever, we can talk to our computering systems. I mean, truly in natural language, we can tell it what we want it to do and what we want that output to look like. Now, this is, you're absolutely right, there's a sort of scary gap here between what these models can do in terms of creating very believable output and whether or not we can actually treat that as correct. And already I think we're seeing a really interesting movement where people are just trusting that output. And I think it says a lot about how good the technology is, but it also points to a pretty scary thing about this is that what does it mean when we no longer check our work or we're no longer applying critical reasoning or solid business processes behind generating these outputs from these models?
Lauren Livak:
And Steve, when people think about AI to that exact point, I think a lot of people see ChatGPT as a way to type something in, get something out and be like, "Okay, I'm done. I can use it. The work is done. It doesn't need, to your point, any editing or proofreading," but it's really not the case, especially from an e-commerce standpoint because they might not have all the information. They might not know what you know, you might have to input some of the information. So how are brands using AI then, to integrate it into their strategy in a way that is scalable and accurate?
Steve Engelbrecht:
Yeah, no, it's an awesome question and we should be clear too. So ChatGPT, and other generative AI models, these large language models or LLMs, this is one of many different applications of artificial intelligence and AI of course has been around for a long time. It's already part of systems that we use and see every day. Things like your Netflix queue showing you things you might be interested in, that is AI, those are predictive analytics that are driving those algorithms. This one's just different. It's different because it's so big and it's so revolutionary and it's so accessible to everyone that you can actually talk to it and interact with it directly. So I think the easiest thing way to get started with this is just to explore it a little bit and play with it. And when somebody tells you it's a chatbot, you go in and you say, "Hey, how are you?" This is how my kids play with it. "Hey, how are you?" And all of a sudden you're having a conversation with this thing.
What's so amazing though is how adaptive it is. Because it's been trained on literally billions of inputs of free text, it does have knowledge. I'm going to make air quotes, it has knowledge, it has information that's been indexed into it, which it can access for those conversations. What we need to be careful about is confusing output, which is just spitting out what it thinks you want to hear versus how correct that is. So getting into applications of this, where I'm fascinated by some of the applications of this technology are two areas. One is where we get into computing and getting into looking at external data sources, basically taking this trained model, but then applying it to real world problems. I think that's really fascinating.
And the other is about scaling. So anything that we want to do on a repeatable process that we want to try to generate consistent output at enterprise scale, you get into some issues here. And this is, I think, a really important point. The allure of ChatGPT is that you can log in and you can type to it. You can type whatever you want. You can even talk to it now with some of these models, with the Whisper technology, they've integrated into it. You can't do that at scale. So no enterprise merchant or merchandising operations team is going to sit at ChatGPT and one by one, grab product records and pull them in and say, "Okay, make me a description, make me a description." I mean, you could, but that's not going to save any time and that's still a pretty miserable approach.
What I'm fascinated in is how we can take this core technology and we can structure how we're interacting with the technology to make it consistent and make it performant and make it grow and scale with us. So not to bury the lead here, but the application of this that Sitation is focused on is generating product content. And in particular, we're starting with the basic information that we have about a product, your master data, maybe a few key talking points that would come from the marketing team or a product team. And from that, we're generating long form content. So this might be, for instance, we've got a product title, we've got a brand, we've got a category, maybe we have a few specifications about size, flavor, whatever it might be, depending on the category. And then maybe we have a couple of features. So sticking with food, maybe we have organic, we have gluten-free, we have non-dairy, have talking points like this, or maybe something about the flavor, flavor combinations.
Just from that, those would be the same inputs that we would take for a human copywriter and say, "Okay, we're going to create a piece of content that is for this particular audience, for this channel, it should be approximately this length, it should use this information. Maybe here's some sample output from previous iterations that we want you to write to a similar style." But those are all the inputs. And then from there, a human writer is going to take those and generate a creative output. That's the point of content for us. That's where the rubber meets the road for our technology, which is to take the same inputs and to make available to the model, all of that information that we need. Here's what good looks like, here's some guidelines, here's some sample skews, here's the tone of voice and the target audience, and to let it generate that first draft of what that content looks like.
And the value here is that this is a very time consuming piece of effort and work for any company really, that's dealing with a lot of product data, but especially when you've got a huge catalog or that's complicated by, you've got multiple languages, you've got multiple channels, you've got multiple audiences, maybe one version for B2B, one for B2C, one for Amazon, one for Walmart. So the complicating factor comes at the scale, and Peter and Lauren, we talked about this a little bit in some of our preps for this, but a lot of companies are tending to just skim what are the top categories, what are the top products? And they just let them fly. They do it once and they let it fly, and they never really revisit their data quality or the output of the long form written content as a strategic asset.
And I think the biggest thing we can do now is that whole thing gets turned on its head. What if we can generate thousands of product descriptions overnight? What if we can take feedback from the field and integrate them into the models and make them smarter and better so they're more compelling, they're more effective, they're more targeted, and they do a better job of compelling people to add to their cart and complete a purchase. For us, that's the promise of this technology and what we're most excited about.
Lauren Livak:
I'm putting on my old hat here and thinking about my small digital shelf team and what we could have accomplished if we had AI to support that from a content perspective. I mean, we had to prioritize the skews we could create content for, to your exact point, but we weren't able to do more and this would really enable us to create more content at scale so it's just super exciting. It just opens so many doors for teams that might not be able to be able to add more resources. But Steve, this could potentially strike some fear into creative teams and content teams. But I know we talked about this before and it shouldn't because we still need them, but can you address that a bit? Because I know there is some fear that it's like, "Well you can have images created and you can have content generated through AI, so what's going to happen to the future of creative and content overall?" What do you think about that?
Steve Engelbrecht:
Sure. No, I think it's a really good point, and I think it probably makes sense to think about previous technological revolutions that these were always growth events. These were not things that caused massive unemployment. The invention of the computer, people were scared of it because it's like, well, what's going to happen to accountants? And then look what it has caused this massive revolution of computing becoming part of everyday life, part of everyday work. Same thing with phones, with internet, with cloud, there are these inflection points where things really change, and I think what it does is a technology like this is certainly going to change the way that we do things that are relatively low in complexity and very high in frequency or scale. And things like generating product descriptions, I think, are a really good example of that. Maybe another would be something like music editing or video editing or photo editing where we have an opportunity here to leverage the technology and say, "Hey, this image that I'm looking at right now, I want you to change this or add this or expand this."
So I think it will have a big impact on some of these creative teams, but I think what's likely to change is that they're going to integrate this technology as part of their toolkit and have an opportunity to say, "This allows us to do better work and be more effective for our customers." And the humans are going to continue to look for exactly those interactions and those points in the value chain where we need people. And that's going to be, I think, in this case, more about marketing and positioning and determining that brand voice and making it as effective as it possibly can be. So certainly it's going to change things, but this is about growth. This is going to going to be a revolution that helps many, many companies do much better work in a very exciting way.
Peter Crosby:
We've been spending so much time recently talking about this next decade of the digital shelf and the headline is every one of the players in this industry need to figure out how to do what they do more profitably than they've been able to so far because of all the investments they've made in innovations to get to this place where digital and omnichannel is starting to be a tightly integrated part of the business rather than separate silos that they've been during this more experimental phase of e-commerce and digital, but now the experiment's over and money's no longer free. It's figuring out how to do all of this A, more profitably and B, drive more growth out of every shot you have at the consumer or the buyer than you've been able to to date. And that's what excites me so much about the possibilities here. When you think of someday, maybe not too far away, the ability to mash enough data together and enough content options for a product that you're actually personalizing a product page experience on the fly.
And so you can imagine the customer data, that consumer data that we'll need, the first party data that we'll require. You can imagine the knowledge of context, the temperature where that person is, all sorts of things that add up to this person might want this product for a slightly different reason than somebody else or have a different set of criteria. Then on the other side of that equation is the content that needs to be present for those switch outs to happen just like they do today in email marketing. They need to happen in the moment on a product page to get more search for those use cases and more conversion because you've satisfied that use case. We only get there at scale if machines are doing these translations by persona for us. And one, does that future vision resonate with you, Steve, and do you agree that that's going to be possible?
Steve Engelbrecht:
Yeah, there's a lot to unpack there. I think-
Peter Crosby:
Yeah, sorry.
Steve Engelbrecht:
But I think you're right on of what the future holds for this. So even right now, you can see the truly global excitement for this new technology and people are embracing something that really, frankly, is in its infancy, I think., That this is the first time that this has spilled from the academic world into the business world in any meaningful way that's accessible to everyday people to start to see it. But you can see the way that people are thinking about how it's going to change, again, virtually every aspect of our daily work and home lives. There's good examples out there already of how personalization at a massive scale can be really effective. I think Facebook's a good example. Some of the personalization or localization around Google results are another example.
So let's continue on that thread a little bit. So think about how we're searching. And if our desire to seek out some new information that we're looking for is part of a broader persona that understands who we are and what our interests are and where we work, and that information that comes back can be more tailored to us in a way that helps to reduce the friction or reduce the time of connecting a person with the information that they're seeking, then this becomes a value multiplier. So take that and multiply it by several billion times every day of everybody interacting with data, with search, with algorithms, with data at work or at home. And I think that's fascinating to think about. So some of the new capabilities of what will be coming out for the Microsoft OfficeSuite, for instance. You may have seen their introductions of what that looks like.
Your ability to really seamlessly ask for references to whether information that has visibility to your files that can say, "I found a spreadsheet that looks like it's relevant," and you can say, "Okay, I want to take that and I want to turn it into output that looks like this." Maybe a PowerPoint presentation or the way now that you can integrate CSVs and Excel files into ChatGPT and you can ask it to do this dynamic analysis or even just to interpret the data that's there without giving it any context at all. It's fascinating because our world runs on big data. Today's world runs on big data. There's a tremendous amount of information that is generated and consumed every moment in our offices, on our phones, at home, in every aspect of what we do, and finding ways to leverage that and create more meaningful experiences with brands that we know and care about, with each other via technology, is very exciting. It's very, very exciting.
So to your point, absolutely, I think a couple years from now, this is going to be so deeply integrated into how we're interacting with technology systems, how we experience advertising and marketing is going to be completely different. And I think it stands to reason that yes, there's a scary big brother side to that as well, but I think it does mean that the way that we are searching for information or for products or for services starts to get better and better at hitting on exactly what we need. And that's very exciting as a service provider and as a technologist in this space, is that we have an opportunity to really make a run at efficiency and correctness to reduce friction between systems and between people and computing systems to make better user experiences.
Peter Crosby:
So Steve, I would love it if you would now bring us to the reality of what's happening. You've been working on this for, and tell me if I'm mistaken, but several months now and have brought a product to market that's integrated with a PIM that rhymes with malsify. I won't mention which one it is because we try and avoid that, but you have customers that are using this today and bringing it. So walk our listeners through an example of what you're starting to see, how things are going, what value people are seeing, what are the hiccups and what's next? Ready? Go.
Steve Engelbrecht:
Yeah, awesome. Thank you for that. Yeah, so we happen to be a major partner to that PIM that rhymes with malsify and that PIM is very popular with global brands who are exactly these users that we're talking about. So these are brands, they're manufacturers, there's retailers, there's distributors, but the common thread here is that there are lots of products. Those products have lots of data and those products need to travel to many destinations or channels. So just like everybody else that has product data to manage, there are problems that start to emerge and become exacerbated at scale when you think about anything you have to do to a product or to a category or a brand, hundreds or thousands or tens or hundreds of thousands of times, even millions of times with some of our larger customers. And the opportunity is to think about where can we shave off some seconds and some time and some cost here in that value chain to help bring those products to market.
Backing up for a second, working in the retail or distribution world, when we think about items set up for one of our customers, by far, the biggest element of time that comes in here is how much time is spent after we've decided to sell a product, but before we can actually get that product into a channel so it's visible to a customer. So broadly speaking, that item setup process needs to go through contract and set up in the ERP, it's going to get pushed over to an MDM or a PIM system, but then somebody has to get it ready for sale, and that's going to be importing information from manufacturers. It's going to be massaging that information, enriching it, making sure it's ready, all of the work to get that product data ready so that we can get it up in a way that is compelling and useful and consistent and true to our brand when that data is available to a customer.
So that is the problem that we've attacked specifically with our offering, RoughDraftPro. And what we've done with RoughDraftPro is that we've built a couple of different key technology offerings here. First is a wrapper essentially to the APIs that power ChatGPT, and other offerings from OpenAI, which allows us to add structure and consistency to how we're talking to these endpoints, and also to inject product data into those prompts so that we can basically say, "Here's a bunch of examples of what product data looks like at this institution. Here's the inputs to this and what we consider to be good outputs." Then we say, "Okay, here's some new fresh inputs. Show us the output." That's basically what it does, and it comes back and we're going to have it generate one, two, maybe three different options, present that into a workflow and help our users to decide which one they want to go with and do they need to make any changes to it.
But very importantly, we can do this thousands of times, very, very quickly. So the benchmarking that we do comes from the scaling of this technology. Because we're trying to build an offering that will appeal to a global audience, and in particular, to enterprise customers, we know that not only the accuracy and the data quality is important, but also our ability to do this quickly, efficiently, and affordably. So our latest benchmarks, these are fresh numbers from last week, is that we were able to do 1,000 products in four minutes, which is 15,000 products per hour. And this is taking all of the inputs from product data, like I was mentioning, the specs, the product title, the brand, whatever, passing it into one of our models, comparing that to all of the fine tuned data from that particular brand that says, "This is what data looks like here," generating the output and saving it.
And we do that through building a really powerful, scalable cloud technology that's creating these user agents, interacting with that API, collecting that data and bringing it back into the flow. Super exciting, and because we're able to both hit on those data quality points, but also on that scalable point, we're getting the most traction so far with really big catalog. So CPG has been a great target for us. And then big distributors, big retailers that have a ton of data that they need to move through a process like this quickly. So just imagine, we're doing 15,000 product descriptions in an hour. How long would that take a human team to do? If you had 15,000 copywriters, maybe you could do it too. Because I think on average we're seeing about 45 minutes to an hour for a 300 word piece out of a human copywriter. So you would need a lot of people and you'd be spending a lot of money to get anywhere close to that throughput.
Lauren Livak:
I just had a slight daydream of an executive going to their C-suite and being like, "I need 15,000 copywriters and $10 million dollars." Would never happen.
Speaker 4:
But if you want to get the work done in an hour, that's the need. You need 15,000 copywriters.
Lauren Livak:
It's game changing though.
Peter Crosby:
Yeah. One of the things that really popped out at me is that you use the phrase... One of the steps is connecting it into a workflow, which I think AI, without a connect, without keeping humans in the loop, well really, the lawyers would kill you. You will need that capability in order to demonstrate even internally that this is safe enough to use. Can you talk about some of those checks and balances and why keeping humans in the loop, but doing that at maximum efficiency is so important?
Steve Engelbrecht:
Yeah, it's a great question and I think it's important to make the point too. So we're not trying to replace copywriting, period. I don't think that that makes any sense. So no one in their right mind, no brand would automate this and never read it and just trust that whatever output comes from their creative process is going to go straight online and be visible to customers. You would never do that with human copywriters, and it doesn't make sense that we would consider doing that with an AI based system like this either. It just doesn't make sense, because you want to make sure that it's consistent, that it's correct, that it's on brand, that it's focused, and product liability is a real thing. So just like any creative process, we're going to have some gates that it has to get through.
Thinking about the AI piece in particular, so there are risks just as there are working with, for instance, an outsourced team of copywriters. They may not understand exactly what that product is. They may make a claim that's incorrect. There may be something factually that was incorrect about the inputs. There may be typos, there could be any number of different things. So it's very important that we're building a process that allows human editors to have an opportunity to interact with the output, change it, request rewrites, et cetera, exactly as we would if we were only using a team of human copywriters. There's an interesting aspect of the AI output that I've actually got a blog on this that's available on sitation.com, and I would invite people to go take a look at this if they'd like, but it's talking about something called non-determinism in LLMs. And basically what this means is that there is a degree of randomness that is built into the output, and we actually have some control over how random we want it to be.
Essentially, we're allowing the model to take some risks and going outside the bounds with its predictive algorithm of what word to say next. And what we can do with that is we can let it essentially be more creative. It can go a little bit further away from that center line and start to take some more risks. What you see by default, even in ChatGPT, is if you ask the exact same question twice in a row and do it in two different sessions, I mean, the exact same wording, you will get different output. And that is non-determinism at work. So what you're seeing is that randomness that is baked into the output by nature of being a natural language system and how it's built and trained and what that output algorithm looks like from what's called a transformer model.
It's good and bad. This is a blessing and a curse. We want some variety because we want some differences in the output language, meaning we may want to request a rewrite and let the system go ahead and give us a different output that we're going to like better. But at the same time, this is the element of this that leads to sometimes some of the output going off the rails. And what we want to avoid is what we call in this world a hallucination. So you can imagine a human copywriter hallucinating, not a good thing. This is basically the same thing. This is-
Peter Crosby:
That's a Thursday for me, Steve.
Steve Engelbrecht:
Yeah, exactly.
Peter Crosby:
Some of my best writing is done hallucinating, but go on, I get your point.
Steve Engelbrecht:
But what this would be is this is when the model is basically, it's saying something that isn't supported by the inputs, so it's going off the rails, again, in a very believable way. Remember, these models are meant to be believable not to be correct. So it's going off the rails in a way that makes you go, "Huh, I don't think I mentioned anything about free shipping. So where did that come from?" So we need to catch those things. To do that, we're doing this a couple different ways. Number one, we can actually guard the guards, so we can have another layer of the AI that's comparing the original input to the output and looking for anything that would not be substantiated by the inputs. But the other element of this is the workflow piece that you're talking about, and I talk about this in my blog as well, but it's actually exactly the term that you used, which is humans in the loop.
So we want a workflow that even if it is machine assisted or heavily driven by AI, there is a human in the loop element, at the very least, for spot checking, but ideally for reviewing the output for consistency and correctness before we're publishing that information into anything that would be visible to customers. And frankly, especially at our biggest customers, the legal and compliance team is going to demand it because again, that product liability aspect of what claims we're making about a product, in many industries, that is heavily regulated, it needs to go through a regulatory process. It needs to be signed off on by multiple partners. So we can't just change things and toss them out there casually and hope for the best. That's not a good business model. Instead, we want to make sure that this is part of an integrated process where we are replacing the copywriting function or maybe the first draft function, maybe the rough draft function of this.
Peter Crosby:
I see what you did there.
Steve Engelbrecht:
And we're letting people then take over and make sure that everything is correct and complete before we publish it and make it available to customers.
Lauren Livak:
And Steve, I think it's an interesting point that you make around having the right prompts and understanding how to use the tool. So you talked about how this is really growth for the industry and it's a step forward. I would encourage anyone listening to this who hasn't tried AI and maybe is a little bit nervous to try it, it's about understanding how to use the tool to help you. It's not trying to replace you, it's trying to help you do your job better and more efficiently. And there's ways to prompt the AI to get what you need, keep the humans in the loop to be able to have the right accurate information, and then as the output be more efficient, have more scale, and be able to produce more content. So I just wanted to make that distinction because there's things to learn about using this tool to help you move forward rather than being afraid that it will blow up how you're working today.
Steve Engelbrecht:
Yeah, that that's totally true. And I think coming back to that aspect of the creativity, part of what it's really good at is maybe helping you to think about something that you wouldn't have thought of before. So it's not about you type in one or two things and it generates an essay for you and, again, you don't read it and you publish it and you put your name on it. That's dangerous. But what it can do, very much the way that a Google search would, it can help to expose you to different points of view, different approaches, maybe think about a problem a little bit differently in a way that's going to lead to a better outcome and hopefully save you some time as well.
On that point, again, I'd mentioned earlier there's different kinds of AI systems that are out there. The predictive systems and the analytical systems, I think, have an opportunity to go hand in hand with the generative systems like a ChatGPT or a RoughDraftPro that's using the underlying APIs in that, because there's so much information that's available, those interfaces between anything that we are creating, whether that's from human output or from a machine assisted workflow like this, with measuring some interaction with that content. I think huge potential there to say, "This is what worked, this is what didn't." Maybe some multivariate analysis, thinking about length of content, about format, tone of voice, really exciting to think about how we can tune these over time and we can make that output better and better specifically for our use case with product data, but generally with how this information is coming out of these models, being able to train them to talk more like us, more like our business, and be able to scale that potentially infinitely.
And before long, right now, these are running on these giant computing systems and cloud centers that are owned by Microsoft and Facebook and Google. Someday we're going to have some of these capabilities literally in the palm of our hand. Our phones are going to be able to do this, and I bet this is within a year or two.
Peter Crosby:
Wow.
Steve Engelbrecht:
And it's not going to be as big of a model, but you're going to be able to interact with these things in real time anytime, and that's really exciting, I think, as well. It's going to change a lot about how we're just dealing with information day to day.
Lauren Livak:
And for anyone who is listening to this who wants to dip their toe into AI, maybe hasn't tried it yet or doesn't know where to start, what would your suggestion be, Steve?
Steve Engelbrecht:
Yeah, so I think generally, regardless of what your role is at your organization, if you've not tried ChatGPT, please try it and please come up with different things that you're interested in learning about that you want it to quiz you on. I'll give you a good example, a couple of good examples. I love learning languages and I'm revisiting my high school of French now and trying to get better at French. You can have conversations with ChatGPT in French, and then at the end of it say, "Okay, give me a summary of things that I did wrong and what I can work on," and it can summarize like, "Well, here's some vocabulary, or maybe you use this verb incorrectly," but just really cool to be able to test yourself along the way.
Another thing that people are doing with us that's really fascinating is using it to help organize their day. Think about, here's the business problem that I'm looking at. Here's what I'm trying to do. Help me put together a plan. And again, it's not about necessarily trusting it as correct, it's more about having another voice, something or somebody to bounce these ideas off and try to come up with some ideas. We have a big team meeting this week at Sitation on Friday. It's our quarterly all hands meeting, and I've been using it to help me fine tune the agenda of, "We've got this big meeting, we're growing, we're 75 people now, we've got a bunch of new people. Help me put together the most powerful itinerary that we can that's going to make sense over 90 minutes." And I'm not copying it verbatim and saying, "This is what we're doing," but it's great to have a sounding board and interact with these things.
So try that. If you're a business person that deals with information, try it, play with it, experience it. It's super cool, and it's amazing to be able to see this in this way. Specifically to our use case is if you're dealing with lots and lots of product data, what I would love to ask you to do is to do some benchmarking of try to get a sense of exactly how complex your copywriting process truly is. What are the inputs and outputs? How do you do it today? Think critically about what that value chain looks like. Are you using in-house resources? Do you outsource it? Perhaps you're just ingesting product descriptions that are coming in from your suppliers.
Think critically about it, about where the opportunities would be. And then let's have a conversation. When we put out this podcast, we're going to have some information that comes along with this benchmarking human copywriters versus the AI, and I want to have as part of that, we'll make sure this is part of the output that you can play with when you're looking at this, trying to guess which one came from people and which one came out of our model. And then think about is it potentially time to think about employing some technology that can help you to build out your complete catalog at a similar level of quality generating content for all of your channels, all of your different personas and target customers and all those different things. That's the opportunity that we're looking at right now.
We'd love to show you how this works. We're very, very proud of it. We're getting some great traction with big companies today. And again, our goal is to be the best partner we can be of embracing not just this aspect of the technology today, but as a thought partner and an innovation partner. As this technology continues to develop over the coming years. It's very exciting. We're putting our bet on this particular table, and we think there's going to be some really amazing things to come.
Peter Crosby:
I was listening to the Pivot podcast the other day with Kara Swisher and Scott Galloway, and sometimes Scott Galloway irks me, but this time he did say something that really resonated for me, particularly in the shouts of employment doom that people see coming because of AI. He said, "AI is not going to take your job. Someone who understands AI is going to take your job." And I think that that's a clear call to action to dip your toe in, to try these things. And that's nice about, it's at your fingertips, and right now it's freaking free to just go on and try the OpenAI, ChatGPT prompt.
I wrote my... Well, I had AI write a press release for the first time the other day, and it was scary and awesome to see that press release get generated at light speed and then to tweak the prompt and see it get closer and closer to the level of detail that I was looking for. And I think that would've been a 90 minute exercise for me that was like three minutes. And so I can't recommend it enough just to put some time aside and figure out, because we've all had these hype cycles of VR, AR, blockchain, all of these things-
Lauren Livak:
The Metaverse.
Peter Crosby:
The Metverse, all of these things, which all have potential, but this feels, like you said, Steve, a real step change, a real threshold of a new way of creation in the world with all the good and bad that comes with any step change. And it just seems to make sense that now is the time to figure out what it means for each one of us.
Steve Engelbrecht:
Yeah, I agree with Professor Galloway's assessment there, and Yann LeCun is the Chief AI Scientist at Meta and also a professor at NYU, as Scott Galloway is, and he talks a lot about this too. I mean, this is a guy that's truly on the cutting edge of the technology side, and he's not one of these gloom and doom types either. He really believes that this is going to be a technology that can and should and will be used to help people. We don't have to all go out and become data scientists and programmers in order to participate in how this is going to change things. I think what we do need to do is encourage our people to at least stay up to date on the news, to try these things and to understand how it impacts their role and how they can do their jobs better at work.
That's what we're doing at Sitation. We're finding lots and lots of ways to integrate this into our day to day, and we want everybody here to, they don't need to understand necessarily the layers and layers of the deep learning, neural networks and programming and the logical pieces that are happening behind the scenes. What they do need to understand is how engineering a good prompt and interacting with these models can help them to do the best possible job that they can do. And I think it's up to all of us , if we're going to be productive members of the working world, to take it upon ourselves to make sure that we're up to date and keeping ourselves informed and educated and relevant in a changing marketplace.
Peter Crosby:
Lauren, the digital shelf maturity curve that you and Joe Gaudreau came up with and premiered here at the DSI, when I think about the stages of it, at each stage I see where this automation could speed not only your progress within a stage, but to get to the farthest stages on the curve in ways that very few of people that we know are on the maturity curve have reached that optimization stage, that ideal state where you're just making things better and better. And for those even that have done that, it's usually for a relative small set of products. When you think about your digital shelf maturity curve, which is still, gosh, people just trying to get through stage one often, what do you think when you look at this?
Lauren Livak:
I mean, I think it can really help accelerate from a content perspective, how you're showing up to your consumers, what you're doing from a digital shelf, above the fold, below the fold, on your PDP. I totally agree, Peter, that it will accelerate that beyond what anyone's been able to do before at any scale. If you're a smaller organization who doesn't have a lot of resources or you're a large CBG company who has the most amount of resources in this space, it will really help everyone move through the maturity curve from a content maturity perspective.
I think the other element that can't be lost here is that it still takes the right people and the right strategy in place to be able to move through that curve. So I feel like AI is the catalyst to help you do that, but you can't look past the fact that you need to have a cross-functional collaboration. You have to have the right team members in place, and everyone needs to understand the importance of this to move forward. So I definitely agree it will help move certain aspects of the maturity curve, but let's not forget that another really big piece of that is making sure that the whole organization is moving in that direction.
Peter Crosby:
People, process, technology, I know.
Lauren Livak:
I know, I know, it's like I'm a broken record.
Peter Crosby:
That freaking three-legged stool, going to be the death of us. So often we talk about how our listeners are going to be, certainly the future CMOs, if they aren't ready, are the future CEOs of this industry. And here's another example of where the burden that has fallen on them to be the educators, the cheerleaders, the bullies to make innovation happen at their companies, this is another opportunity. And Steve, I'm sure you're seeing this at the people that are working on this with you, they're the ones, they're the brains that are like, "I'm leaning into this damn it, because this is A, it's fun, but B, they can see the opportunity for transformation." Is that-
Steve Engelbrecht:
Yeah, they absolutely can. And the meetings that we've been having with prospects and customers as they're starting to embrace this are a lot of fun. Lots of smiling, lots of wows, lots of... And in fact, one of our calls yesterday, we had a call with a global CPG yesterday, and they laughed when I saw the output and they said, "It really feels like a magic trick. It really does feel like there's somebody behind the scenes just typing really fast," because it's just crazy to see what it can do and really exciting. And one more point related to what you were just talking about, the digital maturity curve here, I think the best leaders in the e-commerce world realize that things like product data, product data modeling, content creation, these are not projects, these are programs. It's never done. We can always do better. We can always measure more. We can always refine and target more. There's always more products, more categories, more competitors. This is an extremely dynamic world and it's also fiercely competitive.
And coming back to your initial comments as we got kicked off today, Peter, thinking about doing more with less, thinking about helping our customers to be more effective and create the best possible user experience they can, and realizing and understanding that things like their data model, their categories and attributes and how they structure their PDPs and their search experience, these are not commoditized things that just you do it once and you cross it off the list. Every single one of those is an opportunity to create competitive differentiation and to win a customer for life. And that's what this is about for us. It's about helping those customers to be as effective as they possibly can be at leveraging their data, leveraging their great products, getting them in front of the right people and creating a compelling experience to get people to add to cart and buy. That's what we're after.
Peter Crosby:
Well, thank you for the innovation that you're driving and for sharing it out. And again, for those of you that just want to dip your toe in, there's ChatGPT on Google and go and start playing with prompts. If you really want to see what's happening directly in the e-commerce and content space, then it's roughdraftpro.com?
Steve Engelbrecht:
Yes, that's right.
Peter Crosby:
Okay. Sorry, I didn't have that right in front of me. So thanks for confirming that roughdraftpro.com, exactly like you think it's spelled, and I know Steve and his team would be happy to connect with you and walk you through it.
Steve Engelbrecht:
We absolutely would. We'd love to get a look at some of your content, show you a custom demo with your products. It's a lot of fun and it's amazing to see what this technology can do.
Peter Crosby:
Thanks for being here, Steve.
Steve Engelbrecht:
Thanks so much for having me.
Peter Crosby:
Thanks to Steve for putting the AI in brain. Head on over to digitalshelfinstitute.org for all the latest. Thanks for being part of our community.
