In this episode of Enterprising Insights, host Keith Kirkpatrick discusses two Market Insight Reports that were recently published on FuturumGroup.com, focusing on Text-to-Image Generation Technology for the Enterprise, and on Customer Data Platforms. He discusses the key issues within each market, pulls out key insights from the report, and discusses the pitfalls and challenges to enterprises seeking to implement each type of software.
Finally, he closes out the show with the “Rant or Rave” segment, where Kirkpatrick picks one item in the market, and he either champions or criticizes it.
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Listen to the audio below:
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Transcript:
Keith Kirkpatrick: Hello everyone, I’m Keith Kirkpatrick, Research Director with The Futurum Group, and I’d like to welcome you to Enterprising Insights. It’s our weekly podcast that explores the latest developments in the enterprise software market and the technologies that underpin these platforms, applications, and tools. This week I’d like to talk about a couple of reports that were just published on futurumgroup.com focusing on text to image generation in the enterprise and on customer data platforms. I’m going to go through reports, pull a few key highlights out that I think are newsworthy and noteworthy, and then of course conclude my show with the rant or rave segment where I pick one item in the market and I either champion it or criticize it. So without further ado, let’s get right into it.
As I mentioned, I actually wrote a couple of different reports that just were published recently, these are what we call market insight reports, and really the idea here is to give folks an example or give folks some insights into a particular product type or technology in the market. Why don’t we start out here with the report that I wrote on text to image generation in the enterprise? So what are we talking about here? Well, I think everyone is pretty much familiar with this new capability that Adobe and Canva and many other companies, Microsoft, have come out with, which is allowing people to generate images simply by typing in a text prompt. Basically it’s you would say, “Draw me or create an image of a meadow setting with cows and a barn and make it look like it’s from the 1850s.” And the idea is that that prompt is then going to go out to the large language model that has been trained on various images and those descriptions of those images, and then is going to pull together what it believes is your intent in terms of creating an image.
And, of course, the quality or accuracy of that image is wholly dependent on how descriptive you are with that prompt. Now, of course, the other issue that’s at work here is how well was the underlying model trained on the data, how well those descriptions match up with what is actually in the image. Obviously, I’m sure most people played around with the various technology. As I mentioned, Adobe has a product called Adobe Firefly where you basically can go and try this out for free. Right now it’s free for a basic usage. But the thing that’s really interesting about this is it has so many applications beyond just the gee whiz factor. When you think about text to image generation, it really does enhance creativity and productivity. Let’s take a great example, which is, of course, looking at marketing use cases. In the past, if you wanted to create a very, very personalized campaign, you had to have a designer create a new image for every particular scenario.
For example, if I’m marketing a shirt and I want to place it in a number of different scenarios at the office, at a party, on the beach, in a nightclub, well, you would really need to it used to have to actually have a designer create different images for each of those scenarios. With generative AI, it is so fast and easy to create a number of different variations on that same image, using the same structure, using the same image type, and then just changing things like the background that really make it easy to create these very, very personalized campaigns but at scale. And when I talk about at scale, I’m not talking about 10 images or 20 images, I’m talking about millions of images that may incorporate a number of different types of variables there, different cultural aspects to the image. For example, if you think about what we think here in the United States as a relatively benign image of a cow, if you were to go into other parts of the world there’s a whole different level of reverence toward that animal than there is here, let’s say, in the United States, and being able to create campaigns taking all of that into account is really powerful.
So a little bit about the report itself. What we’ve tried to do here, and I do encourage you to go to futurumgroup.com and download it. You just need to put in your basic details, it does not cost anything, and it is available for you to download. One thing I really wanted to pull out about this report is I do go into detail into how this technology actually works. And the reason all of that is important is because we’ve heard a lot, and I’ve certainly written a lot about some of the challenges in terms of getting these text to image applications to work consistently and to actually reflect what the requesters desire is within the prompt. And I go through exactly what’s at work here in terms of how these models are trained, what goes into it, and then, of course, I talk a little bit about why it’s so very difficult to get that consistency when you try to generate an image every time you put in even the same exact prompt. So I think that’s important to really understand how it works and why essentially we are not there yet in terms of being able to use this in the same way that we use other technology, because it still needs refinement, it still needs guardrails, obviously it still needs people who are essentially designers or creative types to take that output and then fine tune it so it can be used in a commercial campaign.
And that means making sure that obviously, as an example, I believe Adobe and Shutterstock, they both say that all of their models are only trained on content that is licensed or is in the public domain. But even so, it’s still incumbent on companies that use this technology to really make sure that any output doesn’t infringe upon any kind of intellectual property or any type of copyright, because that could have a massive impact on them if they were to get sued by using something that looks very similar to another, a copyrighted image, and it’s found that, well, that actually went into it as the fuel. So, again, I think the important thing to remember here is that this is still new technology, very, very powerful, but certainly needs oversight. One of the other things that I bring up in this report is the risks to the enterprise when it comes to things like bias and misinformation. And what I mean by that is if you think about some of the companies that actually offer these solutions, maybe training their models with images they find wherever, out on the web, and the challenge with that, of course, or using that in a commercial setting is that you could start to wind up getting images that might incorporate historical biases.
An obvious example is if you were to type in some of these, show me a picture of a nurse, well, you might get a very stereotypical outdated image that reflects what perhaps society would see a nurse looking like not from today in 2024, but perhaps from several decades ago with a white uniform and hat, it might be of a certain ethnicity, all of that kind of stuff. So really the important thing is to make sure that whatever model you use, you really make sure that the prompts that you put in, they will provide an output, make sure, of course, that it matches what you’re trying to do there and there isn’t that incorporated bias or inaccuracy. Inaccuracy is another big one that a lot of these companies got hit with where someone would put in a prompt with a historical figure, and that historical figure would not be represented accurately. And, again, that’s partially due to the tuning and training in that model to hopefully eliminate historical biases, but perhaps they went a little too far the other way. So, again, the main takeaway here is making sure that any type of use of this in a commercial setting that you’re looking for all of those things. Let’s see here. The reports also include a small roundup of vendors that are representative of active players in the market. It’s a good starting point if your organization is looking to evaluate different applications or different packages there.
I would say that the most important thing, of course, with all of this is also looking at many of the more powerful things are not free in terms of the actual compute cost it costs in order to generate images from text. Over time we’re going to start to see some shifts in pricing. I think some of the main vendors out there already have a consumption model based on certain tiers, so you get a certain number of generative credits, once you go above that, you wind up paying more. So if you’re generating new images every five seconds, putting in new prompts, that cost will add up, and I expect that ultimately we’re going to see some new pricing models to make sure that the providers of this software are able to recoup their costs and eventually turn a profit. Please, go to the website, take a look at the report, download it. It’s certainly good food for thought if you’re a buyer out there in terms of at least digging into the basics of this technology and how it can be beneficial to you and your business. So with that, I’d like to switch gears to talk about another report that also came out, and this report’s called Leveraging CDPs to Support Access to Data, AI, and Automation. This is about CDPs, which is an acronym for a customer data platform.
And this is really when we talk about CDPs, we’re talking about software that’s really designed to unify an organization’s data into a single centralized location. And why do we even need to do that? Well, if you think about organizations, a lot of them have important customer information sitting in their CRM, maybe they have some information sitting in another database somewhere else, maybe they want to incorporate data from another third party. If it’s all spread out all over the place, that’s a real problem, because, ultimately, if you can’t get all of that information and derive a single source of truth… When was the last time a customer interacted? Well, if you’re CRM says one date and your point of sale system says another, that’s going to be a problem. So what a CDP is designed to do is unify all of that information. And this is not a new offering, they’ve been out for many years, but I think the real catalyst for more interest in this has been, of course, AI, because AIs value can really amplify everything when it’s applied across entire processes, entire workflows, and across an entire customer journey.
So essentially, if you think about how AI is being used, it’s being used for personalization, it is only really powerful when it can identify me as an individual and knowing exactly how I’ve interacted with that company throughout my entire lifecycle or with that company through across every touchpoint. The only way you’re able to really make sense of that is to make sure that there is a source of truth that captures all the data about me in one single location and it’s accessible. And that way, when you’re actually using AI, whether it’s a self surface chatbot or if you’re using an AI predictive engine to figure out a next best offer to me, it has to be in one place, and that’s what a CDP does. So the report delves into some of the basics there, looking at CDP software, pricing models, how CDPs function within the technology stack, I talked a little bit about some of the benefits of using a CDP, and then, of course, I get into some of the trends. And of course one of the big ones is, of course, AI. The other one is that increasingly organizations are looking to figure out a way to leverage information that may not be their own information.
It could be information… I’ll give you an example. If you think of customers and purchasing behavior, there is me who I have my own customer journey information, but perhaps an organization wants to be able to understand what other people who are like me might be interested in purchasing and applying that to me in terms of presenting me an offer, I’m thinking about external data sources, syndicated data sources that take whether it’s survey data or a purchase data across a big aggregate base of customers that say that people like me of about my age like to purchase these items with these particular attributes, bringing in that third party external data and harmonizing it with my own journey information can provide even more personalization opportunities. And the only way you can really do that efficiently is having a central repository to pull that in. Some of them offer zero copy abilities to actually take that data in a federated way, not copy it into that record, but still utilize it. There’s a lot of power there, really creating personalized marketing sales engagement campaigns with all of this extra data.
And why is this all important? Because, in the end, we’re seeing the phase out of third party tracking cookies, which is if you think about the internet where you used to go to a website and they would track you where you went. I went from exporting goods and then I went to another store, and then I went to a music store, and then I went to a clothing store, well, it’s almost like bread crumbs, and they’d be able to track you and ascertain certain behavioral aspects. Well, because that’s seen as being very, very invasive, they’re being phased out. So ultimately marketers are needing to rely on first party data, which is data that is actually generated by that customer on their own properties and their own behavior. So all of that information has become very important. And the way to manage it is to be using a CDP to make sure that there is essentially a single source of truth that a marketer or salesperson or support organization can use to then engage with that customer, making sure that all of those attributes are linked to a single identity.
So, again, one of the other things about the report that I think is really important to take a look at are some of the features, really essential features of CDPs, I go through those, and, of course, perhaps the most important one is how well, if you think about a CDP vendor, how quickly can they implement, if you think about time to value, that is a critical, a critical component when it comes to any type of software purchase. And you think about a CDP where you’re trying to incorporate different data sources, if it’s not done fairly quickly, you could actually wind up being bypassed in terms of an innovation cycle in terms of features, in terms of AI, all of that kind of stuff, so that’s another thing that we get into within the report. And, as always, I have a list of representative vendors, vendors that are representative of the market, so, again, it’s a great starting point for customers or folks who are looking potentially to find a suitable CDP for them and their organization. So, again, this report is available on futurumgroup.com, so I encourage you to go up to that website and download it. Again, it’s free. And, as always, if anyone has any feedback, please do feel free to reach out to me directly. I’d love to hear from you both on the vendor side as well as any buyers out there.
All right. With that, I’m going to move to the final segment of this show, and of course that is the rent or rave segment. And this is, of course, the time when I talk about something that I’m either really excited about or I’m really disappointed. And I’m going to give a number after. We’re into the second half of year, I’m going to start it off with a rant. And this one actually doesn’t even come from me. This comes from a friend of mine who was traveling recently and I believe she had to go to Las Vegas and went to a hotel, it was her and her family, and it was for, I believe, her daughter’s volleyball tournament. So they were staying at this large property, everyone knows it, it is on the strip. And she had asked the person at the front desk if she could get a late checkout and the person said, “No, we don’t do that.” She said, “Well, I am a platinum member of your rewards program and I’d like to speak with your manager.” And the manager came down and said, “Hi, I hear you would like an extended early checkout.” And she said, “yes, I would. I’m a company of platinum member.” He said, “Absolutely, I’d be happy to do that for you. It’s an extra $170.”
And, of course, my friend thought that was ridiculous because that is one of the things that you get as a member of this rewards program is a late checkout, that’s one of the courtesies and benefits of being a member. And of course they went back and forth, and the manager decided that, no, not going to happen, it will be extra. And I call this to everyone’s attention not to hammer any one company, and that’s why I’m not naming names, you could probably figure them out, but it goes to the issue that I’ve been talking about for quite some time, which is these organizations have all of the data, they have policy data, which is, hey, if you’re a rewards member, you get this benefit. They also have the data saying that, okay, obviously if this person is a rewards member, they’re here a lot, they probably will continue to be patronizing my business a lot. Why that manager decided to throw up another barrier, it’s beyond me, and it gets the issue, which is you can have all the data in the world, you can have all the best systems, but ultimately it comes down, to in terms of providing great customer experience, training and aligning everybody’s goals the same. The manager, maybe they were new, maybe they weren’t, but they need to be explained that the extra X amount of dollars that you think you’re generating for you does not do anything in terms of long-term customer value, loyalty, satisfaction, all of that kind of stuff.
And I think that’s where a lot of organizations really fall down. When I talk about a lot of these examples of bad customer experience, most of them are not from small companies that don’t have updated systems. Most of them tend to be larger ones. And they ultimately usually wind up being some sort of either disconnect between data and people, or people who just don’t want to implement the good corporate policies or best practice corporate policies, or whatever, and that’s what really causes a downfall in their customer experience, which, when you think about it, people tell their friends, that’s why I know about this story I’m telling all of you. And it’s really ridiculous when you consider how easy it is to create a great experience as opposed to creating a negative one. So that’s my rant for the week. And certainly if anyone’s interested in hearing more details, feel free to reach out. But that’s all the time I have today, so I want to thank everyone for joining me here on Enterprising Insights. I’ll be back again next week with another episode focused on all the happenings within the enterprise application market. So be sure to subscribe, rate and review this podcast on your preferred platform. And we’ll see you next time.
Author Information
Keith has over 25 years of experience in research, marketing, and consulting-based fields.
He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.
In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.
He is a member of the Association of Independent Information Professionals (AIIP).
Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.
