Andreas Welsch: 0:21

Today, we'll talk about cutting through the generative AI hype and finding out who's ahead. And who better to talk about it than somebody who's actively working on that. Mark Beccue. Hey, Mark. Thank you so much for joining.

Mark Beccue: 0:34

Hi, Andreas. How are you doing?

Andreas Welsch: 0:36

Doing well. So excited to have you with us. And I thought maybe you can tell our audience a little bit about yourself, who you are and what you do.

Mark Beccue: 0:45

Yeah, sure. I've been an analyst for a long time. I won't tell you how long, but it's long. But had, been working on AI, being a market analyst of research. Market Research Analyst. Started following, if you remember the old Slack back in 2015, they started, that started coming out. People were like, what the heck is this? And they had all these little bots that would run on that. And I started writing some research about that, which turned into, quickly, virtual assistant focus. Thinking about that. It was the early days of kind of an AI early, Thoughts about AI. And what that turned into was working for Tractica, which was a small market research firm that became a specialist in AI. So I've been focused on market research around AI since 2015. So I still do that today. I'm a principal analyst with ASG. So that's what I do.

Andreas Welsch: 1:47

Awesome. Wonderful. I know we've, worked quite a bit at SAP and that, that's why I'm so excited to have you here for an independent. Perspective and independent conversation today. on the topic of Gen AI. So folks, for those of you in the audience, if you're just joining the stream, drop a comment in the chat where you're joining us from because I'm always curious to see how global our audience is. Now, Mark, what do you say? Should we play a little game to kick things off?

Mark Beccue: 2:13

Let's do it. Perfect.

Andreas Welsch: 2:16

All right. So this one is called In Your Own Words. And when I hit the buzzer, the wheels will stop spinning. And here we are. So I would like you to answer with the first thing that comes to mind. And why in your own words? If AI were a fruit, what would it be?

Mark Beccue: 2:33

I'm gonna say it's a I'm gonna say gen AI is a dragon fruit.

Andreas Welsch: 2:41

Okay, why is that? Because it's exotic and bizarre and not many people have tried it yet. Maybe they want to, and they're a little afraid to try it. They're intrigued by it, but they think that they're thinking it's intriguing and they want to try it. It's, it just sounds fun. Okay, that sounds good. It can be pretty sweet too, right? I remember having a dragon fruit juice in, in, in Asia when I was traveling there. Sometimes it's also the right mix and, the right place to get it from that you need. Awesome. Great answer. With that out of the way, why don't we talk about some of the questions that we've discussed up, up front that I see many people are, and many leaders are asking about. Just what, two weeks ago or three weeks ago, something like that, Gartner released their latest hype cycle for AI. We see gen AI is moving towards the trough of disillusionment. There are some reports coming out. I think it was Morgan Stanley the other week where they're saying maybe we're missing a trillion dollars in that, that promised economic potential that we forecast last year. So I'm curious, are we still in a hype phase or are leaders already getting disillusioned? What are you seeing?

Mark Beccue: 3:57

It's interesting to me that it's, to me it's following a typical cycle for technology disruption. In a different, in a couple of different ways. And you can stop me if you want when I get too crazy on this. But, we were introduced to this concept of a more economic, or a more, An economic way to bring models to market, right? So if we go back and said, before gen AI, you had to have a data scientist, you had to have your own data, more or less, you had to have a lot of expertise and you had to have your own data to do anything, right? We were trying to do something you couldn't. And then we get this just drops out of almost nowhere. And you were writing this with what, you were doing and, all of a sudden it becomes feasible and plausible that you might not have to have all this data to make an AI model work. And you might not need a market a, a very specific data scientist to make it work. If you think about it, this drops in, and there's a lot of hype, obviously, a lot of promise. I think that's still there. What we're running into and I'd like to hear your thoughts on it, actually, is like, how, what we found is, That doesn't it, it makes some things easier, but it also makes some things the same, right? So it's that, that doesn't change the fact that this isn't a completely baked, solution. There's not completely baked solutions. There, there's the promise of this. And it needed, it's very raw, it's it's so new, and we watched how the models have really just morphed dramatically over a very short amount of time. And I just think we're at the front end of really operationalizing Gen AI and that's causing, so is it disillusionment? In a way it is, but I look back to it as if you look at the, what I originally said, which is technology disruption, nobody hits a home run immediately. It doesn't work like that. We're people are figuring it out and we're going through that sorting process.

Andreas Welsch: 6:25

So do you feel, the industry, the VCs, the big and small companies in the software industry have over promised? Or is it just a natural excitement because we want to be innovative? We need to be innovative. We know that we can be innovative because of this technology and thereby first of all, deliver more value for our customers, but also make more money and from a startup perspective, attract more money. To me, it's almost like this flywheel effect that comes together here.

Mark Beccue: 6:58

It does. If we look at other, so if you, I don't know what we're really calling the first iteration of AI, I call it legacy AI. That kind of happened around 2015. I mentioned earlier when you were doing this, when compute got cheaper compute got cheaper and it then became feasible to run a lot of these pieces. What happens is there's a learning process, right? This cycle, the life cycle of experimentation and what things can do and how to put guardrails around things. We were getting there when Gen AI showed up and all of these companies that had been down this road where we were moving at a pace where you would call, I'd call legacy AI and moving at a pace wasn't lightning speed, but it was moving forward and it was a lot of investment. And what happens is when people all of a sudden go, Oh my God, and what can I do? They don't have any experience. So they were trying to create something from scratch. So if we said when GenAI came in, what did it do? It broke up the models, right? The business model around AI, it was like we can make our own thing? What do you want to make? And we, you and I've talked many times about what are you trying to solve? What is it that you're actually trying to solve? So if you think about where we are in the market adoption of these things. The people that were going to go solve something for someone haven't done it, right? It's I've got some tools. It was, I think, you've said shovels and we've got shovels right? And then when you call it we've got shovels, but we don't have the end result, right? Here's something you can use to go try and figure it out. That's where we are, is that there haven't been a lot of what I call end to end solutions for AI. So if you were Corporation B and you said, what, how do I fix this? And then nobody can come to you. They can give you compute and they can give you models and they can do all this, but it's not an end to end solution. With some exception, and stop me if I go crazy, but the set, some of the SaaS players have built solutions that you could plug and play and try and do something.

Andreas Welsch: 9:23

Yeah and there, I'm also then wondering for you guys in the audience, if you have any questions feel free to pop them in the chat, we'll take a look. And I think just lately we've seen so much coverage also, when it comes to this question of is there disillusionment in the market that I think, first of all, we're just building the foundation, right? If you look at the NVIDIA, if you look at Microsoft with Azure, Google and others, you first need to build that platform, you need to build that foundation for others, for higher value services and offerings to come in and make those available. Maybe, that's a good segue, right? Because I know you've been covering the adults in the AI room for quite a bit. And I've already mentioned a few just now, but who are they from your point of view? And what do they do well, or what do they do extremely well?

Mark Beccue: 10:14

Yeah, so you're talking about a little segment I used to run, called the"Adults in the Generative AI Rumpus Room." So you had people running forward. Some companies running forward with ideas and technology that I would call not so responsible. We've been there. Some of them start with O and some of them start with S and end with AI and things like that. But when you throw software into the wild and just let it percolate there without any guardrails, things happen. If we go back and look at when ChatGPT first came out and the first one of the first things that almost happened was, there was this assault by students of all sorts, high school students to college students starting to write their papers with plagiarism and things like that, and you had to scramble. Where schools and universities and everything were like, oh my God, what do we do with this? These people are cheating, right? That's irresponsible, right? So we go back to adults. So the adults in the room, in the generative AI rumpus room are the ones that I think have put guardrails for themselves and responsible use of AI for themselves and their customers. There's a group that I call, we're mostly AI pioneers, that were AI pioneers in the legacy realm. And they are some of the usual suspects that you might think of. Some of the hyperscalers. They are adults. They write research, they look for guardrails, they look for ways to build guardrails around things that are used that they have access to. But I think Google and AWS, Microsoft, IBM, and I will name several SaaS companies that I think have been very good. So SAP, Salesforce, Adobe, those come to mind. I think some of the chip folks have been good or what's called on prem people like Dell, Qualcomm, Intel, and AMD. All of those kinds of companies have been thoughtful about what they do. Then there's other lots of small ones I've named tons, but what they're doing is saying, before you go, here are things you need to consider. I think they're all generally, in many forms, thinking about responsible use, whether it's transparency, looking at how to reduce inaccuracy that you have with some models, those kinds of things.

Andreas Welsch: 13:08

There's one question in the chat by Ashish that goes in that direction, and he's asking for AI guardrails: is there a concerted effort coming together or forming where each player is putting their own version or interpretation of how we can govern and safeguard from perils of AI?

Mark Beccue: 13:26

Yeah, that's good. So there's multiple, I think it's moving on multiple fronts. I don't know what you think, Andreas, but it, there are things like NIST, which people have started to look, there's multiple things, right? So if you think of the AI Act and what its impact is going to have, so let's say just from a regulation standpoint, there's some guardrails in that sense moving forward. But there are others we look to these influence these really big regulatory players like the E.U. In the U.S. I think NIST has really put out some really good papers and some frameworks around responsible use. So that's not really a law, but there are some frameworks. I've seen a lot from responsible AI org. They have some some interesting uses. But it's a a mishmash of different things where you have some companies like early people like SAP wrote some some guardrails and some frameworks. So did Salesforce. And I think they're one of the ones that I look to that wrote some very, pragmatic ways to approach. And I'll just talk about that for a second and say, they were one of the ones that I first heard talk about thinking about responsible use, not just in the iteration of the idea, but how you manage it over time. You've talked about this before many times about having oversight committees. Playing into your other guardrails like security and those kinds of things. To me, it's a mishmash. What do you think?

Andreas Welsch: 15:18

So to me, I think it's a really good sign that over the last years, vendors have been investing in that topic. And especially many of those have been investing beyond lip service, right? That's, I think the important part. It's easy to say, yes we're responsible and we do this or we feel we need to do this because market seems to expect it, or maybe if we do it but really seeing that become a real practice and something that is actually by large and leading organizations. And I think that also can be a lighthouse or a beacon of light, if you will, and a good example for others to follow, especially smaller companies who might not have all the resources to put these programs together themselves. But if there's something that they can orient themselves around and say if, to your point, Microsoft, Salesforce, SAP, whoever else is doing this, maybe there's something in there that we can adopt on a smaller scale for our mid sized companies.

Mark Beccue: 16:18

I felt just as an, that's not down a slightly different route of that. Microsoft published their AI safety, was it the UK safety conference or something in the summer. Remember that? When they put that out and and Microsoft published their AI safety, what they're doing. And it was important to me because that was their partner was OpenAI. It was like, I, if you read through that, it was really very detailed about how they were building these guardrails about how they use OpenAI models, which was different than when OpenAI does it by themselves.

Andreas Welsch: 16:59

Yes.

Mark Beccue: 16:59

And I thought that was really well thought through. It doesn't necessarily give you a path where if you're an enterprise, but it would say, I felt the market got confidence from using Copilot and those kinds of things based on what Microsoft had built in very detail in these documents that they published, which was great. It's nice that you could see that from a big player.

Andreas Welsch: 17:27

Yeah. And it also gives good assurance to the market and to companies looking to use these systems if there's more transparency and more explanation, more thought behind it overall. So great, perfect. Good question. Please keep them coming in the audience. We'll take another look in a minute or two. Now, reassurance that you're on the right path, that it's a solution to invest in regardless of the vendor, to me also is tightly coupled with the topic of cost and pricing. So I know you talk to many different vendors, and I'm sure you also talk to many different buyers, but we know that buyers prefer cost transparency, planability. I want a fixed bill, so I know every month how I'm, how much I'm paying. And I like the spikes because they're too unpredictable. And you have folks in both camps to be fair, right? I've seen that myself as well. But I'm wondering, what commercial models are you seeing that resonate with B2B buyers around AI, around Generative AI? Is it the 30 per user per month, or is it the pay what you use, calling card, type of phone card?

Mark Beccue: 18:37

This kind of, to me, goes back to your first thoughts around disillusionment. And you mentioned that very well. The point is that we're such at early stages for making Generative AI real in the enterprise was what are the costs? So first, it's what are the costs? And what are you buying? Is really the first question. And what I've noticed is that, I would point this out, and I don't know how you feel about it, but when I looked at this disruption within Generative AI, if you look at earlier iterations where we were with legacy AI, we saw was a big bunch of venture capital money put into what I call end to end solutions. So I got the sense that vast majority of the market in the legacy AI trends before 2022, late 2022, was towards solutions, right? Not tools. And what I see in a difference right now in this early stage of the market is that enterprises want they're not looking for end to end solutions. They're looking for tools and platforms and it's almost like a do it yourself. They want to invest, to build their own expertise. It's just something that I think is a big difference. So that goes back to your point about cost is that means you have to figure in a whole lot of other costs, which is what's the compute cost, right? That's a big, might be one of the reasons we're seeing this hesitancy is we didn't know it costs that much to run cycles on foundation models. What does it cost for compute? What does it cost for that development? What does it cost for securing it? So they're going through all this where they have to think about this complete life cycle and they didn't. I think that's a little backwards. And yeah, it's something that you've mentioned many times, and I'm a big believer in is, I think enterprises are forgetting their basic business school training, which is what am I trying to solve, right? If you went about saying, what am I good at? What do I want to solve? Should I build, buy, or partner? It's all those basic questions, right? And then you get at what do I want to pay for something? I know I'm answering that in a weird way, but it's like to me, what are you trying to buy? It's are you buying, cause then you've got to put it all together. But at the end of the day, it has to make economic sense either that you're saving money or make. Is it going towards cost savings as an enterprise? Is it going towards revenue generation? And all of those answers, this is very much in flux. I'm not sure if I answered it except for open more can of worms. What do you think?

Andreas Welsch: 21:50

Yeah I think it, it really depends on the organization and their preference. So I can see it both ways, right? One is the, hey, change management is tough enough in itself. If you introduce a change process into the way people work. So why not try something that's built in and you get a headstart and you don't need to build everything from scratch in your entire stack. But I also see that where it's more differentiating to your business, again, coming back to your point of really understanding what is that business problem that I'm trying to solve. If it really differentiates my business, my operation, if it gives me an edge in my industry to be a leader or become a leader, then I think investing more on that platform level and looking for those opportunities that you might build yourself makes perfect sense. But the change management around it is still going to be there no matter what.

Mark Beccue: 22:38

Which is why, and I'm interested in what your thought is there, here is to me, this might cycle back where we were before was the emergence of SaaS. And think of it as, to me, it was, remember we enterprises will always say, what am I good at? So when you come back to that, what is the focus of my company? What am I good at? And go back to saying. Is this something I outsource? Is this something I, want to own? Is it something and, I think we're going to end up in when you want to leverage your data, then that's something you might want to own more than outsource. But that being said, you look at some of these SaaS players and they're giving the opportunity for you to buy, right? And to your point, I'm buying this as part of, it's accelerating what I already do with that company. Whether it's supply chain management or collaboration tools with Zoom or whatever. How much more am I paying if I'm just getting a call summary from Zoom? I don't have to do it. They already did it. It's all baked into the price that I already bought. For this collaboration service. So a little bit of a bump up. Sure. If I can see that, I think there's a lot of experimentation with that.

Andreas Welsch: 24:05

I think that's a good segue to our last question for today. And again we already established that you obviously talk to many different vendors and you have a front row seat at what's happening in the industry. It also goes nicely to with Jason's question that he was asking in the chat. I'm wondering, what's your prediction? Where do you think things are going in the next six months on Generative AI? Maybe one of the questions here from Jason, will we see a convergence of LLMs, of conversational interfaces? Is it one of the things that you see in your crystal ball?

Mark Beccue: 24:39

Yeah. All these different vendors. We could talk for days, couldn't we? Two things. I think over the next year just come, like we said, from now and just keep moving forward. There's this struggle to bring data to the AI or bring AI to the data, right? I think at the end of the day, the differentiators over time is not going to be the technology that we've seen. It's not LLMs. It's not. The models are going to become models. They're going to be used for different reasons. Like we I've talked about before. It's just have to use it. Enterprises might use a bunch of different ones, and they'll use them for different reasons, for whatever they want. Why? But at the end of the day, it's like, how do you leverage what's unique to you as a company, generally end up with data? And there's a struggle. This has been a struggle for a long time. Remember when we used to all talk about big data and Hadoop and all those things? It's that hasn't happened yet. To Jason's point, I think one of the great hopes and promises of Generative AI is maybe, with that help and that economy of scale we get with these foundation models, we can tap and unleash that data that companies have so that they can use it for multiple reasons, for whatever it is. One of them just being even that idea that you're going to tap into your data to use it for whatever purposes you want. I think that's one of the big things. I don't think there's going to be a convergence of LLMs. I think that they're becoming more task oriented, right? You'd use a big one for certain things. You're using smaller ones for certain things. And because of that cost, you're balancing that when you need it.

Andreas Welsch: 26:38

Thank you so much. Now look, we're getting close to the end of the show today, and I was wondering if you can summarize the key three takeaways for our audience.

Mark Beccue: 26:45

You asked a tough question up top, which I think is, are we in disillusionment? I think it's not, I wouldn't call it disillusionment. I'd call it the pragmatic phase. We're getting into the pragmatic parts of what enterprises need to do to operationalize generative AI. I think that for our folks that are listening the promise of this is very, there's a lot of upside to this. Some of the predictions seem to be a little heavy for me, for it's going to do this, it's going to do that. It's going to take time. It always does when we really back up and go. Be patient with Generative AI. Take your time. Be pragmatic. Think about what you want it to do. And start with the problem first and then see if it can help you solve those things. And be pragmatic about do I need to build, buy, or partner based on what my needs are?

Andreas Welsch: 27:42

Thank you so much. Also thank you so much for joining us today and for sharing your expertise with us. It was wonderful having you on the show and learning from all your insights of talking to so many different vendors and being in the space for such a long time. Thank you so much, Mark.

Mark Beccue: 27:55

Oh, thanks, Andreas. Appreciate it.

Andreas Welsch: 27:57

Wonderful. And thanks for those of you in the audience for joining us live.