AI Agents Are Changing Procurement—Here’s How to Stay Competitive

Check out the full episode below! Enjoy The Exchange? Don't forget to tune in live Friday at 12 pm EST on the Greenbook LinkedIn and Youtube Channel!

Major corporations like P&G and Coca-Cola are already using AI to automate supplier selection and negotiations, fundamentally changing how business gets done. This isn't a future trend—it's happening now, and companies that don't adapt their processes for AI-driven procurement risk being left behind. We explore how LLMs are becoming the new operating system for business, the critical importance of discoverability in AI ecosystems, and why 83% of Gen Z workers are concerned about AI displacing their jobs.

Many thanks to our producer, Karley Dartouzos. 

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Transcript

Lenny Murphy: And there we go.

Karen Lynch: All right.

Lenny Murphy: We are live. Happy Friday, Karen. Happy Friday.

Karen Lynch: You know, some, uh, some weeks hit harder than others. This is one of those weeks where I'm like, it feels never ending. So I'm actually relieved that it's a Friday just because nothing, nothing, you know, shattering just, Oh, I'm glad it's Friday. There was a lot going on this week and like, yeah, I agree.

Lenny Murphy: Like, like work stuff. And maybe it's because it comes back after the fourth and all that, you know, maybe that's a piece of it, but also it just seemed like, boy, the pace of news, and not just little news, but like, holy crap, whoa, paradigm shifting.

Karen Lynch: I also think you and I, and Lukas, have these. This July, our fiscal year starts July 1, and July always starts these big conversations. It kicks off a year of strategic thinking , planning and everything that we need to do. And so I think there's also just the high-level thinking that's happening when we get into this phase. This happens all the time. Lukas takes vacation time, and he comes back kind of ready to think of the big picture. And we're all thinking a little more higher level. So I think that has a lot to do with it, as our brains are wrangling. We step out of the minutiae into a high level.

Lenny Murphy: Maybe. And maybe that's a good segue, right? We promised last week that we would kind of continue this conversation about this agentic transformation. And, uh, and because that's been a very high level topic that we've been thinking about, I mean, everybody, you know, publishers and it's set, you know, there's so much disruption I think about from that. Yeah. But it's like a whole new angle that, uh, and, and so here's the, here's let me kick it off. Let's start.

Karen Lynch: Yeah. So, so the, the, What we said we'd start off talking about is not just this higher level stuff, but this last week we kind of hinted to the change in browsers, right? So this shift in how people are accessing information online. You know, remember when Googling was a thing and we all Googled things? That verb, right, Googling, is changing because when we go to chat GPT, it's not Googling. We're using LLMs as browsers. So that's what we're gonna start off talking about, right Lenny? This whole idea that like browsers, I mean, not browsers, platforms, LLMs and generative AI platforms are entering the search game and users are using it accordingly to start. That's one of the topics. One of the things, right.

Lenny Murphy: So that's that part of that transformation. For sure, it is from search to LLMs. And that has, we've all experienced it, I'm sure. You know, you're wanting to consolidate information, answering a question that's different than, you know, going through a series of links, and that has implications. And there's one interesting one, actually, that the industry should know about, in terms of content. We'll get to that kind of thing at the end, I think. But now it's shifting from LLMs being the new search engines to LLMs being the new browsers. And what that means effectively is LLMs being the new operating systems for how people are interacting, not just information, but with activities and with tools. And so, Karley, I'm just going to rattle these off. There's three different proof points, and we'll just kind of synthesize this. The great article on the browser wars heats up as rivals target Google search. From TechBuzz, Perplexity launched their Comet browser. Today, OpenAI launched their Chachapiti Agent, which is not quite their browser. They have announced that there is a browser coming, but it is the the operational components that changes this, the functionality, and that's really, let's zero in on that, because that's what I think is, so I had some conversations that occurred this week that put this really in perspective for me, and I have to trust, I have to not break any confidentiality here, but I was talking to a very senior technology professor in our space for one of the largest companies in our space. And we were talking about this shift and they really, they launched a holy crap moment for me when they shared that recently a very large percentage of the RFIs they're receiving from brands are requesting this agentic integration Now, here's what that means, right? So let's, let's go for what's public. P&G, Coke, all these companies, we covered it when they said we're spending, you know, a bajillion dollars. Actually, I think both of them spent a billion dollars on, you know, building out an AI operating system within their business. Now it's coming to focus. What that really means is that that's, that's a component, not just the informational component, they are building in a, so let's say, I'm just using this as an example, I do not know if this is specifically true, but I always pick on P&G, so we'll use P&G as an example. So, in this model that is now confirmed to be in play, right, that this transformation is starting now, brands are asking for this, if you are a researcher at P&G, rather than going through the traditional process that you would use of what supplier do I want to use? I'm going to send them an RFP and I need to get a bid back and blah, blah, blah. No, they are going within their browser that they've developed. In that case, it would be Azure. There is an agent, a very specific tailored prompt, basically. Here is my research objective. Here is all the information necessary to click the button behind the scenes, this agent is then going through and determining which is the best supplier. It is creating the RFI. It is sending the RFI to another agent, right? Within that, their supplier ecosystem, they're negotiating on the price. The agent is saying yes, and it is executing the research without a human being involved. So just pause for a minute.

Karen Lynch: Because when you put your, when I put my, you know, researcher hat back on, and I recall, you know, 10 years ago, sitting for meetings with procurement officers, and making sure that my small business has a rate card or is an approved vendor. And we're in the portal, not even not even 10 years ago, I mean, the most recent, I think I did this was five years ago, with very large consumer forward companies. And I am working with procurement to get everything up to speed. And that was a big headache. Now imagine for those of you who are on the supply side and have done that. Sorry. I cannot talk to you right now. If you haven't done those things, you don't stand, don't stand much of a chance, because those procurement documents are informing these agents. And it's now it to me, it's like, yeah, you've got to get in with procurement and the procurement documents are going to be informing these agents who are going to do that work without the procurement officials, like, or with with, or you're integrated into a marketplace.

Lenny Murphy: So so this hypothetical guys, the point the point here is is a change we are now at the point where we've been kind of focused on changes to methodology and augmentation and those type of things right from an AI perspective now we are we are it is happening now that it is fundamentally changing routine processes and that are absolutely important to your point right so in this case my My guess is that we are going to see a scenario where effectively, as a supplier, you have built an inbound agent, an executional agent that is integrated with either a marketplace of suppliers, or at least functionally, that's what it is. If you're within, let's say, G procurement, there's this list of suppliers and those suppliers have an agent and they're, you know, we, we recognize I'm going to use this one because they do online quality, this one for quad or whatever, right? There's a specialization, but those agents are doing all of the, the early stage process and they are squeezing that down. So one thing this individual had told me is that they were part of this process. They were expecting, the clients were expecting, a reduction of turnaround time from two weeks to 48 hours. Soup to nuts on a project because all of that, really think about it, there's no value add in that process of negotiation and evaluation, all that good stuff. It's valuable from the standpoint it's necessary today to know how to get to an outcome. But if you're, if you are a buyer, the hours spent in doing those things from a time standpoint, don't deliver value. The value is in executing, administering the project and delivering the results back.

Karen Lynch: And what's happened is in today's reality, that push, which a research, you know, an internal research professional or, you know, corporate research might've been able to push back on and say, yes, but we need, you know, we can't execute it that quickly. Now they have options, now they are like, actually I can if I change my approaches. So now I can meet that demand in a way I've not really been able to confidently meet before. Now I need to step out of my comfort zone to meet the demand because it's possible. And I think that that's, that change in kind of, thinking on the corporate level is going to change the playing field yet again. Absolutely. Go ahead.

Lenny Murphy: No, you go ahead.

Karen Lynch: Well, I was just going to say, the reason why it feels particularly poignant that you and I keep talking about these things, and I hope that we are connecting dots for people week after week, but something just came up today that I'll share internally at Green Book, operationally at Green Book for something that has to do with our contributor database. We've been working on making it more efficient. How do we, you know, how do we keep track of all of our speakers and our authors and our podcast guests and all that? And we've been working on the database, blah, blah, so that I can easily extract, you know, oh, I need, you know, I need a Speaker for this event and I can pull things up and find, you know, oh, so-and-so has spoken at all these events and who, you know, all this stuff. So it's a workable database for content purposes. And as we're doing all this, kind of our product manager sends me a link and she's like, sends me, you know, Hey, can we just quick hop on a call? Because of course, Gemini, as long as everything's in Google drive, like Gemini is actually doing a lot of that for us. Now we have been spending like the better part of a year with one of our tasks being database management. And now Gemini is saying, you don't need no stinking database. Like I got you. And it's, it's like, an operational shift, I use it just as an example of, if you look at your, if you're trying to update your processes and streamline for efficiencies, and you haven't considered the tools that just showed up in your workplace, without you having to do that work, you are missing opportunities, and you have to pivot and be agile with your operations, because, yes.

Lenny Murphy: And that's what's happening now, so if you follow the money, regression, right? So I need to own something. We spent a lot of time talking about these types of shifts, and what they meant to consumers. And I was saying a lot about what it meant to the research process. I was not thinking about this aspect of things, right? And it really, it's only in the last two weeks that that's kind of like, Holy crap. Whoa, wait a minute. There's so many implications here to your, your point on nuts, internal efficiencies. I mean, that's the, what guides it, but what that means for both for buyers and suppliers of any kind, right? Again, I'll use P and G as an example, so that the agentic model is going to apply as much to retailers. Yeah. Ordering from them as it does to their suppliers, you know, ordering materials and then internally facilitating across all of their manufacturing facilities, et cetera, et cetera. All their, their, uh, as much as research.

Karen Lynch: I mean, it's just that management is changing. Uh, I, I, you know, I cannot even imagine how the, you know, the, the master's programs, uh, Michigan comes to mind, you know, in supply chain management, for example, how they are having to pivot right now because AI will do a better job managing supply and demand at a manufacturing level than any human being looking at the data.

Lenny Murphy: I'm sorry. 100%. So we become administrators. Now there's a couple of nuances here that were probably important to point out. Obviously, let's just call it as it is. If you're already a tech company, you're probably in a pretty good position already, but you better make that, you better have an outbound agent or an inbound, I'm not sure I even can figure out how to talk about it, but a functionality that allows literally for an AI system to access your platform. So if you're in tech, you have to do that now. If you're more on the service side, this doesn't mean service goes away, but how services are contracted will change. What does this mean for sales? How visible are services?

Karen Lynch: So absolutely, we're gonna get to a shameless plug, folks. We are. Yes, absolutely.

Lenny Murphy: Because that's because here's the thing. Let's just do it now. Right?

Karen Lynch: Let's talk about discoverability. If you if you're, if you are a service provider, and you want to make sure these LLMs that you are discoverable, that they know about you. You can't count on, sorry to tell you, you can't count on your own kind of limited audience because it's not enough. What we are seeing is that partners that are in our ecosystem, Green Book is discoverable because we've built kind of this, you know, this- Authority.

Lenny Murphy: Authority. It's massive information and authority. Yes.

Karen Lynch: And so we are discoverable. So if you are in our, if you are an author in our network, if you, I think those are probably the two biggest, if you do webinars with us, and this is, again, shameless plugging, you are discoverable within us with authority. How else would you build on that?

Lenny Murphy: You've had other conversations around this as well. Right, absolutely. And so, it goes to the essence of this. So effectively, we are seeing the development of ecosystems or markets to your point now where we're seeing this as content ecosystems. Green Book is a content ecosystem. And so is any, every other publisher in the world is trying to get to the same place. So let's be.

Karen Lynch: And the big, and the big guys are in it, right?

Lenny Murphy: The big guys are discovered, can't be, you know, because they have the authority, they have the mass. But if you don't believe us guys, if you own a business, go do a search on a topic, right? Go to, go, go to Google, go to, go to whatever. Right. Any market research. We will come up, if not first, we will come up second or third, at least before you, unless you have a massive content engine. That sounds crappy. You're probably thinking, but wait, I don't mean it that way. It's just the way these systems work. Now, what's the next step? That discoverability is, how are you discoverable within a client ecosystem when their systems are designed to go to authority. So I don't know what all this means for Green Book per se, long-term. For the moment, it means you need to be in our ecosystem so you can be discoverable. We'll see what else that means in the future. But for everybody, the way that you sell, and we've talked about this for the consumer stuff, right? It's agents selling to agents. It's agents marketing to agents, right? That human component unless you're doing heavy-duty consulting, personalized consulting, it just changes the game. It just works. Well, and let's jump down two bullets.

Karen Lynch: We'll come back to Meera Murati and what she's up to. But let's talk about OpenAI's plan.

Lenny Murphy: Yes, their checkout system. There you go.

Karen Lynch: So, basically, they're testing a payment system within ChatGPT that would let users buy products directly. So, you know, I think probably people If you've done a Google search and it shows up products and then you click on, you know, you can click on, Oh, look, they sell this at Amazon or, Oh, look, they sell this at Walmart or whatever retail retailer. Um, so they've, they're streamlining that a little bit, but open AI is like, well, look, I might show you a product and let you buy it right from our platform.

Lenny Murphy: Right. Which is their platform integrating with the retailer, going through, taking all of your information and doing all that. So you don't have to do anything.

Karen Lynch: You know, you just have to say, I'm in the market for the, what's one of the most recently I bought, we were headed to the beach. Uh, I wanted to get a different kind of sunshade over an umbrella because we were bringing, you know, my grandchild and I needed to make sure there was sun coverage. So, um, I, you know, I went into, you know, I went into Google and I didn't even think to go to, you know, chat GPT for this. Cause I knew my end result was I am buying something, but now I'm like, Hmm. So imagine if it was brought, if I check in chat GPT, I need the best quality sunshade out there to protect my grandson. You know, the first time I brought him to the beach and it said you could buy this one, this one, this one. Here are the reviews. Cool. I would say, I actually wouldn't even say here are the reviews. I'd say, which one is the most highly rated? You know, I'd ask that. That feels natural. And then it would tell me what the ratings are and then it'd say, would you like to buy it?

Lenny Murphy: And I'll say yes. Or book the reservation. Right. It was the, you know, this hotel or, you know, or service. Gutter cleaning, I need to clean my gutters, you know, all this, and now take that to the enterprise. Would you like me to schedule that for you? Yeah. Right. Because as efficient as that is for consumers, the adoption will lag on that. Right. But from an enterprise standpoint, it is not, it isn't, it, there is, uh, and that is maybe the difference that we're, I just had this blind spot of not thinking about that application with the, the enterprise Sherman and buying process until recently. And, and then knowing what that conversation I had, what that told me, it's already happening. Yeah.

Karen Lynch: Imagine something as simple as, um, say I'm a startup and I am building my website for the first time and I want to do some quick AB testing. Cause I know I should test which of these two, I should test this logo. I'm developing really quick, easy, you know, typically maybe some, you know, there's some great platform. Or it's for quick A-B testing, user testing, that sort of thing. But imagine if you turn to an LLM and say, I need to do some user testing on my logo as I develop this company. And it will say, well, here are three providers who I'm going to get you a bid. It's like, thanks. We'll do that thinking for you for a lot of people.

Lenny Murphy: The way I keep thinking about it, I don't know if it's exactly right, I don't know if the end state will look like this from a user experience. But effectively, everything becomes an app in the app store, right? I mean, I think whether you're a business or consumer, the agent will identify the right app, the right solution based upon your specifications, which is just basically a chat. Here's what I'm looking for. Here's what I need. Here's the criteria that I require. It will go and pull that together, will go into that app store that is in its agentic ecosystem to identify, right? It must be able to identify it. And then it will execute. Right? Let's put some, let's, let's also talk about this again.

Karen Lynch: I, you know, I said, I was like, we want to talk about Miramarati because this is not the first time we're mentioning thinking machines. So I think it was June. We talked about kind of the first 10 billion that and now they've raised two billion more.

Lenny Murphy: So- 12 billion valuation? 12 billion valuation now.

Karen Lynch: So she's an ex OpenAI chief technology officer. I followed her after I saw her at Qualtrics. I think it was, like , three years ago now. Anyway, she's incredibly bright in this space, really was instrumental in launching that particular platform. And now she's secured this additional seed funding. She still has no product. But it's called Thinking Machines. She has no product yet, but it's called Thinking Machines. Now, we know where we're at. Her team is sure as hell further along in their thinking than anything that's out there right now, because she's a visionary thinker, right? And launched ChatGPT early on. What is she doing? This is one if you don't have a Google alert for what she's up to now because Right.

Lenny Murphy: Well, you know, and there's nothing I just can't wait till it comes out. I'm just interested . There's another. I just want to have another conversation this week with a client who is in the AI space and they pointed out two things to me. We didn't have the links here from a new standpoint. That's fine. It's not important, but you may have seen in the news that Google just wanted the talent. They didn't give a shit about the product. They wanted the talent. And that goes to our other, we do have an article on this. I'm at a super intelligence lab that deals with, uh, with, um, scale, uh, that, that was a multi-billion dollar aqua hire. It was a multi-billion dollar aqua hire. It was a multi-billion dollar aqua hire. It was a multi-billion dollar aqua hire. Yeah, you know and we've all heard that they know how they're going out and poaching talent that a hundred million dollar You know packages for developers and here's an example of that I mean this so you follow that money goes back to follow the money, right? We could say that's stupid money. It doesn't matter, you know, with that much money flowing into these priorities. They are looking for the monetization and the monetization is to turn that into a licensable product that meets, that disrupts and takes away other business needs. And it pays for itself, because suddenly instead of carrying this huge payroll of 100 people to do a task, I'm only paying some licensing fee. And that could be millions of dollars a year, but it's still cheaper than the payroll to perform these functions.

Karen Lynch: And the scramble to get top talent. Is something, you know, it's that, yeah. And I can't even really speak to that because it's enormous. And we'll circle back to some of the stuff for future reading that has a lot to do with talent. So- It does. We're down, like we've already been talking 25 minutes and we haven't even gotten to some- I know.

Lenny Murphy: Well, I think that this topic, this general topic, we're going to continue as a trend for the next couple of weeks because it's an important one. This is the next, this is the next phase.

Karen Lynch: Our friends at NIQ are going public again.

Lenny Murphy: So their IPO, 1.1 billion IPO is the target. I think we talked last week about them going public, but now we have the money.

Karen Lynch: We're going public again. So now we have the dollar figure. Yeah.

Lenny Murphy: I don't know what the date is, but that is happening. They're targeting a 1.1 billion IPO. Yeah, yeah. You found this, uh, uh, Wazi.

Karen Lynch: It's 12 million series and it's market intelligence. Now, look, there are companies that do this sort of thing, right. That has, um, market intelligence tools, the big ones, right. So it's, they are kind of going up against, um, if those GFK, you know, some of these other large things with that type of, with other large, large companies, um, But the funding is behind them because it's, quoting from my notes here, they are not relying on modeled or inferred data. They are building their own infrastructure for, oh my gosh, data that is protected and safe, right? And fit for use. Anyway, so different from its competitors, also in different markets than a lot of these, which are forward in, you know, kind of North America first, or, you know, this is some South America and overseas, you know, countries, which are part of people's global growth strategy. So super interesting bonfire ventures.

Lenny Murphy: I don't know them, but that's who led this funding. So yeah, yeah, interesting. The follow the money guys keep following the money.

Karen Lynch: Intelligence didn't talk about that last week, intelligence.

Lenny Murphy: Yes, yes, yes. So a new couple of new products that just continue on. Our friends at Pure Profile, man, I got to say, again, I have a relationship with them, so full disclosure. But whether I did or did not, man, they are really, people pay attention. In tech, they call this shipping, right? I mean, they have been shipping products relentlessly. And I think that that's a skill set that we all have to think about and develop is, you know, just getting it out there. So they launched a new AI chatbot for survey engagement. Just a lot of respect for any company that is just, okay, we gotta figure this out. And they're a public company, so they actually have a lot of incentive to do that. So cool for them. The Nuance, which I believe is owned by Decision Analyst, I believe, if I recall that correctly.

Karen Lynch: I don't want to debut something called Colibri. Colibri, a verbatim coding tool, but in the press release, if you look it up, this is why I'm shouting this out specifically, it's the Latin American word for hummingbird, which I'm like, all right, I like that. So I feel like I needed to shout out that not only do I like the name of that product, but I understand Right. We're talking about speed and precision and, uh, yeah. Anyway, So, uh, so yeah, speed and precision in that open-ended, you know, verbatim coding. So, okay.

Lenny Murphy: I'd like to see that happen faster. I'm good. Yeah. I'm sorry. I didn't mean to talk to you. Uh, a new, uh, nimble product, nimble explorations, fast turnaround tools, uh, you know, part of the brand, uh, they've always done really amazing and work with metaphor elicitation, kind of the primary component of that merging of software and service. And so again, it's that nimble, fast turnaround. You see the trend where they're going. The Vibe Insights, let's go up further reading real quick, because that Vibe Insights was cool. You should mention it. Yeah, so I think, yeah, before we wrap, that's it for the industry updates.

Karen Lynch: But now we want you to just, you know, we're giving you homework to use your brains. This Vibe Insights concept was very cool. So Lenny grabbed this article from Gray Matter Unloaded, and I spent some time with it this morning in particular, talking about Vibe Insights as the future of market research. So what are Vibe Insights? Because you have to read it. And I remember thinking, because in the piece, they've capitalized Vibe Insights. What is this? Is this a company? Is this a new product? I don't really know what Vibe Insights is. But the idea is, this is kind of a new way to think about research in a world of AI. And it's the idea that like, what's the vibe? You know, what's the vibe that we get when we test this product? Like just doing this kind of vibe checking with synthetic respondents. So I'm like, okay, and what's cool about this is how might, you know, this vibe check, how might we get vibe insights rolled into a process when we use synthetic data instead of waiting for traditional surveys? We're really just trying to do this other type of research. And one of the things they say in here is, I'm going to read this, when researchers dismiss synthetic respondents or find flaws in synthetic data, they're likely not wrong today. Goes on to say, but they're stuck not imagining how quickly AI will solve those issues. So for everybody who is currently cynical about synthetic data or questioning the quality that synthetic respondents can provide, this will challenge you to consider use cases, but also your own thinking and confidence that these synthetic data sets will get better and stronger. And this won't be an issue down the road because, and I quote, disruption will find a way. My favorite quote from the entire piece is disruption will find a way. And certainly synthetic data and synthetic respondents are a disruption that nobody in the industry likes, but guess what? And I shouldn't say nobody, brand side professionals are liking it if it can get them to be able to meet the demands that's placed upon them.

Lenny Murphy: So full circle back to that. Well, and that was, that's Mark Ryan. Uh, so Mark, uh, Cantar, uh, former chief product officer, you got a position. I mean, Mark, Mark is a researcher, right? He is a data guy, particularly a sample guy. I mean that it has been his claim to fame Besides and obviously also brilliant. So for someone like that, I will tell you some else I heard this week from a Very large research company Experimenting with synthetic sample off of their own proprietary panel In collaboration with a leading university, they are now at 90% accuracy, meaning that from their existing data set of real human data, that's high quality, running a study side by side, and I'm not sure what the details were, but just using AI to duplicate it on the synthetic sample is 90% there. As you would get from a live survey. So to your point, and Mark's point, it's just gonna get better and better. Let's end on a good note.

Karen Lynch: This OpenAI thing was interesting.

Lenny Murphy: So. Well, so there's two, really these two things kind of come together.

Karen Lynch: And I don't know if this is a good thing, but we're ending on it anyway, because these are our last two pieces.

Lenny Murphy: That's true.

Karen Lynch: Let's figure out how to spin it. Yeah, so one of them we have is Julian Daly's LinkedIn post talking about open AI job listings. And that's sort of hinting at the continued need for traditional market research skills, even at open AI, right? So traditional skills in methods and traditional experience in research is still appealing even at that level, right? Some of the companies that we've talked about need researchers. I have gone on and frequently look as I'm doing some Speaker curation and outreach, and a lot of it is like, yeah, they have insights functions, they have research functions. So even they are not finding the field obsolete. So appreciate Jillian putting that out there. But also, I kind of want to pair it with this last article, because I think this is the bigger piece of the puzzle to the workforce. Gen Z being a little wary of AI right now...

Lenny Murphy: Not a little, they were very wary looking at that data.

Karen Lynch: 83.3% of respondents, kind of Gen Z respondents, cite job loss as their biggest fear right now because of AI. And it makes sense that many of them will come out of school and are seeking entry level jobs, and they're starting their careers and they are seeing sort of, you know, traditionally what might have been their roles being outsourced to AI. I think that, I feel like it was a few weeks ago that you and I were talking about like, you know, these, you know, educational institutions really need to be upskilling students to get them, to get their skill level to a point where that isn't a hurdle that they feel, get their confidence up that they can join that AI world. My son and I, he's 23, so he has many, many friends who are two years out, and his friends that are engineers, there's one that's doing reverse software engineering. He's thriving, making more money at 23 than any 23-year-old young man should be making, in my opinion. And other engineers and computer science majors, they're all doing okay. They are very highly employable. And, um, and I saw trades.

Lenny Murphy: So let's keep that in mind too. We're trades. Oh, so we're trades.

Karen Lynch: I was, I thought you said, cause those are trades, but yeah, no, no trade stuff, fixing stuff, doing stuff. Yeah. But in the business programming, in the MBA programming, in, in marketing programming, which is what I came out of, like all of that, um, all of that, that curriculum has to make sure that the folks that typically have gone from those programs into research. Digital advertising, one of my sons, he's 25 and he's in digital advertising and e-commerce marketing. He is on a steep learning curve right now and he is seeing a very different reality at his age because he doesn't have 20 years of experience to be able to get on board here. He learned all lots about Google Analytics, and guess what? That was, you know, he's in a sweet spot of stress, I think, with a lot of these others, you know, kind of... Anyway, so they've grown up tech-savvy, these kids, right? And yet, not AI-savvy. Whereas, you know, Gen Alpha will probably be like, yeah, yeah, I got this. But Gen Z might need a little bit more help from employers that are snagging up early entrance into the workforce. You've got a lot of work to do to make sure that their confidence is up and that they can approach, because they're our future, right?

Lenny Murphy: Yeah, and they can think.

Karen Lynch: Yeah.

Lenny Murphy: Think critically, be creative, all of those skills. But that's a whole other conversation. I was going to say, oh.

Karen Lynch: Yeah, now you're going to be gone in the next two weeks.

Lenny Murphy: Oh my gosh, I'm not going to be gone for two weeks, France.

Karen Lynch: I mean, I'm not leaving today. But yes, I will take it all off for a much needed family vacation next Wednesday night. So not quite the whole family, but me and all of my children are going on an adventure. So my children and my grandchild are all headed to Brazil. So it'll be my first time going to see where my daughter-in-law's from. And then we're going to take some time to go to the coast because I don't go anywhere without a beach.

Lenny Murphy: But we will still have lunch. So Tim, Tim Lynch is gonna step in.

Karen Lynch: Tim raised his hand. Well, and one of the reasons why Lenny and I said, you know, it'd be another, a good time to bring Tim back in and we'll literally, you know, we're just discussing it, but you know, Tim is a change management consultant, helping people at the enterprise level navigate changes predominantly right now. They're all dealing with AI. So at the enterprise level. So I think it'll be an interesting perspective for you guys to pick up the conversation around that.

Lenny Murphy: Yes, absolutely. And then the following week, it looks like Susan Griffin, who is obviously a legend and loves Susan, is going to be on. And then we'll talk about the marketing implications for all of this as well. So yeah, we'll have some good substitutes. They won't completely fill your shoes.

Karen Lynch: Oh, well, thank you. No, it'll be great. I wish I could tell you that I will be listening in, but I won't.

Lenny Murphy: I hope not.

Karen Lynch: This is why I feel like it was this time last year where I was going to. I was also taking, you know, taking a trip to a beach with my girlfriend, Tanya. Anyway, I feel like this is the time of year. This is the vacation where Karen checks out. So I will catch up in August and I will be back the first week of August.

Lenny Murphy: And you have one shout out to Anish who commented about creating their AI-based e-commerce analytics tool. Very cool, Anish. Thank you for sharing that. Keep us in the loop on how that goes. You know we'll have the Insight Innovation Competition coming back around here sooner than we think, really.

Karen Lynch: I can't even believe it's already, yeah, it's already.

Lenny Murphy: So keep it up. And that's cool, too. Last thing. All this is like, whoa. But think of the opportunities for innovation. I mean, there's so many cool things that are going to come out of this in ways that we hadn't thought about before. And Anish, obviously, is an example that you shared. So yeah, we're on the roller coaster. There's no getting off. So we better just find a way to enjoy it and make the most out of it. All right.

Karen Lynch: All right, friends. Have a great weekend, and I'll see you in a couple weeks. And Lenny will see you next week. And yeah, that's all we got.

Lenny Murphy: That's it. Thanks, everybody.

Karen Lynch: Bye.

Lenny Murphy: Bye.

Links from the episode:

Beyond Chrome: The AI Interface Wars Heat Up 

Perplexity launches Comet to take on Chrome

OpenAI Enters Browser Wars with ChatGPT Agent

Mira Murati’s Thinking Machines Raises $2B at $12B Valuation

OpenAI Plans Checkout System Integration in ChatGPT

Meta Superintelligence Lab Considers Pivot from Open Source to Closed AI 

NIQ Global Intelligence Files for $1.1B IPO 

Rwazi Raises $12M Series A for Market Intelligence Expansion

Pureprofile Launches AI Chatbot for Survey Engagement

Nuance Debuts Colibri for Open-Ended Data Analytics 

Protobrand Launches Nimble Explorations for Quick, Deep Behavioral Research

Vibe Insights Positions Itself as the Future of Market Research

OpenAI Job Listing Hints at Continued Need for Traditional Market Research Skills

Gen Z Is More Wary of AI Than Older Generations