So What? Top 5 Use Cases of Job Descriptions

Well, hey everyone, happy Thursday. Welcome to So What The Marketing Analytics and Insights Live Show. I am Katie, joined by Chris and John. Howdy, fellas. Hello.

We're at the awkward angle today. I know. Um, somehow I'm always a little bit caught off guard when I do the introduction. Like, because in my brain, I'm like, wow, it's already Thursday again. How did that happen?

Uh, but here we are, and today we're talking about the top five use cases of job descriptions. Yes, there is more to a job description than just what does this job entail, which quite honestly is never accurate. So let's find other ways to strategically use a job description. One of the things that I've really enjoyed about using a large language model is it's helped me do that deeper analysis that I've always been struggling to do on my own because I'm a single human and I look at something and I see a lot of different things in it, but I can never quite put my finger on it. So, you know, you can look at a job description and be like, there's more here.

It's telling me something else, so I'm not quite sure what. What other insights can I glean other than this is the open position? And that's kind of what we want to cover today. So we're using job descriptions as a stand-in, but think about anything, you know, any kind of material content that you have access to. A lot of times, as humans, we can only see it as a singular use.

So this is the thing, this is what it does. But the nice thing about a large language model is it's not constrained to only see it used that one way unless you tell it that's how it works. And so today, Chris is going to walk through how we get there. And you know, using the example of job descriptions, like, what are some other ways we can use that information? Uh yes.

So let's start off but by talking about the importance of a job description specifically. Why a job description? Because in general, you don't hire for fun. Hiring sucks on both the job applicant side and the hiring manager side. It sucks, it's a long process, it's an exhausting process.

There's interviews, and companies, especially these days, do not open headcount for for frivolous stuff. You only open headcount for one of two reasons. Either you are trying to backfill a role, uh, you know, that you have a mission critical need for that you can't farm out to an agency or AI or something, or you have a strategic imperative that you need to build competence for, build a center of excellence for within your organization, and you do not have those people or those agencies or whatever to do that. You need to get net new people on. So those are the kind of the two things that a job description implicitly tells us.

It tells us, like, hey, this is a thing. Like, either we need we're we're doing the thing, and we are about to lose somebody or lost somebody who's doing the thing, or we want to do the thing and we can't do it with people we got. I mean, and that's not new information. Like that's how job descriptions have always worked since the you know, invention of the job description, which I don't think we have that data. But you know, I remember applying for jobs, and job descriptions for the most part are kind of vague, you know.

So they only kind of tell the end user, the applicant what you're meant to be doing. So if I think back to like my job description at Shift and what it said versus what I was doing, it was wildly different. And so that is one of sort of those caveats that I want to sort of keep in mind is that there's an assumption that people kind of know based on a title what a job is meant to do. So a job description is really just a list of high-level tasks and then the dreaded and other you know duties as assigned, which could mean anything from getting John coffee every day to you know trying to clean up Chris's technical debt, even though you've never touched a server in your life. Yep, or you know, making coffee and having to run it to your board of directors, uh, which happened at our old uh our old agency.

That was a delight. Yeah, that was well, anyway. Anyway, so let's look at a job description as an example. So we're gonna pick on uh McKinsey, the largest consulting firm on the planet, um, because they're the largest consulting firm on the planet. Um, and every company has uh every company that's hiring has some place to put career stuff, and most companies, particularly enterprises, use something like uh job bike, uh greenhouse jobs, LinkedIn, uh, or uh what's the other big one?

Workday. Um so there are all these systems where they that are connected to their HR platforms that publish stuff on the web. So if we go to uh McKinsey here, we go to careers, we go to search jobs. That brings us to their job portal. Their job portal, surprisingly, is not a third-party thing.

It's a single-page application. It's it's actually kind of homegrown, which is is interesting in its own right. There's over 500 jobs available at McKinsey. Now, this is out of a 50,000 person firm. So it's actually not that many.

Um, but we see across all the different business lines, and you know, this goes on for quite some time. So let's let's take a look at this one. This is the job description of a McKinse in Mexico City. Uh it says, your impact. Here's what you're gonna do.

You're gonna execute the growth strategy, whatever that means. You're gonna build B2B and B2C paid media programs across multiple social advertising platforms as well as own channels. You're going to generate significant insights. As opposed to insignificant ones? Yes.

Data analysis, generate insights, verify them with the appropriate organization. So the marketing will share your recommendations with key decision makers. You'll tell stories with the data and enable growth marketing to make. I feel like this is a drinking game. I mean, so John, are you is this getting you excited?

You're gonna go apply now? Yeah, I know. It's it's like, could it be any more vague, really? You know, it's uh I'm looking for uh some synergy in here, and we'll be I mean, honestly, at the end of the day, basically what you're doing is you are drafting ad copy and loading them into all of the different ad servers. Um, and then you are responsible for the metrics of those things.

So you're building PowerPoint presentations endlessly. And it says our office culture is casual, fun, and social with an emphasis on education and innovation. I you know what? I need a little red flag that I need to start holding up during meetings when people say certain things, like, but that's one of them. Yeah, all global consultancies are known for their fun workplaces.

That's definitely yeah. I can't think of what a single enterprise company that is considered casual. Yeah, no. Uh so that's the job description. And you'll notice it's what's interesting is they are pretty thin on what they're looking for.

Bachelor's degree in these fields, knowledge of primary paid media, willingness to travel, digital marketing is plus, uh, and bilingual uh are the kind of kind of the big things there. And that's it. That's the application. Hmm. Yeah.

So the first thing that go ahead, Katie. Well, what I was gonna say, and this is a little bit of a digression, but what the the lack of expertise to me says that this is an entry-level role, which for the conversation of what am I supposed to do? I'm just out of college, like there's still entry-level roles to be had if you want them. But that's sort of like a digression because they're clearly looking for people who are pretty much fresh out of college because the two two plus years experience, quite honestly, a lot of times you can skate by with like internships or portfolio projects, things you did in college. You really just have to demonstrate that you know what the thing is.

Um, so that's a little bit of a digression. Um, I'll try not to do that too much this episode, but no promises. Um the first thing we can do is we can dec we can and we should deconstruct a job description into specific tasks. So one of the things that AI, as Katie pointed out at the beginning of the show, is excellent at is because it's language, it can sort of read between the lines to say what's in here. So one of the things that we have, and I believe it is in the Trust Insights Academy, is a job to skill.

Oops, wrong file. A job to skill tool that decomposes a job description into what are the different things that this job does that could be uh automated by AI. So we have things like uh pull and compile campaign performance reports, right? That is a big one. Bilingual translation reports, guys.

That is a that's a shoe-in for AI. Um build and match B2B BDC campaigns. Yeah, there's there's some AI in there for sure. Um, but there's also some integrations. Build and maintain KPI dashboards, that one very easy for AI.

But these top ones here, pulling and creating campaign performance reports and bilingual translation, those are slam dunks for AI. So immediately, just from a single job description, we can under we can decompose into the tasks because this will tell this will help like a job seeker understand what am I actually going to be doing? Mm-hmm. Because we all agree that vague description was kind of vague and squishy. Yeah.

Well, it tells you not only what you will you be doing, but how quickly you need to outpace AI or AI is going to be doing it for you. Mm-hmm. And so anything color-coded green on here, yeah. You're probably either you're gonna be using AI to do that or you will no longer be doing that at the office. Mm-hmm.

Which doesn't leave a whole lot. Yeah. Down here is client site workshops and travel. That's a a human thing. Sure.

Uh collaborate with data scientists and engineers to compile recommendations, coordinate cross-functional digital marketing partners, verify insights with marketing organizations, present recommendations to decision makers. So basically you're gonna be presenting slide decks. Yeah, you're gonna be scheduling a lot of meetings. Which you're gonna be going a lot of places to do it. Yeah.

Um this is and and then when we look at the actual use cases, we can see what are the strategy tactics and execution for this to say, okay, well, here's how you would have AI use its capabilities to actually accomplish this task. So our our first two use cases for job descriptions is a distilling the job down into like what are the specific tasks you're gonna be doing, and then B looking uh doing a sort of uh, like you said, give you a threat analysis, like how likely is that a machine's gonna do this. And I think that that's an important tool. Um, and I did put up the link, but you can find this particular skill in our academy, which you can get at Academy.trustinsights.ai. Um, if you're a job seeker, it's a really good use case right now because you might get hired on for a job that you think you're like, oh, this is great.

I don't have to keep looking, but like three months from now, well, actually, we hired this outside organization to, you know, build in AI efficiencies, and that's 90% of the work that you're doing now. You kind of want to go in with your eyes open to know like what's the likelihood, and it gives you an opportunity to really get skilled up on how to build those workflows in AI so that you're the one in control. Exactly. Now, here's another thing that you can learn from job descriptions. You can learn the technology over the organization itself.

Like so it a lot of the time, for example, you'll see it requires knowledge of Word, Excel, PowerPoint, and things like that. So if it's saying Word Excel PowerPoint, we automatically know what? We know it's got at least part of the website. It's a Microsoft. Yep.

Exactly. Which means that you're likely tied into co-pilot. If you say Google Sheets, Google Docs, et cetera, or you know, general. Exactly. So we can know that.

Uh, you will know, for example, if they're saying social media paid, um, you're probably talking LinkedIn ads, Facebook meta ads, um, adversely interestingly not. Well, maybe it depends. Maybe it's Google Ads, maybe it's not. But if they're saying social media ads specifically, that's not Google Ads. Yeah, maybe that's true.

But it could be YouTube ads. Most likely, well, hopefully. But yeah, no, and I think that that's a really important key as well because that gives an interviewee an opportunity to be prepared for those questions because those are the inferred questions that you'll likely be asked during the interview. It's like, what's your comfort level with LinkedIn ads? What's your comfort level with meta ads, what's your comfort level with this, even though it wasn't explicit, those platforms were not explicitly called out in the job description.

You know, if I were a job seeker and I were looking for this particular kind of job, I would find this useful because then I could go say, okay, so Meta, LinkedIn, Google, so on and so forth. Are there certifications that while I'm looking for a job, I can go get to at least say, hey, I've at least opened the platform before, and I know the basics of how to navigate it. Because you can learn a lot when you get in there. But if you've never opened the platform before, you're going right to the bottom of the list. Now, here's where we start talking about not one job ad, but if we look at this list here, there's 572 of these things.

If you wanted to, and I do, um, you can take a tool like Claude Code and say, build me the Python code necessary to extract all 572 job descriptions. And you go back and forth with Claude, and I'll say, Great, let me help you design a database to store them all in and stuff like that. And then what you can start doing is start gluing together the different things, the different aspects. So I did this uh this morning. I had Claude take one of our existing scraping tools and modify it specifically for McKinsey's website because McKinsey's got its own sort of custom rolled uh job board.

It's not job byte, it's not the other one. So it I didn't have my existing templates didn't work. But when you do that, and then you start saying, Well, hey, what is the Martech at McKenzie look like based on all the job ads? What you end up with is a five-layer sandwich of you know, marketing and commercial applications, their agentic AI product layer, their data science layer, their cloud and DevOps, what technologies are mentioned in their job ads, enterprise platforms, and their BI. So at McKinsey, based on their job ads, they definitely use Gen AI.

It looks like they have for sure, they've for sure got their own rag databases of some kind. They're using Langchain, they're using vector databases. So they're building their own Martech to a degree. They've got they've got a lot of Python and a lot of SQL. They've got some Airflow, they've got some PyTorch, but it's very much a Python shop.

They have a lot of AWS and Azure and GCP, which is unusual until you realize that's not for them. That's because their big enterprise clients all use those platforms. Right. You have McKinsey ID, the McKinsey Internal Platform, the Lily, their internal AI. They've got Okta, SAP, uh, they've got HANA.

And so that that almost certainly means that that is their ERP and their CRM, SAP. Um, service now for their internal ticketing, and then Tableau and Power BI. Now, Power BI, combined with these other things in here, plus Azure tells me they're almost certainly a Microsoft shop. It reminds me when I started working with you uh a decade ago, Chris. There was a plug-in that you would use, not a plug-in, a Chrome extension that you would use, and I think it was called Built With.

And, you know, so I want to sort of, you know, for people who are like, wow, you really did a deep dive on McKinsey. This is not a new thing, not a new technique that companies do to sort of understand their competitors. And McKinsey is way out of our reach. Um, they'll probably never see any of this. But this is all based on publicly available information from their website.

And what we're showing is just a different way to do the same kind of analysis that marketers and sales folks have been doing for decades. Um, you know, so built with you used to be able to scan someone's website and get roughly, you know, this kind of information of like what's in their tech stack. And so I think if I if I remember correctly, Chris, it was limited to things like what pixels or integrations were within the website. So it wasn't a full picture. Whereas this is just another version of that, but it's based on what they're listing in terms of their roles and responsibilities needs, we're inferring what systems they likely have.

Exactly. If you wanted to add that in and you wanted to do it yourself, you didn't want to pay for the premium version of built with, it's actually relatively easy to do. If you go to the McKinsey website, maybe you go to the home page, or ideally go into some page where there's a conversion form of some kind, go to view, go to developer, turn on developer tools, make sure it's set to the network tab and refresh it. You can see all of the Martech that just fired when I clicked on that. And if I scroll down here and I go to the form, now if I download this file, this is a great big honk of data file that tells me what happened when I loaded the McKenzie website.

This thing is 15 megabytes and it's all in JSON format. You and I ain't gonna read this. No. However, we can hand this to a clawed code or Claude Co-work or the AI tool of your choice and say, using JQ, which is a uh JSON querying tool, take this audit file apart and tell me what marketing technology was in operation when I was on this form on McKinsey's website. And I think that that's useful just to know again, this is all publicly available.

Um, every website has the option to take a look at the and view it that way. But when you're looking at using the job descriptions to infer that information, this is a good way to just double check that the information that the job descriptions, as we know are vague, is that you're inferring is roughly accurate. So you're never gonna get the full 100% picture. But you know, I think about you know, people like you, John, who do biz dev, and you might look at that tech stack and be like, wow, trust insights has expertise in like 80% of those things. Like maybe they need help cleaning up their data, or maybe they need help like integrating things.

It might be worth a pitch because we can credibly say, hey, here's our 10 use case uh case studies that speak to all of these different technologies that we can help you with. Like I can see that being a way to use the data that you extracted from the job descriptions. You hand it off to your sales team and like go after the companies that match our expertise. Yeah, to be able to get a profile of what's in there, it just sets up a bunch of discussions as far as you know, okay. We know they're gonna have this set of problems because they're you know, insert your hated CRM system here, and you know, there's a whole bunch of things, and also builtwith is still doing their thing.

I get emails from them. There's they still uh are saying, hey, you know, we can find these people for you. So um, but uh the thing that's jaw-dropping to me though is 15 megabytes. I mean, what the heck happened to Google mobile data speed? I mean, that is just a berserk amount of data to be going through a form like that.

Um, but you know, again, you're a consulting house and you're building your own form, so who cares? They're they're not and the other one was I did see on the list there that SEO does not show up as part of their stack or their analysis. They're literally we SEO doesn't matter anymore. We're we're giving that one up. Well, it's it's basically says they're not hiring for it.

So it's likely that they have those capabilities or they've you know partially outsourced those capabilities. This tells us either where they've got a shortfall in their staffing, or they desperately need uh to expand the capabilities. Just by looking at this alone, I would look at this and go knowing who their clients are, literally the you know, the biggest companies and organizations on the planet, they need to have they're trying to grow their expertise or to grow their client, you know, forward-deployed engineering team to be able to drop anybody into a client and say, okay, we've got this engineer who or this analyst who can come in and work within your Google Cloud to connect it to their system. Their system is called Quantum Black. And so it's like, okay, how can we get your Google Cloud, your BigQuery database, your data warehouse, whatever connected to quantum black so that we can run an analytics on it?

It makes sense. Um, you know, and so again, I just sort of see those as, you know, I think about it from the lens of someone like John, it's just a huge opportunity. Like they can keep hiring, or they can hire, you know, a agency to just like hit the ground running and handle it. Like there's a lot of opportunity there for someone like us to sort of step in and be like, hey, we do that, and we're we're not going to cost you the overhead of you know health benefits and all that sort of stuff. It's funny you mention that because that's sort of the ultimate use of these job descriptions.

If you were to download them all, which we did, uh convert them all, and put them into a system like Claude Code and say, I want to do a very deep analysis of this. I want to understand what this is, and I want to understand it in the context of a company like Trust Insights. So I built this prompt um saying, here is the 572 jobs that I pulled in. You're a competitive intelligence director, right? Um, here's some information on how the that file is formatted.

Here is everything you need to know about trust insights, which I got off of our about page off the website because it's the most current version. And then my core thesis is that companies hire based on need and they the the major needs are either backfill or uh expand. Now, to think this through, we instruct it to use nothing less than the 5P framework by trust insights. I am so surprised by this revelation of information. If you want to learn more, it's trust insights.ai slash 5p hyphen framework.

The five Ps are purpose, people, process, platform performance, purpose, what the heck are you doing? People who's involved both internally and externally, your customers and your end users, process, how are you doing it? These are your instructions, your standard operating procedures, your processes, platform. What tools and performance did you do the thing you set out to do? Exactly.

So in this prompt, purpose, answer one question based on McKinsey's hiring. What is their corporate strategy for the next 12 to 18 months? And what should our boutique firm do about it? Should we counter it? Should we coattail it?

Or should we identify the white space that they're not talking about because they're not hiring for it right now? That's our purpose. The people is our firm's leadership, aka Katie, uh, who's making investment bets? We're going to look at McKinsey, that's their people, and this and you, Claude, are the competitive intelligence director speaking to our leadership. So we define the people.

The process is as follows. Prep the corpus because you gotta split up to big file. Map each job description into a structured record. Give it weight. Junior role is senior role.

Senior role should have more weight in this kind of competitive analysis because you're gonna hire a lot of boots on the ground for sure, but somebody's got to be in charge. And the higher up the role that you're hiring for, the more interesting it is because those are the ex really expensive people, so you don't hire them for fun. Um group together similar roles, right? So you have an analyst in Mexico City and an analyst in Boise, yeah, it's still an analyst. Um triangulate by using your web search tools to check things like news articles and stuff to see if the patterns are informed by recent news changes in the news, and then synthesize a report.

And then you got to state your evidence for this. The platform, of course, is uh the data chunking, our own custom code for this. Uh, and then the outputs are a markdown file, in case we want to use it again for AI somewhere else, and an HTML file. Performance. You are done when you have gone through all these steps and you can explain everything that you did.

Um you can ask questions. You can up uh ask up to five questions. Here's the example, here's the deliverables. So when we run this, what do we get? We get a great big honk and huge playbook.

Here is what McKenzie's up to. I thought this was interesting. They are, at least based on their job description, they're aiming to become an AI deployment company, meaning they're gonna come and build the stuff, they're gonna do the stuff. So it walks through and says, here's what we see. They are hiring people, forward deployed engineers to do AI at their clients.

They're not giving binders, they're saying our clients just want us to come in and build the thing. Um, and they want it based on Mc McKinsey's tech. So it's harder to rip out uh later. And it goes through all these different things. Uh I thought this was really interesting to say they're repositioned from the firm that tells you what to do about AI to the firm that builds the AI and runs your digital transformation internally.

So this is going from from talking about it to doing it, which Katie, you've been talking about a lot lately. Yeah, no, I mean, and it tracks. I mean, clearly they stole it from me. I'm kidding, McKinsey. I know you didn't.

Um, you really have have way more clothes than I do. But no, I mean, it's a common theme that we're seeing across all of the AI thought leaders who are speaking credibly about it. It's that we've moved past the what is AI to how do I get it to run and run tomorrow? And so this is exactly what they're saying. They're repositioning from the firm that tells you what to do about it, which is the education, to the firm that builds it and runs it.

And that's the actual like doing things. That's unsurprising to me. Um, I would actually be shocked if they were still like, no, we're not shifting gears. We're still just gonna tell you what to do about it. It doesn't matter.

You we're way past that. We're three plus years into what is AI, and now the conversation is almost solely focused on how the heck do I get this thing and how do I get it to stay? How do I get it to work? Um, you know, what it says to me, you know, is if you have any of those skills, um, you know, working with code and software development, any of those, you know, foundational skills. Now is the time to get in with a firm like this if you're looking for a job.

Yep. 162 of the 572 job descriptions are forward-deployed engineers. So that's a lot. Yeah. They said they have concluded advice alone no longer commands premium fees in the AI era.

So they're moving down the value chain to become like Palantir. Makes sense. We're gonna we're gonna build the thing for you. Yeah. Well, and over a billion in quantum black, too.

That's just mind-blowing. Yeah, they are moving all the chips onto that. Like, that's it. Yep. Shift two, they are changing uh their internal structure.

So they are basically ditching their junior roles, junior research and back office in favor of engineers, right? So the huge corpus of you know, junior analysts, of course, AI tooling, their internal platform uh Willie, uh 500,000 prompts a month uh according to web research. Yeah, all those those junior, you know, PowerPoint jockey roles are going away and it's they're replacing them with engineering roles. Which makes sense. I mean we talk about the value of doing um deep research and how to get started with it.

That's actually our casino framework. But if you think about the resources that you know a um this is our casino, Trust InsightStratai slash casino, uh that's our deep research prompt. But if you think about someone with McKinsey who has the resources to really truly automate that, yeah, I can understand why they don't need those junior analysts compiling that data anymore. That's something that AI does really, really well. Yep.

The third major shift in here is they are instead of going saying we're everything to everyone, they're doubling down to create industrialized AI per vertical. So they want to be like Palantir, right? They want to say we want to be the AI implementation vendor for life sciences for defense and public sector, et cetera. Um so they're saying this is where we can defend ourselves we have proprietary data we have decades of expertise we got the role of X. And if we can build quantum black to be strong enough we can be the stack for the Pentagon as an example.

It's interesting I never think about companies like McKinsey having aspirational companies, but then McKinsey might want to do this kind of analysis on a Palantir to understand what they're doing. So McKinsey, we can do this for you. Um but no, I mean it's interesting because you never really I don't personally, I don't really think about like the big guys, the enterprise companies really worrying about what somebody else is doing. But in this sense it totally makes sense. Yep.

And they're you know they're in a constant knife fight with Bain, BCG, Deloitte, et cetera. So they're all jockeying for the trillion dollar companies. Yeah. So now that we know what they're up to, we can look at how they're going to do it. What are they going to do?

So industrializing uh AI, what are the specific things that they are doing? They're building production grade AI delivery. So they're gonna be dropping this stuff in as their own applications. Um and it has recommendations for us as Trust Insights, right? We can't obviously be McKinsey, we can't build uh a trillion dollar data system as much as I would like to think that we could uh that realistically we can't build a trillion dollar system.

Um but what we can do is help companies with the two of the five Ps that McKinsey doesn't isn't touching all McKinsey's going all in on platform. So if you have people process and platform as plus purpose and performance they're saying we're platform which means that our opportunity is yes you got the platform let us help you with the people and process and your purpose and your performance so that you don't buy a instead of an expensive binder you've bought an expensive server. Mm-hmm makes sense proprietary products organizations buying these black box products will need an independent partner to validate the outputs. We don't build black boxes, right? We we we need to know what's in the box.

Um, but they are that's definitely the route that they're going with quantum black. And this is what I thought was interesting. McKinsey is running itself like a tech company, right? So they are basically becoming uh a tech vendor like Palantir. Which supports the pivot and supports the job descriptions that they have open.

Now, that is not an easy pivot. There's a lot that goes into that. Um, you know, because I mean, Chris, you and I talk about this all the time. There's a big difference between advisory consulting and software as a service. And there's a big reason why I've kind of held us back from being a software as a service provider because it comes with a lot of headaches.

Sure, there's dollar signs attached, but at the end of the day, depending on the size of your company like ours, it's not worth the headache. Whereas McKinsey, who has what, what did you say, like 50,000 employees, they can probably find someone who's willing to, you know, deal with the headache for enough money. Exactly. Um, what was also interesting is that um they're pushing a lot of their jobs away from the US core. They've got a lot of hiring in APAC and Latin America, especially.

Um, go ahead. Nope. Um, so this tells us that they've got a cost issue, right? And that the talent in the US they're trying to go after is way too expensive, which makes sense, of course. Anthropic, open AI, Google and stuff are all competing for the exact same people.

So they're like, maybe we should try these other places that have functioning education systems. Makes sense. Uh, especially when you're trying to literally pivot your entire organization. I mean, you're gonna have razor thin margins. Mm-hmm.

It's really interesting. They correctly with their the Casablanca graduate program recognize that places like Nigeria have some of the best like uh growing AI talent. Uh Lagos Nigeria, along the butt of jokes first for spam farms, um, actually is one of the places that you see a lot of papers at like NERUPS stuff being submitted because they have invested heavily in their people and their education system, and it is paying dividends. Like they are they have a tremendous AI hub in Lagos, which totally makes sense because McKinsey has the resources to do that research to find out where that talent is. And then, like I said, the the big thing is they have said strategy with that execution is a hallucination, which is our thing.

Like we we um we want we wanted to make sure that you can not just get a binder, but actually do the thing. So then we scroll down past all this stuff into execution. How are they going to do this? Right, and this is uh they're moving around forward deployed engineers, they've got 15 client facing named products, they and they again they're becoming a software company, um, which is what they do. And what I thought was interesting is they have a partnership because they're running into you know, just not enough people with Google on that front.

Which, you know, again makes sense. And I'm assuming that was publicly available information, probably some kind of a press release because companies like McKinsey, that's a big part of how they communicate, especially because they are, I believe they're publicly traded. Um are they? I don't know. Well, anyway, they're a public company, they're a big enterprise company.

Um, press releases are a big part of how they communicate pretty much everything for full transparency of like what's happening. Yeah. So what are they specifically optimizing for? They are looking at revenue per head. Like that is when you look at the big picture pattern, they are trying to figure out how do they make themselves more profitable.

Which again, that that goes completely in line with uh slimming down that huge base of junior analysts and make and making it an engineering firm. And that's that's what how they decide what success looks like. Um, and that they have are measuring on cost based on again uh where the where they are looking for talent. Um and I misspoke, but McKinsey is a privately owned company, they are not publicly traded. Yeah, but like you said, all this information itself is in public, right?

And so then what it delivers for us afterwards is to say, okay, based on everything that McKinsey is doing, what should trust insights be doing? Um and it explains, you know, do we is it a coattail, is it a counter, or is it a white space? Coattail plus white space, you know, vendor neutral AI governance measurement and adoption. That's that's what we do. Um proprietary products, coattail and counter.

We can validate that you're not being sold something that just because it's got the McKinsey logo on it means it actually works. Um delivery we counter, like we're weird, I guess I we're on shore. Um and then we go down here vertical depth. Uh we are going to ignore that. We're going to ignore the defense and government sector, uh, because we're just not equipped for that.

Um, and productizing AI governance and ALOC via the 5P framework for the mid-market. And this one is a huge part because if you think about a company, for example, like Palantir, Palantir has uh what's the name of their model? They're they have a targeting model. Is it? Um either way, oh Maven.

Maven is the name of the thing. Maven is their internal targeting AI that they provide to companies like to organizations like the Department of War. It is in part responsible for some of the tragic accidents that have happened in the Iran conflict. When we talk about like making sure you're using AI properly and there's doing what it's supposed to, those are kinds of the kinds of things that uh we want to be able to help with. So what we're looking at in a nutshell is a competitive analysis.

And this is under the assumption that you know McKinsey is a competitor for us. Now they are an aspirational competitor, but now that they're pivoting towards being more of a software first. I don't know that I would necessarily consider them a competitor because that's not the route that we're going with Trust Insights. But, you know, as we are coming up to the last third of the live stream, you know, so we've covered what three use cases, four use cases for job descriptions. Uh, I think we've hit all five in varying flavors.

Uh, you know, decompose tasks decomposition. So we're just looking at uh what are you hiring for? Um task decomposition so you could as an individual job so you could you can figure out do I have those skills? Um looking at decomposing the tech stack of a company again on an individual basis, and then again on a on a bigger basis, then looking at the strategy overall uh of the company and broadly and then for specifically for competitive analysis, which is where we are now. Makes sense.

And you know, again, this is all based on publicly available information. It just came from the careers page of their website. Um, you know, and a little bit of extra research that Chris added into his prompt that again, all publicly available information. The accuracy of it really depends on the accuracy of the job descriptions themselves, which is you know a big asterisk. So, you know, sort of proceed with caution in that way.

But this, if McKinsey were a true competitor to ours or something, you know, that we wanted to really do something serious with, this gives us enough information to do something. Um, it's directional for sure. Yep. For us, this is the white space that uh language model said, hey, this is what I didn't see that is in the trust insights descriptions, but not anywhere in the 572 job descriptions. Though a couple that stand out to me, obviously our heritage is measurement and analytics, right?

That's sort of the that's our roots. Uh mid-market, obviously, is a big thing. But this one is something that isn't there. McKinsey's saying we just want to do all the AI for you. And our tactics, I mean, if you look at the Trust Insights Academy, you look at how we consult with clients and things, we are very much in the teach you to fish uh things.

If you look at our our speaking packages, our workshop packages, we want, we don't have enough people to do the AI for you for 50 or 100 companies. We would rather have you do the AI for you, get past how do I prompt, and then bring us in to do what some really fun stuff. Yeah. It's interesting. Um, you know, one of the big takeaways, aside from this, because I feel like as a company, this is really important for us, but as someone maybe watching this live stream, if you're looking for a job, it's a great way to prepare for you know what's coming, what the what the interview questions are likely to be, especially if there's technology vaguely alluded to, uh, but you want to get a deeper dive, or you want to sort of really set yourself up for success and start to seek out those certifications.

You know, maybe McKinsey is an aspirational company for you to work for. You know, it's a really good opportunity to be like, what would I need to do with my own skill set to make me a desirable candidate to someone like a McKinsey? Because you can imagine that McKinsey is only one of many firms like them who are moving in this direction. I would completely agree with you on that. If I was a job seeker, and I and I had a skill set in AI, assuming that I just get didn't get hired just for having the words AI on my LinkedIn, um, it would be to put together something just like this, but tailored to my my profile to say, here's what I used AI to find out, and here's how I specifically can help you achieve this, this, and this.

Mm-hmm. And if you're interested in getting a copy of that uh skill that we mentioned, we have it for sale in our academy at Academy.trustinsights.ai. Um, it's one of our digital downloads. But yeah, I think that this is a really interesting way to look at job descriptions because again, we as humans, we sort of have that narrowly focused bias of a job description is literally just going to tell me what tasks are involved. But AI, a large language model isn't restricted, you know, to those guardrails unless you give them, but it's going to look at it be like, hey, this is a whole bunch of cool data you gave me.

Hey, look at all these different things that this tells me. And I think that that's one of those really nice benefits of using a large language model. Oh, yeah, totally. And and when we think about strategy, right? We're thinking about the why.

Why is why are we doing anything? Nothing tells you the why. Like, hey, we're about to spend tens of thousands of dollars on a person to sit in this seat. Right. And so if we can get the data, which today's AI tools can help you do, if you have an opinionated take on that data that you have the tools process, you know, with that lens, and you have your five Ps all mapped out, you can do some pretty darn cool stuff.

There's a stuff above and beyond this that we know we're certainly that we're probably going to take for ourselves to work on. Uh, but for everybody, if you work at a company and you're like, what are my competitors up to? This is how to do it, right? If you're saying, uh, how in dangered is my job, this is how to do it. Uh, if you're saying, should I should I acquire this company?

How, you know, what kinds of tasks, what are they hiring for? What do they think their strategic priorities are? This is how to do it. If you are merging companies together, you take all the job descriptions and you say, well, you know, what not only we're gonna consolidate head count, but we're also going to rationalize and say how many tasks can we unload across the portfolio to AI so that we can make the most of the people we have, what McKinsey's doing, which is trying to up-level value per head count. This is how you do it.

And if you want to learn how to do it, go to trustinsights.ai/slash contact and talk with a real human being, John Wall. He'll they be there to answer your email, but also to tell you all about what you can actually do with this one simple function of our job to AI skill. We Chris, as you mentioned, we just covered, we scratched the surface of the possibilities. But with large language models, the I think one of the things that you said is really the limitation is only your own imagination. And this is proving that point.

You know, so we look at a jobs job description, like, oh, this is what the tasks are. Large language models, like, hey, here's the 20 different things I can do with this data. Which one do you want to start with? And I think that that's fantastic. Exactly.

John, any final thoughts? Again, give us a call if you want to dig in more to this. Because it's a as far as a strategic window, it's fantastic. You know, to be able to, there's no way you're gonna comb through thousands of job descriptions manually and figure out what's going on. So this is the use case that you know can give you a map of where the opponents are going to, and the there's no uh no substitute for that.

I mean, to for clarity, Katie gave me the guidance about which company we were gonna cover on the live stream this morning at 9:30 a.m. So between in the last what four hours, uh, we were able to get AI to now granted, we had a lot of it pre existing code. We had our own web scrapers and stuff pre existing and all that stuff. But just in terms of getting the data and processing it, we were able to come up with something in less than half a day. Which is why you should hire us to do this for you.

John Wall, he's got his switchboard ready. I know. Anyway. I think we're done. I think I think we're done.

Thanks everyone. We will talk to you all on the next one. Thanks for watching today. Be sure to subscribe to our show wherever you're watching it. For more resources and to learn more, check out the Trust Insights Podcast at TrustInsights.ai slash TI podcast.

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