Predictive Hiring does it really work?

Speaker A: Predictive hiring, workforce planning, workforce intelligence, whatever you want to call it. It's been around for a very long time. But very few uh, products or platforms on the market are actually utilizing AI and data, uh, in depth to help organizations with predictive hiring. Here is what you can expect from today's episode.

Speaker B: I think that the biggest challenge within commerce, within business within the next three years, um, and within society in my opinion is going to be job displacement brought about by um, automation and artificial intelligence. So our own models tell us that around between 34 to 40% of jobs will go within the next three years. Job creation brought about by new technologies, um is looking between 3 to 5%. So when you start to factor in the displacement with creation, there is a huge um, void that um, we would not have experienced in a lifetime. As recruiters, as people within talent acquisition, hr, um business and people leaders, we need to understand that we are entering a changing world M and the dynamic impacts on the workforce uh, are going to be vast and huge. Predictive hiring capability. It looks at uh, what's going to happen in the future. Um, it looks at um, how your team is going to be shaped. It looks at what headcount planning decisions you'll need to make based upon historical data points. Yes, but um, obviously there's a, there's a lot of data points in the models which also examine um, skills, labor demand, um, market force dynamics, your financial um, performance within your industry and you know, how you're looking to grow but goes beyond that and actually looks at uh, um, automation and how that will impact the workforce, how that impacts uh, the way you go about hiring and the way you go about recruiting.

Speaker A: I won't go into too much detail about the individual, but he is a former ten acquisition specialist. He's been in the trenches that we are and he's fed up, or was rather fed up of random notice periods, random hiring plan, decided to utilize the capabilities of AI and his knowledge to bring a uh, predictive hiring tool or predictive workforce planning tool deeper into the market. And at the end if you stick around, you'll see it in action. So if you're listening on Spotify or Apple, please head over to YouTube. When you get to that point, if you're on YouTube already, sit back, relax and enjoy. Welcome to Tea Time with Tan Acquisition, the podcast aimed at uh, fostering knowledge and sharing stories within the vibrant Tan Acquisition community. I'm your host Eden, and for the next hour or so I'm going to be joined by someone who's challenging the way businesses think, uh, hiring at its very core. This individual spent years watching companies make critical hiring decisions based on instincts, spreadsheets, guesswork, and decided that the industry needed a better way. With an incredible background in talent acquisition, he's taken all of his learns and opportunities he's had over his career and building something around workforce planning in a predictive data driven and aligned to real business outcomes. From helping companies forecast hiring demands before it spikes, to identifying attrition risks before resignations happen. His work sits at that intersection of talent acquisition, workforce intelligence, and we can't have any conversation these days without mentioning AI. He's showing that hiring can very much be proactive instead of reactive, no matter the size of the organization. So when you're ready, grab your favorite tea or coffee if you prefer, and join me in welcoming today's guest, the CEO of Talent Forecast, Jamie Dillon. So, thanks so much for joining. Jamie, how you doing?

Speaker B: Yeah, I'm good. Thank you much for that introduction. I couldn't, uh, have written that better myself. So, um, yeah, I won't lie, I

Speaker A: took a little bit from your LinkedIn. It massively helped. Your LinkedIn was very nicely created for me. So it did help with it. But I think the concept of what we're going to go through, I think going to capture quite a lot of people, especially when this is released, which will be around September, October. So August, September, provided my dates are correct in my head, we start getting into end of year planning and this is where this, this intersection really comes into it. So I think it's going to be one where if you're a manager or if you're responsible for hiring in any shape or form, please grab your notepad and pen. There's going to be a lot that we can kind of discuss here before we get into predictive hiring and you know, what you're working towards, I want to kind of start the conversation with something that's a little bit further forward. And like I said when I spoke to you the other day, something that I kind of, uh, asked the same question to everyone at the beginning, shall we say, is what do you think the biggest challenge, not necessarily just for talent acquisition managers, but for recruiters in general, what do you think the biggest challenge is that we're going to have to overcome three years from now?

Speaker B: Yeah, look, great question. Um, so I think that the biggest challenge within commerce, within business within the next three years, um, and within society, in my opinion, is going to be job displacement brought about by, um, automation and artificial intelligence. So our own models Tell us that, uh, around between 34 to 40% of jobs will go within the next three years. And job creation brought about, uh, by new technologies, um, is looking between 3 to 5%. So when you start to factor in the displacement with creation, there is a huge um, void that um, we would not have experienced in a lifetime. So, um, as recruiters, as people within Talent Acquisition, hr, um, business and people leaders, we need to understand that we are entering a changing world. Um, and the dynamic impacts on the workforce are going to be vast and huge. Um, and it becomes clear in a few areas it's not just about the um, the displacement in figures and numbers. We're talking about, uh, an adaption and almost like a evolution of skills within people's jobs will change as well. Um, I mean, you know, we've seen it very much in our own profession within Talent Acquisition. Um, you know, I wouldn't hire anyone today that uh, you know, that doesn't work with or know some kind of, you know, artificial intelligence capability, uh, to help in their productivity. Because most people within Talent Acquisition are, they will tell you that they're managing a bigger workload, that they've got bigger, uh, deliverables, that they're trying to do more with less. Um, and the only way that they're able to do that is through efficiencies driven by the use of, um, you know, AI tools. So I think that's probably going to be some of the biggest challenges ahead of us in the next two, three years.

Speaker A: So that part you're saying there about obviously having more roles to do, more tasks to do with less, it's something that I'm hearing, I'm seeing through conversations on the podcast, through people just in the space. There's a big, there's uh, this big gap now I think, between those that are using AI and now those that are still not, um, and you know, or using just ChatGPT, for example, um, and I think, you know, the conversation that we'll go through with predictive hiring and skill changing. I mean, we spoke the other day about kind of how different the roles are and how people who are phenomenal flying five years ago are really struggling in this current market. So I'm curious to kind of blend that together and see kind of what does the future kind of look like and you know, what predictive hiring can help with that. But before we get into that part, the phrasing of predictive hiring. Right. Um, it's not new, let's say that. Right. Many people talk about it. We, you Know, be under predictive hiring, workforce planning. You know, you have these meetings at the beginning of the year and you go okay, who are you going to hire? What month are you going to hire them? And it's like sometimes guesswork or it feels like it's guesswork. When you talk about predictive hiring technology, which is obviously what talent forecast is. What do you mean?

Speaker B: Yeah, it's a very good question and let me kind of explain that. But first let me set the context and the scene. You're absolutely right. Predictive hiring, um, the coined term predictive hiring has been around for a while, um, even longer has been around, you know, headcount workforce planning, workforce intelligence. The only difference between now and you know, five years ago is that we've got um, models which forward project what the team and what your workforce is going to look like, how it's going to be shaped, what jobs are needed or aren't needed. Um, whereas the sort of pivotal difference between that and how I'd say 95% of businesses currently plan and manage their headcount is that historically they look at ah, what's already happened in the business to make headcount planning decisions. Um, and while that gives a good indicator of what they could need, what a business could need in terms of um, future headcount, uh, and workforce planning, um, it's fundamentally flawed because you're relying on data sets um, that tell you what kind of. You already know. Yeah, I mean even in some of the most popular um systems and uh, high risk systems today, which I won't name names um, but they uh, they give you analytics um, which are, are useful but in terms of impactful they're very limited because it's painting a picture on you know, what's already gone on in your business um, prior. Whereas predictive hiring capability, it looks at ah, um what's going to happen in the future. It looks at ah, um how your team is going to be shaped. It looks at what headcount planning decisions you'll need to make based upon um, uh, historical data points. Yes, but um, obviously there's a, there's a lot of data points in the models which also examine um, you know, skills, labor demand, it, um, market force dynamics, your financial um, performance within your industry and you know, how you're looking to grow but goes beyond that and actually looks at uh, um, automation and how that will impact the workforce, how that impacts the way you go about hiring and the way you go about recruiting um people in the future. So it paints you a bit more of a detailed granular picture of what you need tomorrow rather than tell you, uh, and guess what you need tomorrow based upon what you've learned from yesterday.

Speaker A: For the, this, let's call it the. The current models that most teams are adopting is look at past, try to guess the future. Whereas what you're suggesting is look at past as a single data point, but bring forward other elements such as where you want to grow, what your current market is looking like, what your sales is looking like, and then merge that all together to then predict something that's hopefully more accurate.

Speaker B: Yeah. So m. Uh, I mean, you know, one of the, one of the surprising things that a lot of people might not be aware of, um, and this relies in, um, attrition as well, is that um, as humans inherently we have uh, certain behavior traits which are typical. Yeah. Which are predictable. You know, if I'm looking to leave a job, for example, there's 99% of the time, um, there's 11 reasons which would lead me to leave a job. Yeah. And then when you start looking at these on a, on a, on a big data basis, what we can do is we can spot patterns within that data and those patterns tell a story, and that story gives us the data, ah, that we need to make impactful decisions when it comes to, you know, who we hire, what they look like, when we need to hire them. Okay. So what I mean by that is that um, ultimately when a business loses a member of staff, let's say they've left of their own accord, um, there would be factors leading up to that. And then we can look at that on a micro level and a macro level. So on a micro level we can look at things like, um, you know, the manager relationship with that employee. We can look at the historical labor turnover within that department within that company. Um, on a macro level, we can look at the, um, the turnover for those skills, you know, in the, in the whole market. So, and it can build us quite, uh, a detailed picture and forecast of, you know, why somebody would leave and when they would, um, and it's surprisingly, it is surprisingly accurate. Um, so yeah, it's, it's really interesting to see because obviously if we know someone's going to leave before they do from looking at data, um, that obviously empowers us and empowers businesses to actually do something about that in advance of time to obviously put retention strategies in place, uh, and keep that employee, uh, engaged, hopefully happy, um, and employed for longer in that business.

Speaker A: Yeah, I mean it's react, it's not being reactive, it's being proactive. You know, either, you know, you've hit the nail on the head and they are actually going to leave but you've always started something behind the scenes to replace or as you said they're. The first thing ideally is retention especially you know, if they're a valued member, you don't be losing them. You know, let's try and put what we need to. Obviously we. You mentioned at the beginning there is a shift in the skills and I've seen it in a quite a stark way. So I do a lot of engineering hiring early stage companies. Um, and the difference now hiring is so dramatically like complete, worlds apart. What it was four or five years ago, no one cares about the technology anymore. You know it used to be like you have to have worked with Java, you have to have done this, you have to. Now it's just like ignore that. You know, if you're using Claude cursor, uh, insert, whatever, fine. It's communication. It's the biggest thing that most people are asking for. And you know product engineering, it's communication, it's stakeholder relations. And these were skills that were nice to have you know, four years ago. Now they are the absolute must haves. It's like a complete flip. Obviously that's just from what I'm seeing. What are some of the skills I guess outside of maybe the communication or more specific communication that you've seen from this, you know the data that you kind of had access to and the companies you've been working with around predictive hiring, you know, what are you observing the shifts in skills?

Speaker B: Yeah, look, great question Eden. I think when, when we try to look at the data and understand what's happening in terms of these skill shifts changes. Um one, one thing is first of all become evident in that when 60% of somebody's job um, can be done through automated efforts, um, that person's job is no longer viable. And that's, and that's sector and skills discipline, you know, agnostic. That is across the board. Yeah. When you can replicate an employee's role up to 60% of their duties through automation, that employee is no longer viable in their current form in their jobs, can't form within the business. Um, so that's the first point making. But second, and what you mentioned, um, and I know we spoke about this briefly previously is um, the way technology and automation is changing how a lot of us go about our uh, day to day tasks and roles. Um, and I think this is no more apparent than um, within technical and engineering functions, um, we've seen a huge sea change in, in the market. Um, and this is predominantly because um, the way engineers go about coding, um, and go about their job today is so different to three years ago, you know, uh, and I mean it has impacted the way they work, um, fundamentally right down to the deepest level between you know, most job disciplines out there. Um, and the reason for that is because they can use large language models um, to basically do all of the coding. Um, so what does this mean for, for them today compared with um, uh, say an average engineer three years ago? I think the fundamental difference is that there's a different skills need and different skills demands on these engineers today. Um, you mentioned communication. Yes. You have to be uh, more articulate in the way you're um, you know, you approach your work because the way you interact with different stakeholders has changed the shape and um, dynamics of traffic. Traditional agile engineering teams is changing as well. Um, and the skills are changing. If you're an engineer and 90% of your time was just pumping out code and now you've got a large language module which can do that for you, what does that mean for your job and the way that you work? Um, it means that you've got to be more solution oriented. You've got to have um, perhaps a deeper understanding around not just you know, what these lines of code does in terms of the output. Ah, but you've got to understand the um, how that impacts other features, how that impacts the platform at large, what architectural challenges that that gives you. Um, and trying to reskill and do more um, is going to make you more valuable, not less. And I think this is where, this is where the huge changes is currently happening like it's happening today. Right. Um, you know, engineers that are uh, adapting um, and are learning how to use the tools, how to use the language models, um, to their effect to make them more productive, um, what questions, how to interact with these tools. Um, and actually you know, gaining more skills from an architectural perspective is making these engineers more valuable. Um, however, by and large the engineers who haven't adapted, they will unfortunately, and I'm sure people watching this today will not like to hear this, but they, they will become irrelevant, um, and they

Speaker A: will find harder and harder displace part you was talking about. Right? Because I, yeah, I, I do think, you know, the, there's a, there's a large portion of those software engineers that are not adapting and as a result won't be needed. Right. It sucks and I, I feel bad. I Do feel really bad. It's not just engineers, it's, it's a whole host of different types of jobs because you've gone through your entire career and I think an engineering was always one of those careers where it's like, it's a dead set uh, career always going to need technology. And we still need technology, we just need it in a different way. And it's like there is this um, kind of blocker where some people want to get into these types of companies. They can't get into these types of companies without the experience. It's like this never ending cycle. But there's never, I don't think there's going to be enough job anymore to fill the people who are looking. And you see, you know, some people that work for 18, 24 months and you can see it's really crippling opportunities for themselves now.

Speaker B: Yeah, well, I mean uh, it's already happening. I mean um, you know, Meta made huge levels of uh, engineering and technical dances a few months back.

Speaker A: Ah yeah. After investing ridiculous amounts into uh, AI and stupid virtual worlds. But hey, you know, Zuck can be Zucker guess. Do you think, Jerry, from your perspective, um, do you think companies are truly hiring for today's needs or do you think they're hiring for tomorrow's needs? Or final point, do you think they're just following trends? And let me put a caveat to that last point. Product engineering, it's been around for forever in a day. But it's all of a sudden just got wind in like mainstream hiring and now everyone and their nan wants to hire a product engineer. Found an engineer. Used to be called just a software engineer, but now it's all founding engineers. They're like these little pockets of crane. Like no, we need a product engine. Like do you. Or you saying that because Jamie's hiring for one and you think you need to do that because they're also a similar type of company. Regardless where do you think most companies fall based on today, based on hiring for the future or hiring because of a trend?

Speaker B: I think it depends on the discipline. Right. Because uh, there is certainly some of that uh, within sort of the trend. I'm trying to think of one off the top of my head. But um, like um, I like them when software teams were run by project managers and then um, actually there was an evolution and then people um, learned product management skills and those kind of disciplines kind of became more product management. Um, um, that might have started as an early trend but obviously there was key value, um, and beneficial Gains for structuring, um, and giving those skills for people, particularly in product, um, orientated, uh, environments where quicker iterations and software product lifecycles, um, meant that they could release quickly and um, be more adaptable to, um, getting customer feedback, um, and then building on those, on um, that feedback and m. Developing those products. Um, I, I know that there are a number of trends. Like if we see a certain sort of job title, it's like, okay, um, you know, that's, yeah, that, that's kind of what I, that's what I want to hire. I've seen a lot of them in the market. I think that sort of sums up what we need, um, without any kind of real kind of gravitas behind that kind of, um, uh, or clarity behind that decision making. Um. So, yeah, there is a little bit of, of the trends happening. I mean, like, if we take, if we take AI for example, within talent Acquisition, you know, that, that is a, that is a trend. Um, but it's, it's not just about, um, looking for recruiters who can use CHAT GPT. Yeah. Um, if, uh, if a recruiter could use chat GPT 18 months ago, two years ago, you'd be like, oh, that's cool. Yep. You're obviously working with the latest tech. Um, you know, you'd probably be really good. We need to hire you, uh, today. If you said that you, um, you know, there's nothing innovative about it. It's, uh, it's a tool that everybody uses. Yeah. And in fact, I would argue it would actually, um, be detrimental if you was in a job interview, uh, and you said that, um, the reason for that is because, you know, how are you utilizing that as a tool? You know, are you using it to draft job specs quickly? You know, um, yes, that will save you time. But then you have to understand the way the models work or that particular model or, um, works in that it's just pumping out, you know, similar content, then that's going to have detrimental impact to your employer brands, um, and your, you know, your recruitment m. Marketing strategy. Right. Because you're using the same tool that everybody is. Um, you know, does that impact your, your tone of voice, uh, and you know, how you impact that job creation message to the market. Um, so today it will be about, you know, how you're using artificial intelligence. How are you using large language models to, um, recruit? You know, have you done any, um, sort of vibe coding, you know, which is, uh, you know, a phrase which I hear more and more. You done any vibe coding? And and just going a bit deeper to actually understand how they're using artificial intelligence, um, not just to tick a box, but, um, to add real value, um, to the business that they're going into and how they approach hiring and, um, recruitment and finding and acquiring talent.

Speaker A: I think on that point of uniquely looking at talent acquisition, because I actually had a conversation with yesterday about this and she's looking for a job. Right. And I won't mention her on this, but if anyone is hiring, she's in Berlin. Ping me a message, I'll connect the two of you. All good. Um, but she was basically explaining, like, how can I show it? You know, she's working quite a large organization. I don't have the prowess or the power to come and introduce the brand new system because we're structured. Like my opinion. Please correct me wrong on this one. It's not about how are you adding 10x as a 10 acquisition into your department is are you experimenting? Are you looking for angles? You trying different tools? You know, there's all of the tools available pretty much have free trials. You do not need permission to trial something. Tried it for two weeks, test it, it didn't work. That's an AI experience that you now have. Okay, then use that into the next one and test the validity, bottle the next tool, so on and so forth. I think sometimes we get bogged down of going, I'm not building it. You know, nan agents Paul. Right. Doesn't matter. Or I'm m not using Claude or Vibe code, whatever. Right. But there's so many ways you can adopt AI without just sitting on ChatGPT. I mean, ChatGPT is my best friend. I'll be honest with you. Um, know it's for everything that I'm doing. Just have conversations, just bar ideas. And then you take those ideas and you see, is there something out there? Something's not out there. Okay, how can we build it? You know that that's the process you need to get through. So on that, I don't think you need to be the Vibe coder, but I think you need to push yourself to say, I'm acknowledging that the AI side exists and I'm not just waiting to be told by my manager, this is a tool you've got. Now go use it. I'm being proactive.

Speaker B: Yeah, I think you've actually touched on, which I'm sure we probably won't go into time today, but you've actually touched on, um, uh, something that, that is an issue. There are a huge volume of tools, AI tools out there. And I think what people have got, particularly in sort of, um, people management or recruiting or, you know, talent acquisition, um, I think they've got a little bit of fatigue. Tool fatigue. Right.

Speaker A: Yeah.

Speaker B: Because there has been a lot of tools come out onto the market. Um, you know, probably more so within the last 12 months than. Than in the previous 12 years. Right, yeah. Um, and that is a lot for people to get their head rounds. You know, if you're. And again, it's not productive or conducive if you're trialing at all, your team's trying at all every other week or every month. Right. Um, that can only go on so long before you just like, you know, another.

Speaker A: You know what kind of reminds me of. And this is kind of bashing my own part of recruitment here because obviously I'm an external. It reminds me a little bit about external recruitment. Right. Because there always seems to be another external agency popping up. Hey, Jamie. Like, this is what I do again. I'm taking a piss out of myself here because that's uh, me. It kind of feels like that, you know, I'm not going to name the searching calls, but they by and large do the same thing that recruiter does. They look a little bit better, they're a little bit faster. You can do natural language. Right. But they're all having access to similar or the same data sets. It's the same thing. And that's the part that makes me laugh. It's like we can, you know, we can hire find you people super quickly. Like. And then they start saying of outside traditional agencies, like, uh, but you're now competing against 15 of the same other tools doing the same thing. And prime example, one of my clients actually showed me the tool a couple of days ago. They've hired nobody through it because, you know, none of the people are screened. It's just constantly just in your slack. It just becomes noise. I just ignore it now. And that's. That's where the tools don't become effective. I'm yet to find one that I sit and go, I love this. I will use it all the time. So if anyone has that also, I'm very open to it really.

Speaker B: There's um, that's quite interesting to hear. Is that just because you feel that there's a lot of replication between.

Speaker A: Yeah. So I think, I think Juice Box is probably the closest where I was interested enough to use it. I liked some of their searching capabilities, um, but the time it took me to upskill myself onto that, find them similar Sort of profiles or the same profile on LinkedIn. Right. And yes, Juice Box eventually will become quicker for me as it learned me and I learned the system more. But there wasn't a huge difference where I sat back and thought, holy shit, that's, that's a tool we need. Like we are so far behind without doing it. Maybe another recruiter will have a different opinion on it. But I get the fatigue of it. It's like I trialed that and uh, multiple. And I was sitting there thinking they all are the same.

Speaker B: Yeah, look, I get that. I mean, um, like, I mean uh, obviously Talent Forecast is a, is a platform. Most of it is focused around workforce intelligence. But there is an applicant tracking suite which has got a sourcing tool. And the, the idea around that is to look for talent outside of LinkedIn. Um, and that the thinking was actually predominantly because LinkedIn, as you probably know, even has got a stranglehold over uh, uh, the market, uh, and over B2 data, um, and it's constantly going up and up in price with offering less and less value. So um, you know, our sourcing tool looks for talent across, you know, Apollo, GitHub, stack, overflow, hugging Face, Kaggle, Beyonce, um, Product Hunt, Reddit, um, you know, basically pockets of talent online outside of LinkedIn. And the reason for that is, is because of that, um, LinkedIn is so expensive. Um, you know, in m. My last company when I was reviewing um, budgets, I, uh, I, it was eye watering, you know, what we were paying as, as a relatively small business, you know, 100 employees, um, you know, it was, uh, it was, it was, it was eye watering. Um, and, and I think, look, what you said there about replication of functionality, um, I think inherently that is a, that is a challenge when um, we're obviously engaging the market. I think Talent Forecast is a workforce intelligence solution, uh, unrivaled in what we do. Because I mean you could Google predictive hiring and you'll find 200 blogs, uh, and articles, you know, talking about a topic, but very, very few, um, providers that have a genuine solution, um, to workforce, uh, intelligence and predictive hiring capability.

Speaker A: Um, if you've got time, I'd like to ideally show it. So this is the part now if someone's on Spotify, you've got to now get onto YouTube or Apple onto YouTube. If you're on YouTube, you can stick around. Because this is the part where I feel like we take it away from theoretical. Let's talk about predictive hiring and actually see what it looks like because you are right there's. Very few people that actually are building something like this and we've gone through the process I guess of why, why it's important in general we've got displacement of jobs, we've got the skills. But I think until someone sees it it's, it makes it a little bit difficult to tangible to understand what it is it, it looks like. So I'm going to let you j screen. I'll, I'll pass it over to you and then uh.

Speaker B: Um. So can you see, can you see my screen?

Speaker A: Yes.

Speaker B: Yeah. Okay, great. So um, so yeah, look really good point. Um, and uh, I think it's worth seeing because um, nobody really kind of visually sees predictive hiring now. What does it mean? What does it look like? Um, so if you're using tenant forecast here you've got the dashboard. So immediately you can see who your high risk employees are, what the potential impact cost is to the business, um, and what critical roles are at risk. Um, so immediately you can see like the cost impacts where your future um job attrition is, what potential employees are going to be impacted in what departments. Um, so um, inactive workforce. We can just, if we just click on an employee for example, um, this particular person has 95% chance of leaving within 12 months. So that is, that is pretty much guaranteed. Obviously we can, you know, you can never be 100% um, however 95 is a strong, is a strong indication and we break down the risk factors. We give the um business insight into why this employee is at risk of future um attrition. Obviously AI recommends, you know what you can do to mitigate that risk. Um and then obviously it's com. There's features here that give you kind of real um, compensation with the market. Um now while I'm not going to go through the, the whole platform uh with you today because it is fast. Um, I can show you some of the functionality around um, modeling for um, AI resource optimization. So here we can employ different types of scenarios. Um, baseline conservative, aggressive. And this is based, all this data is based on the current business uh users own employment data. Yeah. So this m calculates from their um, from their own internal data sets and it gives them indications of what departments are going to be at risk of AI skill reskilling, um, over what timeline, uh, the departments and, and the employees as well. Um so if we look at uh, if we look at Kiara here, so we look at Cara here, we've got reskill. So um, this is somebody works in customer support for this business, um, it breaks down the Contributing factors of why this employee is at risk. And then here we can see what skills are being automated in their role. Um, I approach this problem, Eden, with a very human centric, A very human centric way. Yeah. So here we can see how we can make this employee more valuable. We can future proof their skills. And um, and obviously any recommendations, certificates, external, additional training, um, that, that's because I fundamentally believe if you know something is going to happen in far advance, you can do something positively and proactively about it. Um, there are obviously it depends on the stakeholder and the lens they're looking through this data. Um, obviously if you've got someone in financial planning analysis, um, you know, they're

Speaker A: gonna see the number it's gonna cost

Speaker B: us and yeah, they see the, yeah, they'll see the salary risk, uh, exposure, they'll see the finances. Um, so, um. And yeah, if you don't mind me

Speaker A: jumping in a second. Of course there was, there's an element of salary and tenure and then um, there's. There's a salary. Basically let's take the top person. Uh, I can see the salary at 30,000 for example. Does it compare that salary to the market to say if that person below or above the market rate or is it across the company as you're comparing it to?

Speaker B: So, uh, it will be externally matched data metrics in terms of the salary. So um, for example, this particular person is marketing m associate. Um, they're paid 3,494 with this company. Um, that's telling them that um, the market typically pays around the 50 to 55th percentile, which means that they're actually underplaying this employee by 15,000. Um, so, uh, and again we go into the data a lot more granularly as well. So uh, if this was an engineer, um, engineers don't get paid at 50 to 55 percentile. They get paid at 75, 80, 90 plus. Um, so it again gives uh, an indication of what that skills are worth in the wider market. Um, and again it's giving the business that data, uh, so they can do something about it. Right.

Speaker A: The reason I bring that up is the amount of times I get asked, you know, the market rate. So like as an external recruiter, I see a small, small, slight slice of that market. You know, companies that give me jobs and candidates that I speak to about salaries. That's all I'm seeing. There's something like this, I know obviously this isn't the, the focus of the platform, but even having something built in like that is so valuable to companies, you know. Yes, Predictive hiring, you know, salary information, the AI options and how to upskill that person. You can now see how predictive is, uh, how your predictive high models are essentially allowing you to plan better an organization. Because again, you know, coming back to the introduction, we said that people use spreadsheets and I mean we used to literally do it, um, where we'd sat down the CFO and he was like, hey, cool, who are you hiring this year? I'm like, I don't know. What do you mean, who am I hiring this year? I'm like, yeah, you didn't tell me this was what the meeting was about. And then you'd start putting numbers in like, no, we'll move that person to there and that person's there. And how much do you, do you think they're going to build? Like, I don't know who we're hiring, I don't know how much they're going to build. That is wild. I mean that was five plus years ago, don't get me wrong. But I imagine a lot of the teams that I work with in the early stage companies don't have something like this. So do you think your model or talent forecast as a platform, do you think it's beneficial to everybody or who would you say is like the sweet spot of customers that, that would really gain the most benefit out of this?

Speaker B: So yeah, it's a good question. Um, I think, look, if you, if you've got um, uh, a business where you've got you know, less than 100 employees and you've got, you know, 10 attrition or less, uh, this isn't going to be for you. Yeah, um, this is for larger uh, businesses. This is for businesses experiencing turnover. This is for businesses that want to be more productive, that you want to use workforce intelligence to model future hiring demands and plan what vacancies they need, what they look like, um, what the costs, ah, are just giving them uh, a higher data visibility into their workforce and um, those sorts of businesses, you know, if you've got 2, 3, 4, 500,000, 2,000 employees plus, um, and you know, you can see huge, huge, um, impacts, drivers in terms of cost, in terms of um, uh, staff retention. Um, you know, there's a lot of benefits to these businesses of a bigger size. Um, you know, if you're, if you're a 500 employee business with a 25 staff turnover, uh, I, um, mean this is transformative, um, you know, be able to, we believe if you're Using the data in the right way, um, that you, there's no reason why you shouldn't be able to um, see, you know, use, retain your talent within your business, um, up to 50% improvement then prior to using uh, workforce intelligence. So there's huge gains to be made and I mean just, just on the screen in front of me now we've got like home forecast. So uh, this, this obviously tells, tells the business, you know, when they need to hire, for what types of roles, when they need to start hiring, buyer, when they need to fuel buyer. Obviously it uses a lot of data metrics in terms of their own time to hire. Obviously break down the cost for these types of hires as well. Um, and it's just about giving data ah, visibility to the business. Yeah, because when a business hires against a role, um, they see the cost as well. Uh, we need to pay an agency, you know, 15,000 pounds. Yeah. Uh, or $20,000 or what have you. What they don't understand is all of the associated costs that goes into that, that replacement, um, and making that higher. Um, so obviously our data models give them that visibility um, and it breaks them down and obviously it's different costs for different types of roles, um, depending on the resources that's, that's required to, to, to, to hire against that.

Speaker A: I love the fact you put in by the way, recruiter time, hiring manager time, interviewing time. We built a very crude um, data like a, um, well it's not a model by any means, but it's like a cost calculator which focuses on that because that's the part where most people forget is like your job's been open for months before you decide to use an agency or not. But how much time was really wasted? I know, you know, we have one recruiter. If they've done 30 interviews, 30 hours gone, what's that actually cost? The business is changing that conversation away from it's 15k to what's it already cost you and what's it going to continue costing you. And then so I love the fact you've actually embedded that in. That's class. Um, from, from my side. Look, not everyone's going to jump on board and go talent forecast is the best thing ever. Like I want to go and buy it. I would love some people to come and find on LinkedIn and test it out for sure. But let's just take it back a second and say, you know, what's the first step or two? Somebody in the, in the TA space, you know, leader, ah, manager, whoever, if they're if they're more interested in getting deeper into predictive hiring on kind of a more granular level like this, what's the first step or two they can themselves, um, to kind of start that journey.

Speaker B: Yeah, the first step is, um, if you, the first step is to understand your problem. Right. What are you trying to solve? Um, there's no good in coming to us or any vendor, uh, um, and trying to look for a band aid or a quick patch fix. You need to understand what you're looking to get. Gain from having this capability, what you're looking to get out of it. So if you've got high staff turnover, um, if you've got higher variations between, you know, contracts and temporary, Temporary labor, um, and you're always hiring behind. If you've got, um, you know, long time to hire or increasing cost per hire, whatever, whatever the metrics are, you know, you have to have a problem that needs to be solved. Um, and then when you've got clarity on, um, what those problems are, then we can obviously focus and look at, you know, how we can go about helping to, you know, overcome those problems, um, to drive transformation, you know, within the business. Um, but look, I mean, you know, we, we never, we never sell the technology. On a first interaction with a business. It's. It's about understanding them. It's about understanding what they need, need. Um, and then, you know, from that we can obviously, um, work with them to develop solutions, um, which are going to be, you know, fit for what they need. Um, but it is worth me saying, Eden, I mean, this, this whole business came about is because, um, working in talent acquisition, I was fed up of, you know, hearing about a resignation, um, when it happened like three or four weeks before and the person was just about to leave the business, um, and then all of a sudden you're like, okay, now you've got to get sign off with finance. You get signed off with finance, then you can begin the hiring campaign. Uh, the person you're placing has already left, um, extra demands and stress on the, you know, the people are left within that team. Um, and then you're almost hiring under pressure. Yeah. Even, uh, whether the recruiter feels like that or whether the hiring stakeholders feel like, oh, uh, we just, you know, we just need someone. We need someone. Um, and that's why I thought, actually, there's another way. If you could see these problems, if you could see these, um, departures and this attrition happening before it arrived, you could actually do something about it in advance. Um, and I Couldn't think of a better way to make talent acquisition more strategic than using workforce M intelligence to drive decision making at the top of the business?

Speaker A: No, I think it's the first time I've actually seen the product. It looks brilliant and I can see, I can see the value added there. Again, if you're not watching and you're on Spotify or Apple, please go and check the YouTube links out because you can actually see it in its action. But I think that the biggest takeaway one is going to help the business tremendously and it's also going to help the talent teams take away that stress and burden. And how many times we had people, colleagues, friends of ours that have gone off, you know, from burnout or left the industry altogether because you're right, you know, everything needs to be done yesterday. It's always been like that in recruitment. I feed it all the time. Don't get me wrong. Um, it's like whenever a job vacancy comes in, it's like, when do you need to hire ASAP? Uh, it's like, well, you could have told me four weeks. You could have told me four weeks ago. least we could have done something from that period of time. It's. I love the fact of something like this. Um, I really appreciate you, you sitting down showing this. You know, talk about predictive hiring, because like I said, it's the first time in 10 seasons that someone is really focusing in on that, which shows how specific it is. But if somebody does want to pick your brains a little bit further, learn a bit more about talent forecast, where's the best place, uh, that they can come and find you?

Speaker B: Yeah, so, uh, the website's talentforecast. AI. Uh, or by all means, send, um, even my team or myself an email. Jamiealentforecast. AI. And, um, yeah, be more than happy to answer any questions, um, in relation to the technology and see where we can go from there.

Speaker A: Sounds good. I appreciate you taking the time. Thank you.

Speaker B: Thanks, Aidan.

Speaker A: Bye. Bye.