Watch Eightfold product experts give a behind-the-scenes look at how the single AI platform for all talent comes to life. You’ll see how the latest product innovations across upskilling, skills intelligence, and more are coming together to help companies drive talent transformation and get you future-ready.
Brandy: All righty. That’s 10 o’clock. Welcome, everybody. Hi. Thank you for joining us at the Eightfold Innovation Showcase, the single AI platform for all talent. My name is Brandy, this is Maggie. We are solutions consultants here at Eightfold. And our job, basically every single day is to talk to customers and prospects just like you about what problems they’re facing, what mountains they’re looking to climb, goals they’re looking to achieve, and how Eightfold can help them get exactly where it is they’re trying to go. Now, throughout all of these conversations we’ve been having both in the real world and in the little bubble that is HR tech. All this week folks have been telling us how much the landscape of talent has changed over time, especially in the last few years. They’ve been telling us how talent is just so mobile now. We’re not just talking about folks moving into new careers, we’re talking about moving into brand-new geographies.
Brandy: People have been telling us that the competition is fiercer than ever, that organizations are shifting into new industries, which means now they need new roles, which means now they need new skills, which tends to mean that organizations are competing over the same small pool of highly skilled talent. And all of these right skills are becoming the new buzzword. I don’t know how many times I’ve heard it through HR Tech this week, but it’s like skills this, skills that, right? It’s now the linchpin. Like there’s no question that you should be thinking about it. And I guess if you’re here, you’ve already started thinking about that. So congrats to you throughout all of this. HR teams, and talent teams are trying to figure out how they navigate these changing landscapes, but they’re tending to do it in silos. They got l and d over here and the workforce planning team over there, and talent acquisition over here when everyone is essentially trying to do the exact same thing, get the right people with the right skills and the right roles at the right time. So how do you deal with all of that? How do you bring all of this together?
Brandy: Well, imagine if you had a single platform, one place that you could go to and with a click of a button would enable you to see the future of your talent, see the future. What do I mean by that, right? Just imagine what you could do if you could see the future. You could see how your top talent measures up against the top talent across the industry. I mean, if you could really see the future, you could proactively hire before your competition, even those, who need that skill. If you could see the future of your talent, you could see not just what’s the next step in your own career or your employee’s career, but you could see everyone’s potential, the potential for someone within your organization to be your next VP. All of this is possible with Eightfold, and we wanna show you today how exactly that happens. But before we do, before I lift the hood, so to speak, and walk you through this, we wanna tell you a little story because I think if all we do is show you what’s possible, you won’t get what’s happening here, what people are actually unlocking with Eightfold. So we’re gonna start off with a true story, and that’s from Maggie.
Maggie: Hey everyone. So before joining Eightfold, I was a career advisor at the University of California San Diego, supporting one of the first undergraduate data science programs offered in the country. I crossed paths with Eightfold because I couldn’t get my new emerging talent past the screening phase of the recruitment process because they didn’t have that expected master’s degree education. Eightfold CEO, Ashu Garg, came and spoke to my faculty and he explained how the Eightfold talent intelligence platform could help my industry partners better understand this new emerging level of talent we were creating with our program. More importantly, he explained how Eightfold could make this understanding actionable, guiding these brand-new graduates into the right roles and identifying necessary upscaling so that they could enter those roles immediately after graduating with an undergraduate degree. Full disclosure, I became obsessed with Eightfold then and there. I joined their talent network so I could receive updates and resources relevant to my role. And as the landscape scape started changing, I started to rethink my own career path. Like many during the pandemic, I questioned my future in academia, but I didn’t really know how my skills would transfer or how I would get the opportunity to explain how I think they could to a potential employer.
Maggie: I received this webinar campaign from Eightfold as it related to my role mid-webinar. After agreeing with literally everything that Eightfold leadership was sharing, I thought to myself, Why don’t I work for this company that I love so much? Before the webinar even ended, I went to the Eightfold career site. I had no idea what I was looking for. I had experience in academia and non-profit mental health support. What was I gonna do at a tech company? But the thing is, I didn’t have to know when I went to the Eightfold career site, I was prompted to immediately upload my resume. And when I did, I was attached, or I was matched to this current role. I had no idea what pre-sales was. I had never heard of a solutions engineer. I was a liberal arts grad who had never taken a business class in her life, but I could see how my skills would transfer.
Maggie: And specifically I saw that my presentation skills and my public speaking skills could set me up for success in this new role. I looked through the insights available around their previous hires, and I started to see my skills differently, differently than how they were presented on my resume, but still true to my capabilities. With one click I applied, and two days later, I received a verbal offer. Fast forward a year, and those presentation skills and those public speaking skills are the foundation of my new career. We wanted to share my story with you today because I am the result of using AI to navigate career change focused on potential. That’s the power of being able to look into the future and know what someone is capable of. That’s the power of Eightfold. We’re gonna demonstrate this power to you today by sharing some additional stories. Here we’ll hear from an H R B P trying to navigate building a brand-new team within her organization on a limited budget will learn about a recruiter who has to then fill those new positions while on an understaffed and underdeveloped recruiting team and will hear about an employee who’s interested in moving into the next step of her career, but only sees that next step as management and isn’t interested in taking that step. So Brandy, are you ready to tell us more about our first story with Stacy?
Brandy: Yeah, Stacy is my favorite person because she is honestly underappreciated. , Stacy’s an HR business partner and she has been charged with planning for a dedicated data science team. Now her organization is saying, Yeah, we’re doubling down on data. Data’s cool, and she’s been charged with putting in the actual work to get that done. Now, unfortunately, she also has some budget constraints. So she is trying to figure out how do we build this and save as much money as humanly possible. At the same time, she has to think about a few things. She’s got to think, well, what does it mean to pull in a data science role into the organization? What skills should we be looking at? What does good look like here at the organization? And if we’ve got to pull in these skills, well, do we have any of this at the company currently?
Brandy: Like do we have to go out and get that externally or could we maybe upskill or reskill folks here and now to fill those future roles? And that if we do have to go out into the world and pull in some new people, how do we make sure that they have not only the right skills that we need but that later on, we’ll be able to hold them to those skills that we’re hiring them for? All of this is possible with Eightfold, we’re gonna put that market data and those business needs into action. So Stacey and her team are prepared for the future, not tomorrow, but today. And so that starts with doing a little bit of research. So Stacey goes into Eightfold and goes into the job intelligence engine. This is the one-stop shop for all the roles and skills and their prevalence inside their organization.
Brandy: Now, along with a nice role library, Stacy and her team are gonna get access to eight folds own market data so that she can do research, she can figure out what it means to build out that data science team. Now, in particular, she’s interested in looking at a data engineer. And so she goes into that job intelligence engine and she takes a look at that data engineer role. Now, this is gonna give her access to the entire global market around data engineering, but really, she needs specifically for her organization, for her industry, what skills are needed for this role there. And so she really wants to hone in on transportation and as she does, she can see those top skills for a data engineer in that particular industry. And we can see this is more of a practitioner-type role. These are people who know Python.
Brandy: These are people who know machine learning. These are people who know data analysis. She can also see the prevalence of this role over time. So how often are folks saying, Yeah, I know this skill set, I have this role. We can see that this is one of those roles that have greatly increased over the last five years, in particular, she’s definitely on the right path looking at this data engineer position. So now she knows if she wants to be in an organization like a Tesla or an Uber or an Amazon, especially when it comes to data, this is the path their organization needs to go on. This is the type of role they need to bring in. All right, she’s done a little bit of research, she, she’s got a starting off place and now that she knows what kind of skills are needed for this type of role, she’s got to pivot a little because obviously with budget constraints, we can’t just go out into the world and start shelling out tons of money trying to bring people in.
Brandy: We got to repurpose a little pull in some folks who are already within the organization. And so Eightfold is actually gonna give her insight into her employees and more particularly into their skills. So inside Eightfold, we have tons of analytics. We are tracking literally everything. And so for Stacy here, she can understand how many folks actually have that Python skill, How many people actually know machine learning? And there’s a really good subset of folks who have this skill set that’s great. This means that there are tons of opportunities here to either immediately move folks into these positions that they’re trying to find and trying to build or to upskill or reskill folks into these new roles.
Brandy: Now that that research phase is done, she’s gone out, she’s checked out that market data, she’s gone into her employee base and looked at their skills. Now she can make plans for what good looks like at her organization. So she builds out inside that role library, the data engineer position. And when we say that we wanna understand for her what good looks like, sometimes that means being real explicit and saying, oh, I know in particular what skills we want and the proficiency levels we want for this particular role. But sometimes that also means being able to highlight some ideal candidates. Some people who currently work at the organization or a random person that she met one day are like, Yeah, you’re cool, you’re, you’re a great data engineer. We wanna highlight those folks because they have the sorts of skills that the organization is looking for in this particular role.
Brandy: They’ve got the same, experience that they’re looking for. And the AI is able to take those ideal candidates and the skills and proficiencies that are set inside of that job’s intelligence engine and use it to help match the right candidate to these rules, both in recruiting as you’re looking for external talent, and also as you’re looking for internal talent. And that is automatic. Literally, as soon as this is done, she can walk away and go, I have done my job. I have put this in place. Now it’s time to hand over the reins to our recruiter. So say hello to Thomas.
Maggie: I’m Thomas. I’m a recruiter, and I’m very tired because, for the majority of my pan for the pandemic, my team has been understaffed. And now I need to hire a lot of new roles and roles that I’ve never hired before. So what I, Thomas, really need is help to identify the right candidate and get as many of those right candidates in the top of my funnel. Essentially, Thomas needs to be able to do more with fewer people. As we move into the talent acquisition module, we see this data engineer role that Brandy and Stacy were creating. What you’re also going to see is that the Eightfold talent intelligence platform has a bidirectional sync with your ATS and H R I S systems. When that happens, when that data is brought into the Eightfold platform, it is deuced, and it is refreshed from public data sources like LinkedIn and GitHub, and career sites.
Maggie: What that means for Thomas and his team is that they are evaluating their talent database with the most updated information about these candidates. For example, someone applied to his organization three years ago and didn’t have the skills necessary at the time. Within those three years, that candidate has developed those skills and is now the perfect fit for one of the open roles with Eightfold, Thomas and his recruiting team can access those qualified candidates that already had a previous interest in his organization. So when Thomas opens this data engineer requisition, he’s not starting with zero. Eightfold AI is based on the skills and proficiencies and ideal candidates that were defined by his leadership. Eightfold is now surfacing and recommending previous candidates, current candidates, previous employees, and current employees, surfacing them so that Thomas and his team, don’t have to go out and look externally. They can start with the talent that is already interested in their organization.
Maggie: Now, what we like to call this is talent rediscovery because there’s no way that individual recruiters can manually update and track every single individual that has ever expressed an interest in their organization. But AI can. Furthermore, AI can help Thomas look at this talent to build that top of the funnel and potentially increase the diversity of the top of the funnel. Along with all the other initiatives going on, there is a larger organizational initiative to increase female representation within their technical roles. And MOS sees building this data science team as the perfect opportunity to prioritize this initiative. He looks at the candidates that have already gone in and applied, and there’s no woman that is not going to help him, his team, and his organization achieves their goals. So Thomas goes to his talent pool. He filters to focus on female candidates within the talent pool specifically. And a quick note, these are not just female candidates within his database. These are qualified candidates who happen to be female. The qualification is detailed in a match score describing explaining to Thomas and his recruiting team exactly why this candidate is being recommended. This allows Thomas to proactively outreach and encourage qualified candidates to consider applying and joining his top-of-funnel.
Maggie: Thomas saw Cali’s profile when he took a deeper look. He was able to understand exactly how this candidate’s skills aligned with the skills needed for this role. Eightfold AI helps Thomas understand that, yes, this individual, we are confident that this individual has these skills because the Eightfold AI has validated it against a global data set of 1.5 billion unique profiles and 1.5 million unique skills and profiles to understand skills associated with careers. So when Thomas is looking at Cali’s profile, the AI informs him those other individuals in similar roles at similar companies during a similar time also have these skills. More importantly, Eightfold AI catches those candidates who maybe didn’t list all their skills on their resume, but the AI knows that they most likely have this skill because other people have this skill within the database, within those public data sources. And the AI can infer and catch these candidates that would maybe otherwise be overlooked with a more keyword bully and search system.
Maggie: Thomas is very excited about Cali. He’s contacted her, and he found that she was responding more quickly to his text messages than she was to his emails. You can see that they’ve already started a conversation here. So Thomas is going to come back and invite this individual to consider applying. He wants to talk to this individual about the update on the status of this requisition. Now without tripping, I’m gonna come down here, and I noticed my friends from Vail have decided to join us. Very nice to see you today. I’m wondering if you wouldn’t mind playing the role of a candidate for me. So first and foremost, before you respond, how does that feel to have a recruiter reaching out with a role that you are interested in from a company you’re interested in? Yeah, I’d be excited. Excited? More so feel free to say I’m excited.
Maggie: Let’s see what this conversation can look like as long as I get out of the way so you can see it. Thank you. I’m excited. Perfect. Thank you so much. So what’s most important is that you’re not just engaging your candidates; you’re engaging them with opportunities they care about. As a solutions engineer that previously worked at a data science institute, I can’t tell you how many recruiters send me engineering roles. I’ve never coded a day in my life. That means nothing to me. Why are you sending this to me? With Eightfold, it helps Thomas and his recruiting team target the right people with the right role at the right time. Now, as I mentioned, Thomas’s team has spread pretty thin, and so he has been asked to help his campus recruiting team with their campus recruiting events, and Thomas has no idea what he is getting into. But that doesn’t matter because Eightfold is a solution to support, automate and facilitate this workflow. The team can all come into this event where they have been able to customize a checklist to support their pre-event, current event, and post-event activities. These activities include campaign registrations, where candidates can register through a barcode with a button click.
Maggie: As these candidates are registering, Thomas and his team can leverage this full platform to bring in the positions that they’re recruiting for so that as candidates register, they get matched to the opportunities that they need to fill. Now, I don’t know if every one of you has been to a campus recruiting fair, but these are the longest lines you’ll ever see in your life. But now Thomas and his team can be proactive. No more letting your top-tier talent wait in line for 30 minutes and potentially leave the event before speaking with you. No more letting that top-tier talent go to your competitor’s line cuz it’s shorter. With Eightfold, with this matching, Thomas and his recruiting team can proactively contact these individuals to let them know they’re just as excited to meet them as the candidates are to meet their team.
Maggie: They could maybe even send them a special registration pass where they can bypass the line so they can talk to them as soon as they arrive. They could even schedule an interview for when his team is on campus. This reminds me of one of my faculty member stories from when I worked at UC San Diego. He was the first data scientist at Uber, and he was responsible for building their team. He explained how he used to have a box of resumes, and then some resumes would make it into his backpack, and those were the individuals he wanted to contact immediately as he was in his Uber to the airport. That’s stressful. Everybody else is contacting them at that same time as well. With Eightfold as a solution, Thomas and his recruiters can, as we talked about, proactively identify that talent before they’ve even met your competition and before they’ve even applied to your organization.
Maggie: Now Thomas, as he’s supporting this campus recruiting team, he knows that he’s got a lot of data science positions that he needs to fill, not just now he’s gonna have to continue recruiting for those positions. So he wants to make the most of this developing data science talent that his team is engaging at this event. So he can pull those attendees into what we call a community where he can nurture and target the right messaging to these individuals so that he’s already engaging his future talent before they’re even ready to apply. Now with this other initiative in mind, how he needs to increase female representation in his technical roles, he can actually filter down this already niche community and add the females of this developing data science group into a community that he’s created to nurture women in stem specifically
Maggie: From here, Thomas can take additional action. He can now start targeting and campaigning and communicating and building relationships with this talent, with his future talent from the campaign. He can utilize AI to help make sure that content is getting to the right people, much like it did in my situation. So I don’t know if you’ve heard about Eightfold’s new podcast, The New Talent Code. We recently had a special speaker, the CEO of Women Who Code, who spoke on how to design for equity. I’m gonna use this as an example for Thomas so that you can see how within his campaign, the AI helps him target the right audience—further supporting Thomas’s efforts with getting the right messaging to the right candidate at the right time. There are additional filters to help make sure that this is getting to the right audience. You can filter by experience, location, by application details. You can even filter by how recently you contacted them to avoid overspamming these individuals with automated follow-ups. Based on these candidates’ behavior from this campaign, Thomas can now prioritize more essential tasks as these more manual tasks have become automated to support his larger goals.
Maggie: Now, we’ve talked a lot about how we really engage this external talent so that we connect with the candidates we need to hire right now and the candidates that we need to hire in the future. But what about internal employees,
Brandy: Brandy? Yeah, let’s talk internal. Let’s meet.
Brandy: Our next story, Sheila. Now Sheila’s a lot like me. We got an engineering background but we’re looking to grow, looking to do new things. She’s been in our organization for about three years. She enjoys what she’s doing, but in order to grow what she’s hearing from her manager and her manager’s manager and his manager’s, manager’s, manager, if that, she should probably get into management. Unfortunately for her, she kind of hates people, so doesn’t seem like a thing that mixes right? So what options does she have? Where can she go next? How does she grow into her career and maybe even figure out how she does what she’s doing right now better? How does she get to that next step? She’s gonna be able to do all of that inside of Eightfold to see that career development and even maybe develop into something the organization really needs.
Brandy: And the way that Eightfold looks at that is through the lens of her own career. So there’s something that we say here. We got a big old mission statement that says we believe in this idea of finding the right career for everyone in the world. Lofty goals. I know , but what we mean by that is that Sheila here should have control over her own career. She should be able to say, I wanna explore this and I wanna do that. And she shouldn’t have to wait on her manager or her manager’s manager or his manager’s, manager’s, managers. There’s a lot of managers in here to tap her on the shoulder and tell her what she should be doing next. Now whether she’s at work on her laptop or she’s on her cell phone, on her lunch break, or she’s sitting at home on her iPad, I don’t know, in her underwear or something like that, eating bond bonds, she’s gonna get access to the Eightfold career hub, which is a centralized place for her to develop her own career now here at Eightfold for us to understand where she can go and to help her figure out what her future is, we got to take a look a little bit into her past.
Brandy: And so what will provide to her is a profile. Now you might be thinking in your head, Oh, another profile , and that’s okay, right? We totally understand that. She’s probably got a profile floating somewhere for herself. We’re gonna pull in that data, right? Cause she shouldn’t have to fill this out 12 different times. So if you’ve got something in the HRIS or you’ve got some courses that she’s taken, any data that you have on her and what she’s learned and her skills, we want that. We’ll pull that in. If she would like to submit her resume or give us her LinkedIn or point us at her GitHub, we’ll also take that data in in those skills and experience that she’s had and populate it inside of this profile. And so the profile becomes more like a living resume where we’re capturing her skills, but we’re also capturing the projects that she’s done, the courses that she’s engaged with and the experience that she’s had both at her organization but also everywhere else.
Brandy: And because we’re looking at this from her perspective, she needs to take control of her career. That is the lens we’re looking at. We also really wanna understand everything that she’s done, not just at her organization but outside of it. So on the side, she’s a dance instructor, which is great cause I have two left feet and don’t know how to dance. So we should talk right now in her head she’s thinking dance instructor, what does that have to do with my career? I don’t wanna do this for a career. But inside of that are real skills like dancing for sure. But also she’s probably doing some event planning here. She obviously also has some public speaking experience cause she’s sitting in front of a crowd telling them how to do those moves. She obviously is also has some teaching skills. So these are the kinds of things that the AI can bubble up for Sheila here, so that she’s not expected to know all of these things.
Brandy: And let’s be honest, I don’t know about you. I am terrible at writing resumes, , and I’m a terrible at identifying skills. And she is too with eight fold. She doesn’t have to know Eightfold knows, eight folds gonna be able to help her. All right? So now we know her past. Now we got to help her get to wherever it is she’s looking to go. And some of that is gonna happen inside that jobs marketplace because what Sheila’s asking herself right now is, well, if I don’t go into management, what next? And she wants to explore just like everybody else does right now. She could be going out to some other website, some other company and going, well, what do they have? What’s here? Instead of doing that, we want her to think about, well, what’s here at my organization? What could I be doing?
Brandy: And for that, we want her to explore what is currently available. And that’s all gonna bubble up in that jobs marketplace. This serves as an internal career site, just like we saw with Maggie when she applied for Eightfold. Sheila’s also gonna be able to apply for new roles and see where she fits the best. Now she already knows she’s a great solutions architect. She knows what this role is. That’s totally an option. And it looks like she’s a strong match. She’s not particularly interested in exploring that though. What’s getting her real excited is this data science role. Man, she had no idea she could be a match for that. It looks like she’s a good match, which means she’s not quite there yet. There’s some skills that she needs to learn, some things that she needs to pick up. So we wanna show her exactly how it is that she gets there.
Brandy: Now, if she’s on that jobs marketplace, she can definitely see the types of people that were hired for this role in the past, what skills they had and what experience they came with to the table. Not just like how many years they’ve been in that particular role, but things like what kind of type titles did they have in the past? Where else did they work? What type of skills are they bringing to the table? And she can see, well actually I have some of these things. She can see herself in this role that she could possibly have in the future. Now she’s also having to think, Well, if I don’t do that, what else is there? What other options do I have? And with that, she has career planner to make some of those decisions for herself. I like to think of career planner as the land of possibility.
Brandy: This is where we’re talking potentiality. I don’t wanna think about the constraints of the organization. I wanna think if I decided one day to completely change my stars, what would that look like? What would it take for me to get to that next stage? And for Sheila here, she’s thinking about all sorts of things. She’s thinking about that engineering manager role she could possibly go into that. She’s thinking about what if she went into product instead. Eightfold is also bubbling up additional rules that she could pull into because we can see, because we have access to all this data that next stage in her career, we can see what other people have done along their career paths and where they’ve also gone. So we can bubble up these ideas for her as well. So she can see, well, these are also possibilities.
Brandy: Now, I did say before that she’s really interested in this data science position. So she really wants to understand what it takes to get there. She’s not a good match. Now how does she become that strong match in the future? On the left, she can see the skills that she currently has and how she fits and on the skills that she’s gonna need in order to get to that next level in order to become that data scientist. Now we wanna understand how she bridges that gap. Well, we’re gonna match her to courses, projects, and we think of projects as experiential learning opportunities, mentors as well. And these mentors are bubbled up across the organization. So she doesn’t have to just look at what’s in her very small lens of folks that she knows. She can look across the globe at folks who are within her organization who have raised their hand and said, Yeah, I’ll totally talk to folks about this. I’ll totally help you get where you’re looking to go. All of these so that she can explore where she has potential, where she has the PO possibility of moving to in her career.
Brandy: Now, understanding where she could possibly go, that’s really good. But we all know as managers and HR folks, she should probably be pretty good at what she’s doing right now before she moves into something new. We wanna make sure that she is being assessed on the skills that she needs for her current position and even for her future roles. If we remember Stacy at the beginning here, she built out that role and that lives inside of the jobs intelligence engine for that data engineer. And inside of that, she had those skills and proficiency set now on the front end that helped Thomas understand what needed to be brought into the organization. On the back end here, internally, it serves as the basis for how we assess how well Sheila’s doing in her current position. And so here we can see the skills that are core to her current role, and more importantly, the benchmarks that have been automatically pulled over from that jobs intelligence engine that she now has to assess herself against.
Brandy: And so what we’re providing here is both self and manager assessments and then it also integrates with other assessment tools. So if we really need to understand what her actual skills and capabilities are, we can pull that in as well and populate that against the benchmarks that were set inside the job intelligence engine. So now everyone within her role, they’re all heading in the same direction. They’re all looking at the same benchmarks. They’re all measured against the same skills and proficiency levels. This is amazing both for Sheila and understanding where she’s at right now, but also for the organization as a whole. And the fact that Stacy doesn’t have to come in and do this for every single person, that helps too, right? automated. Now we’ve been talking about this whole time the future. We’ve talked about Sheila’s future and her trying to plan for what possibilities look like, what potential looks like. We’ve talked about the future of external talent as Thomas has brought that those folks in. And from Stacey’s perspective, we’ve talked about the future of the organization. I wanna talk a little bit about the future of Eightfold.
Brandy: So I don’t know if you’ve been hearing but you’ve probably have quite a bit about our upskilling, right? Full upskilling. That’s what I wanna show you today because Sheila is looking to do some really exciting things in the future. But what we wanna do is pair what the organization needs with what the employee is also trying to do. So imagine in that you’re a manager, you’re a manager, and someone on high has come down to you and said, hey, there’s a new role, there’s some new skills we’re looking to bring into the organization. Before we do all of this recruitment and stuff, we need to make sure that we are utilizing the employees we currently have at the organization. So I want you to take a look inside of Eightfold that everyone that works for you and that’s on your team and create some development plans, some upskilling plans for your team.
Brandy: Because we need to get them to that next level. We need to get them to those future roles. What we can see here inside of Eightfold is that Elena is a really good fit for the new role that we’re looking for. This data science engineer, it’s got a high role criticality. She’s probably my top choice here for that upskilling potential because it’s so high. Again, what we’re looking at here is her potentiality to fulfill a new role to fulfill, in particular this data science engineer role. And we could see that because we understand what that means, not just here at the organization, but in the market as a whole. So we need to upskill her. We wanna go ahead and create that upskill plan. We wanna talk about what it takes to get from that quality engineer position all the way out to that data science engineer role.
Brandy: And for this, we can see where Elena is right now, what skills she currently has versus what skills she needs to have in order to be really successful at that next role. In this, just like we can with career planner, we’re gonna bubble up courses, projects, maybe even certifications that she’ll need to get to that next step. These are things that she’s gonna have to engage with and her manager is actually going to track her progress on. We’re also gonna be assigning a mentor here. This is someone that she can go to and engage with and learn from as she’s going through this development process to become that new data science engineer. Now her manager’s gonna go ahead and create that plan. And so when Elena comes in here and takes a look at what she’s doing, she can see the whole timeline, not just for the plan that she’s working on right now, but plans that she’s worked on in the past and what she’s probably gonna be working on in the future.
Brandy: Now, the plan that she’s currently working on or will be working on in the next little bit is that data science engineer role. And it’s gonna take her a little over 90 days to learn what she’s gonna need to gain the skills that she needs to get to that next step. Inside of this Eightfold is gonna be able to track her actual progress. Now this is a space for Elena to come to. And as she completes courses, as she is engaging with progress to move those into different phases. So at the end of this process we can see everything that she’s completed. And if along the way she steps off the path, so to speak, her manager can come back and go, Oh, hey, where are you? Is there anything I can help you with? Because everyone can see exactly what it is that she’s working on.
Brandy: Now for Elena, she’s super excited about where she’s going, but really all anyone has told her is like, oh, you’re gonna be a data science engineer one day. What she wants to know is after this, what’s next? A data science engineer is what’s now. That’s what she’s training for, but the future holds some really amazing things for her. And so with career navigator, she’s gonna be able to see the path that she’s on right now, but also paths that she could go on in the future. Maybe right now data science is where she’s at. And one day she’ll become the director of engineering or she could diverge, she could move into software engineering, learn again, start from scratch, do something completely different, and also be able to see where that could possibly lead her into the future. Either way, the future is now in her hands. The future is now in Sheila’s hands to decide exactly where it is that she wants to go next, preferably at her same organization and not somewhere else. So this is the future with Eightfold. This is the future that we are planning out both for internal candidates and all your internal employees as well as externally and with the organization as a whole.
Maggie: So we’ve talked a lot about the process, but what about the outcomes?
Maggie: We come back to our three stories. With the Eightfold talent intelligence platform, Stacey was able to accomplish her goal of hiring for this new data science team while remaining within her tight budget. She was able to do that because 53% of these new hires came from her own talent database. Thomas with the talent acquisition module was a recruiting machine. Him and his team saw a 35% faster time to fill and a 50% candidate engagement. And Sheila was able to move forward into that data science position and she wasn’t the only one who was able to grow at the organization. As across the organization, they saw a 52% increase in internal mobility within just a year. Now we’ve presented these to you as stories today, but these are real Eightfold customer outcomes. When you talk about Eightfold, we had positioned at the beginning of this presentation a question, what if with a single platform and the click of a button, you could see the future of your talent with Eightfold with the outcomes that we’re seeing with our customers? It’s no longer a question of what if. It’s a matter of when. Thank you so much. We’d like to open it up for questions for about the last 10, 12 minutes that we have here today. Thank you so much for attending.
Brandy: We got a question in the front, sir.
Participant 3: Can just talk about of the data that you’re actually not are all coming from, do they have Yeah, I’ll…
Brandy: Answer. That is a great question. So the question was where does our data come from? We have a proprietary data set that encompasses about a 1.5 billion profiles, a million and a half skills and job titles. What that means in context, there are about 3.2 billion people in the global workforce. If he excludes the elderly and I don’t know, people who don’t work. So we know about half before we ever look at your data. We have a really clear understanding about how people move within their careers and what their skills actually are, and then what those skills mean in context with potentiality, right? So we take that to train our ai, that’s our data that’s coming from us. What we also do is take your data and refresh it. So when I say your data, I mean inside of your applicant tracking system. Some organizations have literally millions of candidate profiles that they’re not looking at because it’s really hard to find that data. It’s really hard to sort through it and then it’s also very stale. So what we do is we go out to publicly available sources like LinkedIn. There’s a few other places that I won’t list out right now and use that to refresh that data. So Maggie gave the example of seeing someone, I don’t know, three years ago who happened to apply. Well now because we can go out into LinkedIn and places like that and refresh her profile. We can see in that span of time exactly what it is that she’s done.
Brandy: Absolutely. I got another question here. So I think the question was are we using a standard set of skills? Taxonomy.
Participant 4: Taxonomy?
Brandy: Could you give him a mic so I can understand what he’s saying? Oh, here’s a mic for everyone leaving. Thank you for coming in and hanging out with us.
Participant 4: My question,
Brandy: Oh, he’s turning on the mic now. Give him a second.
Participant 5: My question was, in the public data sources that you consume, in your algorithms, do you consume standard jobs, skilled taxonomies on net? I believe there is another major like T taxonomy used in Europe as well.
Brandy: Yeah, that’s helpful as well. So the question is, are we looking at other skills taxonomies and bringing that into the organization? We are not integrated with any of those systems. That is not to say that we could not integrate with those systems. Tends to be if an organization already has a skills taxonomy that they wanna bring into Eightfold jobs, intelligence engine is gonna hold that. But for eight fold, we’re looking at skills a little bit differently than the, here is the skill description, here is the skills sort of way. We look it as a series of related skills. It’s kind of a best way to describe it. So imagine this, right? If you know how to tap your foot, there’s high probability that you already know how to stomp your foot. And if you can stop your foot, you can probably do a jig of some sort, right? That’s kind of skills clustering. That’s what we’re looking at. Skills adjacency. I think I saw more questions. I thought I saw a handout.
Participant 6: So since we are looking at the, Oh, there we go. Since we are looking at the past data, right? So is there any shortcoming until now? Did you find anybody missing something? Because sometimes the job may not be existing or the skills may not be existing.
Brandy: Yeah, so the question was if we’re looking at past data, how are we seeing new skills that arise? Past data is not the only thing we’re looking at. So the system is learning all the time from new customers who come in as well as new resumes that come in. And so what we’re seeing is how skills change over time and that’s happening in real time. So we’re looking at both the future and the past all at the same time. So I wouldn’t say that there’s any drawbacks to having a basis for what someone is previously doing, cuz that’s gives us an idea of what they could possibly do in the future. More questions? We’ve got about seven minutes, so I think we can take these last three. Go ahead.
Participant 6: Hi.
Participant 7: So is it, who is your target segment? Small businesses, large corporates. And how do you charge? What is the business model?
Brandy: Yeah, I’m not gonna talk money in this setting. I don’t think you’ll be surprised by that. But what we are looking for is a fairly good pool of data from the organizations. So by that I don’t mean that you have to have a hundred million people in your organization but what we do need is some pretty good context for you and how you hire. So we’ve worked with organizations, I mean small business all the way on up right now. We’re very focused on enterprise customers, but we work with everything in between. The more data that we have about you gives us a better idea and how you hire and how people move within your organization gives us a better idea of how we can help you and what context we can give you for that potentiality.
Participant 8: Can you hear me all right? Yeah. So how much does internal feedback build the AI and ML for a specific organization relative to the dataset? So for Sheila, if it ends up being very successful, does that weigh in terms of how that fits into the organization? And then if someone is unsuccessful, does that help predict future placements within the
Brandy: Organization? Oh, a hundred percent. So the question was are we able to see both the successes and the failures for all these career moves? And Yes. So we are a data company first and foremost. That’s where we started. So before we even really thought about how this could be applied, we said, Well wait, let’s start with the ai. So that means we are tracking everything, all the movements, all of the skills all of the successes and failures. It’s always super helpful when we can see, especially if you’re using a integrated assessment tool, what it means to be successful at that organization. And that all goes into that model as well. And that kind of leads us to that first or second question that we had about are we still learning? Yes, we’re learning every day. Last question here.
Participant 8: I’m curious what it looks like to validate the skills that someone is putting in as their own skills and projects
Brandy: Yeah, so skills validation, let’s talk about it in a moment. So there’s a validation piece that happens when someone gets applies to a role. So as you’re looking at a role or a position that’s open, a new rec, recruiters sources, hiring managers can see how that person matches to a position. And also within that, the skill set they have, how those skills are validated for skills validation. What we’re looking at is contextually that organization and how they’ve hired in the past and what skills people have, but also because we have this huge data set, how similar people and similar roles at the same companies that this person has worked at around the same time period. What skills do these people have and validate it from there. We also take in and integrate with assessment tools, both on the front end there as they’re applying and then the back end as we’re trying to see how well people are doing within their role.
Brandy: So that’s part of also that validation. So it’s happening in ton of places. Now sometimes the question that I get on the career planner side is, well, they could say that they’re, I don’t know, ride horses on the side and they’ve never seen a horse . That doesn’t really matter for us, right? Because our understanding from a career side is that you should be able to do whatever you want to do as long as you can fit the skills that are needed for that current role. So yeah, you could put in that you run horses or that you, I don’t know, were you saying bolt or something like that, But if that particular skill does not align with the role you’re trying to go towards, the thing that you’re applying for, that’s not gonna matter, right? Also, we’re gonna highlight that skill as a thing that’s like, wait, I can’t validate this. I don’t know where that’s coming from. And so your recruiters and sources and everyone else involved in the process can go, Hey, what does this have to do with data science? Hopefully that helps. I might be able to squeeze in one more question. We got three minutes. First hand up all the way at the back.
Participant 9: I wanted to understand the burden on the company in terms of getting data in and then maintaining the data so that it stays accurate because we all know good data in good data out.
Brandy: Yeah, good data in, good data out. Totally believe in that. So the question was about lift. How much effort does it take? On the outset, what we’re looking to do is integrate with the systems that you already have, and we work with some of the biggest companies out there, including Workday, Success Factors, I can list more. So those integrations are already in place. So all we need to do is you give us access to that data and we can pull that in. Not such a huge lift, right? On the internal mobility side of the house, again, we’re pulling in your data and we’re pulling in the data about that person. So as they’re using the platform, it’s already updating itself, so not so much lift if they’re actually using it. Right? On the workforce planning side, as you’re doing your research, as you’re doing building out those roles as well, that work is on you guys, right? We don’t want to presume to know what it means to be a great data engineer at your company, but what we will do is help to perpetuate that throughout the organization. So you’ll set that up inside of that job’s intelligence engine and then that’ll get perpetuated out. So certainly not as much Lift as some other companies that I’ve worked for , but it’s not zero lift, I can say that.
Brandy: All right, we are about at time and I wanna be respectful of who’s coming next. Yep. Thank you so much for your time. We totally appreciate you.