In this podcast, Mike Hofer, SAP Executive HXM Value Advisor, and Dan Hopkins, Eightfold.AI Global Public Sector VP, review emerging trends in HR and discuss how integrating SAP and Eightfold.AI technologies improve employee experiences in the public sector.
Corey Baumgartner
Welcome back to CarahCast, the podcast from Carahsoft, the trusted government IT solutions provider subscribe to get the latest technology updates in the public sector. I'm Corey Baumgartner, your host from the Carahsoft production team. On behalf of SAP and Eightfold.AI, we would like to welcome you to today's podcast focused around how the search for top and retainable talent human resources and talent acquisition departments must consider the effects of digitalization and demographics changes to future proof their workforce. Michael Hofer HR transformation executive of SAP success factors and Dan Hopkins, Vice President of Eightfold.AI will discuss how organizations in the public sector can hire and retain talent effectively by focusing on mobilizing the workforce adopting emerging technologies like AI, embracing D,I and B principles and preparing internal contributors for managerial roles.
Mike Hofer
Thank you all for taking your time to join our discussion today. Just a little quick about me is so I'm an HSM executive Value Advisor. What does that mean? Well, in my role, I'm fortunate enough to help organizations find the answers they're thinking through and help facilitate their HR transformation journey. I bring HR expertise, transformational experience to the conversation, and I've also been in that, you know, quote, sat in their shoes unquote, seat before so it's really helpful in that perspective. I help organizations think through the business objective, and goals they have said and assist organizations in defining the current pain points and value that change will bring. So that in essence, is what I do, and I and I'm very fortunate enough to work with all of our existing customers and prospects. But now I'm gonna pass it really quick back over to Dan. Dan, you want to introduce yourself just really quick.
Dan Hopkins
Absolutely, Mike. Thanks so much. And thanks, everybody for joining today. My name is Dan Hopkins, I'm the VP of global public sector at Eightfold.AI, based out of Washington, DC and been with Eightfold for almost five years now, I really responsible for bringing Eightfold into the public sector, both government as an employer. So we all know that the government hires people and has employees, and also supporting government as an enabler for employment, where we do a lot of work with state department's of labor, for instance, to help displace workers find more rapid reemployment. So really happy to be here today and talk to you specifically about the impact of AI on the workforce.
Mike Hofer
Thanks, Dan. So together just a little bit about who we are. As we look at success factors in aid full AI really help customers drive more effective forward looking talent strategies. And what I mean by that is by connecting the comprehensive foundation of the enterprise, and employee data in SAP SuccessFactors with the eight fold talent intelligence platform, organizations can find recruit the right talent faster, accelerate employee development and grow, promote internal mobility and drive the organizational agility needed for the future of their business. So today, I'm going to be covering at the high levels of HR megatrends as researched by SAP SuccessFactors. And I O psychologists that we have, as well as how to fuel your company's growth with integrated talent, talent, AI. Dan is going to cover really the AI's impact on the workforce, as we double click down into that and the responsible use of AI throughout organizations. And then we'll wrap up today's session with a Q&A. Let's start our conversation today with our HR mega trends, it's important to know what's going on globally so that you can apply it locally. This is the year of VX. It's no longer a trend but a foundation or an embedded part of our H HX. M, or HR mega trends, and what our customers expect to see throughout our solutions. So from a macro perspective, quoting our Dr. Steven hunt from SAP success factors and his book on talent tectonics, this really impacted me, and I wanted to share it is the digitalization and demographics and the collision of these two forces impacting at the highest levels. And so what does that mean? Digitalization, it's really about the speed of change. industries are being altered with changes in one industry can creating changes for another. Think about companies creating unmanned aircrafts or boats. This will not just impact a talent that builds and deploys these assets but will likely change forever how we defend our own country. These are big things and then we look at demographics. And I know we hear about all the different, you know, different, you know what demographic that you tie to, but people are just basically they're living longer and are having fewer children, and not by a small amount. Life expectancies has increased by 38 years since 1910. And global birth rates have declined by 51%. Since 1950. In some cases, industries or even countries more people are leaving the workforce than entering it. Those two factors combined; we haven't even got into human resources. Those are just two things that what I see our customers and talk with our customers every day about what's happening in their workforces, and why we're struggling to find the right talent. So when we look at, you know, we'll go, I'll double click into these trends. But when we look at mobilizing the workforce for the future, it's really more about a shift towards skill based hiring, right? And when we look at emerging trends in technology, it's about regulation and explainability. And I'll go into that here in a second. But broadening the scope of any company's HR transformation, why we're here today is to talk about the embracing or adopting of emerging technologies. And then, of course, expanding D, E and AI and B. It's more of a lifecycle approach as we look at how the E and AI, not just the processes, but the technology has come so far and is embedded throughout the employees journey. And then reading leaders you have manager or read readiness among individual contributors. It's more of a people centric skills, I guess you would say. So we're going to double click into all of those right now, mobilizing the workforce for the future. So organizations are continuing to turn to development as a mechanism for improving employee experiences in 2023. Developing unique individualized career plans for employees will demonstrate their value and future with the organization so supporting that long term career trajectory, with a variety of shorter-term developmental activities motivates employees to reach these goals and helps them re engage and organizations also benefit from their short term development gains. 2023 will be the year that organizations also shift their focus from short term skills gaps to predicting long term business needs, and engaging in a strategic workforce planning to prepare for the future. performing skills audits, accessing current skills and knowledge to identify potential gap at the individual employee level will not only inform employees developmental plans, but will also provide valuable information about the broader skills gaps across the organization when viewed in the aggregate. And of course, we can't not mention the global economic uncertainty. It's shaping this trend in 2023, in some organizations to focus more on internal mobility than external hiring. So organizations subject to external hiring freezes, and limits to backfilling roles, which we've all seen. It's all about the RE skilling their existing talent for new roles to get more out of the workforce they have, right versus just hiring for it to this in the concept of quiet hiring or filling skills gaps without adding new full time employees, is it really expected to continue throughout this year and into next, within many organizations returning to physical workspaces organizations in 23, we'll consider how to move forward with providing the right balance of learning and development opportunities within a hybrid work context. So you have two learning methods that are expected to rise in popularity. And that's really around experiential learning and coaching, that are going to work well in any setting, adopting emerging technologies. So this is this is kind of why we're here today. And to me, this is, this is so exciting, you see it all over the news, and how a lot of people are scared of it. But understand that from SAP and SuccessFactors, and Eightfold, we're not we don't it's a very deliberate and integrated approach to our HR technology. So as use cases of AI and machine learning and HR continue to gain traction. So to this global legislation, right related to the intelligent technology. So as a result, organizations and HR are being forced to be much more critical and very cautious about their use when you have the added scrutiny of needing to be audited by a third party for bias. You know, beyond mitigating bias. Another focus of new regulations is explainability. The ability to explain both a technical process and the AI system uses the rationale for the decision or predictions made by the system. It just makes sense. In years past, we've had critique, you know, narrow focuses on adopting intelligent technologies to support, you know, let's say recruiting and onboarding, with limited attention paid to the rest of the employee journey. However, 23 trends suggests that organizations are finally broadening their scope of intelligent technology investments by exploring applications across the talent management and development, employee engagement, D and AI and employee well-being. What's great about this is these trends map to exactly the conversations that I'm having with, you know, HR leadership across the globe, you know, and then lastly, we hear we've heard about Metaverse and web, you know, 3.0. You know, they were Hot Topics in 2023 trends. And progressive organizations really started looking at potential use cases for these technologies. But as early as early examples, virtual and augmented reality have really been leveraged for both learning and development, and collaboration. But a high cost of adoption may prevent organizations from leaning into this trend anytime soon. But we're seeing significant interest also in blockchain, you know around credentialing and tamper proof and private self-sovereign portable capabilities. So embracing the complexity of diversity, equity, inclusion, and belonging, organizations and 23 are expected to take a lifecycle approach that I talked about earlier. So Dee and I embedded its principles across to all people related to practices. So although it's unclear what these specific interventions will look like, companies will build on a foundation of employee listening data, people analytics to identify bias and all their practices. Given broader labor market macro-economic shifts, organizations will focus more and 23 on practices related to talent acquisition and retention, such as recruitment approaches that diversify talent pipeline, and more inclusive retention strategies that take into account the different needs and expectations of different groups or workforce segments. Continuing on these transmission to AI are mentioned two new AI attributes that organizations will consider in their strategy. So first, the plight of the deathless worker, including labor shortages, unacceptable working conditions, and even labeled as unskilled workers has been somewhat ignored by organizations. But they focused on their remote and their hybrid work strategy. So organizations this year will consider how to provide equitable opportunities and experiences to this workforce and shape initiatives that align with their unique needs, especially because women and ethnic minorities are typically overrepresented in this population. Second, there's a renewed focus on generational differences in the workforce, as the workforce becomes increasingly multigenerational organizations are considering how employees at different ages must have a different expectation at work, and tailoring their retention incentives and engagement practices accordingly. These are, you know, definitely some things to consider when we're looking at, you know, these trends around D, E and AI. And then lastly, preparing people leaders for today and tomorrow. I think this one's really interesting as well. So the 23-labor market and economic landscape is driving really a renewed focus on managerial readiness. So among individual contributors, like some organizations are struggling with that, like, let's just say a lack of leadership experience in their internal and external pipelines. And with talent shortages, they will have no choice but to hire less experienced candidates. So managerial promotions will also be used as a retention tool or for high performing individual contributors. Organizations this year will focus on developing manager or managerial readiness to address these gaps as leaders at all levels, face difficulties, you know, we're, it's, it's important, more now than ever, that they continue to improve their people centric skills. So imagine having to go lay off a team member, or you know, talk about a bonus, or I need you to go this year without a raise or with and without a bonus, you're going to have to, you know, employees need their direct managers and upper leadership to address these troubling topics with emotional intelligence, empathy, and sensitive communication. And these are skills that we all know that not everybody has, but it's so important for everybody to develop. With the continued changes in their role, frontline and middle managers well-being will be top of mind in 23. So after a notable absence in the 22 trends managed managers are actually expected to build and maintain empathetic, unique relationships. with their individual team members to improve performance, imagine that and prevent turnover, right and, in, in the case of a hybrid and remote work, serve as their team's most important connection to the organization and its culture. I can't tell you how often that I'm having conversations around that one very topic. Feeling like you're not involved when you're working remote has a big impact on that culture. And it takes you know, the leaders to help focus those employees on the most pressing priorities and keep them motivated going forward. So how to fuel your company's future? So work and work. The workforce itself has significantly transformed over the last few years creating several challenges, or opportunities for more organizations, depending on how you look at it. Some of the biggest challenges we face today are supply chain disruptions. I think we're all familiar with that one and how it's impacted our lives over the last several years. And really an ongoing labor shortage that I spoke about when I you know, started this discussion. So global employee, global employers are struggling acquiring the talent they need to anticipate they will have the challenges in the future. According to a PWC survey, one in five people plan to leave their jobs in the next 12 months. It's been a roller coaster for talent acquisition teams across federal, state and local, you know, our industry's workforce expectations are undergoing profound shifts as well. today's workforce is one inclusive workplaces, flexible careers, continuous learning, and equitable path promotion, all great things. And with the half-life of a skill somewhere near I don't know, three to four years, organizations and HR teams need to do more to quickly improve the skills of each worker, while helping everyone gain control of their careers and career paths. action needs to be taken now to fuel the future of your organization. So first steps towards fueling the future is really understanding the skills that exist across your workforce, better understanding the talent at your company. So for example, looking at rising stable and declining skills, right? Where are we getting the most effectiveness, one of the things that I also see, as within federal, state and local companies that I work with, are that there's so many disparate systems that they're working in, it makes it very difficult and not because it's their fault. It's the managing multiple spreadsheets, multiple departments, multiple databases, to try to come up with, you know, one vision into determining where those skills are. So understanding your talent, we also have a critical understanding of emerging skills. So these emerging skills, you know, have a role in the market to learn how the role is evolving over time, what are skills valued or in demand across industries. And finally, understanding the rising stable and declining skills, you know, that that are aligning to skills and to business objectives, how your skill distribution compares, and identify in identifying relative skills advantages that might give you have a competitive edge. By bringing all of these together though, you can do amazing things to transform your talent outcomes. Now, you know, with a drumroll all of these examples are possible today through SAP success factors and Eightfold.AI. So let me explain this a little bit before I pass this off to Dan. Everything that I just talked about, can be achieved these trends can be looked at, analyzed, and successfully worked through during a transformation by looking at success factors in Eightfold.AI together. We help customers drive more effective, forward-looking talent strategies. We do this by connecting the comprehensive foundation of enterprise and employee data and success factors with the Eightfold talent intelligence platform, so organizations can find and recruit the right talent faster. Eightfold brings talent intelligence into a broader deeper AI model. And this is where the magic happens. It starts with your talent data from success factors, then this data is enriched or enriched. Yeah, sorry, too, with public data. So like LinkedIn, updating more experienced and recently acquired skills automatically. So with talent intelligence, you can better understand your talent, like what skills my candidates or employees are capable of learning or easily determine the health of your talent pipeline. And finally, talent intelligence, you can better understand your market, like one of our customers Prudential. Here's an example of identified emerging skills to ensure requirements reflected the evolution of the role. It could not have done this without visibility, you know, through success factors. And April. So to close out some dry forward talent strategies, build your company's talent, intelligence, understand the workforce skills and potential, find and recruit the best talent, boost development and really focus internally on how you can scale and rescale those employees. And then, obviously, most important, advance your diversity goals and allow technology to come alongside the processes that you're using today. Why don't I pass this over to Dan, and Dan can give us some examples of what he's seeing in the market.
Dan Hopkins
Thanks so much for passing me the mic to go through everything we want to talk about today. My name is Dan Hopkins, VP of global public sector for Eightfold, as mentioned, and what I wanted to do is really kind of tease out in a little bit more detail, some of the themes that Mike was going through specifically around skills, AI enablement, insight into your workforce and transformation from not only an HR standpoint, but also from just an overall workforce perspective with the inclusion of D, E, I, N, A as well. But first, I really wanted to just stop for a moment and just tell you a little bit about who Eightfold is and why we're talking to you here today. Eightfold was founded in 2016, really, with the audacious goal of solving the right opportunity for everybody in the world. And I know that might sound a little Silicon Valley, which is where we are based. But it really truly is the organizing principle that our founders sort of created Eightfold to solve for it really effectuates not only all of our products or cultures, but really truly our mission. And so what we've done is created a single AI platform for all talent needs, really the world's first talent intelligence platform, we've created a number of patents around AI job matching. So being able to match the right individual for the right role, being able to mitigate bias and support diversity and inclusion within the hiring process. And we are the most well capitalized in our space with over $410 million in funding from some of the most marquee Silicon Valley, the venture capitalists. We are also trusted today by a number of large global brands who use us on a daily basis for their talent outcomes that might be around talent acquisition, talent management, workforce insights. And what you're seeing here is just a small capture of some of those customers both on the public sector and private sector, we are the industry leader on the private sector, where we have some of the largest names using our AI for their talent outcomes. And we are operating in the public sector for the last two years supporting both government as an employer, and also an enabler for employment. So what I wanted to talk to you today about is just a couple of things first, because there's so much confusion about exactly what AI is, I want to just spend a moment to level set. So we're all kind of singing from the same song sheet. And then we get into the AI AI's impact on the government workforce. So I've just sort of highlighted some of the key themes that I'll be talking about. And then we'll get into responsible use of AI within the organization. So first, when we talk about AI, there's a lot of definitions that are out there. Really one that I like is that artificial intelligence is a branch of computer science that focuses on building and managing technology that can learn from data autonomously make decisions and carry out actions without explicit instruction from a human being. So that's a really critical component and understanding, you might have heard things like machine learning and deep learning and for our purposes of our conversation, those are really all subsets of artificial intelligence. So in an attempt to sort of demystify what we're talking about artificial intelligence is sort of the umbrella term with the definition that I gave you. And then machine learning and deep learning are sort of techniques, if you will, to simplify things within artificial intelligence. And one of the key parts of AI is that, you know, AI really makes predictions about the future. And it does so by looking at past data, and to understand insights and learn from that to make predictions. And so, you know, for our purposes, what we'll be talking about, really what we want to understand is skills within an workforce, and then start to understand how those skills might be applied. And for the first time ever, we'll really start to talk about unlocking true potential within an organization. You know, prior to probably chat GPT when we talked about artificial intelligence, you know, most people thought AI was just robots, and worse than they thought that it was, you know, Terminator robots. And now it's also you know, are these robots or is it AI is going to take all of our jobs. So, you know, it's a pretty scary when you hear some of the news about artificial intelligence out there. It's almost apocalyptic. And so, you know, what do we do? Do we act like the Luddites to sort of tear everything down and start from scratch? Well, the good news is, is that artificial intelligence is, in fact, already are all around us today. Not as scary as some of those misconceptions. You know, you might use artificial intelligence today and some of your personalization apps, personal assistant apps, whether it's Siri or Alexa using natural language processing, from some of the self-driving technologies that are out there and say, at Tesla, to some of the new emerging medical technologies, like in the example here, detecting diabetic retinopathy with more accuracy than even a human to as simple as making recommendations on what movie you're going to watch on your weekly movie night. Why is AI sort of all around us? And why are we hearing it so much? Well, there's really kind of three things first is just the overall level of compute that's out there through GPUs is now sort of enabled us to sort of have multi-threaded processing technologies that technologies like artificial intelligence, specifically deep learning really need and thrive on. So this GPUs, which started in graphical processing unit started in gaming technologies have now been sort of adopted and retrofitted to solve some really big, multi-threaded data challenges, to an explosion of data. There's just more data out there, right, we have more connected devices, creating new data, there's been emergence of new data, think of like GPS data, for instance. And then also now the creation of neural networks, which is enabling, enabling us to have multiple levels of processing that understands and can decipher information from large datasets all together. And being able to bring all these things together is what's driving this sort of explosion in artificial intelligence. So really, instead of thinking about robots think about artificial intelligence, enabling the human in the loop, and supercharging, their effectiveness, right. So you can all become sort of Superman's as you're sort of harnessing artificial intelligence. And if you think that is a little bit too, pie in the sky, or egregious of a, an analogy, simply think of it as a washing machine, right? So you want to use artificial intelligence to do a specific task, not busy yourself on that specific task, and it sort of set it and forget it and enable you to do more high value. So that's sort of the ask, in terms of how to think about artificial intelligence as we go through the rest of the presentation. And so let's dive into it. So what impact can AI have on the workforce? Well, to kind of tease out some of the themes that Mike was talking about, but also some of the things that we're specifically seeing. And these are just really three global macro mega trends that are out there, the first two are really almost kind of the same, right, which is less people simply to do the work. The nature of work is rapidly changing. As Mike mentioned, the half life of skills, is going down to three to five years at best, there are emerging new technologies, the workforce is changing, the essence of our job is changing. And to be quite frank, the population is simply declining, as well. So you're having less people to choose from, there's changing jobs that require greater specialization. So there's just not a lot of people out there. And really, that's evidenced by if you look at any of some of the technical roles that are open within the government say even cybersecurity, for instance, which you have almost a 50% open rate across, you know, the government for those some of those specialized roles. The irony in these megatrends is, however, is that the GDP per hour of work for individual is skyrocketing. And so we're able to really do more with less. And that really is a testament to technology enabling us to be more effective at our job. And so one of the key themes we're that we're seeing and that we can bring to bear is enablement of the human in the loop, how can we enable an individual to make the best decisions, the right decisions and do so more effectively? When we look at AI and where it's found in the workforce, it's actually being used in a number of different silos. And my point of view on that within the workforce is illustrated by a thumbs up and thumbs down. So we are seeing that AI be deployed to understand skills advanced dei and a to supercharge our productivity and really being able to fit the right person in the right role. There are use cases where AI is also being used for interviewing candidates, employee surveillance, and also downsizing. In other words, who should we let go in the organization? I think in general, that's just a really bad use of AI. I think there's some ethical issues on the downside. And as we see some of the bills and we talk about sort of the responsible use of AI. I think the general consensus is really shying away from the those three categories. But the upside here on how we can use AI really means a sort of a better aligned workforce, and one that is more diverse and really mitigates unconscious bias that's found today with the human in the loop. So let's talk about how an AI can enable an agile skills-based foundation. And you know, as Mike mentioned, really all today, it's about understanding the skills that you have within the organization so that you can think better about what it is in fact that you need, right, when you do workforce planning, you really need to understand current state before you can properly address future state. And one of the things that we're thinking about, or how organizations are shifting their thinking is around these five categories. Number one is like well, how much data do we actually need to make these decisions. And because we have an explosion of data now, we can actually really address that before we you know, we had siloed information and really didn't even have good access to data. And those might have been siloed, within Excel spreadsheets, for instance. And what we're seeing is, is that really, that data is starting to be unlocked. We're also seeing new data emerge, as well think, again, about that sort of GPS data. That's just data that we didn't have before in the past that now we might actually want to consider new data within the overall process. What if anything, should be automated, right? What tasks should we automate? Versus what tasks? Should we still have the human in the loop do? And what should we sort of work together with AI to sort of make sure that the AI can do a lot of the automation but not make ultimately the end decision and enable the human to be able to do those? Also, how are we thinking about detecting our skills? What areas should we look at before in the past, that was true, traditionally done through assessments. But with AI, as we'll see in a moment, we're now able to really better unlock an individual's skills and more importantly, their potential. And then finally, you know, in the past, we didn't really think about external data, and when we were making internal decisions, but now we have the ability to sort of bring in for lack of a better word market data, what are trends in the workforce? What jobs are increasing? What jobs are decreasing? What should we be hiring for right? To take a look externally to better inform those internal decisions. And really, the challenges to get there today are really moving from this system of record, which was really in place for reporting and compliance and sort of think of this as process based check the Box technology to move to truly a future focused on making true talent decisions that require agility that require a real true understanding of skills and competency within the organization to drive that future demand and future workforce planning. And just sort of another illustration here is this move from just this notion of having these systems of records that manage your jobs and position and structure data, to a, really a system of intelligence, and this is where the Eightfold SAP SuccessFactors relationship works so well, is it because it brings best of breed technologies together so that you're really solving these challenges holistically? And, you know, the deep learning that AI sort of can provide here starts with a neural net of skills, we've really been able to understand through billions of data points, what skills mean, and what skills are associated with what job and that cuts across industries that cuts across job titles and organizations, so that when an individual articulates their past role, we know what skills that they have? And we can also match adjacent skills? In other words, if they have if they know Skill A might they know Skill B, could they learn skill, C, and to what velocity so understanding sort of all of the things on the left, which is includes a connection into the SAP SuccessFactors data platform that has so much data unlocks everything on the right, a true understanding of skills and capabilities? learnability? What can a person sort of know? And what can they learn quickly, and sort of what roles are best for them. And when we're talking about internal mobility, this becomes really critical as well. If you think about in the past, how skills were defined, it usually consisted of a high paying consulting firm or a high dollar consulting firm, rather, coming in really going through a mapping of all of your roles, and then rash trying to rationalize all the skills. And you know, I think the problem with that is, let's assume for a moment that you've captured all of those skills in that exercise, which in and of itself is difficult. Those skills typically lacked context. And worse, they change almost as soon as that exercise is over. And so when you rely on an understanding of what you need through a sort of a static taxonomy within your organization, you very quickly Miss align what your organization really needs, and worse that people that you're trying to bring into your organization and fill those roles. And so what artificial intelligence can help create as a more dynamic framework, where we're looking at not just the skills that you have within your organization from your people, but the skills that are actually required to do those roles. And if we go back to that sort of market data approach that can be informed both internally of what it is actually that you need, and externally as well. So artificial intelligence can better help you understand your organization, understand each specific role of what it is not only that you have, but what it is that you need within your organization. And it can also help you understand sort of the disruptions that are existing, to better plan for what it is that you need. And so as you build your talent plan, artificial intelligence can allow you to align your future business needs, to what skills that you have, what skills that you develop, what skills that you need, and then enable you to have a real full ecosystem to be able to attract talent, engage the talent that you either are trying to bring into your organization or have within your organization, upskill or reskill? As jobs sort of change, as Mike even mentioned, most organizations find it hard to continue to attract individuals from outside. So enablement internally becomes more critical and upskilling them so that they're job ready is a critical component of that. And then, of course, redeploy those individuals on those roles. If we talk about the second point, which is optimizing your hiring and total talent lifecycle, you know, we first have to really understand what are the challenges with how we've been doing things today. And the reality is, is that the job and talent discovery process of today is fundamentally broken. And in an efficient, that might sound a little bombastic. But if you think about jobs and talent as two sides of a network effect, what you realize is, is that the only thing that's matching those individuals are job descriptions, and resumes. Well, if you've ever read a job description, I don't mean to pick on the United Nations here, this is really sort of emblematic of all job descriptions, you realize very quickly that job descriptions are a poor articulation of need. They're typically vaguely worded, and they require an individual to interpret what they believe you're looking for, and then self-assess against those vaguely worded interpretations. And so no surprise that most people get this wrong. In fact, this really speaks to sort of self-selection bias and exacerbates D and Ima, which we'll talk about sort of later on down the road. If you're thinking about also job descriptions and resumes, you know, internally from your organization, the only thing you really have to go upon is beyond self-assessment is a CV or a resume. And the problem is, is that a resume really only shows the surface level items, their education as self-attested skills experience, for instance, but what you really care about are all the things below the waterline, right? So can they actually do the job? Do they have potential, which by the way, opens up the aperture of talent that you want to consider for your particular roles, but really resumes or self-assessment to a survey doesn't give you this type of information. And so with artificial intelligence, we can now create a capabilities metrics of not only what it is that we need, so a predictive model for each opportunity. But we can also really understand the skills through the neural network of an individual of what they have, and what skills that they could easily learn Unlocking Potential, we also understand the career pathways that they're on. So we might need a data scientist, but the candidate that we ultimately want, has never been a data scientist, but they have all of the requisite skill sets, or they're on a career trajectory to be a data scientist, we should consider those candidates. And so when we talk about sort of transforming the ability to match individuals to the right role, we're now able to personalize those experiences at scale for net new external candidates or for internal employees by looking at their past by looking at what it is that they can do by looking at what it is that they want to do and match those to the role for those individuals. So ultimately, the AI in the talent acquisition component not only enables that wider selection pool, it reduces and overall time to hire in a cost to hire. There are some instances and probably many people on this call, probably feel this pain firsthand. Where you know, the time to hire is over a year. And so it used to be that if you wanted to go launch rockets, NASA was the only place to go. But now SpaceX can hire you in a short amount of time and also pay you probably more so it becomes more critical as we try to capture those skilled individuals to move very quickly. It Identify those candidates, match them to the right role and get them into the organization. And we talk about the individuals that are already in your organization. skilling development for internal mobility becomes critical when we talk about reducing regrettable attrition. Every organization I talked to always says our greatest asset is our people. But in order to actually give the employees what they demand, you really have to think about sort of three buckets. Number one is internal mobility, how do we actually give people line of sight to their career within our organization? How do we enable them to learn new things? And how do we get them involved in important work for our business? And historically, you know, we've kind of tried to tease that out with our employees by asking them, what is it that they want to be when they grow up, and by presenting sort of all options, we don't present any great options. And instead, what we really want to do is understand the skills that our employees have, where are they want to go within their organization, and enable them to choose their own adventure within our organizations. And so really, it becomes about identifying the pathways within our organizations that they could do. And by the way, that might not just mean a career move or job change, it might be enablement of mentors or projects or recommended courses that set them in on that journey. And so that they have more visibility on their role and their long-term employment within the organization. Because today, ironically, it's much easier to find your next role by looking externally. And then using AI within the process starts to change that paradigm, and give them that visibility and predictability of growth within the organization. We talked a little bit about AI advancing diversity, equity inclusion. So I want to tease that out just for a moment here. But most organizations today really sort of have an a narrow focus on what it is that they're looking for. And that's usually people that have done that job before in the past, they don't consider individuals that could do the job that should be considered for the role simply because there's no way to actually to that they're not looking at skills. Historically, there's a number of self-selection bias in that process, right, because ultimately, the human in the loop are making decisions. And when we talk about sort of a black box, that is the ultimate black Box, right? And so there's not a lot of data supporting those decisions. We talked about resumes, providing really only that surface level, it will really truly want to understand context of skills, why what capabilities those individual have, you know, there is unconscious bias that still exists, unfortunately, and how we measure individuals also create bias as well within the organization. But when you start to consider individuals for potential, you start to give candidates the break that they need, right? It's not just about how have they done the job before in the past, but do they have the requisite skills to learn the job and move into the position that we want to, we want to do in care encourage candidates to apply the right job. So instead of self-assessing, and interpreting those job descriptions, we want to encourage individuals to apply to the job that we want to, you may be familiar with the study that Harvard did why women don't apply for jobs unless they're 100% qualified, which found that female candidates hold themselves to a much greater standard of job rigged meeting job requirements than embarrassingly so for us male candidates, which means they're not even imply applying to these roles, which means that you have a pipeline of candidates that are probably skewed more male. Also, with artificial intelligence, we can introduce context into the role, which we've talked about a little bit before in the past. And guarding against unconscious bias and the human in the loop. Masking profiles is another enabling technology that is just good practice in introducing that within your hiring lifecycle. And we've talked about hiring lifecycle, we also want to make sure that we know exactly how we're doing. Right. So in this example, we're showing as we can compare gender for instance, how are we hiring male and female candidates side by side and where white we have problem areas this can inform and infatuate sort of offline processes. And then we've all talked about we know today in the news about Chad GPT and Ellen's and talking about AI. And it really how does that fit? Well, you know, first of all chat, GBT is really on its way to sort of be an emerging technology that sticking with us and highly adopted more so than any other technology so far, and forecasts that we will continue to see growth like chat GPT within this space, and impact a number of different jobs and those jobs. While they may not go away, they certainly fundamentally will transform. And when you're talking about hiring and creating workforce plans, you know, you need to understand how those jobs change and how the skills associated with those jobs change. Otherwise, you're going to be having an outdated workforce. When we talk about how HR can be impacted. Really what you're wanting to do is that the candidates themselves can are going to be generating their own cover letters. They can customize the resume as employees can better manage their careers and find open jobs in a conversational way. And recruiters may sort of manage candidates and task almost as sort of an entry point into some of those process technologies. But really, when we talk about anything that is sort of chat GPT, like, when we talk about these in terms of CO pilots, it would be really a bad idea to just sort of release that out there, what you really need is an intelligence layer that sort of governing that conversational user interface. And so, you know, solution like Eightfold and SuccessFactors, can leverage the data that you have in place and the intelligence to be able to ask questions in a co pilot way to give you those results. And finally, as we sort of wind up my presentation, I want to talk just briefly on the responsible use of AI. And done correctly, AI does have a number of inherent advantages over the people in the process, I sort of broken this out by the fact that you know, there is bias, there is privacy, potential violations, and gamification and fairness all as sort of four key pillars within HR that need to be addressed. And you know, people in that process are subject to their own air. But what AI can do, if done correctly, can sort of augment that to smooth out some of these challenges so that AI and people in the process can come together to solve some of these really tricky and important problems within HR. And if you think about the way that policy is developing, and think about AI is used within your organization, what we look at and what we sort of advise our customers out is doing. First of all, we do risk assessments and all high-risk use cases. So you know, we audit our AI, we use third party validation for AI. Anytime you bring AI into the process, especially HR, that's a good idea. And try to adhere to the guiding principles that are out there, White House has a guiding principle and the responsible use of AI and there's a number out there as well. And so incorporating sort of that best practice is critically important. And then also adhering to existing laws. Right. So EEOC, you know, they've been governing employment law for a long time, AI doesn't change that. And so anything that you do with artificial intelligence should also manage and maintain the existing laws that are out there. But all in all, I think there are a few best practices to consider when you are designing or implementing any artificial intelligence system. And that is, number one, it should be representative data, most organizations have limited data sets. And so being able to leverage a global data set as well, in addition to your data set gives you that contextualization short representation across the data, it's generally a very good idea to explain why the algorithm is making decisions within the process, and what factors are coming into that, in fact, in some jurisdictions, that is law, these are all things that Eightfold abides by. And also, I think, probably the most critical component is it's probably a bad idea to have artificial intelligence, automate your hiring decisions, whereas I think the critical component and the philosophy that we take is in enabling the human in the loop, right? How can we augment that human in the loop with more knowledge than they had before, give them in them insights, create inferences to skills that they might not have seen so that there's more even evaluation of candidates and enable them to make the right decision. But ultimately, it is still human that's making those decisions. And then finally, what I'll leave you with is a commitment to universal AI principles mean, you find these across some of those standards that are being released throughout government today. But you know, being able to make sure that the human in the loop and making sure that AI is designed with, you know, an equitable equity first approach to mitigate bias and test those are all critically important. And it also will ensure long term viability of your AI project and the AI that you put within your organization. So with that, I know slightly over time, so I want to hand back to the rest of the team.
Corey Baumgartner
Thanks for listening. And thank you to our guests, Michael Hofer and Dan Hopkins. Don't forget to like, comment, and subscribe to CarahCast and be sure to listen to our other discussions. If you'd like more information on how SAP and Eightfold.AI can assist your organization, please visit www.carahsoft.com or email us at sap@carahsoft.com and eightfold@carahsoft.com. Thanks again for listening and have a great day.