Stella Ioannidou, Senior Director of Research at Bersin, discussed the evolving landscape of talent acquisition in 2026, emphasizing the impact of AI and automation. Key themes include job creation slowdown, increased automation, and the rise of generative AI, with large organizations spending up to $10 million annually on AI. Ioannidou highlighted the shift from skill-based to talent-based organizations, focusing on talent density and internal mobility. AI is transforming talent acquisition by automating tasks, improving efficiency, and enhancing candidate experiences. The role of recruiters is evolving to strategic advisors, leveraging AI to make better hiring decisions and optimize talent management.

Introduction and Overview of the Webinar

• Host welcomes attendees and explains the use of the chat widget for questions and technical issues.
• Stella Ioannidou introduces herself as the Senior Director of Research at Bersin and mentions the partnership with Eightfold.
• Stella outlines the focus of the webinar on future trends in talent acquisition, including job creation, automation, and generative AI.
• She mentions the importance of talent acquisition in the broader ecosystem and the core themes for 2026.

Key Forces Shaping Talent Acquisition

• Stella discusses the economic growth and job creation slowdown, leading to increased automation and productivity.
• Generative AI is now an established concept, with large organizations spending significant amounts on AI.
• Early excitement about personal co-pilots is not paying off, leading to a focus on enterprise AI in 2026.
• Companies are trying to understand and deploy AI to automate workflows and build AI-empowered ecosystems.

Impact of AI on Talent Acquisition and Organizational Expectations

• Employees are uncertain about job mobility and career growth, leading to higher expectations from employers.
• The rise of the super worker and the need to redesign jobs and support teams are key themes.
• Companies are grappling with decisions on headcount, business process redesign, and AI system investments.
• The conversation shifts from individual productivity to broader organizational transformation and AI-driven productivity.

Evolution of Management Philosophies and Talent Management

• The shift from skill-based organizations to talent-based organizations is emphasized.
• The goal is to maximize potential and productivity across the organization without hoarding talent.
• Embracing AI to drive productivity and support continuous transformation is crucial.
• AI transformation is about growth through efficient talent allocation, not just cost control.

Stages of AI Revolution in Talent Acquisition

• Stella outlines four stages of AI revolution: assistance, automation, redesign, and transformation.
• AI is integrating across all areas of the organization, starting with HR.
• The impact of AI goes beyond efficiency to improving work outcomes and employee performance.
• AI is used in various HR functions, including rewards, employee experience, career mobility, and leadership succession.

Impact of AI on HR and Talent Acquisition

• AI helps streamline recruitment, support hiring managers, and solve business problems.
• AI is used for interview support, skills inference, and candidate screening.
• Eightfold’s study quantifies the impact of AI in HR, showing significant improvements in efficiency and outcomes.
• AI helps change the narrative from supporting business goals to solving business problems.

Talent Acquisition as a Precision Science

• Talent acquisition is evolving from an operational process to a precision science.
• The focus is on hiring for the right roles with the right skills for future needs.
• AI helps design future-ready roles by analyzing market trends and emerging skills.
• The shift from fulfillment to growth and talent advisory is emphasized.

Personalization and Automation in Talent Acquisition

• AI enables personalized candidate experiences, tailored assessments, and career websites.
• Automation frees time for recruiters to focus on strategic tasks and candidate experience.
• AI-powered talent marketplaces help rediscover internal talent.
• The landscape of talent acquisition technology is becoming richer and more complex.

Role of Recruiters in the AI-Driven Talent Acquisition Revolution

• Recruiters shift from operational tasks to strategic advisors.
• The focus is on understanding business goals, aligning hiring tasks, and managing AI tools.
• Recruiters need to interpret AI insights, manage expectations, and have complex negotiations.
• The role of recruiters is enhanced with new skills in data analysis, AI integration, and strategic advisory.

Conclusion and Q&A

• Stella concludes the presentation and opens the floor for questions.
• Questions from attendees cover topics like measuring better hiring decisions, leveraging offshore resources, and adding value to the hiring process.
• Stella emphasizes the importance of understanding tasks and orchestrating AI and human resources effectively.
• The webinar ends with a thank you to attendees and a reminder of the recording availability.

Host 0:00

Hello and thank you for joining today’s webinar. We’re excited to have you here. If you have any questions during the webinar, please put them in the chat widget located at the bottom of your console. If you experience any technical difficulties, you can place those in the chat, and our team will help you troubleshoot with that. I’ll turn it over to Stella to get started on today’s presentation.

Stella Ioannidou 0:24

Hello, everyone. So nice to be with you here today. My name is Stella Ioannidou, and as I almost always present myself, the weird Greek name that sounds so Greek to you is exactly that. I’m the Senior Director of Research here at The Bersin Company, and we are honored to be partnering with Eightfold on this webinar. I’m really pumped to share with you the latest research on where we’re seeing talent acquisition go into the future. What are some of the forces reshaping how we understand what the right talent is, how we attract the right talent, what we do with talent once they’re into the organization, how we onboard them, and then how we grow them? I’m happy to take any questions you might have through the Q&A, and I also have some questions that some of you have sent in advance that we’ll also be covering. So, let’s slowly get this started. There is a big year ahead of us. We’re almost at the beginning of the year, I want to say. A week or a couple of days ago, Josh Bersin presented the new imperatives for the year. What we are seeing as a company becoming the core themes, the core trends of 2026, and you’re going to see how important, how pivotal a role talent acquisition is playing into that further, broader ecosystem.

Well, let me start with the key forces at play. For the first time in a lot of years, we’re seeing economies are growing, but job creation is actually halting. We’re seeing CEOs and C-suite folks push for more automation, push for discussions around fewer jobs with the promise of productivity, and that appears to be a core theme for the year ahead. The second theme is that generative AI is now an established concept. Many of the companies we speak to on a daily basis do tell us that they use it at least sometimes per week and/or even once per day. They are spending—large organizations—40% are spending 10 million per year on AI in some shape or form. So if you’re not up to speed or up to date with what it is, this is a good time to get started, for sure. Another theme we’re picking up is that early excitement about personal copilots is actually not paying off. We are trying to understand and see what the enterprise is going to look like beyond personal productivity. So we’re going to be looking into that ourselves in 2026; we actually have a big study ahead of us in AI excellence and enterprise AI, so more on that near the end. Well, I can share ways that you can be a part. A fourth theme is that companies are trying and starting to understand artificial intelligence. They’re deploying some agents, but they’re not sure yet how to automate some of the workflows, how to clean up or share the data, how to build these super-agent collections of agents that need to actually make this an AI-empowered ecosystem, rather than a standalone AI agent doing one simple task. So I think we’re going to be seeing a lot of traction in 2026 and 2027 in that landscape. Meanwhile, on top of everything that has been reshaping and shaking the work ecosystem, employees are uncertain. There is lower job mobility. There is general uncertainty about careers, including frontline work. And employers definitely get a lot of expectations from their workforce around what they expect as an experience, what they expect as a growth opportunity, what they expect as a place to be and to deliver their best work. We outline in our Super Worker Organization imperatives for 2026 all the 11 imperatives that we are seeing. As the mandates for you to consider, you will see that talent acquisition is no less one of them, and we will be talking considerably today about why that is the case and why that matters to you.

A little bit of background of what we say, what we mean when we say it’s… it started off as the “Rise of the Super Worker” from 2025, where companies are trying to gain momentum and advantage by increasing their productivity. We were trying to understand how that would translate into the broader ecosystem. We were able to see that it’s not only about individual productivity, about employees doing more with less, and reinventing jobs and learning everything about AI; it’s also a lot to do about supporting work redesign, about thinking differently. How are the roles shifting? How is AI impacting the tasks that are being done? How do we support teams learning, and how do we address the concerns about losing staff or losing power, losing positions, or falling behind? On the broader organizational level, that translates to a more complex series of imperatives, like deciding on: Do we reduce or do we freeze headcount? Do we redesign the business processes? Do we invest in AI systems? Which ones do we trust? How can we trust them? This is a big conversation to be had, definitely for 2026 going into 2027—how that effect is going to translate and cascade from the individual to the systemic to the collective. We are definitely going through some form of revolution in management philosophies and in culture. We started off talking, and it kind of feels like it was a long time ago, we were talking about skill-based organizations. It’s still part of the discussion, but now we have moved ahead into: we don’t just need to prioritize, let’s say, skills and competencies over job titles and roles and levels. We’ve been having that discussion for many, many years. We also want to make sure that for those skills, we are putting the right talent at the right position, in the right time across the organization to maximize the overall productivity of the team. So how do we build a talent-dense organization, for that matter, where we, at any given point, maximize the potential and the productivity of our workforce? And on top of that, how do we design for constant change and continuous transformation and move people around without hoarding talent, while promoting growth? And topping that list is, on everything and all that being said, how do we embrace AI to drive productivity and have individuals as managers of agents, rather than mere individual contributors or experts in the specialist roles? This is becoming a very, very complex conversation and discussion, and I will explain in a bit how that impacts talent acquisition and why it is important. But what we’ve seen is that organizations who understand that—for example, in artificial intelligence and in scaling the talent acquisition revolution—it goes beyond choosing the right skills and choosing the right technology. It actually boils down into six different types of strategy mindsets that organizations who are great at this understand very well.

One is that AI transformation and everything we will be discussing after this slide is not about controlling costs. It’s not about necessarily doing more with less. It’s about growth through better and more efficient and more timely allocation of talent across the organization, enabled by artificial intelligence. The second thing on everyone’s mind when it comes to pacesetting the AI transformation is about looking at everything—every change, every shift, every rift—as an opportunity to innovate at the core. Even if we try out something—let’s say us in talent acquisition, we try out something, and it doesn’t work—we still get to learn a lot of things that we didn’t know, that we can leverage to move ahead and innovate even at a later stage. Pacesetter organizations understand very well that none of the things that we’re going to be discussing about artificial intelligence in talent acquisition today matters unless we actually design the work around what needs to be done and not just include AI and bring in technology in talent acquisition without doing the actual task analysis, job analysis, and redesigning every work of the job families that we start to, we begin to focus on. It is a game of people quality over people quantity. It is a game of change agility rather than change management. It is a game of ensuring that our HR teams, on the emergence of AI, are more systemic, more cross-connected, more cross-pollinated than ever, because a lot of the decisions that need to be made are not TA team decisions. I’m going to be very clear on that. The more you incorporate, the more you come together, the more you work cross-functionally, and the more you embrace the systemic mindset, it’s going to be more supportive and bring you faster in what you want to achieve by transforming the TA function.

Moving ahead, I want to touch a little bit on how AI came and is starting to change our organizations. And we will spend a considerable amount of our time today talking about how it will change TA, but let’s take a step back for a minute and remember how it’s changing the company landscapes. It is integrating in bold new ways across all the areas of the organization. Most of the industries have had some form of AI in some area of their organization for quite a long time before HR was introduced to AI. We used to think that AI was a novelty back in 2022—and kind of, it feels like a long time ago, but it’s only like four years ago. It kind of felt like it was something amazing, something maybe scary. Can we trust this thing? Can we figure out what it is? From the years 2023 to 2024, we started having fun and exciting and disruptive small use cases, like, how do we use it well? How can we learn how to use it well? It’s the first copilot and the first, let’s say, fun experiments. But then we started wondering, okay, this sounds cool, but it actually makes more mistakes than I expected it to do. So why is that so? And then we started having these conversations: okay, how much can we automate? How do we program and control this thing that we call AI? How do we learn to deploy it well, so that it doesn’t make so many mistakes, and it can transcend beyond personal use, and we can have like a tool to build them, to automate stuff? And now we’re having the discussion that, well, we’re talking about the toolset, but at the enterprise level, not at the personal level, not at the team level, but the enterprise level. How do we re-engineer entire workflows? And we will be having the conversation of, okay, we brought this thing. We are an AI-enabled or augmented, or AI-first organization. What is the economic value? Why do we really need that? Where is the opportunity for us to re-engineer the business at the enterprise level and actually build a platform for automation and accelerate the organization leveraging AI as a super agent and not as an individual agent, doing one thing? We are seeing currently four stages of AI revolution.

Stage one is where most companies are. For example, we’re seeing companies use assistants for employees to find HR policies. Nobody’s job is changing, but things just get a little bit faster and a little bit easier. For example, it takes 15 seconds, not five minutes, to find out if December 24 is a holiday or not. Something in that stage of complexity, the improvement is quite small, but it’s still on the 15 to 30% range, in terms of how many tasks are being handled by AI assistants. Then the second stage: we’re still in the same work, in the same roles, but we’re using agents, and we’re scheduling interviews, for example, in TA, and we’re supporting candidates in any question. And in many cases, our candidates tell us that it’s so realistic that sometimes candidates bring flowers or want to meet that agent that supported them in their onboarding journey or in their hiring journey. We’re seeing removing steps in existing work roles for more productivity, and the improvement can go as high as 50% of the tasks to be completely taken over by the AI agents. In stages three and four, currently, we’re seeing fewer companies overall, at least from the discussions and the case studies that we are capturing. In stage three, we’re seeing the redesign of work and roles for greater productivity. Now that we know what AI can do or cannot do, we are actually redesigning what tasks need to be done around that automated landscape. And this is where the improvement becomes significant. And on stage four is where we fundamentally change the roles in the work to enable humans to work at what we call “top of license,” the top of their productivity, the top human capabilities that cannot be automated, and the top areas that you definitely need a human not just in the loop, but taking charge of the action. Why I’m showing you these stages is that I want you to start thinking slowly and surely of some use cases of AI emergence and implementation in talent acquisition, and we’re going to be sharing a lot more around that in just a bit. But think about how many tasks, or what tasks, or where the opportunity is, and what stage you would be if you were to think about evolving AI in talent acquisition in your organization overall.

Bringing it a little step closer. We started with the ecosystem and the economy, and then the company, and now HR. Now HR has been front and center at least for a year and a half on: okay, what do we do with that new fancy thing about AI? What are the opportunities? What can our tech stack do? What can it not do? So what do we need to update or upgrade or simplify or tone down or trim down, or whatever? When we’re thinking about artificial intelligence in HR, we’re not just thinking of modernizing an existing HR tech stack. In most cases, this means scrapping entire parts of the tech stack and bringing in agents to work on our behalf. And AI in HR is actually going everywhere, from rewards, from employee experience, from career mobility, from Employee Self Service, from learning and development—one of the most impacted areas of HR by AI—leadership, succession, and even recruiting. We are seeing that especially in HR, the impact of AI goes beyond HR efficiency. Yes, the first thing that came into our minds is that we can complete actions faster with AI, but the more organizations are incorporating, playing, piloting, adopting, scaling AI-based tools, the more they see that it actually helps make work smoother and easier in some of the cases, and it actually helps people accomplish the right outcomes through increasing effectiveness. And at the final, let’s say, stage of impact, we’re seeing gains in performance and productivity for every employee. This used to be something that we were discussing, like, two years ago, and said, “Well, the impact is coming. It will come in interview support. It will come in skills inference. AI will come as a candidate screener. AI will come as a skills interpreter.” Well, guess what? It’s already here. We have captured actual case studies. We’ve been capturing those for the last two years at least, where AI, for example, is used as an interview supporter, reducing the cost for both the manager and the recruiter in terms of the time that they spend and where they are included in the loop. We’re seeing a major increase in employee satisfaction and mobility and growth opportunities, because AI is supporting skill inference and is generating more opportunity through talent marketplaces for the talent of the organization. And Eightfold is an example in that. We are seeing a reduction in time to hire for even the hardest-to-fill positions because we have improved candidate screening, because we have 24/7 abilities of people to apply to jobs through their cell phone in a matter of minutes. We are seeing a reduction in employee turnover because we know better what type of skills we need for people to grow on, and we can enhance their talent and skill development. Kathi Enderes, from our team, actually did a big study quantifying the impact of AI in HR. We saw that it goes well beyond efficiencies and cost savings, improving overall the outcomes and the impact of HR. And the biggest, let’s say, transforming factor is not that we can definitely prove the case that it’s good and it’s saving us X amount of time, or it’s eliminating X amount of steps. The biggest opportunity for HR with AI is that it helps change the narrative from supporting the business to achieve their goals to actually solving business problems and being part of broader conversations. We’re seeing organizations, for example, using AI to streamline the recruitment and support, of course, their recruiters in hiring, and hiring managers with more accurate insights, but that actually helps solve the problem of lengthy hiring and inconsistent quality of hire. We’re seeing AI help HR solve the problem of a confusing candidate experience. We’re seeing organizations and HR departments having difficulties assessing candidate job fit or experiencing high turnover and limited internal mobility, and these are some of the business problems that AI has been solving for the organization for the last two years at least. And we’re seeing examples from a variety of industries, which makes that interesting, because the initial assumption we had is that some industries are more prone to adopting than others, but we’re now seeing that to become like uniform adoption—no matter where you are in any industry, rumor has it, there is something about AI that’s been going on. There is some conversation, or at least a pilot going on, and this was not the case a year and a half or two years ago.

Bringing it home to talent acquisition slowly, through the lens of talent management, we are seeing, for example, in organizations, when they use AI as a growth goal guide, they see reduced time to drafting performance goals, reduced time for managers and recruiters to spend during interviews, reduced time to draft job descriptions, reduced time to hire, reduced time to hire for harder positions. And the most interesting thing, I think, in that part, is now we actually have the data to prove it. We thought it would work. We had an idea. The systems are also helping us be more vocal and more able to explain what is the impact in the area and the point in time that we currently are. We’re seeing that AI has the power and the ability to support not only every part and every person and every talent in the talent acquisition ecosystem, but everyone in the organization to improve their impact and their understanding. But in HR in particular, it helps also benchmark the business impact and help prioritize and determine what type of impact is HR having during the AI transformation. With the emergence of AI, it had us completely rethink how we understand and manage talent. Back in the day, it was about: we have a job, we need a person to fill that job and do some sort of work. And workers were abandoned, and we could have this traditional, let’s say, approach to work into HR. But now we are moving into a talent architecture that evolves around the person. It is the person that has the skills that is engaging in some work activities, and can also leverage talent intelligence, generative AI, or any other copilot-related tools at a what we call “Systemic HR” ecosystem. Why is that important? Because it shifts the whole concept of talent management and talent acquisition from “how fast can we bring people in” to “how do we ensure that at any given point, if someone would do like a talent audit in the organization, they would see that we’re placing the right type of talent at the right type of role and the right point in time for the organization to achieve the outcomes that we need?” And we call that talent density, and it is really important for us to understand in talent acquisition, because the more we think about the people that we’re looking to hire, the talent that we want to bring in the organization, through that lens—the macroscopic lens—how do we make sure that we bring not just as many people as we can, but the right people? It will support the broader ecosystem and help us be even more impactful in the organization. And some of you have asked me in the questions that you said beforehand about what are some of the less traditional KPIs, for example, that we can have in the new era of talent acquisition. And can we go beyond time to hire, for example, time to fill? And we can definitely go beyond. But to go beyond, we first need to shift our mentality from: we are no longer a fulfillment center to get, you know, people and talent in as fast as we can. We are the growth department. We are the talent advisors. We’re the career coaches. Our mandate is to grow the organization’s capability. And when you adopt that lens, you understand that, okay, it’s less about time and cost to hire, but it’s about skills and capability focus. It’s less about scaling to scale up to hire more. It’s about strengthening internal mobility and growth to scale. These are some of the more important questions to be had in our minds in TA, especially with the emergence of AI, because, let’s face it, us in TA, we always had some form of a data-driven, KPI-driven culture. We had some shape or form, or even in many cases, we have the most advanced systems throughout HR. Now we can better and even more enable our people in talent acquisition, our recruiters, our sourcers, our hiring managers, with tools to support the mandate. So where do we actually come and maximize to stay our potential to work at top of license, as I said earlier? Well, this is the opportunity for us to actually shift from this idea that, okay, each new hire creates some process that I need to manage, to “each new hire increases everyone’s productivity.” And maybe, say, maybe when we’re having discussions with our hiring managers, we’re not actually taking the requests at face value, and we’re having discussions like, “Okay, why do we need this hire? Can we find those types of skills internally? Oh, let me check your request for a minute and see… okay, with those specific skill requirements, there are literally three people on the planet that we can hire. How can we be more strategic in what we want, not just for now and for today and for where the organization is, but for where the organization is going?”

And this is the core, at the very, very core, of the revolution as we call it, that we’re witnessing and we’re capturing and we’re narrating with our research around talent acquisition for the last year, and where we see this heading into 2026 and beyond. First off, talent acquisition is no longer a, let’s say, operational, process-managed, let’s say, state that we need to follow steps 1, 2, 3, 4, and then go from posting to the first day that we hire into onboarding. It’s actually more of a precision science. And what we mean by precision science is actually more: “Hey, are we actually hiring for the right role with the right skills and to have people, let’s say, do the right tasks? How do we know are we hiring for skills that are declining in the market or not going to be around in three to five years? How do we know? If we don’t know, can we include that into our overall analysis?” And when we are designing a new requisition, we can find, for example, that, okay, this is not necessarily the right type of profile for the work that we need to be done. For example, in many cases, we know where a new job post starts: someone leaves, or someone, you know, transfers to a new part of the organization, or there is growth in the department. So we say, “Oh, I have a Stella. So can I hire another Stella?” Yeah, but Stella has specific types of skills, and half of them are actually declining. How different would the conversation be if I knew which of the skills would be declining compared to which of the skills are rising and which of the skills are actually stable? And maybe, let’s say, maybe, when I’m designing the actual job post, I don’t look for the declining skills. I only look for the rising and the, at least the stable skills to make sure that we’re bringing in talent not just to cover today’s need, but also to cover the needs looking ahead. This is an example of leveraging, for example, Eightfold data to describe, to design a future-ready role, taking into account what is the skill status for all the skills in that role, and what are some of the emerging skills in the role, in the industry, in the economy, in the geography that we can include, and say, “Okay, the future-ready role actually now looks like this. It has the stable or the rising skills from what I used to know, and has these new extra ones for us to actually be on the lookout for.”

You cannot expect to do this out of a hunch, out of making this up, right? You actually need technology. You need AI for that. You need to be scraping the market. You need to understand where the vibes are, how the skills are progressing. You need this time-series data. So you really need the AI and the technology there.

The second part that we are seeing is that AI is integrating almost everything in almost every part of the talent acquisition pipeline. We are capturing examples of organizations who are actually automating literally every part of the steps that lead from the job search to day one, and they’re only keeping the moments that matter, where the human actually needs to be in the loop. For example, this is an example from Great Wolf Resorts. They were able to significantly bring down their time to hire—to actually their time to interview—to single digits. But they did decide that, “You know what, AI can automate almost everything. But I still want to have the in-person interview. I still want the human in the loop to make the decision for hiring, and I still want a person to meet and greet my new talent on day one. The rest, I couldn’t care less. I want them automated, and I want my recruiter to not manage them anymore, but instead serve as the person that oversees the agents and makes sure that, okay, this part was done. I can see no errors here. And this was done in time. And this is at the quality standards of our organization, and we can proceed. And this is the list of people to have in-person interviews. And these are the recommended, let’s say, these are the outcomes of our assessment, and this is a shortlist, and we can decide who we want to interview in person.” So instead of worrying about, you know, “Does Sue have time on her calendar to meet with Stella, who’s interviewing for the job?” AI takes care of that, and the role of the recruiter completely shifts away from the mundane and into managing the ecosystem.

The other part that is very prevalent in the TA revolution is how much personalized they can get, how much tailored the assessments can be, the language, even the career websites—they can be tailored to the person going into the website to see the roles and to understand if this is an organization or if there’s a role that I am good considering. This is something that we were not able to do before, at least at that scale, and with that ease, and it also frees a considerable amount of time from both the recruiter and the hiring manager to let go of the, “Okay, let me know about, you know, where is the address? Who do I meet? Where do I come?” Or “How much does this role pay?” Everything that we were going to get asked by candidates can now be, in 90% of cases, covered by conversational AI. The assessments also being tailored per role, per person, per geography, spoken in people’s, candidates’ own languages. Not everyone needs to speak, let’s say, English to apply to our organization, because the conversational AI assistant can speak up to 20 different languages. So it completely changes the candidate experience from “I need to literally do steps 1, 2, 3, 4” into “Okay, I can actually have a tailored experience to my geography, my language preferences, my role, and the way I’m approaching the organization.” And we’re seeing that especially prevalent, both in the candidate, let’s say, assessment, and in the testing and scoring. One of the earliest applications of artificial intelligence in AI has been letting go of the need for someone to actually manually send the score, the test into someone’s email so that they can take the test, take the assessment, and we can check if this is a talent that meets our quality standards. We’re seeing this. It’s one of the, let’s say, fastest growing areas of AI adoption in talent acquisition—letting go of the manual testing and scoring and enabling that either by automatically sending the test and/or by including conversational agents, AI agents testing our candidates in scenarios that are very close, or even closer than before, to the actual environment of work, the actual work that they’re going to be doing. Which is another interesting area that a lot of debate and conversation is currently going on, like, “Can we trust the actual outcomes? What if this is a real person? What if this is an avatar?” And there’s a lot of discussion and a lot of solutions rising in the field of being able to understand, let’s say, deep fakes from real, actual candidates.

Another part that we’re seeing in talent acquisition to be heavily, heavily impacted by the emergence of AI is because now we have the infrastructure to analyze and process and run algorithms at scale. We actually have the ability to produce AI-powered talent pipelines and rediscover the talent that we already have. Maybe we have, let’s say, X, Y amount of people in our organization, but let me know if you had the same experience as I had when I was back in the industry. I was always very hesitant to do a skills questionnaire and ask people, “Okay, what are your skills?” Right now, I wouldn’t know if half of them would say the truth or inflate their skills to further promote themselves or downplay the skills because they would feel like they would get into trouble, or they would question, “Why are you actually asking about my skills?” AI is helping significantly enrich our talent, our knowledge of understanding, at least at the first level, around what skills they might have based on the roles that they currently have in the organization. And of course, it is a blooming, blooming market that transcends beyond the ATS. Someone asked a question about the ATS. Well, if only it was about bringing AI into the ATS, right? It’s actually everywhere in every part and every type of solution, whether it is an ATS solution, or it is a scheduling solution, or it is an assessment solution, or it is a sourcing solution, or it is an interview or onboarding or career design solution. We are seeing the landscape become richer and in some cases quite niche and overall complex in terms of what type of technology is leveraged, and how can we actually select what type of technology solution we can tap into depending on the real need.

What I do want you to take away is that when we think about the impact that AI is having on talent acquisition, it is a big paradigm shift for at least the recruiter and the way that they are generating the impact in the organization. They move away from the operational, nitty-gritty, everyday management, organizational stuff and going into having an oversight of tools, of the candidate experience, of the data that we’re using for, let’s say, predicting or enriching the candidate pool. And on top of that, “I am freeing time from things that I was doing, like figuring out how and when to schedule interviews of candidate one, two, and three, into what type of conversations can I have with my hiring managers, with other people across the organization, to understand where the organization is going? What are our skill and talent needs? Do we really need to hire for that role externally? Can we look internally? How can we ensure that we’re making better decisions about placing talent where they are, instead of worrying about, okay, how fast can I fill this role? How quickly can I bring people in the organization?” And this shift of the recruiter from becoming a, let’s say, support of the process into a talent advisor, actually comes with a new and a different set of skills that are going to be present in just a bit. We are moving, especially in TA, away from working a lot on the interview part, because a lot of the hard tasks are being or will be automated. I personally predict that at least in this year, we’re going to be seeing a much higher population of companies who are completely automating the interview stage in terms of doing the background checks, figuring out if we have the right data points, scraping the CVs, ensuring that we have everyone’s assessment done, and we’re going to be thinking more about the skills architecture and the talent and where they are in the organization. And that is why we’re seeing a lot of companies actually move from having distinct talent acquisition functions into a broader talent and culture, for example, or talent and culture and talent wherever. I’ve seen a lot of names under the same organization, overseeing the entire talent experience lifeline, from before you join into the organization, even after, when you become like an alumni of the organization that in many cases, has been seen to come under the same organizational roof.

We are seeing that, for example, even having access to specific copilots or support like our very own, the Galileo one, is actually enabling a series of tasks that even if they can be extremely automated, or 100% automated, they can still get a lot of momentum by receiving the right guidance at the right time. Maybe you’re looking for guidance in consolidating TA technology. Maybe you want to understand how to support autonomous recruiting as a strategy. Maybe you want to map skill adjacencies to design your own talent marketplaces. These are some of the solutions that tools like Galileo can support. But I want us to spend a few moments more on what that means for people in talent acquisition, for the recruiters, for the sourcers. They have a new role and a new set of skills.

Yes, some of the skills and some of the parts in their profile that we used to have—for example, the adaptability, the mitigating bias in recruiting, the responding to changing businesses, collaborating with all parts of the business—these are definitely not going away. But we are seeing an enhanced need for capabilities. Like, do we really know the business deeply? Are we aligned? Do we align the hiring tasks with the organizational goals? Do we understand the technology integration and the impact of AI transformation not only on the recruitment task, but on the broader idea of bringing the right talent into the organization at the right time with the right skills at the right area in the organization? We need additional abilities to draw insights from data and also interpret the AI-generated insights, understand where the AI could be right, but also understand where AI could be wrong, and we need a second oversight, a second human in the loop, to make sure that what is being given or generated by AI, for example, can be translated into safe and actionable strategies, because AI can hallucinate, and the recruiter, especially in the generative AI environments, needs to be very well aware and wary of that. We always knew that recruiters were one of the most empathetic and connecting people in the HR organization, but now they also need to manage expectations, have complex negotiations, and have higher-touch experiences with both the candidates and the hiring managers, and have the weird conversations like, for example, “Do we really need to hire for that talent externally? Can we find this talent internally, or can we hire from adjacent roles in adjacent job families? Because we can bring, we can bring directly that job role that we’re asking for, or the cost is too high, but we can, in some shape or form, still access the skills.” These are advanced and enhanced skills and capabilities that recruiters need to develop. And on top of that, they need to understand how to configure, how to optimize, how to troubleshoot the AI tools, how to manage conversational AI agents, how to manage digital twins, and be the strategic advisor that the business leaders need to design their talent strategies and the workforce planning and understand also the market insights. It is a big discussion, and a lot of organizations that we speak to are currently working very diligently on further supporting that evolved recruiter. This is an example from AMS that they’re talking about the similar concept where they think about, okay, what are some of the human tasks? What are some of the tasks that we can have? Some parts are human, and some parts are AI, some parts that we need an oversight, and some parts are completely AI tasks, and the recruiter doesn’t need to worry about them anymore.

What is interesting for you is to start by mapping out the work, mapping out the data, mapping out the ecosystem. In most cases, and we’ve been saying this for a very long time, the devil is in the details and in interconnecting the data and the systems and redesigning around the work that needs to be done. Think of this… think of the shift from into the talent acquisition revolution as a shift from handcrafted to precision-based. Think that your job description should be standardized by AI. Think that your sourcing intelligence should be based on skills. Think about how do we include AI in our interviews and our assessments in ways that don’t bring in more risk than we need, but at the same time, simplify the lives of our recruiters and our hiring managers? Think that in how many… in what ways can we do screening as fast as possible? How do we integrate internal mobility into our processes, in our discussions, and in our conversations with our hiring manager? How can we have discussions around salary and rewards based on roles? How do we integrate that into workforce planning, while focusing on growing the business, not hiring as many people as we can? The biggest thing, the biggest shift and the biggest challenge for a lot of organizations we speak to is not on the ability or the inability of people to understand what’s in it for them when you’re talking about AI in talent acquisition; it’s how we need to understand that the HR as an organization, and talent acquisition as a part of the HR organization, needs to evolve from service delivery to product offering to consulting. We need to be cross-trained. The org, in many cases, needs to be flatter, and people must work in cross-functional teams, including the TA team. There are multiple ways for you to be supported in that front. Definitely, especially when you are in the early stages of AI adoption, you can tap into specific agents that have HR knowledge and can support you. Galileo is one of them. But if there is one takeaway I want you to, let’s say, have stuck in your head when you’re thinking about AI and talent acquisition, is that it is a big shift that goes beyond the tools. The tool, AI, is just a means. The transformation and the shifts that are coming in the organizational context and in the context of the talent acquisition team actually rely on working differently and being ready to adapt and cultivating for that readiness to adapt. Talent acquisition has always been at the forefront of, let’s say, operating in a dynamic environment, but now more than ever, we are expected to not only transform ourselves, but manage expectations of everyone else across the organization while we are changing. So that being said, this completes the first part of my presentation today.

So let me go into addressing some of the more specific questions that I see in our Q&A and in the questions that you’ve sent my way prior to the webinar. So question number one says, “Can you share with us how modern organizations are measuring better hiring decisions? In other words, how is quality of hire different today, if at all, from the historical data points of retention and performance, and for that matter, how AI-driven ADS systems are informing better hiring decisions?” Where it’s more… it’s more complex than that now, isn’t it? Based on what we’ve said right now, it’s not about how fast we are bringing people in or how many regrettable losses afterwards we’re having. It is more about: do we have the right skills at the right time, in the right place within the organization to achieve the right productivity or impact, so that we can make the measured, let’s say, estimate that this is a high-quality hire. It’s not only about productivity, though. I want to be very upfront about this. It has to do with understanding the broader ecosystem, and especially in TA. Let me go back into my slides a little bit to show you this. The main parts of the revolution. Let me push this.

Stella Ioannidou 52:38

Have technology… so a good quality of hire is actually combining a lot of the five imperatives of the TA revolution. Quality of hire is it? Is it precise? Do we know exactly who or what, or what skills we are hiring for and for what area in the organization, and are we covering our needs today? And not only our needs today, or also our needs in, let’s say, the next year or the next couple of years. A good quality of hire is a hire that is coming at the right time for their organization, regardless of where we found them. Maybe it’s internally from the organization, maybe it’s an alumni network, maybe it’s early career talent that we were able to secure. Maybe there are so many sources we can draw from. The interesting thing for us is to understand from a wide variety of, let’s say, points of view: What is the impact this person is having? How much growth potential and growth opportunity are they having? How are they collaborating with their peers? And how can we actually see the outcome of their impact? And that comes into the shape or form of multiple KPIs that goes beyond the, let’s say, quality of hire that we think about: “Oh, if they don’t leave, maybe it’s a good quality of hire.” Let me know, because I don’t know who sent this one. Let me know if I’ve answered your question. Tell me in the chat, and I’ll make sure to add more to my question.

There’s another in the Q&A says, “How are teams leveraging offshore resources versus AI to help support the administrative and manual tasks, to free up TA team members to be more strategic human elements in the hiring process?” Well, we’re seeing a couple of things in that area. When we say, like, offshore resources versus AI, it’s not about the resource; it’s about what type of job is needed, what type of work needs to be done, and who is the best to do it. Maybe the task is best done by AI. Maybe the task is best done by someone offshore. Maybe the task, for example, in talent acquisition, is best taken care of by an RPO. Maybe this type of work is best done by AI, overseen by a human in the loop. So it’s more about understanding the task rather than: is it in or is it out of the organization? How do we maximize the precision and the quality of the outcome of our work? This is how we’re thinking about this, not so much anymore about: do we keep this in the organization, or do we give take this outside of the organization? Remember the big shift in the recruiter. The recruiter now stars at the strategic agent, orchestrator, the person that has an overview of whatever it is is going on within the organization, and they are calling the shots. “I’m okay with the outcome of this process. I’m not okay with what these AI agents did there. I will intervene here. I will orchestrate.” So it’s more about orchestration at the task level, rather than the resource level. And this is a big, big shift in the TA revolution, let’s say landscape, the way that we are capturing this through our research and our stories.

Stella Ioannidou 56:33

I think we’re nearing the end, so it’s probably the last one. “How can this add value and save time to hire, improve quality of hire and employee experience?” I think we touched upon the measured impact of AI in talent acquisition. Think of this as a lot… as an approach, as a tool, as the impact being: it can simplify the things that are too time-consuming for us in talent acquisition, and we trust it enough to completely, let’s say, or at least partially automate. Think of the emergence of AI in TA, especially the more we are seeing agents come together, it will be more about understanding how they communicate to one another, what type of data they rely on, and what type of problems they’re helping us solve, rather than: “How can I tell you that, okay, it’s minus 0.5 seconds in the time it takes for someone to apply to a job?” Although I should put an asterisk there to say that, yes, AI will help us be even more data-driven, because we will have a lot of these data points at any point of the candidate experience and proof points to actually see what the impact it is having could be. I think we’re at time. So I would like to thank you for spending your morning or evening or afternoon—don’t know wherever it is you are—joining in. Someone has asked the chat for the recording. I understand that this will be provided. So that being said, thank you very, very much. And have a lovely rest of your day. You.