Name: From AI Pilots to Proven TA Impact: What High-Performing Organizations Do Differently
Uploaded: 2026-09-10T18:30:06.310Z
Duration: 1 h 5 min 28 s
Description: From AI Pilots to Proven TA Impact: What High-Performing Organizations Do Differently

Transcript for "From AI Pilots to Proven TA Impact: What High-Performing Organizations Do Differently": Alright. Good morning. Good afternoon. Good evening to everybody. We're gonna get a we're gonna give everybody, just a couple seconds to get logged in, and accessing into the webinar, and we'll get kicked off here in a few moments. Alright. Perfect. We'll go ahead and get kicked off. Again, thank you everybody for joining our webinar today. We are going to be going through our webinar today from from AI Pilots to Proven TA Impact, what high performing organizations are doing differently than other organizations to receive the value position. To start off, my name is Andrew McManus. I'm a senior principal on our AI strategy team here at Workday. My role responsibility here at Workday is engaging with our strategic customers, helping all of you understand our vision strategy from an AI perspective, and also run a number of different workshops building out road map strategies alongside of your AI strategy at the organization. Prior to joining Workday, I actually came through the Paradox acquisition. So I was the vice president leading the Workday practice, partnering very closely with Workday prior to the acquisition for the last five years. But I am most importantly joined by an esteemed colleague, Amanda. And, Amanda, if you wouldn't mind, I would love for you to give a quick introduction of yourself, a little bit of your role, and your background at Hackett Group. Hey, Max. Absolutely, Andrew. I'm Angie Nusfeld. I am the leader of our global HR applied intelligence practice here at the Hackett Group. I specialize really across HR. One of my specialty areas is talent acquisition. Twenty five years in the HR space, and primarily what I'm working on here is how do we help. All of the functions of HR really operate better and deliver better value for the business overall. Awesome. Thank you. Looking forward to some of the insights that you're gonna share with us today. But before we get started into the content, we have to go through some housekeeping issues or some some housekeeping items. First off, you do have to start with our product statement. We are going to be mentioning some things around road map, strategy, vision, where Workday is going from from that perspective. Just make your purchasing decisions off current functionality, not future functionality. We reserve the right to pivot our road map as we see fit. Now with the product statement out of the way, the last housekeeping items that we're gonna run through is, just as a reminder, this webinar is being recorded. So if you do, or if you do have to drop, we will be sending out a copy of the recording twenty four hours after. That's That's also gonna be the case if you get any technical difficulties where you miss some of the the questions or comments that you might be talking about to the audio issues or tech issues. We'll send those out. Additionally, we are gonna be holding q and a throughout the entire session, and we'll have a section that we're gonna go that we're gonna go through and answer some of those questions. So there is a q and a section next to the chat. So if you toggle into that, it's gonna give the opportunity to ask some questions, to myself and Amanda and some of our colleagues in the background, and we'll be happy to answer some of those at the tail end of the session. And last but not least, we will be having a survey, and we would love your feedback about the webinar, what we could do differently, additional content that we can start to bring in. But we'll be very happy to to get some of your feedback around the the content that we went through today. Then our last housekeeping item is our agenda, talking you know, framing up what we're gonna be talking about today. So it's you're not gonna be listening to me talk a ton. I'll have some some color commentary to share, but it's really gonna be the show focused on Amanda and sharing some of the great research that the Hackett Group has done. We're really gonna start to focus on kind of the the value realization gap that exists in talent acquisition, specifically around AI utilization, and then really diving into what high performing talent acquisition organizations are doing differently than other organizations. Those orgs that are seeing these great benefits of AI and utilization, we're actually gonna run through some of those use cases. And then we'll also touch on some of the ways that these organizations are selecting and funding the right use cases that are going to drive value for their organization. And then last but not least, we're actually gonna walk through a framework that focuses on four specific areas in talent acquisition and use cases that you can start applying to your business in your processes as a Workday customer and some of the features and functions from Workday that are helping to achieve and address some of those use cases. And then we'll end with our next steps and q and a from there. But with that, we'll go ahead and and move into the fun stuff, Amanda. I'd love to hear about some of the the different topics that we're gonna discuss today. Yeah. Absolutely. Thank you so much. So let's begin and start with by looking at what the gap is between AI adoption and actual value realization and talent acquisition. We all know the technology is advancing very quickly, but a meaningful impact really depends on how effectively organizations align to business needs and then redesign the work around that. So as we we move forward here, in a world where the business must move faster than ever, AI capabilities aren't enough to really lift the constraint talent acquisition places on the business's ability to staff and skills and skill the skills, sorry, needed for the future. The challenge really is it's a combination of multiple things. So there's scarce skills available in the marketplace overall. TAs still continues to be fairly reactive, planning from a role based perspective with relatively weak alignment to the business strategy in a lot of areas. Location bound operating models and fragmented AI adoption also kind of further that gap in in helping TA really kind of accomplish what it needs to accomplish from a business perspective. These factors tend to slow the organization's ability to support growth, and transformation. To be more responsive, TA has to think of two things. First, how does TA evolve the way it works? And then how does the enterprise think differently about where to source talent? Especially as we're looking at definitely the proliferation of AI and its consideration from a labor perspective, but then also if we look at where labor comes from, the skills that are available within the workforce, and how the workforce is even shifting, and how many people are even opting out of the workforce overall. The key takeaway here is that the technology alone is not gonna solve the problems that we're seeing here. To realize value from AI and, really, systems in general, TA has to evolve from thinking about itself as an execution engine into a strategic capability, one that anticipates future skills, connects directly with business practices and priorities, and operates with much greater speed and further reach than it does today, which really creates an interesting conundrum. If we dive deeper into this just a little bit, according to our most recent 2026 strategic talent acquisition quick poll, AI is already widely used in talent acquisition. So it's not a matter of the fact does TA had the technology to be able to enable what it needs to do. But what we're seeing is that the adoption and the maturity of that use is fairly concentrated and uneven. Only a few organizations have really reached full maturity in AI adoption, within TA itself. 15% are have reported mature AI supported solutions, where another 15% report mature AI enabled sourcing, screening, and matching capabilities. And then finally, just 8% report maturity across the three candidate engagement life cycle, especially with things like talent intelligence as well as end to end automation. As more organizations kind of think about their TA maturity, a lot of them we're seeing still sit in this piloting and planning phase, waiting to understand what shift they need to take next. Candidate engagement platforms really lead the way as far as where organizations are thinking about going next at 69%, while AI supported solutions and talent intelligence or even internal mobility platforms are each at 62%. The message here is not that organizations need more experimentation. We actually need to get unstuck from from the pilot and the experimentation, premise, and we need to instead prioritize where the use cases have documented proof of value and build more discipline from an operating perspective to be able to scale that value across the broader, enterprise from a TA perspective. Love to be able to dive deeper into that a little bit further. So if we kind of go into that just a bit further, many organizations we're seeing again, they're really stuck in this evaluation or pilot mode. But why is that? I will tell you from our quick poll perspective, most participants shared that the constraint is often business alignment, not necessarily the technology itself. So products are delivering on what they need to be able to enable the processes, but the ability to understand how to leverage the product, and align with the business is creating a lot of, issues for organizations. An overwhelming 47% describe alignment between the talent acquisition strategy and the business as fairly informal and undocumented. So without understanding how the two merge together and can work together, it really leaves a lot of reactivity, into the work that's being done today within talent acquisition. 15% of our respondents remains largely reactive to demand overall. That means there's no, items in place to be able to predict where demand might lead, no strategic sourcing or or thoughts, there from a strategic perspective around talent acquisition. By comparison, 15% of participants reported talent acquisition strategy that's fully integrated and aligned at the board level. So for organizations, talent acquisition is a strategic area, and it is something that should be thought of very critically. But another 15% are really only aligned that way for critical roles. We know that without this clear connection to business priorities, organizations are gonna continue to struggle to select the right use cases for which, to be able to to leverage AI to optimize their processes and their work. They're going to struggle to redesign the work in the way that they need to to make those deeper connections with the business, and will also struggle to establish governance and measure outcomes. The decision, therefore, is not simply about whether to experiment with AI at all. It's how do you actually redesign talent acquisition around measurable business value with AI used as an augmenting capability to drive forward in that thought process. As we move forward, the opportunity, associated with concepts around advanced AI platforms is already starting to take hold in visibility, as seen in our latest vendor, assessment results. So platform providers across all solutions reporting averages that include 32% improvement in time to fill and time to hire, 72% improvement in automation, a 70% increase in recruiting efficiency, and 74% of business requirements met by a single vendor. The platform providers are there to enable what TA needs to do. The market is moving quickly. It's being responsive. It's shifting towards concepts like Agintiq AI, conversational recruiter interfaces, stronger matching and content generation, improved analytics, and emerging fraud detection capabilities, which is key, because these are the things that are gonna help increase, the decision making and judgment that's needed in the hiring process, putting the focus on and the capacity where it provides the most value overall to the business, overall. The buyer lesson here is to stay focused on measurable outcomes. Separate the capabilities that improve actual real downstream enterprise business value, like speed, quality, or efficiency from features that just seem interesting but really add cost and complexity without a clear business case. So as we kind of think about the state of the world and kind of where we're seeing impacts, let's kinda shift our focus into where we're seeing organizations that are actually realizing value, and taking advantage of the opportunities that are before them. So what are high performing talent acquisition organizations doing differently that is enabling them to translate AI investment into that measurable, scalable value that delivers back to the business overall? When we take a look at high performers, what we see consistently, and this is inclusive of all processes across, the map in and and in TA specifically. They consistently follow a repeatable process where they're thinking about AI adoption, and how it aligns their priorities. So there's an increased focus on aligning priorities, redesigning work in a way that's important, building governance around, where decisions need to be made, and then ensuring outcome measurement. So if we dive into each of these areas, so first, these high performing organizations align talent acquisition priorities and investments with the business strategy. High performers are focused on what creates value for the business exclusively, and they're focused on that rigorously. Second is understanding that not just plugging in the AI technology creates the value. Redesigning the workflow is really needed rather than adding AI on top of weak process. We all know that if we automate a bad process, it just makes the bad process faster. AI will create capacity. The people in the workflow have to be able to absorb that capacity, in different ways in order to actually generate value. That means that the work has to be designed in the right way, to to take advantage of those, capabilities and that, the people have to have the capability to be able to navigate within the new process. Third, these organizations are establishing explicit governance for data, decisions, transparency, and human accountability, which is critically important as we continue to see, new laws and compliance issues emerge specifically related to the utilization of AI within the recruiting process overall. This element where decisions are gonna continue to have higher elements of scrutiny and people have to retain decision rights, especially in these high risk situations. Finally, we don't or cannot achieve value from AI unless we're actually measuring our outcomes. So organizations here are exclusively measuring value such as speed, cost, quality, adoption, scale, and I would even say experience. Action alone does not equal value. The ability to drive the outcomes that we need from a from a talent acquisition strategy perspective are where the the value lies, and we have to measure that accordingly. These four disciplines reinforce one another. They can't be, worked in a vacuum. They can't be worked in silos. Alignment identifies where the value matters. Redesign changes how work gets done. The governance makes the model responsible and sustainable, and measurement shows whether to scale or to stop. My recommendation is as you're looking to improve the performance of your TA organization, use a framework like this as the road map. And we're gonna go through that in a little bit more detail on the next few slides. High performers begin with that material business problem. That's really where we're talking about. All of the conversations that I have had recently, with the clients that I'm working with regularly is starting with the question of what is the problem that we're trying to solve, or what is the opportunity that we don't wanna miss out on. We're not starting with what is the available AI feature and trying to work backwards from that. They really instead work through five questions. What are the hiring constraints that are impacting our organization's ability to grow, transform, or access critical skills? Really seeing a lot of language changes around how we talk about skills within the organization that's leading to be able to define what these business constraints are. We have to understand what the work is that the organization is doing both now and what it wants to accomplish in the future in order to be able to do that and to understand where the business constraints exist today. From there, we have to understand what's the baseline. How do we establish what today's performance looks like and concepts like time, cost, and and quality experience or capacity, so that we can understand what we wanna achieve in the future and define how we're gonna measure that. We also need to understand the roles or the skills, the locations, or the talent pools that are gonna carry the greatest impact. Many organizations look at, strategic workforce planning as an element to be able to feed this, managing their overall talent portfolio as if you would manage, a portfolio, of work. Where do we need to differentiate and focus our efforts at versus where might we need where might maintenance be the appropriate place to to live for those talent pools right now? Ultimately, we have to understand, is AI genuinely suited to either assist, augment, or automate the work that we're trying to do with the appropriate oversight? And we have to understand that from a value perspective. Otherwise, the solution can't scale beyond a contained pilot. We have to name the value. We have to name what, capacity gains that might happen from that value and then how we're gonna reinvest that value in order to be able to drive more more, improved experiences or more value for the business overall. This sequence creates discipline, and that's what's really important here is because we're doing a lot more around making, estimates or assumptions around where investment should be placed and then proving it out and doing a lot of quick cycles of pilots that then begin to scale quickly. By doing this discipline, it connects each use case to evidence, an effective segment, a clear role for the AI for AI as well as for the human in the future in a credible path to showing the broader value that the solutions will create for the organization overall. Not implementing AI for AI's sakes because everybody else is implementing these types of capability, but truly driving what's gonna make the right sense for your organization based on what the work and the, roles that need to be hired at your organization. The key takeaway here is that we should be funding outcomes, not necessarily the technology for its own sake. As we take a a deeper dive into this, the second concept here is that high performers are able to convert AI into operating advantage, mostly by building a better and stronger management system around it itself. So it's not necessarily the technology. In many cases, I would even argue to say AI is less technology as it is a variable labor component to be treated as such as a part of the overall organization. The system starts with operating discipline, understanding the use cases that clearly address material business constraints, redesigning the work so that we understand what the AI share of the work, that variable labor from AI is going to do, what recruiters need to own, and what hiring managers are gonna own. Roles are clear, including decision rights and who has accountability for what decisions throughout the process. Value governance then defines what the baseline is, the expected benefit, and criteria for scaling before additional investment is made. Cross functionally, that supports across talent acquisition, the technology, data, and even the legal and and compliance parameters that we need to keep to to consider. Ultimately, this helps the business sustain the change. Without the capability and understanding how we are going to absorb the capacity gains that AI takes or gives us, we're unable to actually realize any value overall. When these elements work together, the outcomes include impacts to margin, impacts to an experience, as well as speed and even growth. You can lower process cost. We have greater automation that can drive speed and then a course growth and faster hiring cycle so we can see where our organizations are looking to grow overall. The advantage comes from how AI is managed, not simply by adopting the AI tools themselves. So this comparison here that I'm sharing, really shows why work design separates, an incremental pilot pattern and the typical pilot patterns from what high performers are doing, which is really scalable impact. In a typical pilot pattern, a tool is ex is inserted into existing process. We take the approach of process redesign, and understand where we can improve process steps through automation. Adoption tends to vary in these components. Decision rights aren't necessarily addressed, so they will remain unclear. And the organization itself struggles to demonstrate what the return on investment is for the technology, that's implemented. Now the technology may work, but the operating model around it hasn't changed to fully realize the value. And so value leaks, And we're unable to actually see down into the the p and l what that value actually is. High performers reverse that sequence. They start first by redesigning the workflow before they've ever even selected what the technology is going to be. They look at explicitly defining what activities are suitable for AI to perform and where human judgment has to remain. Then from there, they build in escalation, accountability, governance, and they use an outcome dashboard to govern the continued investment. They're relentlessly tracking that the value is actually being achieved before they ever prove scaling a pilot to the next phase. But they have an answer, and they can prove the value because they're they're monitoring it, and they've already know where they're expecting to see the value. The practical question here is not only whether the tool performs or not, it's whether the end to end work flow is designed to use it consistently, responsibly, and measurably, most important. So, again, we're driving towards what are the outcomes that we can actually achieve. So finally, we've talked a lot about about data and governance and accountability. And a lot of times, these key components become almost the roadblocks, for people to continue to moving, to move forward. And scalable AI adoption does depend on five foundational requirements. So data has to be clean and usable, across candidates, requisitions, skills, and workflow information. Your governance has to be transparent. You have to have bias controls in place, escalation pass as things may deviate, and then compliance reviews. We have to be able to be transparent about, both holding AI accountable as well as human accountability. We have to be explicit about where recruiters or hiring manager judgment is required. The higher the risk of the decision, the more prone or or where we need to focus our efforts from a human perspective. The lower the risk, the higher volume, the more standardized, those are areas that are suitable to start to think about AI in a more, ad hoc fashion. Adoption also needs active management through training, a clear change story, usage expectations, and ongoing support. We have to think about training not just as a one time point in time element as well. We have to think about training as a learning system where we can provide knowledge, yes, but also make sure that recruiters and hiring managers understand how to apply, that knowledge with through the course of the work So they're actually realizing the benefits of the improvements that we've delivered through the AI solution. Management helps tell that story. It helps define the baseline of where we are at, sets a target for us of where how we could see improvements, provides cadence for us to review how we're doing, and thresholds for for signaling that we should scale or, in some cases, even stop a use case because it's not producing the value that we anticipated. The takeaway here from our top performers is that these are operating requirements, not after the fact controls. There are things that have to be defined as a part of the business case development for the use case itself. Establishing these things early are gonna enable AI adoption that's compliant. Yes. Trusted. But most importantly, capable of actually delivering sustainable value for the organizations overall. So that leads us with with one last question here. How do we actually select and fund the right use cases? When from a Hackett perspective, here's a framework that we often provide as a discipline path for AI experimentation to proven business impact. We begin by selecting a material problem and a use case that addresses it. We then fund it through a clear business case and stakeholder commitment. Next, we operationalize the solution by redesigning the workflow, establishing governance, and supporting adoption. We prove value with actual performance measures and only then decide whether to scale, modify, or stop. We have actual I have actual clients who are in the stages of delivering this right now. And it's taking a lot of effort to understand what each of these phases look like, and it is reversed a lot of the work that typically is done through the course of transformation or process redesign. The stages are intentionally sequenced this way, though. Each one creates evidence for the next investment decision. So we're driving decisions that are that are proved out with value proof along the way. And it's gonna protect the organization from spending on solutions that aren't going to produce meaningful value. We have a lot of examples over the past year. If what we've learned in in late twenty twenty five leading into 2026 is that process improvement alone and, again, layering that AI on top does not deliver value. We have to be able to to source the start from that outcome that we're desiring and be able to prove out the value through, redesign of work. Our next five slides, we're gonna dive deeper into each of these stages in a bit more detail so you can understand what's involved in each of the stages. First, let's start with the select stage. This starts with, again, that business constraint, not the technology. And I always like to say this is the constraint might be an actual problem, but it could also be an opportunity that the organization is unable to take advantage of right now because of lack of capacity. So consider the constraint to be one of those two options. We have to identify from a talent acquisition perspective where hiring friction is most material, which roles or segments affect business outcomes the most, what baseline measures already exist, and what improved performance would unlock. This keeps the discussion connected to real organizational need. Now one of the things that we have to connect talent acquisition to is not just headcount planning, not just short term workforce planning, but true longer term strategic workforce planning. What do we see as not just our capacity that we need to hire for, but also what is the capability that we need to hire for? And we also have to now openly look at, is that hiring from our own base of employees from an internal perspective, or are we also looking externally into the market? And so this concept of talent acquisition becomes a lot different when we start to think about about the work that they're doing because we're now just not solely focused on the external market as the sole source for bringing in talent. We're also thinking about our own market within our own organization as well. Once we've identified the organizational need, we then now need to start to screen what are potential opportunities or use cases that could solve that constraint for us. We need to look at things like expected value, what the data readiness is, for the solution that we're proposing, the complexity that we might create, risk as well as governance requirements, and its ability to scale beyond the pilot. We have to get more comfortable in living in a world we where we balance the fact of what we know about the work as well as some assumptions that we need to make around how AI can drive improvements in the work. Selection usually requires some type of trade off. There is no perfect scenario where you're gonna solve for all the demands at one time. A high value idea may not yet have the value or process readiness to succeed yet, but it can set us up to make changes to get us there. An easy use case, on the other hand, may not really matter enough to justify the investment. Sure. It might use AI. It might speed up the process. But if the volume isn't high enough, it doesn't actually achieve value. And in some cases, it might even cost more to leverage AI to do that process. Fund the opportunities that combine meaningful value, sufficient readiness, and a credible path to scale, as what you're gonna take forward from a value case perspective. Do not fund projects just for the sake of funding projects to use the technology. Next, let's now look at the fund stage. Once a use case is selected, it must be translated into credible business a credible business case. We have to be able to, make assumptions and prove out what the value we anticipate achieving, and we have to be able to measure whether we're achieving it or not through our testing phases. A case that could, or that case should connect really four dimensions if you think about the value case. The expected value, in speed, cost, quality, or capacity, and even experience, the readiness across the data, the process, the workflows, and the people that are gonna be operating within the process of the adoption. We also need to consider risks. Yes, we need to think about things like bias, but we also need to understand where's the transparency in our process, especially as we have more and more client, compliance, concerns, local, state, federal, international laws that are being created that are demanding that transparency from us, and then especially where do we need to ensure human oversight exists in order to be able to mitigate risk. The support required from talent acquisition and other areas are also important. HR in general, finance, IT and technology organizations, as well as our business leaders. Different stakeholders are going to evaluate, our use cases through different lenses, and we need to understand the value of each of those stakeholders, as we're presenting the value to them. CHROs will likely focus on productivity, workload, and quality of hire. A CFO is gonna look at what's the cost per hire, what are the workload economics, and what's the AI cost model. Whereas a technologist, CIO, will assess integration, technology stack fit, and governance. Finally, the CEO or the chief operating officer will connect the investment to the availability for the talent that's needed, business execution, and speed to hire. The funding decision is not an automatic yes or no. The funding decision is scale, modify, or stop. Always starting with the first phase of a pilot and then looking at how to continue to expand and scale that pilot over time. This is going to enable you to gather stronger evidence and prove out that the value is there to continue going. Or it's gonna tell you the value is not there and that you need to stop and modify and rethink the use case. Now that we've we've, proved out the funding and the value of the, the business case. It's now time to operationalize what we're actually doing. And the first step to operationalizing is understanding how to redefine the work, before we ever attempt to scale it. Because the key critical component is most of the capacity gains are gonna be in cap capacity release for people who are already in the process, and we have to be able to name and assign ownership of where that capacity is going to go next and how is it going to contribute back into driving value for our organization. We've already started with a talent acquisition problem and its baseline. Now it's identifying, as as we've identified what's the AI capability that's actually gonna address it, such as we're gonna deploy a matching capability. Maybe it's a scheduling capability or a way to be able to surface insights and intelligence, whether it's on talent or skills or what have you. Then we have to define what is the actual human role. What percentage of share can we fit within an AI capability, and where must judgment and accountability remain essential to the process? That's gonna translate into a set of skills and, eventually, a set of roles into the workflow with explicit decision rights and controls, and that then now connects to the workflow, the flow of the work. How is the work gonna be undone with a big, or a true kind of guidance of showing where we're actually driving to the intended outcome? These elements have to be designed together. Deploying the capability without clarifying the human responsibility or changing the workflow, is going to create inconsistency. It's gonna create risk. And worst, it's gonna create value leakage. You're not going to achieve the value anticipated unless the work gets redesigned itself. That's why you need operational guardrails. So your guardrails should also be in place from the beginning. You have to understand things like that. Again, data quality, transparency, and escalation, and that human in the loop accountability. This is how the technical capability becomes repeatable operational value. Without it, it just gets stranded and the value gets stranded overall. Next, we have to prove. We've operationalized. We've we've identified how we're gonna find it. We've redesigned the workflow. Now we have to prove that the actual value is happening. This is where we actually put the rubber to the road in improving talent acquisition performance, not simply where people are using it. A lot of organizations or our top organizations are using some type of dashboard that covers the outcomes that matter for the use case specifically, in order to be able to demonstrate that they're proving or showing actual value. This might be measuring speed through time to fill or time to hire ratios, measuring economics through cost per hire, or HR process costs, productivity through recruiting efficiency and placement rate per FTE, or quality of hire through qualified applicants and first year retention. You also have experience measures here through candidate engagement, scheduling friction or some areas where you can measure. And then finally, how are we supporting internal mobility through internal fill rates and talent pool utilization? Not every initiative that you may drive from a TA perspective is gonna need every single one of the metrics that I just named, but you should be measuring those that connect directly to the original constraint and show a tie back all the way through to the enterprise value lever that we're driving here. We have to establish the baseline before implementation so that we can show what the delta of the changes once we get going. The decision rule here is straightforward. We use the evidence to determine whether we scale. We start at pilot. We evaluate the evidence. We determine if the evidence is showing us what we expect to see, and then we choose to scale, change what we're doing, or stop the initiative based on that evidence. And then we continue to scale from there. Adoption, yes, is an input, but measurable performance improvement is the proof of value here, not just adoption and not just the activity of launching the tool itself. Activity does not equal value. So finally, we are now at the point where we've proved the value and it's time to scale. How are we gonna move from pilot evidence to durable capability that's gonna ongoing standardize and deliver value for our organization? This is not simply running a larger pilot. This is making that transition from isolated evidence into durable organizational capability. This sequence begins with the pilot itself. The pilot has proven measurable, measurable input. Now the organization is standardizing the workflows, and controls and operating expectations. We've shown why the standardization needs to exist versus trying to push the standardization upfront without any demonstratable, proof. With that foundation, that capability can expand across additional role segments, perhaps, geographies, for example. Finally, we have optimized by addressing the next constraint and repeating the value loop. Consider these to be less around strings with a defined end and beginning and more circles, cycles that we continue to produce over time. Each stage requires evidence and discipline before we move forward. We risk expanding too early, adopting inconsistent practices, being unclear about accountability, or weakened measurement if we do not have the standardized consistent, process of being able to to navigate. So, again, durable scale depends on standardized workflows, governance, metrics, and adoption support. Quite frankly, that's not any different than we've been saying all along from a technology perspective. The difference here is that we have to think about changing the work, and we have to consider AI, again, as that variable labor, not as just the tool. The objective here is not a bigger experiment. It's how can we repeat the capability that continues to create value over and over again from a bigger perspective. So that leads us to how we apply this framework and practice. First, let's address the core recruiter challenge, identifying qualified candidates quickly when application volume is high. The research shows that most recruiters are experiencing a 70% average, increase in applicants per opening and a 53% average increase in number of qualified candidates. The distinction here matters. Not only is pure volume increasing, but more candidates make it past the existing qualification gates. And more volume alone can create additional screening work. The goal here is to how do we improve reach and precision. Andrea, I'd love to hear a little bit about how Workday is positioned to help solve this challenge. Yeah. Absolutely. Happy to happy to share our our perspective on on what we've, built, what we're building, and and where we're going and and kind of planning to address some of this challenge. The the biggest thing is, yeah, that that influx of candidates coming in, more and more candidates are applying. You know, how do we help recruiters kind of screen through the rank and file to find the right candidates? And this is where our recruiting agent actually comes into play. So our recruiting agent essentially, helps screen candidates for minimum qualifications and. desired qualifications where we're actually taking the resume that they upload during their application process alongside some screening questions that they may be answering and actually grade that candidate against the job description of the requisition that they're applying for. We're looking at the minimum qualifications, desired qualifications, and kind of ideal qualifications, and then providing a grade back to that individual that or providing a grade to that candidate that is an a, b, c, or d. A is a candidate that really kind of exceeds expectations and, achieves above what the the requirements are within the role. A b is one that has the majority of the the main qualifications that may be missing one or two. A c, again, is missing some of the major qualifications, and a d is just not a a right fit for the role because they're missing some of those those core qualifications. But, really, this is really, the insight here and and the the vision and strategy behind our recruiting agent is really helping recruiters look at those candidates, be able to assess who are potential top candidates so they can take a second click into it. The intent of the grade is not to say every a candidate moves forward and b, c, and d can see your way out the door, but really more of of how do we kind of help with different signals that identify what might be what might be a good candidate and then allow recruiters can make some of those decisions on that side. This is where we're seeing decreases in 50% recruiter screening time. But most importantly, when we see these candidates that get graded at a's or or b's, we're seeing that they're two times more likely to make it into that offer stage and stick through some of those complex hiring processes to kind of really kind of excel in the role then. That's excellent, to see. I know this is definitely one of the critical areas where a a lot of my clients get stuck is how can we get better at finding and and pulling forward the qualified candidates. So next use case here really focuses on how do we get more value from the talent that our the organization already knows. I mean, really kinda think about this as two complimentary pipelines. You've got your internal mobility process. So who how do we understand what skills employees have today and then matching those people to new roles that we have tomorrow and help them to explore their careers and opportunities for movement within the organization. The second is really rediscovering candidates that have participated in our process in the past. Perhaps it's capturing, again, those prior candidate profiles, refreshing and understanding what skills they may have and how that may apply, to reengage relevant talent into new roles that we're looking for, today. Either way, this is really a key component of being able to drive and speed up, hiring processes. And so, Andrew, from from a Workday perspective, what are some of the ways, Workday is helping move or make more candidates and employees visible, in the talent acquisition, Yeah. Yeah. Of course. Happy to happy to share. And and this is one of the ways where it's actually a a combination. of of two of our agents, or a a skill of one of our agents and then another one of our core agents. So the first off is thinking about, the internal mobility. So that's actually driven primarily through our internal mobility agent. Our internal mobility agent is really kind of focused on keeping employees engaged and kind of surfacing roles to those employees that have been in roles within a certain allotted period of time. So for organizations that I've worked with that have very strong internal mobility strategies, they generally have specific guardrails around internal mobility in terms of what employees are eligible to move to different roles, when are those employees eligible to move to different roles, and what are different skills that they should have and transferable skills as they progress through. And this is where our internal mobility agent really helps with that because you can establish those guardrails and and establish for specific roles across, you know, job families or role specific and establish, hey. This is the employee or these are the employees that sit within this role. This is the time that they're required to be in this role. And our internal mobility agent respects all of those guardrails and those policies and those procedures, and we can then surface potential jobs or, you know, upward upward jobs or or diagonal roles. One of the things that we're we're not gonna do is, recommending lateral moves to employees. We're we're kind of looking at some of that. Our internal mobility agent is really focused on kind of what's the next potential step in your career. It's not like when I go on to LinkedIn and LinkedIn surfaces jobs for me and says, oh, you're senior principal. Here's seven other senior principal roles that you could do at other organizations. It's really kind of looking that career trajectory and how can it make those next steps and those recommendations for the employees. So that's kinda the internal mobility strategy, and that's our our our, stand alone agent that can, plug into our talent acquisition suite as well as our, talent optimization suite. But then we start to think about the, the candidate rediscovery that you touched on. That's focused as a skill of our recruiting agent. So that's what we call, fetch back into the the Workday database. So there's so many customers that I've been engaging with that have really kind of wanted to start to tap into this more and more. Because when we think about it, there's a massive repository of candidates that have applied to roles in the past, expressed an interest in this organization. And oftentimes, they're not gonna go back if they, you know, weren't considered for the role the first time. They're not gonna go back and always pay attention to new jobs or new positions that are opening. And this is where our recruiter agent has that ability to go back and fetch those candidates that are sitting inside of the Workday applicant tracking system or within our CRM or an external CRM, those candidates back to or to the recruiter when, that requisition is open. So then recruiters can go in and say, hey. I wanna invite this individual to apply. So now we're not only getting very granular on who are qualified candidates that exist within the database, but now you can be very pointed and precision based on who you invite to apply for this new role. So you're getting very strong candidates that are coming through the funnel. I think that's amazing. One of the things that we talk out about a lot is kind of the galaxy of careers from an internal perspective. So being able to enable that, ability to look at diagonal type of moves is really critically important, as well as being able to surface kind of those top candidates that maybe didn't make it all the way through that you can pull in that are pretty strong, into future hiring. I think it's critically important to have that type of visibility, to cut down on on. work. So the next item is around really something that we hear a lot about, and it's this concept around candidate engagement experience, and especially scheduling. Scheduling is something that I talk a lot about with my clients. And our vendor assessment showed that 69% of of the participants, organizations are piloting some type of candidate engagement experience platform, solutions. So this is really kind of an area where we see a lot of high volume repeatable work that we can provide some significant experience impact for. What are we seeing, from a Workday perspective and how it's handling, streamlining the engagement and scheduling, experience for candidates? Yeah. This has been, this has been a a a focus area, from our recruiting teams for time. We we built a lot of things very early on, kind of our candidate self scheduling, the ability for candidates to apply now via social and kinda simplify some of that process. But then we decided to make the acquisition of Paradox, where really being able to through our candidate experience agent now, we now have the ability to embed a an agent on external career sites, whether it's the Workday external career site or a custom career site or now the ability to build custom career sites for customers, that that we can have a candidate that's or an agent that's there that candidates are able to engage with and navigate through the hiring process easier. They can ask frequently asked questions about culture, benefits, what's the hiring process like, what's how should they prepare for their interview, Really kind of centralized around improving that overall candidate experience and and helping them search for jobs as well through natural language and conversation. But then the application process becomes very simple at that point, being able to go through the application via a quick conversation where we're pulling in screening questions from the requisition inside of Workday. This has been immensely helpful when we start to think about high volume, you know, early careers, frontline, organizations, but also in professional environments too where there's really kind of if you're asking somebody to to fill out 40 pages of an application, they're gonna be dropping off very quickly. But our our agent doesn't stop there around helping with the application. Our agent actually steps back in after a recruiter has worked with the recruiting agent to surface who are the right candidates that we should be looking at, and then being able to pass those candidates over to the candidate experience agent to say, hey. I think this is a great candidate. I want you to go ahead and schedule these four these five candidates for interviews with me over the next ten or so days or the next couple days. And that's where our agent automates the entirety of that process, which is really kind of helping us drive some massive reductions in time to hire for our customers, but at the same point, not sacrificing the candidate experience at all. I think that's amazing because, so much kinda gets lost in that kind of management, and then there's so much just kind of the relationship and managing their relationship and ongoing kind of continuing that engagement in a seamless process is really important. Finally, one of the the last things that we think about is how do we enable more timely hiring manager decisions? This is probably one of the most fragmented parts of the process if you think about. Hiring managers themselves may get fragmented candidate information. It's often a battle to chase to get interview feedback post interviews, and often a battle talking about what the actual market context and situation is. This usually results in delays, a lot of rework and questions, from the hiring manager. Our research shows that with good timing, 70% average increase in recruiting efficiency can be achieved, and a 72 average improvement in automation can be achieved just by trying to help managers make better decisions. What's what's Workday how how does Workday helping recruiters hold hiring leaders accountable here? Yeah. This is where we go back to another skill of our recruiting agent. So our recruiting agent is is essentially the AI for any hiring team, and that can be recruiters. That can be hiring managers. That can be anybody involved in the recruiting process. And and what the recruiting agents primarily focusing on when we start to talk about hiring manager engagement is one of the core concepts that we we kind of evaluated very early on in development of the recruiting agent and looking at it that managers are not always in hiring processes. They may not always be going through interviews. There may be managers that hire once every one or two years. But then there's other managers that are in it day in and day out all the time in this hiring cycle. And how do you establish or create a concierge or an agent that is able to nurture that hiring manager through so that whether it's a manager that's hiring all the time or a manager that's hiring very sporadically, it's the same experience. It's the same deliverable. We're hitting the same SLAs, and we're keeping in touch with that, that candidate progressing them forward. So this is where our recruiting agent acts as that concierge, servicing next best steps to them, simplifying the user experience, being able to surface communication in Teams and Slack and other channels instead of having to go back and navigate directly into Workday to kind of complete some of those actions. So it's about allowing them to operate in the flow of work and remove some of those friction points. Absolutely. Yeah. But I know we are getting a time, so I I wanna for anybody that can stick around for a few more minutes, I I do have a a few questions that we do want to, that I know that were asked throughout, and all as well, we also have upcoming webinars that you can scan the QR code and see what's available. Amanda, one of the questions that that I I always get, so personally, this is kind of somebody asked it, and I I wanna ask it as well. How much insight are you seeing or how much of a trend are you seeing from AI interviewers in the customers and kind of, you know, research that you guys are doing? Are you seeing these virtual interviews or on demand interviewers being the the next big trend? Or give me a little bit of insight for what you guys are seeing. I'm seeing a lot of hesitancy in that place act in that place, actually. I kinda see two camps. I do have some more kind of tech forward organizations that are looking at deploying interviewer agents, with some type of human in the loop to be able to evaluate outcomes before there's any decisions made. Most of the clients that I'm working with are still kind of on the hesitancy's camp. They're highly concerned around candidate experience. I've actually had the, ability to actually watch some of these, just personally, with my husband, and it was a really frustrating experience. So kind of if you're looking at those types of avenues from an interviewer, perspective, I would focus on what can you do to be able to protect the candidate experience. Can you replicate some of the more simpler, type of workflows for the interview, not replicating, like, prescreening questions or things like that, in the process and to be very clear about what the true time will take to complete the interview. One of the frustrations that I'm hearing from, others is, understating the amount of time it might take to be able to interact with, an agent, especially if it's a if it's a text based typing type of agent and being able to explain the responses, appropriately. Yeah. I've I've seen very similar thoughts and and had some of those those insights as well. But it is I've seen some customers that are very much I love it. I want it, and. and wanna see more of it. So, yeah, one of those mindsets. The other the other one that I have is a real quick one. Are you seeing, are you seeing more customers building custom agents or buying custom agents in the or buying agents in the talent acquisition space? I'm seeing a fair mix of both. Early on in the stages, there was a lot of, can I leverage at cost things that I already have in my arsenal? So, you know, can I leverage Copilot in part parts of of my process or, whatever the case may have you? As is typically seen, I think talent acquisition, I always tell people, it's the most complicated tech tech, interaction space in in in all of HR because there's so many different things. You're gonna have a start up that's gonna create a solution that's gonna help fulfill a very specific niche need. I do think that there are some good players in the space already, that are providing agents. But I think each organization has to weigh what their needs are against their, ultimately, what their actual business issue is in order to be able to, drive to what's the right solution for them. So in some cases, buying something off the shelf may fit the need. In other cases, you may need something that's that's more specialized, in which case you embark on creating something that's custom. Yep. I it's it's exactly what I'm I'm seeing as well. There's so much utilization of Cloud Code, Cursor, Codecs, etcetera, where. people are are thinking about what are these custom agents that we can build. And I I think the the appetite from CIOs and and IT teams are starting to open up to it a little bit more, and take on some of the the risks there. But well, with that, I know we're a few minutes over. Amanda, thank you very much, for for spending some time with us, walking us through all the fantastic research and some of the insights. And, again, for anybody that's still around, we're gonna send a recording out, within the next twenty four hours. But, again, is, next steps or or other items that you could see a little bit more. We have an upcoming webinar on the twenty third. Scan the QR code. You can go ahead and register for that. But other than that, I wanna thank everybody for joining us today and all of the questions through the chat. We'll send some follow ups to a lot of the questions, but thank you again, and and have a fantastic rest of the day. Thank you.