HR transformation in the AI era: Designing work that delivers better HR performance

I have here with me today two of our Eagle Hill experts in process and automation, Steph Mullen and David Mouser. Steph will detail for us how we can understand the work, identify administrative burden, establish meaningful performance measures, and determine whether an intervention actually improves performance. David will elaborate on how, once we understand the work, how we can determine where technology belongs, and whether the right answer is workflow automation, robotics process automation, AI, or something else entirely. David, Steph, thank you for joining me. Before we get into AI, I think where I want to start this conversation is, what leaders should understand about the work they’re trying to improve. Steph, can I start with you, and then David will have you build on that?

Stephanie Mullen: Absolutely, thanks so much, Joe. At a high level, I’d encourage leaders to take a people-centered approach to transformation, because ultimately, it’s your people that are going to decide whether a transformation, and really any transformation, as you mentioned, is going to be successful or not. So, to do this, we want to start by thinking holistically, considering how work is done today, how it should be done tomorrow, and then how technology can support and enable that. So while we’ll spend today focused on AI and automation and talking through what that looks like for organizational transformation, it’s really just one part of a larger story that drives that big change of organizational transformation. David?

David Mouser: Yeah, thanks, Joe. Thanks, Steph. Specifically, I think it’s understanding where’s data in your organization, and what part does that play in this process? Specifically, where does it sit? What insights can you derive from it? And leveraging what you have. I think reinforcing what Steph outlined, data should be able to inform and validate where there’s inefficiencies or bottlenecks in the process, and clue in where to look further for where there’s efficiencies to be had in the HR functions. That could be things like you’re missing service targets or performance thresholds as kind of quick and easy signs that there might be something more afoot, but there’s certainly opportunities and where to layer technology for improvements.

Joe Macaluso: Interesting. So what I hear you all saying, and I think where we’re starting this conversation, is that organizations often approach transformation by starting with technology or a capability, and then looking for places to apply it. They’re not looking at the way the work is designed, the way the work is set up, the extra steps that occur, unnecessary friction, the administrative burden. They’re not changing how HR operates? Is that the correct understanding?

00:04:55

Stephanie Mullen: Absolutely, and I think that’s where organizations stumble. So, as you mentioned, it’s almost the inverse, right? So instead of starting with technology, organizational transformation begins with the work itself. So, what is being done, and how it’s being done, and then technology enables and supports that. So, your technology solutions and your workflow should be going hand-in-hand, and addressing one without the other is just likely going to lead to more problems down the road. If we think about this at a higher level, technology fragmentation, so having different systems, different data entry points, a lot of manual verification points, that’s ultimately going to become workflow fragmentation. Again, it’s your people that are doing the work. So, as you move between systems and technology, people are the ones that are tying those things together.

They’re also not experiencing technology in isolation. They’re experiencing the work and what it takes to move across that, whether that is one grand system or multiple systems and multiple data points. That’s also going to create handoffs, duplicate data entry, manual verification points, where that’s going to create opportunities for delay, inconsistency, or rework in your overall processes and workflows, and that’s where productivity is going to erode. Kind of hearkening back, that’s where organizational transformation can stumble.

So we do have an example of this from the research findings that we teed up at the beginning of this webinar. One of the clearest findings from this research is that while organizations are investing in technology, many employees and leaders are still spending significant portions of their week on low-value administrative work that are actually pretty prime for AI and automation opportunities. To just put a finer point on that, we found that leaders and HR employees across an organization running the gambit, are spending about 20 hours or more a week on administrative work. That is almost half your week spent on repetitive, routine tasks, but again, are prime candidates for some optimization and opportunities in AI and automation. It tells us that technology isn’t the only answer. It’s not automatically translating into better ways of working, but it could. If we successfully use AI and automation, you’re going to really transform the administrative burden and the time and tasks that people are spending their time on and ultimately pivot that into doing higher-value work. So, as organizations look to transform their organization and look to technology to support that transformation, the best place to start is really focusing on how the work is done today and how it can be done tomorrow.

Joe Macaluso: Thank you, Steph. I really appreciate the way that you’re looking at this. I mean, our own research, as you mentioned here reinforces this point. I think more than 60% of directors and HR professionals often experience process and workflow issues, including slow response times, too many handoffs, manual processes, unclear ownership. In fact, process and workflow issues, I think, ranked as our top challenge for both HR service providers, as well as the business leaders receiving those services. So, it makes sense to me that we need to establish what good performance looks like before deciding how to improve it. We need to leverage those closest to the work, as they are the most important source of evidence within a structured transformation approach. So, once you’ve broken down the process in that way, and you look at these different elements in terms of the functions and the burdens on the existing organization, David, how can you determine where technology then belongs, or how it can actually help improve those processes?

David Mouser: Yeah, thanks, Joe. I think Steph kind of started to hit upon this. I mean, first, it’s just having an understanding of what does that process look like end-to-end. Ideally, coming out of the stuff that Steph is talking about, you’ll have a fully documented process, so you understand who’s involved, what are the steps, what are the processes, the activities they’re undertaking, as well as the systems and data at play. Equally, you’ll get a sense of, is it homogeneous, or is it fragmented, as Steph outlined? But then it’s looking at a blend of qualitative and quantitative information to help tease out and understand where those efficiencies are. We’re talking about things about looking at, for example, where are bottlenecks in the process? Looking at individual or collective activities, are those ripe or available to be supported by technology? What types of technology are suitable for any given activity? I think we touched upon this a little bit at the start, but, traditionally, that’s been RPA or workflows, and now, as we’re kind of in this new tool suite of AI, there’s different types of AI that can be brought to bear to affect different tasks.

00:09:58

But equally, as I mentioned, the systems. What systems are involved? What capabilities are inherent in the systems? That might be a matter of there’s something there that you need to turn on, maybe sometimes you need to pay extra for it, or sometimes that’s just absent outright, so that kind of is a barrier to stringing these things together. I think the other part of that, also that Steph was alluding to, is a lot of individuals are just focused on their day-to-day tasks and activities. Part of it is taking a step back and somebody looking and owning that process end-to-end and evaluating it. For example, I might be able to automate a few individual tasks that I’m doing, and I certainly can see efficiencies from those individual activities, but if I’m still having to move things around from system to system, there’s only so much efficiency that we’ve realized in that process. So I think a lot of that question is, what do we as an organization, as a client, as an organization, want the operating model to look like? So there’s certainly some time and effort, I think, to sit down and say, where are we at today? What are those efficiencies? Are we in agreement and alignment on that to find what that future state looks like? And from there, then you’ve got more of a blueprint to move forward and affect some of these things by applying the technology, too.

Joe Macaluso: And if I may, then, is this where the discussion becomes bigger than just HR itself? I mean, we’re using HR as our transformation lens here, but couldn’t we make essentially the same diagnosis for any other back office function, whether it be finance, operations, etc?

Stephanie Mullen: Absolutely, and I think the short answer is yes, that we are talking about the key components of any organizational transformation, whatever business unit or function that may be. So, especially in those units and functions where there’s shared services, HR being a very prime example of that, finance, legal, also being functions of that normally across organizations. This is where transformation’s really going to get you in the door with your employees and have big impact on your organizational performance. Because, particularly with HR, it’s one of the maybe only functions in your organization that’s going to touch every single employee, right? It is built into your operating model. At one point or another, every employee is going to interact with HR, interact with an HR system, interact with HR functions, and then, of course, you have the people in HR that are doing the work that are a core business function. So, yes, we’re using HR as a model, but these foundational principles of looking at that end-to-end perspective, understanding the process, understanding where technology can really drive organizational performance, that can be applied to any business unit.

Joe Macaluso: And this is, I think, where the conversation gets more especially nuanced, is while there is a traditional transformation approach, understanding what you’re trying to change before trying to change it, I think the common thing that we hear a lot of buzz about today is obviously AI. And it seems as though that everything that we talk about, that AI has become shorthand for an enormous range of capabilities. But I think there’s additional nuance there. Is it that organizations are starting the conversation at the wrong level, asking where can we use AI instead of what problem we’re trying to solve? David, can you elaborate a little bit on this for me?

David Mouser: Yeah, I think I started to touch upon this a little earlier as well, Joe, right? I think it’s understanding AI is now a tool in the broader toolkit of automation, and just like any other job that involves tools, recognizing what’s the appropriate tool for any given task at hand. And so, that could be, again, traditional workflow automation, the RPA, or the generative or the Agentic AIs. And equally, these are kind of complementary tools, right? One’s not inherently better than the other, it’s just using the right tool for the right task at hand. I think a simple way to think about these is, for example, does the task follow a set of given rules and required judgment? These rules-based tasks are more repeatable, and they’re suited for those legacy or traditional things, like RPA or workflows. If the task is more thinking forward, so to speak, that has interpretation or understanding and context involved, or making a decision, that might be something to look at applying different types of AI to accordingly. And to bring this back to what we’re talking about today with HR specifically, we’ve kind of dug into this. So, we looked at a standardized process that represents anybody’s hiring functions, and across that, we had identified 88 opportunities where there might be technology to be applied to realize efficiencies.

00:15:04

Again, I’ll stress, not every single one of these is appropriate or ripe to apply AI to. Some are more well-suited for traditional things, again, like workflows or RPA, depending on what it is. But also, more importantly, some of them are not really ripe for technology at all. I think something to keep in mind throughout all of this is how and where do you want to keep the human in the element? Or maybe there’s just a cultural component to the organization that needs to take front and center, and technology’s not going to solve for that. One of the examples that came up very recently in a conversation with one of my clients was, their hiring process involves having panels that personnel sit in on and conduct with the employees. I think that’s something common that a lot of organizations apply in some way, shape, or form. In this particular instance, the limiting factor is the availability of the employees to sit on those panels and maybe some limited set of employees that are even capable or able to sit on those panels. Technology itself is not going to solve for that. It might be able to apply some options to sift through calendars and recommend optimal panel time slots. That certainly is a help, but that’s not going to solve for the capacity limitation of people at the end of the day. I think another thing to keep in mind through all this that we also looked at is, this day and age, a lot of the services and software’s out there that are available to HR functions are starting to roll out or have baked in their own custom AI capabilities. Those are certainly something that are available, or at least to look into further and see if they might be able to be turned on and implemented in your organization. But equally, those two did not solve for the whole process end-to-end. So, I think as we again take a step back and we’re looking at how do we realize efficiencies? As Steph mentioned, especially if you’re operating in a fragmented environment, even if there’s these baked-in tools or you have third-party AI services, like a Claude or a GPT or something like that, it’s going to be an amalgamation of things to realize these end-to-end efficiencies. Other things that might trip that up to be mindful of is if you’re working with legacy tools or services that have limited data connectivity or ability to move information between systems, that’s going to be something that you’re certainly going to have to look into more bespoke solutions to realize these efficiencies.

Joe Macaluso: So, if I understand this correctly, I’m hearing that the organization, the structure of a transformation often affects what the ultimate outcome is. David, you’re saying here that the idea of where we are applying technology, whether it be rule-based deterministic workflows, things that require interpretation, maybe consequential or non-consequential judgment, those all are different criteria in terms of selecting what the appropriate tool is. So, Steph, if there are different criteria for selecting the appropriate tool, whether it be a technology-based solution or a non-technology-based solution, can I ask you, does this affect how you think about organizational readiness for that intervention?

Stephanie Mullen: Great question, and the short answer is no. It’s probably pretty obvious that David and I work closely together often for this exact reason, that AI and automation is a tool, and it’s going to accelerate whatever it touches. But really, it’s the underarching processes, the workflows, that are going to dictate where there may be inconsistencies, unclear ownership, fragmentation of data, and understanding that, knowing where those exist, knowing what parameters you’re functioning in as David kind of mentioned with your legacy systems, or what AI functionality you already maybe have within an HRIS system, that’s all going to dictate what the final product can be, or what your organizational transformation can look like. It gets back to understanding where your organization is today to prepare for the future. So, just to give an example of this. In our survey, we found that about 80% of HR employees often or always enter the same information into multiple systems, and 70% rely on spreadsheets, manual tracking, or other workarounds outside of their formal HR systems. What that’s indicating is that employees are compensating for gaps in the process and their system integration and their data flows. And as David mentioned, knowing what that looks like and where there’s friction and pain points in the current operating structure is ultimately going to give you a sense of when and where AI and automation can support your workflows, ease those burdens, and give you that organizational performance that you’re looking for.

00:20:08

If you don’t do that first, oftentimes, AI’s going to just accelerate those problems rather than solving it. Now, in addition to understanding the environment that you’re working in, there’s another couple of factors that we haven’t really covered that also impact organizational readiness. So, these should be factors that guide any transformation. Again, we’re talking about HR today, but truly for any organizational transformation, there are things like risk tolerance, compliance requirements, regulatory requirements that may dictate if an organization is ready for a transformation, and what and how transformations can be applied, especially in the technology field. And that can vary by business unit and department, by industry, by sector, but these areas around risk and compliance become your guardrails to help drive where AI and automation can improve your workflows while still minimizing risk, maintaining your compliance, and ultimately, supporting long-term growth. So, getting back to that holistic look at organizational transformation, if anything, as leaders consider implementing new tools or technologies, it’s really a call to document your current processes, streamline how work functions today, and clarify the workflows that technology touches. It’s going to give you that opportunity to identify, understand, set, and confirm those guardrails around risk and compliance that ultimately are going to impact and influence any AI or automation solution you choose. So it’s a little bit of a shift when we talk about organizational readiness, to what can we implement, to how has automation, or how can automation position us for the future in a way that drives organizational performance while maintaining our risk tolerance and compliance?

Joe Macaluso: So, when someone’s then telling you that AI is the answer, is then the natural question, the answer to what? As you’re talking about these guardrails of risk and compliance, obviously, the solution that we tailor is very much informed by these guardrails. What immediately occurs to me is that while AI may be the hot topic, we really need to understand, what is it solving for? Is that right?

Stephanie Mullen: Yeah, absolutely. I think, as we mentioned, it’s a holistic exercise when you’re looking at organizational transformation. AI and automation are one tool in the toolbox, but understanding the rest of the toolbox, and even to take this metaphor one step further, what toolbox you’re in. What are the requirements that you need? What are the risk tolerance of the organization as a whole? What are the goals, objectives, the strategy? What’s our data environment look like? These all become integral before we even get to the question of, AI is a solution, or the solution to what?

David Mouser: And I think to build off that, too, especially as we dig a little bit more, peel back the onion on some of the AI stuff. The way AI can be set up and used to really be rigorous and validate, both for the outputs, but consistency and repeatability is, there’s a lot of nuance on how to set that up appropriately. Not to get too technical, but things with the instructions and the markdowns behind the scenes that feed it, and being able to extrapolate information or data, whether that’s already pre-structured, or you have to infer that. It’s very different than, say, hey, I realized we don’t seem to be interviewing good candidates. So, it’s not just ask a prompt to AI and say, hey, here’s a pool of candidates, give me the best class of who I should interview. Give me a rank order. It’d be great if it was that simple, but you don’t have any insight of what’s happening behind the scenes. So to do it well and accurately, you want to set that up and put, Steph’s already alluded to this, put those guardrails in place. So that you’ve fed it the criteria, what constitutes a good candidate, and things of that matter. That’s getting into a little bit of the technical side of things, but I think it’s just something to be mindful of to branch off of that question and Steph’s thoughts.

Joe Macaluso: No, I mean, it’s interesting. It occurs to me that just like any other organization, this is really a muscle that needs to be prepared and flexed before it can be developed and cultivated. David, you’re talking about the preparation of data and the structure. It brings us back to the age-old adage of garbage in, garbage out. So this is the idea that whatever you apply AI or automation to, the better the structure and the organization is from the forefront, the better returns you will get on the back end. But consequently, the worse it is, the higher the cost will be, because you’re essentially leveraging maybe an overpowered tool to do some menial or cleanup that could have been preempted in the first place. Am I understanding that correctly?

00:25:22

David Mouser: Yes, absolutely, yeah.

Joe Macaluso: Perfect. So, alright, let’s bring this back to the HR leaders that are on the call here today. So, if an HR leader is listening and they recognize that they have some of these problems in their organization, where do they start? Steph, can I start with you? And David we’ll have you build on that one again.

Stephanie Mullen: Absolutely. I think this is a case of where you sit is where you stand. So, I would position a process for a starting point, really understanding what the work is, how it’s being done today, which means mapping how work actually gets done, where there’s the burden, the friction points, what systems are at play, what people are at play, what kind of checks and balances are already within the system? That’s going to help inform what stays, what goes, and help you identify where there’s some duplication, where there’s fragmentation, where there’s rework. All of these that are ripe for automation. The second step would be to engage people closest in the work, not just as a listening exercise, but really part of a structured method for identifying where processes break down and what practical constraints exist because the people actually doing the work are going to know that better than anybody else in your organization. And ultimately, you want something that’s going to work for the people doing the work, if you will. So engaging employees in both defining the problem and then creating the solution is ultimately going to drive investment and adoption for any potential AI or automation solution you decide on down the road. And then finally, using those inputs from your process understanding and your people understanding, that’s where you can determine what pain points exist that AI and automation should address. Now, that might be a little bit different than, what can they address? As David mentioned, there’s a lot of different guardrails and considerations to make in that, but if you’re grounding decisions in what’s currently happening on the ground, where employees are currently duplicating effort, running into manual administrative burdens, where there’s reconciliation and quality controls that are slowing down the process, that’s ultimately going to become the foundation for anywhere where AI and automation can enhance your work, and ultimately your organizational performance. David, what do you think?

David Mouser: I think you already hit the most important thing up front, which is, figure out where you want to start. What is the piece or the pieces that you want to start with for the most opportunity? And that’s a theme that we’ve brought up time and time again. But again, another thing I’ve already mentioned: traceability. Not only the data that’s going into that, but more importantly, if I’m somebody downstream in that HR process and I’m having to make a determination on that, I need visibility and want to know where AI has been used and applied in the process up to then, so that I’m making a fully informed decision. I don’t want to say, Steph, to think that that’s coming from you, even though it is, right? But I want to know that you made that, versus you made that with the aid of AI. The other thing we hit upon here, and you were just talking to a little bit, is the structure of the data and the information, to branch off of what I talked about a little bit earlier. AI doesn’t need structured data to operate, but Joe, as you said, it certainly makes it much more efficient and effective. So just to pull on a little bit of an example, think about that as if you’re just requesting applicants for a job and they send in the resume. That’s something you can parse through, interpret, and do individually, but if you’re asking AI to do that, it can certainly do that. But that’s a much stronger ask to have it to interpret 10 different resume formats, and have it understand what’s a skills block, where is that, are they all in the same format, are they bullets, are they not bullets? Did somebody put it as a run-on sentence with commas? Somebody else didn’t even do anything to separate the pieces. So thinking about how can we perhaps maybe change the upfront of our business process to maybe have the applicant spend a few extra minutes and itemize that data, so now I know it’s a structured block of data per applicant that’s much easier for me to process downstream. And then the other thing I touched upon earlier as well is thinking about, how do we start stringing these together to realize true efficiencies in the process? It might be a moonshot to try and get the whole process automated end-to-end, and that might not even be something that’s not desirable for a lot of the aforementioned reasons we’ve talked about with risk and guardrails, but I think that’s what you’re after, ultimately. How can we lessen the burden of people and realize efficiencies in a more holistic manner? And then I think the last piece that we haven’t really touched upon is, don’t think of anything that gets implemented as a one-time event. How do you bake this into the new norm, the new way of doing things, and become part of the culture? Just don’t view it as a singular transaction, like flipping a light switch, and then, hey, we’re done, and moving on. It’s a bit of a journey to get there.

00:30:48

Joe Macaluso: I mean, it’s really interesting. I hear you both touching on these ideas of continuous improvement, and Steph, through your lens, I think you’re really segmenting the idea of intent of how processes are performed, and the reality of how processes are performed. And then, David, on the other side of the spectrum, I think you’re talking about the overall operating model and integration. When we think about HR, as we mentioned before, being a shared service across the organization and permeating through many different individuals, many different functions, etc, I think the whole idea becomes, if you can improve something in one place, don’t shift the burden from that place to another in your organization, because at the end of the day, that is not transformation, let alone optimization. It is still just wasteful movement. So, if I can here then, we’ve framed this around HR transformation. But that sequence that we talked about here: understand the work, establish the baseline, select the right intervention, and then integrate it and continue to measure it to ensure that it is continuously improving. Again, that’s not uniquely an HR framework. That’s a transformation framework. So, if I could, as we are closing here today, Steph. If you could get an executive to ask just one question before approving the next transformation investment, what would it be?

Stephanie Mullen: Great question. Answering a question with a question here, but I think the one question I would say is, are we adding value to our workflows that drive organizational performance? Because if there’s one thing to take away from this webinar, from all of our points at different places in this, it is that AI and automation don’t create transformation on their own. They amplify whatever process they’re built on. They optimize the work first, the technology has a much greater chance of delivering the outcomes that your organization is investing in, and ultimately going to drive that better performance. To shift a little bit, this is where we get into measuring performance and tracking success, because it goes beyond just adoption rates, and that’s ultimately what’s going to drive value. It becomes a conversation of not just are people using technology, are they using an AI solution, but is the technology improving our performance? A shift from the adoption metrics around how people are using it, how frequently they’re using it, to what does our capacity look like? Did it increase? Did our quality and service improve? Did our administrative burden and rework decline? These are ultimately going to let you know if an organizational transformation is working. And tell you whether it’s truly being embedded and adopted by your folks, because adoption alone is not evidence of transformation. We really want to look at organizational performance to know if that transformation has taken hold.

Joe Macaluso: David, I want to pose the same exact question to you here. But maybe from a technology perspective. What’s the technology question that you would want an executive to ask before engaging in that next transformation investment?

David Mouser: Yeah, absolutely. What is the decision or outcome that we’re trying to make? Where does data fit into that? And what technology applications do we need to put in place to realize that? And then validating, making sure that the outcome, similar to the ROI that Steph was just talking about, is realized. I think just to stress on a few points, too, is we talked about AI is not necessarily the be-all, end-all answer to all of this. It certainly has a role to play. But there’s other technology and automation options available to apply here as well. Some other pieces around what Steph’s talked about is if the process is poorly designed to begin with, that’s not something you just want to be quick to start layering technology on, whether it’s simple or advanced.

00:35:03

Because it gets back into bad input, bad output. So, that might be time to take a stock of where do we need to inform and affect the process to make some changes fundamentally to the workflow itself before layering technology on to try and realize some changes. I think, also, awareness of where it fits into decisions, which is what I’ve talked about as well. There’s some different aspects here that we haven’t necessarily touched upon, but things like risk. What’s the tolerance? What’s the cultural tolerance of the organization? But also, and not to freak anybody out, but do you have both the data governance in place for the underlying data? But now, you’re layering on top of this AI. I’ve supported organizations where we’ve set something up, and then it breaks, and then we’re trying to figure out what’s going on, only to find out somebody across the hall, two buildings over, made a change somewhere unilaterally, and it broke something downstream. To emphasize the importance of having that data governance in place, right? But equally, if you’re an organization and you’re taking the time and effort to put all these more advanced AI pieces in place, do you have governance and oversight over the underlying components that we talked about? The technical components, the prompts, things like that. Heaven forbid you’re invested in an AI service, and they fold shop. Do you have hands on those assets? What’s your remediation plan to get those set up? Or you have to go back to the old way of doing things. What’s the contingency plan to make sure the operation keeps moving if a service needs to change? Or that can even be that the service is down. You come in Monday morning and the service is down because there’s an internet outage or something, which we experience for other things, like email. Apply the same thing to AI. It’s an always-on service in the internet. So, just being mindful of having those elements in place as well. And I think the larger opportunity here is just, just, again, what are the people, the processes, and  the technology, and how does all that fit together? At the end of the day, that’s what we’re talking about. And while we’re advocating for this end-to-end process, I think the other thing to keep in mind is, while this might seem daunting, it’s not something that has to be done all at once. You can put little pieces of this together and string them together over time, again, as that risk tolerance or comfort level matures to then start realizing that more end-to-end efficiencies in return.

Joe Macaluso: Thank you both. I’m hearing this idea of continuous operations, continuous improvement, and sustainability. And if I could summarize this all for us here, I think what we’re saying at the end of the day is that AI creates significant opportunities in HR transformation and just broader workforce transformation. But again, the age-old story of this webinar today, the technology itself isn’t the transformation. Organizations can capture value when they understand the work intentionally improve the work design, select the appropriate technology for the problem, and embed it into the operating model. And finally, measure whether it actually improves performance, or whether it has an adverse effect on the organization at large. And while HR is our lens for today’s conversation these principles extend well beyond HR into broader organizational transformation. Thank you to you both for joining us, and having this conversation today, and thank you to everyone who joined us for this discussion.

Moderator: And just as a reminder, everyone, you can explore Eagle Hill’s research on this topic: HR’s problem isn’t too much work, it’s poorly designed work. You can scan that QR code there and read more about the research. If you’d like to know more about this topic, or talk to any of the experts that are with us today, feel free to reach out to us. You can also send us questions at info@eaglehillconsulting.com. Visit the website at www.eaglehillconsulting.com, and take a follow of Eagle Hill Consulting on LinkedIn.com. So, we thank everyone so much for joining us today. If you, again, have any questions, we encourage you to reach out, and we hope you have a great day. Thank you so much.

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